Petrophysics Practices And Pitfalls.pdf

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Petrophysics Practice and Pitfalls Tutor: Graham Webber Edinburgh November 2010

What you should expect to be able to do by the end of the course? Understand what logs (wire-line and LWD) are available and what they contribute to formation evaluation. Plan well data acquisition programmes. Value of Information Data Types Available

Quality check log data. Make a quick-look interpretation of logs.

Make a simple deterministic interpretation of logs to include: Lithology – Clay Volume Porosity Permeability Sw Know when to use shaly sand analysis and when not.

Net & Pay 2

What you should expect to be able to do by the end of the course?

Understand the necessity and means of using core measurements to calibrate petrophysical models. Porosity Calibration Permeability Predictors Use of Capillary Pressure Data

Understand the main differences between clastic and carbonate petrophysics. Appreciate the importance of the petrophysicist communicating with the whole subsurface team to ensure: Other specialists insights are considered when selecting appropriate parameters and methods.

Other specialists requirements of the petrophysical model are understood before the model is developed. 3

Course Outline and Timetable Day 1 Module 1: Petrophysics Definition and Contribution

Section 1.1: Introduction Section 1.2: Petrophysical Properties Section 1.3: Capillarity and Fluid Distribution Section 1.4: Net and Pay

Module 2: Well Environment and Data Available Section 2.1: …..The Borehole Environment Section 2.2:……Petrophysical Data Types 1: Wire-line Log Data

Module 3: Looking at Logs Section 3.1: …..Log Quality Assurance Section 3.2:……Quick-look Analysis of Logs Exercise 1 4

Petrophysics Practice and Pitfalls Practice Performance; actual doing, proceeding; habitual action (Chambers Twentieth Century Dictionary).

Pitfall A concealed hole in the ground that serves as a trap.

An unpleasant source of trouble or danger; a hidden hazard. 5

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Module 1

Petrophysics Definition and Contribution

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Section 1.1 Introduction

Introduction: Petrophysics Definitions Petrophysics is the study of rock properties and their interactions with fluids. Description of oil and/or gas distributions and the production flow capacity of reservoirs, from interpretations of pore systems and fluid interactions using all available data.

8

Introduction: What do we want to learn from Well logs?

Depth Permeable formations Porosity Thickness of reservoirs Net Sand / Net Pay Subsurface Pressures Fluid phase, gas, oil, water Fluid saturations Sw, So, Sg Moveable Hydrocarbons Depth of formations Environment of Deposition Lithology Temperature Velocity/Time Seismic responses Correlation with other wells

9

Where petrophysicists sit in the sub-surface world? Geophysicist

Geologist

• Rock Physics

• Porosity

• Gassmann Substitution

• Permeability • Saturation Height

Reservoir Engineer

• Net Vclay

• Petrel

• Fluid contacts

• Relative Permeability • Saturation Height

Petrophysics

Geomechanics

• Core

Commercial

Production Technologists

• Sonic / Density

• Porosity

• Formation Strength

• Permeability

• Sanding Tendency

• Fluid Analysis • Perforation Depths

Drilling • Pore Pressure • Bit Selection

10

The Petrophysicists Contribution to Calculating STOIIP More of the parameters used in the calculation of STOIIP are provided by Petrophysics than any other discipline!

Petrophysicist

Geophysicist

STOIIP Where,

STOIIP GRV Net Gross Ø Sw B0

= = = = = = =

GRV

N G

1 (1 S w ) B0

Stock tank oil initially in place. Gross rock volume. Net Reservoir Gross Reservoir Porosity Water Saturation Formation Volume Factor

Reservoir Engineer Geologist 11

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Section 1.2 Petrophysical Properties

Porosity Defined as the ratio of Void space to Bulk Volume of the rock: Porosity is a measure of the space available for storage of fluids:

Vp Vt Where,

Ø = Porosity Vp = Pore Volume Vt = Total Volume

Expressed as Percentage (%) or Decimal (v/v) 13

Porosity Types by mode of formation Types of porosity Primary – originating as the sands were laid down Inter-granular or inter-particle Intra-granular Inter-crystalline Bedding planes Secondary – formed by various processes after sands were formed Solution porosity or Dissolution Dolomitisation Fractures Vugs Shale Porosity Secondary porosity is generally far more important in carbonates than sandstones

Fracture

Inter-granular or intercrystalline pores

Micropores

Vugs

For clean sandstones and carbonates, Porosity can readily be derived from logs For complex formations porosity data from core is required to calibrate the log response 14

Porosity Types: Total versus Effective

Total Porosity Øt Ratio of all pore space (and clay structural water seen by some tools) to bulk volume. Includes all pores regardless of the degree of connectivity or pore size. Includes water in clay structure.

Effective Porosity Øe Ratio of interconnected pore volume to the bulk volume.

15

Volumes and Porosity

Porosity Definitions

Absolute or Total Porosity Øt Matrix

Effective Porosity Øe

VSHALE Quartz

Clay Layers

Clay surfaces & Interlayers

Small Pores

Large Pores

Hydration or Bound Water

Capillary Water

Hydrocarbon Pore Volume

Isolated Pores

Structural Water

Irreducible or Immobile Water

16

Controls on Porosity Carbonates

Sabkha Facies Rocks – Carbonate Dominated

Dolostone

Inter-granular Intra-granular Mouldic Reefal Dolomitisation

Sandstones Grain size Grain shape Sorting Packing Cementation Clay volume and type Compaction/depth of burial

5 cm

5 cm

Core Photo

17

Porosity Ranges

Type

Porosity Range (%)

Recent Sands – Unconsolidated

35-45

Sandstones

15-35

Tight Sandstones

5-15

Limestones

2-20

Dolomites

2-30

Chalks

5-40

Note: Theoretical maximum inter-granular porosity for cubic-packed spherical grains is 47.6% 18

Porosity Measurements Core porosity Measure two of: pore volume, grain volume and bulk volume of core plug and ratio them. Direct measurement but: Measure Øt or Øe (or something in between) depending on pore types present, clay content and method of cleaning and drying. Measured under laboratory conditions rather than reservoir stress. Require correction to reservoir conditions for comparison with or calibration of log porosity.

Log Porosity Sonic, Density, Density/Neutron, NMR. Porosities measured differ. No log measures porosity directly. Calibrate to core when possible. 19

Porosity and measuring techniques Log and core Porosity Measurements Total Porosity, Sonic Log Total Porosity, Neutron Log Total Porosity, Density Log Absolute or Total Porosity

VSHALE Quartz

**

Oven-dried Core Porosity

Matrix

Clay Layers

**

Humidity-dried Core Porosity

Clay surfaces & Interlayers

Small Pores

Large Pores

Hydration or Bound Water

Capillary Water

Hydrocarbon Pore Volume

Isolated Pores

Structural Water

Irreducible or Immobile Water

** If sample is completely disaggregated (after Eslinger and Pevear, 1988)

20

Permeability Permeability is a measure of the ability of a porous medium to allow fluids to flow through interconnected pores. Fundamental to the success of oil and gas production.

Controls on permeability: Effective porosity. Hence: Grain size, grain shape, grain size distribution (sorting), grain packing, degree of consolidation, cementation.

Types of clay present: Swelling (when in contact with fresh water) clays, smectite and montmorillonite. Pore filling clays, illite. Fibrous Pore Filling Illite in Sandstone*

21 * Reference D. R. Pevear Proc. Natl. Acad. Sci. Vol 96. March 1999.

Darcy Equation for fluid flow

Flowrate  Q

k . A. P µ.L

Where, Q = Flow Rate in cm3/sec (m3/sec) k = permeability in Darcy (m2) A = cross sectional area of sample in cm2 (m2) ∆P = Pressure differential across the sample in atm (Pa) µ = viscosity of flowing fluid in centipoise (Pa.sec) L = Length of sample in cm (m) (The conversion of CGS to SI units is 1 Darcy = 0.9869 x 1012 m2)

Poor

< 2mD

Fair

2-10m

Good

50-100mD

Excellent

500mD 22

Permeability Measurement and Estimation Carman-Kozeny Correlation Kozeny modelled permeability in a set of capillary tubes. He related permeability to porosity and specific surface area:

k

Where,

k = SVGR = Ø =

1 2 SVGR

3

1

2

Permeability Specific surface area (total area exposed in pore space/grain volume) Porosity

Permeability is measured on core. In most (all) cases it is difficult to estimate permeability from logs without core calibration. No log measures permeability directly.

23 Image Reference http://faber.ms.northwestern.edu/shanti.html

Fluid Saturation Water saturation Sw is the fraction of the porosity filled with water. Expressed in % or v/v. The objective in formation evaluation is derivation of Hydrocarbon Saturation (Shc).

It is normally easier to derive Water Sw and calculate Shc:

S hc (1 S w ) 24

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Section 1.3 Capillarity and Fluid Distribution

Capillarity and distribution of fluids in the reservoir 3. At increasing height above the FWL progressively smaller pores are filled and oil saturation increases.

Depth Capillary Pressure Pc

Oil Gradient Water Gradient Free Water Level

Pressure 1. The reservoir is initially water saturated. Migration of oil into the reservoir causes drainage of water.

2. Close to the FWL only large pores are invaded by oil. Low oil saturation.

26

Capillarity, the drainage process

Oil replaces water Water

Oil

Po

Pw

So

Drainage Oil enters largest pores: Pentry

Po-Pw 350

Matrix The higher the pressure within the oil Po the higher the curvature of oil/water interface and the smaller are the pores penetrated by oil.

Swirr

30

250

25

200

20

150

15

100

10

50

Capillary Pressure psi

Pw

Height above FWL ft

Oil

Po

300

35

5

Pentry

0

0 0

0.2

0.4

0.6 SW

0.8

1

27

Hydrocarbon / Transition and Water Zones

Mixed Hydrocarbon and Water Production

Hydrocarbons

Transition Zone OIL / WATER CONTACT FREE WATER LEVEL

Water

Water Production

0

Water Saturation

Decreasing Sw to Swirr / Increasing Hydrocarbon Saturation

Water-free Hydrocarbon Production

Increasing Height above the Free Water Level

Sw Irreducible (Swirr)

1.0

28

Saturation Height: Relation to Rock Quality

Poor quality rock:

Swirr

Asymptote

Swirr

Low Ø, Low K small pores Transition Zone

Depth

Poor Rock Oil/Water Contact

Poor Rock Entry Height

Plateau

Good Rock

Good quality rock:

High Ø , High K larger pores

Free Water Level

0

Oil/Water Contact

1 Water Saturation

29

Fluid Distribution with Varying Rock Type and/or Quality

Rock Type

B

A

C

B

B A B C B C

Capillary pressure or Height

Sedimentary Sequence

C

OWC

OWC

A OWC

-

Water Saturation

+

-

Sw Log +

30

Dependence of Permeability on Saturation: Relative Permeability Previously described permeability to a single fluid.

1

In the presence of a second fluid permeability to the first is reduced. Relative Permeability

0.8

Relative permeability:

0.6 Kro Krw

0.4

0.2

Permeability to one fluid in the presence of a saturation of a second fluid.

0 0

0.2

It is fraction relative to the permeability for a single fluid and is reduced as the saturation of the second fluid increases. Sw Critical

0.4

0.6

0.8

1

Water Saturation

So Irreducible 350

Sw Irreducible

At SCritical dry oil is produced. In the transition zone produce oil with a water-cut.

300

Height above FWL ft

Kro (or Krg) and Krw

35 30

Dry Oil Zone

250

25

200

20

150

15

Transition Zone

100

10

50

At oil saturations below Sor produce only water.

Capillary Pressure psi

This effect is quantified by relative permeability.

5

Water Zone

0 0

0.2

0.4

0.6

0 0.8

1

SW

31

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Section 1.4 Net and Pay

Net and Pay Gross Rock: Comprises all rock in the evaluation interval.

Net Sand: Comprises those rocks which may have useful reservoir properties. Sand is a generic oilfield term for lithologically clean sedimentary rock. Determined using a Vclay cut-off. Vcl cut off

Net Reservoir Comprises those rocks which do have useful reservoir properties. Determined using a porosity cut-off on Net sand. cut off

Net Pay: Comprises the net sands that contain hydrocarbon. Determined using a water saturation cut-off on Net Reservoir Sw

cut

off

33

Determining Net cut-offs

Determine using cut-offs equivalent to appropriate permeability: Oil field k=1mD Gas field k=0.1mD

Check sensitivity to cut-offs Compare with core data if possible Compare with production profile from PLT 34

Determination of Net Pay

„Net Pay‟ is derived from Net

Sw cut off of 50% -60% commonly used – arbitrary.

120 Relative Permeability (%)

Reservoir with the additional cutoff of water saturation to take into account the relative permeabilites of hydrocarbons and water.

Example: Relative Permeability v. Water Saturation

Kro

100

KrW

80 60 40 20 0 0

20

40

60

80

100

Water Saturation (%)

May be justified by examination of relative permeability data. 35

Course Outline and Timetable Day 1 Module 1: Petrophysics Definition and Contribution Section 1.1: Introduction Section 1.2: Petrophysics Properties Section 1.3: Capillarity and Fluid Contacts Section 1.4: Net and Pay

Module 2: Well Environment and Data Available Section 2.1: …..The Borehole Environment Section 2.2:……Petrophysical Data Types 1: Wire-line Log Data

Module 3: Looking at Logs Section 3.1: …..Log Quality Assurance Section 3.2:……Quick-look Analysis of Logs Exercise 1

36

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Module 2: Well Environment and Data Available to the Petrophysicist

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Section 2.1 The Borehole Environment

The Borehole Environment

Introduction The borehole is a very „extreme‟ environment, subject to changes in: temperature pressure chemistry

as drilling progresses. Mud circulation stops and starts, drilling rates vary; mud/rock interactions can cause formations at the borehole wall to swell, fracture or disintegrate, leading to tight hole or enlargement.

All borehole measurements are therefore affected to some degree by drilling mechanics and drilling fluid characteristics. 39

The Borehole Environment

Muds There are many types of drilling mud systems: Water, Oil, Synthetic Oil, Foam or Air based. (The most commonly used are Water- or Oil-based systems). Water-based muds are conductive. Oil-based muds are resistive.

All fluid muds perform a number of functions: 1. 2. 3. 4. 5.

Cooling the drill bit as it cuts through rock formations Clearing the rock „cuttings‟ from the bit and circulating them out of the hole Lubrication of the drilling bottom hole assembly (BHA) decreasing drag Preventing formation fluids, especially hydrocarbons, from escaping to surface Preventing rock cuttings from settling out from the mud column while drill pipe connections are made

6.

Sealing the borehole wall to reduce fluid loss to porous / permeable formations 40

The Borehole Environment

Effect of Muds on Log Data Water-based mud (WBM) often cause chemical reactions with clay minerals in the formations (mudstones / shales), which lead to clay swelling or clay movement within the pore spaces of the rock. Potassium Chloride (KCl) is sometimes used in the water based muds to help to reduce this reaction. However, the Potassium, being radioactive, has an effect on wire-line log quality (Gamma Ray). Barite is often used as a weighting agent in drilling mud. When present it masks the formation PEF log curve but may provide a fracture indicator. Oil-based mud (OBM) avoids, to a greater degree, the chemical interaction of water-based mud with reactive clays. From a formation evaluation standpoint oil-based muds are non-conductive and water based muds are conductive. This influences the type of logging tool used to measure formation resistivity (and the SP cannot be logged in OBM). 41

The Borehole Environment

Washouts Hole enlargement mechanical / chemical. Weakly cemented sands or chemically-reactive shales / mudstones can be eroded by the force of the mud circulation in the well bore. This can enhance the cutting effect at the bit and enlarge the hole diameter. The longer the hole is left uncased, the more chance of increased hole enlargement.

Over gauge

42

The Borehole Environment

Tight Hole Conditions Hole diameter may be reduced in a number of situations: 1. When swelling shales „squeeze‟ into the borehole 2. When a mud cake is developed across a porous and permeable formation 3. When halite (Rock Salt) „flows‟ as pressure of overburden is released by drilling to the well bore under gauge in gauge

Over gauge

43

The Borehole Bit Diameter

under gauge

overgauge gauge

In gauge

Under-gauge Hole - due to development of drilling mud cake against porous and permeable beds but may also indicate chemical instability e.g. swelling clays which are reacting to mud chemistry.

Over-gauge Hole - due to weak formation mechanical or chemical properties - exploited by drilling mud pressure and drill and BHA action.

In-Gauge Hole - due to mechanically and chemically stable rock properties and good mud chemistry and overbalance.

44

The Borehole Environment

Invasion Formation fluids are kept in place by maintaining an „overbalance‟ pressure of the mud in the borehole. The pressure exerted by the weight of the mud column is greater than the pressure of fluids trapped in the formation. In porous and permeable formations this overbalance pressure will force a net inflow of a portion of the drilling mud - the mud filtrate - into the pore spaces of porous and permeable formations. This phenomenon is called „Invasion‟. The larger mud solids collect at the borehole wall and develop a „mud cake‟, which has a very low permeability and thus tends to seal off the formation to further filtrate invasion. Build up of Mud Cake (under gauge)

45

The Borehole Environment

SECTION VIEW PLAN VIEW

Rmc Ro

Hmc

Rxo

Rm

Sxo

Sw

Rt Ri Invaded Zone

Non-invaded

Si

R = Resistivity S = saturation m = mud mc = mudcake xo = flushed zone i = invaded zone t = uninvaded zone w = formation water o = 100% water saturated, uninvaded zone

Transition Zone Flushed Zone Mudcake

Borehole

46

The Borehole Environment

Invasion The depth of invasion is controlled by the formation porosity and permeability and the mud characteristics (pressure differential between mud column and formation, viscosity and fluid loss).

Rmc Ro Rt

Rxo

Rm

Sxo

Ri

Sw Si

Invaded Zone

High permeability beds generally tend to show less invasion, due to fast mudcake build-up, while lower permeability beds tend to have more invasion.

Non-invaded Transition Zone Flushed Zone Mudcake Borehole

As mud invasion is a volume system, the depth of invasion in high porosity beds is shallow and correspondingly the depth of invasion in low porosity beds is deep.

The effect of invasion will decrease away from the wellbore so that there is a „transition zone‟ developed, from mud filtrate at the well, through a zone of mixed filtrate and formation fluid, to the „non-invaded zone‟ where original formation fluids are found.

47

Mud Filtrate Invasion

Well Bore

Mud Cake

FORMATION FORMATION WATER WATER

MUD MUD FILTRATE FILTRATE

Resistivity

Mud Cake

Well Bore

MUD FILTRATE

OIL

FORMATION WATER Flushed Zone

Flushed Zone Transition Zone

Non-Invaded Zone

Transition Zone

Non-Invaded Zone

Well Bore

OIL FILTRATE

FORMATION WATER

Well Bore

Mud Cake

Oil-Based Mud System (c) Water-bearing formation (d) Oil-bearing formation

Mud Cake

Resistivity

Water-Based Mud System (Rmf >> Rw) (a) Water-bearing formation (b) Oil-bearing formation

OIL FILTRATE

OIL

FORMATION WATER Flushed Zone

Flushed Zone Transition Zone

Non-Invaded Zone

Transition Zone

Non-Invaded Zone

48

The Borehole Environment

Log Corrections Poor hole conditions (or large hole diameters) will affect logging devices that are calibrated to measure in a specific borehole diameter. Log measurements may have to be corrected for a number of borehole effects such as: • • • •

Borehole diameter Presence of mud-cake Depth of mud filtrate invasion The proximity of the tools to boundaries of beds with differing lithology, and hence, log characteristics

49

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Section 2.2 Petrophysical Data Types 1 Wire-Line Log Data

Data Types Wire-line Logs Down-hole Pressure Measurements LWD (FEWD) Logs Core Data Sidewall Core Plugs Percussion Mechanical

Drilling Data All drilling data is captured on mud logs, these comprise: Cuttings Description and Percentage Inferred Geological column Hydrocarbon Shows (Gas and Fluorescence) ROP and other Drilling Parameters 51

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Wire-line Log Data

Wire-line Logging Tool Development

Wire-line 1927 First well log Pechelbronn field - Electric Log Hand plotting of log data 1931 SP (spontaneous potential)

1931 Continuous pen recorder 1936 Photographic film recording Typical logging combo 1936- late ‟50s SP short normal long normal lateral log resistivity

Recorded separately until 1946 1941 GR and Neutron tools 1946 Sonic log 1949 Induction Resistivity 1951 Laterolog (focussed deep resistivity) 1953 MLL 1953 MSFL 1962 SNP (Sidewall neutron porosity) 1963 DIL (dual induction log) 1964 FDC (compensated formation density) 1970 CNL (compensated neutron log) 1981 LDT (litho-density log) 53

Logging Development: Evolution of Logging and data implications 1950s/60s

Platform Express (PEX)

GR/Neutron, Electric Log, Lateralog, Microlog 3 separate runs, film recording, prints/film only, logs needed digitising if computer interpretation required

1970s/80s GR/Bore hole compensated sonic /Dual Laterologs/Microlaterologs or Induction logs GR/Neutron-density GR/WFT Fluid sampling

2 separate runs to acquire basic logs Computerised data recording and depth shifting Digital data recording to tape Data transmission started at the end of period (used rarely)

1990-Date LWD as part or whole substitute for wire-line GR/Density-Neutron/resistivity) single run GR/Modular formation testers Down-hole fluid typing Additional logs NMR, Array sonic etc Data transmission commonplace

54

Data Types

Wire-line Logs Wire-line logging tools are lowered from the logging unit (Self-contained unit offshore / truck-mounted unit onshore) on an electrical cable into the borehole. The wire-line cable provides two-way communication between the logging unit and the logging tool. Instructions can be sent to the tools / logging measurements are transmitted to the logging unit. Depth measured by length of cable spooled out. Logging is normally carried out when the tools are pulled out of the hole. Allowance for cable stretch and slippage are made by reference to magnetic marks registered at regular intervals on the cable.

Additional measurements made while logging include cable tension, mud resistivity and borehole temperature. The common logging tools are described in the following sections.

55 Schlumberger

Log Names – a few common examples

Description

Mnemonic

Alias

Caliper

CAL

CALI

Gamma Ray

GR

SGR

Spontaneous Potential

SP

Deep Resistivity

LLD

RD, RDEEP

Deep Induction

ILD

RILD

Sonic Compressional

DT

AC, DTCO, DT24

Sonic Shear

DTS

DTSM

Density

RHOB

ZDEN

Neutron

NPHI

CNC

Photoelectric Factor

PE

PEF

Full list of curve mnemonics, listed by company, in SPLWA website: http://www.spwla.org/

56

Caliper Log –

Units Inches or mm, Typical Log scale (6 – 16”)

The calliper log measures borehole diameter by means of either: 1. Mechanical arms attached to the tool which extend to contact the borehole wall as the tool is pulled up through the wellbore. 2. An acoustic signal measurement. – LWD tools.

Uses For calculation of borehole volume in casing cement jobs. Identification of hole enlargement (washouts) or swelling clays. Identification of mud cake development across porous / permeable formations. Breakout analysis for down-hole stress calculations. Input to environmental corrections.

Important Considerations There are different types of callipers: Mechanical 1, 2, 4 Arms / Acoustic Borehole size may exceed the arm length of the calliper tool in very badly washed out holes 57

Gamma Ray Log -

Units GAPI, Typical Log Scale (0 – 150)

Some chemical elements in naturally occurring minerals emit radiation in the form of gamma rays.

The commonest radioactive elements in the earths crust are Potassium (K40), Thorium (Th232) and Uranium (U238). Claystones / Shales, have a large amount of Potassium and Thorium and correspondingly exhibit high total gamma ray readings. Most clean reservoir rocks (sandstones, limestones and dolomite) have very low concentrations of radioactive minerals and therefore are characterized by low Gamma Ray log response. The difference in gamma ray reading is therefore used as a way to discriminate reservoir from non-reservoir rock. 58

Natural Gamma Ray Tool The Gamma Ray tool consists of a sensitive Gamma Ray detector that measures the natural gamma ray emissions of the rock column as the tool is passed in and out of the well. The total gamma ray value is recorded against depth. Uses Lithology determination Correlation between wells Clay volume (“shaliness”) calculation Depth matching or tie-in of multiple logging runs in a well

Important Considerations The tool can be run with most other logging tools The Gamma Ray log is a statistical tool. Therefore exact reproduction of the log curve may not be attained from one logging run to the next. Potassium-bearing muds (KCl) will increase the gamma ray readings Some reservoirs contain radioactive minerals which will mask the contrast between the reservoir and adjacent shale beds. Nuclear Source Tools (Density & Neutron) „activate‟ the formation, hence a higher gamma ray response may be apparent when run in combination with these tools. Some non-reservoir rocks such as coals, salt, anhydrite, gypsum and occasionally shales contain little or no radioactive minerals The log is affected by: formation density, borehole size (large washouts cause a decrease in log value), mud density (the heavier the mud, the more material between the detector and the borehole wall) and the presence of casing (tool response is attenuated by the presence of the steel and cement). 59

Spectral Gamma Ray Log

The Spectral Gamma Ray log is used to analyse the contributions of the main radioactive elements (K, Th and U) to the gamma ray signal. The main occurrences of the three radioactive minerals are: Potassium (%): micaceous clays, feldspars, micas, radioactive evaporites Thorium (ppm) : shales, heavy minerals Uranium (ppm) : phosphates, organic matter

60

Spectral Gamma Ray Tool

The Spectral Gamma Ray tool consists of a sensitive Gamma Ray detector that measures the natural gamma ray emissions of the rock. The gamma ray spectrum is measured while the conventional GR log measures the total count rate. The gamma ray counts are „binned‟ around the energy peaks for K, Th and U and the readings converted to individual log readings for these elements. SGR total GR . CGR GR with U component subtracted.

Th/K Th/U U/K

Uses Shale volume calculation – in sands without appreciable clay volumes, the Spectral Gamma Ray may permit better calculation of shale volume (CGR). Heavy mineral sand identification. Log correlation. Lithology determination – K vs Pe (photo-electric factor), Th / K ratio vs Pe Clay type – ratios e.g. Th / K are used to distinguish particular clay minerals Source rock potential – relationship between U / K ratio and organic carbon in shales.

Important Considerations Environmental corrections for hole size and mud weight are required. Statistical tool requires slow logging speed. 61

Spontaneous Potential Log –

Units (mV)

Uses Detection of permeable and non-permeable beds Shaliness indicator Formation water resistivity (Rw) calculation

Important Considerations The shale base line is frequently not a fixed value from surface to TD but tends to „drift‟ with increasing depth in the well. Poor resolution in thinly bedded formations. Cannot be run in wells with non-conductive muds. If salinities of mud and formation waters are similar there will be very little deflection of the SP curve. SP curve requires environmental corrections for bed thickness, hole size, invasion and resistivity contrasts. The calculation of Rw requires a clean, non-shaly bed response on the SP. SP response is dampened by the presence of hydrocarbons. 62

Spontaneous Potential Log

SP measures the difference between the electrical potential of a fixed electrode (Ground) at the surface and a moving electrode on the logging tool.

Shale

The logging response is generally constant in shale. This is called the “Shale Base line”. Permeable

Deflection of the log response occurs at permeable beds. SHALE

The direction of the response depends upon the mud filtrate salinity and the formation fluid salinity: „Negative‟ deflection to the left = formation water more saline than mud filtrate. „Positive‟ deflection to right = formation water is less saline than the mud filtrate. The magnitude of the SP deflection from base line depends on many factors.

Bore Hole

Permeable

63

Spontaneous Potential Log

xxx

Scale : 1 : 400

DEPTH (8390.FT - 8520.FT) DEPTH FT

GR (GAPI) 0.

SP (MV) 150. 0.

29/05/2004 19:54 LLD (OHMM)

100. 0.2

2000. MSFL (OHMM)

0.2

2000.

8400

8500

64

Sonic Log -

Units µsec/ft, Typical Log Scale (140 – 40 µsec/ft)

The Sonic tool essentially comprises a transmitter that emits a sound pulse and 2 or more receivers that record the returning signal. The first arrival at the receivers is the compressional (p) wave, which travels by the fastest path through the formation. The difference in arrival time of the compressional wave at the two receivers is called the interval transit time (Δt). Other sound waves, like the shear waves, travel through the mud and formation more slowly and arrive later at the receivers.

Modern Sonic tools commonly use multiple transmitters and receivers to correct or „compensate‟ for borehole enlargement or irregularity and tool tilt. 65

Sonic Log Tool

Uses Velocity derivation. Time to depth correlation. Acoustic Impedance calculation. Porosity calculation particularly when Neutron / Density logs are affected by large hole size. Evaluation of secondary porosity in combination with Neutron and / or Density logs – secondary porosity (vugs and fractures) is generally not seen by the Sonic log. Fracture identification – as above for secondary porosity. Overpressure evaluation – change in shale Δt with increasing depth. Lithology identification – some rock types, in their pure state, have diagnostic sonic Δt‟s (halite, anhydrite, gypsum) . Shear velocity from Array sonic data. 66

Sonic Tool and Principles

T1

Mud Dt Sensors

Transmitter compression

R1 R2

R3

8 Receiver Array

Compressional (P) Wave

Upper Receivers

Lower Receivers

Shear (S) Wave

R4

Lower Receivers

T2

Transmitter

Transmitters P Arrival

S Arrival

Stoneley Arrival

1

2 3

Borehole Compensated Sonic (BHC)

Array Sonic 4 5 6 7 8

t

T2 R1

T2 R2

T1 R4

T1 R3

2

67

Sonic Log Tool Important Considerations Cycle skipping – where the first arrival (p wave) is too weak to trigger the far receiver. Instead the tool records a later arrival, leading to a large travel time measurement. The Sonic log shows a large, abrupt „spike‟ to a higher transit time value. This may occur where there is washed out hole or where an inappropriate threshold setting has been applied during acquisition.

Δt is 57 μsec/ft in casing and provides a simple check that the tool is functioning properly. Stoneley Shear Compressional

First motion

68

Shear Sonic Log QC (Monopole & Dipole)

A quality check of the Monopole and Dipole measured shear log should be carried out in order to determine if the shear data is fit for purpose.

GreenbergCastagna lines

Areas where the log is affected by mud arrivals, processing artefacts and poor data are identified from Vp/Vs cross-plots by comparison with GreenbergCastagna empirical lines. Note: Dipole data can also be affected by mud arrivals.

Scale : 1 : 500

Test Well 1 DEPTH (8500.FT - 8600.FT)

DEPTH FT

09/02/2004 15:15

DTS_Mon (uSec/f t) 240.

PHIE (Dec) 40. 0.5

0.

DTLN (US/F) 240.

40.

Mud Arrival 69

Density Tool Tool Skid-mounted tool with radioactive source, shielded receivers and a calliper arm to record hole rugosity.

Uses Density determination. Caliper Arm Porosity determination. γ Fracture identification. Gamma Far γ Detectors Near γ Identification of minerals in evaporite deposits. γ γ Detection of gas. Gamma Source Determination of hydrocarbon density. Evaluation of shaly sands. Calculation of overburden pressures. Acoustic impedance calculation. Photoelectric 'absorption' Index (Pe) [second generation tools onward] lithology sensitive measurement largely unaffected by porosity and fluids. Fluids have low atomic numbers and very little influence on Pe.

γ

γ γ

70

Density Log -

Units gm/cc, Typical Log Scale (1.95 – 2.95 gm/cc)

A radioactive source (Caesium137 / Cobalt60) bombards the formation with focused medium-energy gamma rays (661 keV energy). The gamma rays collide with electrons of the formation in three different types of interaction. These interactions occur at different energy levels.

Pair Production – (>2M MeV high energy interaction) Not important in Density log energy window. Compton Scattering (0.5 – 2 MeV medium energy interaction) Important for the measurement of formation density. Photo-electric Absorption (<0.5 MeV – low energy interaction) Important lithology measurement.

71

Density Tool and Principles

Gamma Far Detectors Near

Gamma Source

High Pe

γ

Incident Gamma Ray

γ

Energy loss

γ γ γ γ γ

Large

γ γ

Readings at the gamma ray sensors depend on the „Electron Density‟ of the formation being logged. Electron Density is related to true bulk density, which in turn depends on rock matrix material, porosity and density of fluids in the pores. High Electron Density – Low gamma count – Low Porosity Low Electron Density – High gamma count – High Porosity

Low Pe

Capture Cross Section

Small

Pe ( Z

3.6

Energy loss

γ

e

e

γ e

e e Electrons

/ 10) e

Counts per Second / KeV

Caliper Arm

The difference between „Near‟ to „Far‟ Detector count rates is used to correct the Average Bulk Density displayed as a log curve.

Compton Scattering

Photoelectric Factor

Pe Region Compton Scattering Region Caesium Source Low Gamma Ray Energy (KeV)

(661KeV) High

72

Density Measurement

The remaining, scattered gamma rays are counted at two detectors in the tool at fixed distances from the source. The measured count rates are inversely proportional to the „electron density‟ of the formation i.e. a low count rate will be recorded in a high-density formation. The readings at the near and far sensors are used to correct for any remaining mud-cake or mud between the tool and the borehole wall. The correction (DRHO) is usually plotted next to the Density curve and provides a quality check for it. If the correction exceeds 0.05 g / cc, the quality of the main log is questionable.

73

Density Tool and Principles In earlier tools (Schlumberger FDC, Western Atlas Densilog). A Cross-plot of Long spaced detector versus Short spaced detector defines a “Spine and Rib” plot. For the tool in contact with the formation data fall on the spine and the position on it defines the formation density. In the presence of caving or mud-cake data plot off the spine and a corrected density is determined by projecting along the appropriate rib onto the spine. The correction is applied automatically to the log bulk density b. The borehole correction b is displayed as a log and provides a check on the reliability of the log bulk-density. Later generation tools (Schlumberger LDT etc) apply a more complex and less transparent calibration but still provide b as a quality check. 74

Pe Measurement

The probability of photo-electric absorption occurring increases with the volume of atoms with high atomic numbers (Z), thus more electrons, in the formation. The measurement of the probability of a reaction occurring between a gamma ray and an element with a particular atomic number is called the Photoelectric 'cross-section' Index (Pe). The higher the atomic number, the greater the value of Pe. Modern Density tools count the gamma rays detected in the low and high energy ranges separately. The low energy gamma rays count is a result of the Pe of the elements in the formation.

75

Density Tool

Important Considerations Density tool is a pad device. The tool is mounted on a skid to try to ensure contact with the borehole wall.

Hole rugosity adversely affects the density tool response. Any mud between the skid and the borehole wall is recorded in the „total density‟ measurement. Measurement is affected by the type, thickness and density of the mud cake. (Spine and rib corrections) It is a statistical tool and requires a slower logging speed ~1,800 ft / hr Pe is sensitive to the barite in barite-weighted muds. Barite has a Pe some 150 times greater than that of most common minerals and tends to dominate the log response. In this situation it can be used for fracture detection. 76

Neutron Log -

Units pu or v/v, Typical Log Scale (45 – -15 pu)

A radioactive source (usually Americium Beryllium) bombards the formation with high-energy neutrons. Neutrons colliding with heavy atoms of the formation matrix are effectively bounced, analogous to „billiard ball‟ collisions, losing a small amount of energy at each collision. However, when the neutrons collide with the nuclei of Hydrogen atoms, which are of near-equivalent mass, they lose a significant amount of energy, slowing their movement quickly to lower „thermal‟ energy levels. The slowed neutrons are „captured‟ by the nuclei of atoms such as Chlorine, Hydrogen and Silicon, which emit a gamma ray in the process. Depending on the tool type, either this „capture gamma ray‟ or the returning/surviving low energy „thermal‟ neutrons themselves are counted at the detectors on the logging tool.

Thus, the Neutron tool responds primarily to the amount of Hydrogen in the formation. The count rate at the detectors increases as hydrogen concentration decreases. i.e. count rate is high in low porosity rock and low in high porosity rock.

77

Neutron Tool and Principles

N

ENERGY

N

N N

Bowspring

γ

Far

Near

Neutrons slowed by repeated collisions with matrix nuclei & either captured (with release of a Gamma ray) or slowed & detected as Thermal neutrons.

Neutron Source

„Epithermal‟ Detectors

Near Far

Neutron Scattering Each change of direction represents one collision With a nucleus Mud cake

Borehole

H

Thermal Neutron

Increasing Neutron Energy (eV)

„Thermal‟ Detectors

Hydrogen Atom

N

Neutrons slowed rapidly to thermal levels by collision with Hydrogen nuclei.

Fast Neutrons (> 10k eV, Source – 4.5MeV) Intermediate Neutrons (100 – 10k eV) Epithermal Neutrons (0.1-100 eV) Thermal Neutrons (< 0.1 eV)

Increasing Time

78

Neutron Log In a Compensated Neutron Log (CNL) the source and 2 (near and far) detectors are mounted on a tool that is pressed against the borehole wall to minimize the borehole and mud-cake effect. Different neutron detectors tools measure neutron activity at either „thermal‟ or „epithermal‟ energy levels. The ratio of the count rates at the 2 detectors is processed by the computer to produce a linearly-scaled recording of neutron porosity index. Uses Porosity determination. Lithology interpretation. Identification of gas bearing intervals.

79

Neutron Log Important Considerations Shales / micas – shales and some other minerals e.g. gypsum, contain hydrogen in the crystal lattice, as bound water. Since the Neutron responds to all hydrogen, it results in large neutron porosity readings when logging through shale sections. Neutron absorbers – count rates at the thermal neutron detectors are affected by the presence of chemicals such as Chlorine and Boron in the formation water and rock matrix. Gas effect – Gas bearing formations have a reduced hydrogen density and hence an apparently low neutron porosity. Neutron log can be recorded through casing Porosity determination - in gas bearing or shaly formations can be made using the neutron and density logs combined. Neutron porosities are calibrated for clean, water-bearing limestone. Porosity measurements for other lithologies must be corrected for lithology. Borehole effects – Neutron logs can be affected by hole size, temperature, salinity standoff and pressure and are usually corrected for borehole salinity and hole size when processed at the well-site. 80

Summary Log Plot

81

Laterolog Resistivity - Units ohm.m, Typical Log Scale (0.2–2000 ohm.m)

Laterolog devices are „focused‟ electrode tools designed to minimise the effects of drilling mud and adjacent beds. Laterologs provide better vertical resolution than induction logs. The measuring current is forced to flow radially as a thin sheet of current into the formation being logged, thus minimizing the influence of the borehole and of the surrounding formations. Laterolog tools use focusing, or „Bucking‟ currents to force the current into the disc shape. The potential drop varies as the measure current and the formation resistivity change. The Dual Laterolog tool provides 2 depths of investigation for deep and shallow resisitivity (LLD / LLS) 82

Dual Laterolog Tool and Principles

Mud cake

Deep Laterolog (LLd)

Shallow Laterolog (LLs)

Focusing Electrodes Focusing Electrodes

Transmitting Electrode

Focusing current is returned to nearby electrodes causing the measure current to diverge more quickly as it enters the Formation leading to shallow depth of investigation. Both Shallow and Deep Laterolog measurements use the same electrodes and have the same current beam thickness. The different focusing current characteristics produce the different depths of investigation.

Focusing Transmitter Electrodes

MLL Pad

Saturation in the invaded zone as well as the diameter of the Invaded zone must be known to be able to correct apparent resistivity Ra to Rt. 83

Laterolog Resistivity Tool

Uses Can be used only in WBM. Lithology determination and correlation Recognition of resistive fluids (usually hydrocarbons) in the formation. Water Saturation estimation. Important Considerations Invasion and mud type can severely affect laterolog measurements. Fresh water muds cause log readings to be overly influenced by the resistivity of the invaded zone. Laterologs are generally recommended for use in saline muds, lower porosity and high resistivity formations Groningen Effect – A shift in deep resistivity measurement arises when high-resistivity formations (anhydrite, salt) force currents returning to the surface electrode into the borehole. An artificially high formation resistivity results and can lead to incorrect saturation calculations. The shallow resistivity (LLS) is not affected. 84

Induction Resistivity – Units ohm.m, Typical Log Scale (0.2–2000 ohm.m)

Purpose :Measurement of Formation Resistivity. High frequency alternating current of strength is sent through a transmitter coil.

constant

The electromagnetic field thus created induces secondary currents in the formation. These currents flow in circular ground loops and create, in turn, a magnetic field that induces a voltage in the receiver coil. The receiver signals are essentially proportional to the conductivity of the formations. 85

Induction Resistivity Tool and Principles

Mud cake

Receiver Coil Receiver Amplifier

Transmitter Amplifier

Foucault Current Ground Loop

The Foucault Current provides current focussing that ensures the transmitted current travels deep through the formation before reaching the receiver. „Bucking‟ currents eliminate the direct coupling of the transmitter and receiver coils

Transmitter Coil

Originally developed to measure formation resistivity in boreholes containing oil-based muds or air. 86

Induction Tool Uses Can be used in WBM or OBM. Recognition of resistive fluids (usually hydrocarbons) in the formation. Water Saturation estimation. Lithology determination and correlation.

Important Considerations The Induction log works best where the borehole fluid is of low conductivity. (This is the only tool for measurement of Rt in oil-based mud.) The tool also works in water-based mud wells as long as the mud is not too saline, the formations too resistive or the borehole diameter too large. Data should be environmentally corrected for borehole size, adjacent bed effects and invasion (in that order of priority). Limitations on the use of Induction logs are dictated by bed thickness, the depth of invasion, and the ratio between formation and mud resistivity. Induction devices do not read accurately at values > 200 Ohmm. 87

Micro Resistivity Log (MSFL or MLL)

Purpose: Measurement of formation resistivity close to the borehole. Pad mounted tool, focused electrode device, pressed against the borehole wall. The measuring current is forced to flow radially as a thin sheet of current into the formation, thus minimizing the influence of the borehole and surrounding formations. Uses Determination of Rxo, flushed zone resistivity. Lithology determination and correlation. Recognition of resistive fluids (usually hydrocarbons). Detection of mud-cake, hence permeable beds. Detection of thin beds.

Important Considerations Check hole rugosity and mud-cake on caliper log. Computed Sxo should always be greater than Sw in hydrocarbon-bearing zone. Zones of interest should be re-logged if pad contact is poor. 88

Nuclear Magnetic Resonance (NMR) Log

The Halliburton MRIL tool operates in a centralised mode, the Schlumberger CMR tool in a sidewall mode. NMR logs can provide information on Porosity (Total and Effective) and Hydrocarbon types. Largely unaffected by the rock matrix material. and requires little calibration to formation lithology.

In practice, NMR responses have been found to be complex and are frequently calibrated with respect to core measurements.

89

Nuclear Magnetic Resonance Log

Before NMR logging, the protons in the formation fluids are randomly oriented. As the NMR logging tool passes through the formation the magnetic fields generated by the tool „activate‟ the protons.

A permanent magnet firstly aligns (polarizes) the „spin axes‟ of the protons in a particular orientation. (This polarization increases exponentially with a time constant T1). An oscillating magnetic field is then applied, via the antennae on the tool, in order to „tip‟ these protons away from their new orientation. As the oscillating field is switched off, the protons try to re-align, or „relax‟, to their previously imposed orientation. Specific pulse sequences are used to generate a series of so-called „spin-echoes‟ which are measured by the NMR tool.

90

Nuclear Magnetic Resonance (NMR)

mm scale of measurement

1.Randomly oriented Hydrogen Protons in formation fluids

ffff

Spin-echo Amplitude (calibrated to Ø)

Magnet

ØTotal ØNMR

2. Hydrogen Protons aligned to imposed static magnetic field

3. Hydrogen Protons „tipped‟ by oscillating radiofrequency pulses

4. Hydrogen Protons „relaxing‟ to orientation of static magnetic field

T2 Cut-off for Clay-bound water < 3 msec T2 Cut-off Capillary-bound water >3 – 3?msec

T2 Cut-off for moveable fluids Moveable Fluids

FFI

Inter-echo Spacing (TE)

0

10

20

30

40

50

Time (milliseconds)

Bulk Volume Irreducible (BVI) & Clay Bound Water (MCBW)

60

70

80

T2 Spinecho Decay Curve

Free Fluids Index (MFFI)

T2 cut-off relates to pore radius or cap. pressure

Area under the curve equals Porosity (with proper calibration)

T2 Relaxation Time (milliseconds) Log Scale

91

fff

Nuclear Magnetic Resonance Log

The amplitude of a spin echo train is proportional to the number of hydrogen nuclei associated with the pore filling fluid. Thus amplitude can be calibrated to calculate porosity.

Properties of the fluids that affect the echo trains are: Hydrogen Index (HI) - the measure of the density of Hydrogen atoms in the fluid. Longitudinal Relaxation Time Constant (T1 - milliseconds) – a measure of how fast the randomly orientated protons align parallel to the imposition of a static magnetic field by the tools permanent magnet. Transverse Relaxation Time Constant (T2 - milliseconds) - a measure of how fast the „tipped‟ protons in the fluids relax perpendicular to the static magnetic field, after being disturbed by the radio-frequency, oscillating, pulse. T2 Cut-off (milliseconds) a value of T2 empirically related to a rock or fluid property such as pore size (inter-crystalline versus vuggy pore) or oil versus water saturation. Diffusivity (D) – is a measure of the extent to which molecules move at random in the fluid.

92

Nuclear Magnetic Resonance (NMR):

Mnemonics and

Porosity/Fluids breakdown

Conductive Fluids

Matrix

Dry Clay

Clay Bound

Capillary

Water

Bound

Mobile water

Hydrocarbon

Water

(MCBW)

(MBVI)

(MBVW)

Free Fluid (MFFI) Effective Porosity (MPHI) Total Porosity (MSIG)

Porosity Log Response Resistivity Log Response

(After cross-plot Corrections)

(After Clay Corrections)

MRIL Response 93

NMR Processed Example

Cumulative Amplitudes of Binned T2 distribution Permeability derived using Porosity, and Movable volumes (Coates Eqn)

Differential Spectrum used to remove water signal and identify hydrocarbons Hydrocarbons

Movable Water (mud filtrate)

Bound Water Waveforms of T2 distribution 94

Nuclear Magnetic Resonance Log The tool provides: A continuous measurement of fluids, including: effective porosity, capillary bound water, free fluid, clay-bound water and hydrocarbon types. Indications of pore size distribution, formation permeability, fluid characterisation.

Important Considerations: Limitations in carbonates – unable to see large vugs. Limitations in gas reservoirs – small signal. Stationary measurements and the stacking of signals greatly improves signal to noise ratio. Operates in the flushed zone. 95

Geochemical Logging - Principals

Schlumberger ECS (Elemental Capture Spectroscopy Sonde). The geochemical sonde measures relative elemental yields based on neutron-induced capture gamma ray spectroscopy.

The primary elements measured in both open and cased holes are for the formation elements: Silicon (Si) Iron (Fe) Calcium (Ca) Sulphur (S) Titanium (Ti) Gadolinium (Gd) Chlorine (Cl) Barium (Ba) Hydrogen (H)

Matrix properties and quantitative dryweight lithologies are calculated from the dry-weight elemental fractions using the empirical relationships derived from an extensive core chemistry and mineralogy database. Dry-weight lithology fractions (from elements) total clay total carbonate anhydrite + gypsum from S and Ca QFM (quartz + feldspar + mica) pyrite siderite coal salt

Matrix properties (from elements) matrix grain density matrix thermal and epithermal neutron matrix sigma.

96

Geochemical Logging - Applications

Independent determination of Clay fraction. Complex reservoir analysis defining: Carbonate Gypsum or anhydrite Pyrite Siderite

A matrix density for more accurate porosity calculation. Sigma matrix for sigma saturation analysis. Mineralogy-based permeability estimates. Geochemical stratigraphy (chemo-stratigraphy) for well-to-well correlation. Enhanced completion and drilling fluid recommendations based on clay versus carbonate cementation. Coal bed methane bed delineation, producibility, and in situ reserves estimation. 97

Wire-line Image Tools

Four or six arm tools. Resistivity or sonic wire-line devices. Complement coring and formation tester programmes. The resulting high-resolution borehole images can be used to identify geological and borehole features. These include: Planar features such as bedding, fractures, faults. Thin beds and new pay definition. Rock texture, grain size profile. Anisotropy. Permeability barriers. Paleocurrent directions. Stratigraphic features such as crossbedding and ichnofabrics. Borehole wall features such as breakout and drilling-induced fracturing. 98

Wire-line Formation Tester - Units Psia or Bar

The purpose of the tool is to obtain formation pressures and to sample formation fluids.

Probe Packer Piston

A retractable probe is sealed, using a rubber packer, against the borehole wall. A pressure draw-down is then applied at the probe by retracting the small cylinders in Pre-test chambers 1 and 2. The formation fluid will start to flow through the probe into the tool. The pressure measured by the tool will equilibrate to formation pressure if the formation is sufficiently permeable and the wait time long enough.

Pressure Gauge

x

Equalising Valve

Tool Probe Pre-test Chambers (10cc each)

Fluid Sample Chambers (5-20 litres)

Mud cake

Borehole

99

Formation Pressure Devices

Formation Pressure Wire-line data types:

MDT

FIT – through casing explosive setting- few points (psig)* RFT/MDT first generation strain gauges used (psig)* Note psia = psig+14.7 psi

Later RFT/MDT quartz gaugeshigher resolution (psia) MDT quartz gauges (psia) & alternative arrangements (dual probe, dual packer, through casing) XPT slimmed down express MDT Dual Packer Arrangements

LWD Tools since early 2000‟s Schlumberger Stethoscope Baker Hughes TesTrak Halliburton GeoTap 100

WFT Tool and Principles

The time taken for formation pressure to equilibrate gives a measure of the permeability (Fast equilibration - good permeability). Mobility is determined based on the drawdown achieved in the pretests.

To recover larger quantities of formation fluid the pressure draw-down can continue with fluid flow diverted to large containers within the body of the tool. Water samples and oil /gas samples can be segregated and sealed in pressurised or non-pressurised containers before recovering the tool at surface. Fluids can be typed down-hole using optical spectrometry and fluorescence sensors (Schlumberger LFA and CFA) which characterise fluid flowed through the tool. Pressure (psi)

Idealised Pressure Draw-down Recording

Mud Hydrostatic Pressure

Pre-Test Pre-Test 1 2

Mud Hydrostatic Pressure Formation Pressure

Time

101

WFT Fluid Densities & Contacts and FWL Pressure Versus Depth Plot

GOC defined by intersection of gas and oil gradients.

depth (tvdss)

Multiple tests

pressure vs. depth

FWL defined by intersection of oil and water gradients. OWC is above the FWL if the formation is water wet. – OWC close to FWL if the entry height is small as is often the case in sandstones.

Fluid gradients give formation fluid densities

Gas/Oil Contact

FWL

formation pressure 102

Wire-line Formation Tester Tool Uses Formation pressure measurements are important to help establish fluid density (oil, gas, water). To determine reservoir pressure. Permeability can also be estimated from pressure stabilisation data. Formation fluid samples can be collected to determine water salinity / resistivity, oil and gas properties. Fluid contact depths (FWL, OWC, GWC, GOC) can be evaluated given good quality pressure data. Inference of reservoir continuity or lack of it in field under production.

Important Considerations If the pressure returns to the higher „mud‟ pressure it is likely the packer is not sealing against the formation. In tight formation (low permeability) the pressure of the mud filtrate may not be dissipated within the formation, leading to pressure readings intermediate between mud and formation pressures. This effect is called „supercharging‟. Operating conditions, such as tight formation, usually limit the use of the Formation Test tool. Pressure / sampling points should be selected from in-gauge hole, avoiding washouts. Measurements from the tool are from very small reservoir volumes. Measurements should be taken going from shallow to deep to avoid gauge hysteresis.

103

Induction log

80 cm

Laterolog

80 cm

Neutron

40 cm

Gamma-ray

30 cm

Density

20 cm

Sonic

60 cm

Micro resistivity Micro log Dipmeter FMI

Resolution

Logging Tool Depths of Investigation and Vertical Resolution

5 cm 2 cm

250 cm

200 cm

150 cm

100 cm

Depth of Investigation

50 cm

0 cm

104

Course Outline and Timetable Day 1 Module 1: Petrophysics Definition and Contribution

Section 1.1: Introduction Section 1.2: Petrophysics Properties Section 1.3: Capillarity and Fluid Contacts Section 1.4: Net and Pay

Module 2: Well Environment and Data Available Section 2.1: …..The Borehole Environment Section 2.2:……Petrophysical Data Types 1: Wire-line Log Data

Module 3: Looking at Logs Section 3.1: …..Log Quality Assurance Section 3.2:……Quick-look Analysis of Logs Exercise 1

105

www.senergyltd.com/training

Module 3: Looking at Logs

www.senergyltd.com/training

Section 3.1: Log Quality Assurance

Log Quality Control and Quality Assurance: Use of Prints ├ The paper (or image file) log header presentation contains important acquisition details

and data. ├ Header Data can be obtained from other sources - from composite logs, routine drilling and geological reports if necessary. ├ A comprehensive log header should document the logs run, the mud type and properties in the well, bottom hole temperatures, casing shoe depths, the environmental corrections applied. ├ Digital databases are frequently presented without adequate log acquisition information and potential interpretation errors may result.

├ In the case of old log data, depending on it‟s origin, it may be necessary to confirm that it data matches the original field prints to be sure whether environmental corrections or depth shifts have been applied. ├ In the case of new data a repeat section is usually logged, the repeat section should be compared with the main log to confirm log repeatability. 108

Log Quality Control: Log Header Information

Log Header Information

Purpose

Tool Types

Environmental Corrections.

Casing points

Identify potential gaps, poor data.

Drill and Log TD

Identify depth discrepancies.

Bottom Hole Temperature (BHT)

Environmental Corrections & estimation of formation temperature.

Mud Type

Environmental Corrections & expected log types, Potassium in mud.

Mud Weight

Environmental Corrections.

Mud resistivities

Environmental corrections, Rw from SP.

List of Logs acquired

Identification of available curves.

Engineers remarks

Warning of problems acquiring logs.

109

Wire-line Depth Measurement and Control Depth Measurement, Wireline

Wire-line depth is measured by measuring the length of cable reeled out.

Tension device

Magnetic mark detector

Depth wheels

Uses Measuring wheels on the logging unit.

Tool zero

Derrick floor

Datum level

Copyright 2001 SIEP B.V.

Magnetic markers on cable. Shell Learning

Stretch corrections are applied to the cable. Depth zero established by lowering the tool until the zero measure point is level with the KB or rotary table.

Logging Wheels

110 May 23rd 2000: Petrophysics for Non-Petrophysicists: Operational Petrophysics

Wire-line Depth Measurement and Control

First Run in hole Tool zero. Calculate stretch correction near TD. Log up.

Subsequent runs in hole Tool zero. Run repeat section. Start main log after repeat section has been put on depth with first run log. Usually use GR for tie-in. Continue checking tie-in during logging.

GR in previous hole section

+/- 50m overlap

Subsequent hole sections Tool zero. Run repeat section. Start main log only after the repeat section has been put on depth with the previous hole sections first run log. Usually use the GR with ~50m of overlap – May need more overlap if there are no clear features near the previous hole section TD,

GR

TD

111

LWD Depth Measurement and Control LWD data stored on a time basis. Drillers Depth attributed to the LWD data on the basis of time and pipe in hole. Drillers Depth: Cumulative tally of all drill-pipe, stabilisers, drill collars etc. in the hole. Usually measured vertically in the derrick or pipe rack. Rarely measured under tension. 112

Log Quality Control and Quality Assurance

Depth Logs can be off-depth for several reasons: ├ Incorrect log offset adjustments can be applied by logging engineer. ├ Successive runs in a well may not be correctly depth matched. ├ Tool sticking can cause apparent tool movement due to cable stretch – see tension logs. ├ Problems are often restricted to pad tools, for example Density and Neutron logs. Hence GR for first run non-pad tool usually used as reference log.

113

Log Quality Control and Quality Assurance: Example Depth error Depth is the most important measurement made in logging! Check Drill depth and Loggers depth are not in conflict.

IC-1

Scale : 1 : 200

DEPTH (9149.98FT - 9300.24FT)

DB : IPDB (1)

DEPTH BS (in) 6. 20. 0. FT CALI (in) 6. 20. 0.

GR (API)

07/03/2006 18:01

ILD (ohm.m) 150. 0.2

GR (API)

DT (US/F) 200. 180.

40.

ILD (ohm.m) 150. 0.2

200.

If they are investigate why. Hole fill? Wrong pipe tally?

Drillers depth should always be greater than loggers by the amount of pipe stretch.

Ensure logs from each hole section tie in to those from previous section. 9200

Usually use GR from first run tool string in each hole section. Engineer may tie-in to casing-shoe if GR is featureless.

Top Upp Isongo Lwr Sd

Ensure logs in the same hole section are all on depth with each other. If mixing LWD and wire-line data need to check that the two are on depth.

DST 5 GAS 202mcum/d 9250

Usually shift LWD to wire-line depth. Exceptions for example if shallow sections are logged with LWD.

DST4 Water +TR GAS?

114

Log Quality Control and Quality Assurance

Calliper ├ Is it valid? Check its value inside casing. Gamma Ray ├ In a sand / shale sequence the GR log normally responds to lithology change unless there is low GR shale or high GR sand. ├ Note GR readings decrease in large diameter hole or if run through casing.

Spontaneous Potential ├ In a sand / shale sequence the SP log normally responds to lithology change, given that salinities of mud filtrate and formation water are different.

115

Log Quality Control and Quality Assurance

Resistivity logs should track porosity logs except in the presence of hydrocarbons. Induction Resistivity ├ There are induction limitations when run in saline muds and resistive formations. ├

Works best in low resistivity formations.

Laterolog Resistivity ├ Problems with the Deep Laterolog can occur below thick resistive beds (Groningen effect - A special tool configuration can be utilised to overcome this problem). ├ Works best in resistive formations. ├

A Porosity v. Rmf/Rw plot is a useful guide for selecting whether a Laterolog or an Induction log is most suitable. 116

Log Quality Control and Quality Assurance Microlaterolog Resistivity ├ Microlaterolog Resistivity logs should track deeper reading Resistivity logs, except where mud filtrate invasion occurs. ├

If poor pad contact occurs then the tool will respond to the mud resistivity rather than formation resistivity.

├

The (compressional) Sonic log should track the other porosity logs in a given lithology.

├

Cycle skipping is the most common problem, produces larger values of ∆T (slower velocity) and can occur in washed out hole.

├

Noise can also be picked up and manifests as smaller values of ∆T.

├

Check value in casing (steel transit time 57 us/ft).

Sonic

117

Log Quality Control and Quality Assurance Shear Sonic ├ Shear logs have a slower velocity than the compressional sonic but the two logs normally track each other for a given lithology apart from in shale. ├ Shear log processing often generates multiple versions of shear curves and identifying the correct one is not always straight forward. ├ A quality control check can be made by using a Vs v. Vp plot with reference to a GreenbergCastagna sand and mud line overlay.

Density ├ The Density Log should track the Sonic or Neutron log in sands / limestones. ├ May be affected in washed out or rugose holes due to lack of pad contact. ├ Always check the Calliper and Drho ( ρb) curves. ρb should be less than 0.05 gm/cc; if larger the density log is likely to be unusable.

Neutron ├ The Neutron log should track the Sonic log or Density log in sands /limestones.

118

Environmental Corrections All logging companies publish chart-books of log environmental corrections. Logging tools are calibrated to work in a particular environment. The further you get away from this environment the greater the need to apply an environmental correction to the resultant log curves. Sometimes environmental corrections are applied at the wellsite, computer centre post processed before delivery to the client or done by the client/consultant sometime later. Understanding what has or has not been corrected for can often be a challenge, especially on older data where all curve history has been lost.

In such circumstances its better not to correct than over correct. Unless a correction is obviously required. 119

Environmental Corrections Common corrections: GR casing and borehole correction (correction should increases GR)

Density borehole mud correction (correction usually very small in 9 inch or less hole)

Laterolog Borehole Correction small in 8.5” and smaller hole Adjacent Bed Correction Invasion corrections Conductive WBM suppresses resistivity response

Neutron Correction Hole size correction usually done in the field, need to remove before applying other corrections. Often when all corrections applied you get back to near starting point. Better to correct by choice of parameters in individual wells. 120

Environmental Corrections A main motive for environmental correcting data is to try and standardise curves and then perhaps your interpretation parameters. However many of the parameters required to allow correction are ill-defined and may vary with depth. They therefore need to be guesstimated or adjusted to provide a result which matches something that is well known. For example Neutron Standoff adjusted to provide N/D porosity which matches core porosity.

An alternative to applying environment corrections is to compensate within your interpretation by zoning and parameter selection. 121

Deep lateralog Borehole correction Differ if tool is centred or or eccentred. Use appropriate chart.

Dependant on: RLLD/Rm. Hole size Small correction for: Small hole RLLD Large

122

Dual lateralog LLD Tornado plot Enter with Rlld/Rlls & Rlld/Rxo Determine Depth of invasion Rt

123

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Section 3.2 Quick-look Analysis of Logs

Quick-look Interpretation: Part of the log QC process Scale : 1 : 100

SYNTHETIC NEW

DB : IPDB (7)

DEPTH (5010.FT - 5098.5FT)

1 DEPTH (FT)

3

5

GR (GAPI) 0.

RDEP (OHMM) 150. 0.2

Caliper (inches) 0.

7

RHOB (GM/CC) 2.95 140. NPHI (%)

RMIC (OHMM) 12. 0.2

40. PFMN_Depleted (psia)

200. 0.45

-0.15 2100. DRHO (gm/cc)

3

200. -1.05 PFMN (psia) 2150.

1

DT (US/FT)

200. 1.95 RMED (OHMM)

12. 0.2 BS (inch)

0.

05/01/2009 15:13

6

2200.

0.1 2200.

Define Gas and Oil Legs from D/N

5020

Evaluate Lithology and Net from GR and D/N 5040

6 5060

Calculate Sw 2 4

Identify

5080

Water Leg from Resistivity

5 Calculate Rw

Calculate Ø from D/N and Sonic

125

Quicklook Interpretation Resistivity/Porosity “Mae West” Effect Curves Responding in opposite directions

Porosity (%)

Resistivity (Ohm.m)

Wet or Tight

OWC TRAMLINE

Curves Tram-lining

MAE WEST

Hydrocarbon

126

• Exercise 1 Group Quicklook Analysis

Course Outline and Timetable Day 2 Module 4: Petrophysics Data Types 2 Section 4.1: Logging While Drilling Section 4.2: Conventional and Sidewall Core Data Section 4.3: Mud Logging Data

Module 5: Basic Deterministic Interpretation Section 5.1: ….. Preparation for Interpretation Section 5.2:…… Clay Volume and Lithology Section 5.3:……Porosity Section 5.4:……Water Saturation Section 5.5:……Permeability Section 5.5:……Net and Pay Exercises 2-6

Module 6: Reporting and Pitfalls Section 6.1: …..Petrophysical Report Writing Section 6.2:……Hints, Tips and Pitfalls

Module 7: Water Saturation in Shaly Sands

127

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Module 4 Petrophysical Data Types 2

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Section 4.1 Logging While Drilling

Logging While Drilling (LWD) or FEWD

Measurement While Drilling (mid 80s on)

MWD

Logging While Drilling (Late 80s, Early 90s on)

LWD

Formation Evaluation While Drilling

FEWD

Insurance Logging Thin Bed Resolution Correlation

Replacing Wire-line logs Geosteering/pore pressure indication Geomechanics during drilling

130

Logging While Drilling (LWD) Conventional wire-line logs are recorded at convenient points in the drilling program LWD is acquired in real time while drilling. Most of the principal logs can be acquired using LWD tools during drilling, providing data for timely, effective formation evaluation. Gamma Ray Resistivity Density Neutron Sonic NMR Formation pressures (during pauses in drilling)

LWD tools overcome the problems of logging in high angle / horizontal wells where tool access on wire-line can be difficult, expensive, time consuming or impossible. Furthermore, the logging is made while the bore-hole is in (usually) reasonable condition and where there has been minimal mud invasion. The logging environment is, however, more „extreme‟ than for conventional wire-line logging (vibration & noise). 131

LWD Logging Tools The tools are integral to the bottom-hole assembly (BHA). Power is supplied to the tools by batteries or by turbines powered by the mud circulation. A subset (selection of curves at larger depth increment than memory data) of the log data is transmitted to the surface using a mud-pulse telemetry system through the mud column in the well. Transmitted or Real-time data. Data are also stored in down-hole memory for later recovery at surface. Memory data.

Telemetry Systems Two modes of data transfer from tool to surface: Positive pulse – The tool extends an hydraulically driven „poppet‟ into the orifice aperture in order to cause a momentary flow restriction. This causes a positive pressure wave. Negative Pulse – The tool creates a pulsed pressure decrease in the mud column by opening and closing an electrically driven sleeve valve across the orifice aperture in the collar wall.

Issues Memory Size - Ensure large enough for length of bit run. Battery Life - Ensure long enough for length of bit run. Rotating and sliding modes. 132

LWD Logging Telemetry

Telemetry Receiver

Pressure Transducer

Computer

MUD PUMP

Drill String Positive Pulse Telemetry

Pressure Pulse Signal Orifice Aperture Mud Pulsor Sensors Mud Motor Bit

Hydraulic „Poppet‟ Mud Flow

133

Data Acquisition: While Drilling - Geosteering

MWD inclination

Bit inclination

Courtesy of Schlumberger

Drill to the Geology encountered rather than to a planned trajectory

134

LWD GR/D/N/Resistivity Azimuthal Density and Azimuthal GR Images –Example U L D R U U L D R U

Azimuthal Density

Azimuthal Gamma Ray

Density/Neutron

Resistivity

Gamma Ray © 2003 Baker Hughes Incorporated All rights reserved.

135

Integrated LWD tool strings

Second Generation LWD/FEWD

Most Service Companies have now launched integrated LWD strings which can offer services comparable to wire-line equivalents GR/Resistivity/D/N/Sonic/Formation Pressure/NMR Baker Hughes: Star series StarTrak, LithoTrak, SoundTrak,TesTrak, MagTrak Schlumberger: Scope series: Stethoscope, Telescope, Ecoscope, Periscope; etc Vision series: ArcVision, AdnVision, SonicVision etc LWD StarTrak Resistivity Tool 137

LWD Sonic

138

LWD Formation Pressure Measurements

Halliburton GeoTap (probe)

Ha llib ur ton - GeoTa p Schlumberger Stethoscope (probe) Baker Hughes TesTrak (probe) Pathfinder DFT (dual-packer)

Formation Pressure Measurements made during pauses in drilling.

Pathfinder DFT

141

LWD Logging Tool considerations

Density tool variations ADN is built into a stabiliser blade and records 4 densities ROBB, ROBL, ROBR, ROBU; hence have to make a choice which is appropriate. Other LWD densities centralised sensors and single measurement.

Neutron/density source arrangements ADN sources wire-line recoverable from surface. EcoScope – no chemical source for Neutron and Density - reduced consequences if tool lost in hole.

LWD/FEWD tools and services are rapidly developing; check out service company web-sites for current capabilities: http://www.slb.com/ http://www.halliburton.com/ http://www.bakerhughesdirect.com/cgibin/bhi/myHomePage/myHomePage.jsp 143

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Section 4.2 Conventional and Sidewall Core Data

Core Types

Conventional Coring core barrel sleeved core rubber, fibreglass, aluminium

sponge core pressure barrel gel coring

Sidewall Cores percussion rotary sidewall 145

Core Analysis – Why Is it done?

RHIIP

CAh (1 S wi ) RF Boi

Dynamic model

Static model Description

Data Source

RHIIP

Recoverable HIIP

C

Constant

Depends on oil or gas

A

Area

Maps, Seismic, Logs

h

Net Pay

Welltest, Logs, Core (perm)

Porosity

Logs, Core (log calibration)

Swi Initial Water Saturation

Logs, Core (m & n, Dean-Stark, Pc)

Boi Fluid Expansion Factor

PVT

RF Recovery factor

Technical, Economic (Core K, Rel K)

146

Core Coring provides essential calibration data for the integration of log analysis with actual reservoir rock samples. This calibration data includes: Routine Core Analysis: Grain Density. Porosity. Permeability. Water & Oil Saturation. Use of (1)Low Invasion Core Bits (2) Sample Preservation Techniques and (3) Mud Tracers can provide reliable saturation data.

Special Core Analysis: Core Compaction (Porosity and Permeability stress corrections). Archie Parameters (a, m and n). Capillary Pressure. Cation Exchange Capacity (CEC). XRD (Mineralogy).

147

Sidewall Cores

Usually limited in diameter/length. Small volume – greater inaccuracy.

Samples taken directly at sand face.

Percussion

In invaded zone. In potentially weakened zone as a result of possible wellbore failure due to too low (or sometimes too high) mud weights. Strength tests often invalid.

Rotary sidewall cores are superior.

Rotary

Often only grain density, lithology, etc from percussion. Not a substitute for conventional core. Small samples. From zone weakened by drilling. Flushed by mud filtrate. May contain mud solids. 148

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Section 4.3 Mud Logging Data

Mud Logs

The Mud Log records the rate of penetration (ROP), weight on bit (WOB), rotary torque, revolutions per minute (RPM) and pump pressure (SPP) measured by sensors connected to the drilling machinery on the rig. Drilling mud also carries rock cuttings and released formation fluids to the surface where they can be detected and recorded at regular intervals. Analysis of this information can give the Petrophysicist key information for the formation evaluation exercise. Relative increases and decreases in gas concentrations can indicate the penetration of hydrocarbon-bearing reservoirs and source rocks. Gas ratio analysis can be used to characterize the hydrocarbon type. 150

Example Mud Log

Example Mud Log

151

Course Outline and Timetable Day 2 Module 4: Petrophysics Data Types 2 Section 4.1: Logging While Drilling Section 4.2: Conventional and Sidewall Core Data Section 4.3: Mud Logging Data

Module 5: Basic Deterministic Interpretation Section 5.1: ….. Preparation for Interpretation Section 5.2:…… Clay Volume and Lithology Section 5.3:……Porosity Section 5.4:……Water Saturation Section 5.5:……Permeability Section 5.5:……Net and Pay Exercises 2-6

Module 6: Reporting and Pitfalls Section 6.1: …..Petrophysical Report Writing Section 6.2:……Hints, Tips and Pitfalls

Module 7: Water Saturation in Shaly Sands

152

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Module 5: Basic Deterministic Interpretation

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Section 5.1 Preparation for Interpretation

Evaluation Sequence

Gas Hydrocarbons  Reservoir  Rock 

Oil Water

Non-reservoir Lithologies: Sand Limestone Chalk Dolomite Clays Coals Calcite Anhydrite Halite

Porosity Permeability Reservoir quality Net sands

Water saturation

Hydrocarbon Type Fluid Contacts

GR SP D/N SGR

Sonic Density Neutron NMR WFT draw-downs

Porosity & DIL DLL

D/N WFT

Cuttings description CSTs Core Description Thin Sections

Core porosity Core permeability Core photographs

Formation water samples: WFT samples Produced water samples

PVT samples: WFT samples Down-hole samples

155

Preparation for Interpretation Talk to the rest of the team Stratigraphy - Tops- Geologist / Geophysicist. Mineralogy & Petrology - Geologist. Heterogeneity – Geologist. Modelling strategy – what do you need to deliver – Geologist and Reservoir Engineer. Production history – expected pressures, reservoir fluids – Reservoir Engineer. Drilling events (losses, kicks etc) Drilling engineer or end of well reports.

Assemble Well Header Data Contractor and Dates logged. Logs run and intervals logged. TD Logger and Driller. Logging problems noted (variable tension, cycle skipping etc). Bottom Hole Temperature (BHT). Mud Type (OB, WB, KCL) and Weight. Mud resistivity's.

Examine all data: Shows. Lithology log/cuttings description. core data and photographs. test and fluid sample data. Offset logs etc.

Make environmental corrections GR. Density – borehole correction in large holes. Neutron – care needed depending on corrections applied at well-site. Resistivity – depending on tool type and mud properties.

Pre-calculate Formation Temperature log. Determine Lithology flags (coals, calcite stringers, anhydrite, salt). Washouts flags.

QC logs 156

Important Conversations With the Geologist

The Reservoir Engineer

Make sure they understand the importance of tops to your interpretation!

Ensure that you deliver a saturationheight function that fits the Reservoir simulation requirements.

That changes to tops may change your interpretation!

What type of permeability input do they need in the model?

Make sure you understand the nature of the reservoir.

With the 3-D Modeller Make sure that the parameters required by your saturation height function will be available in the model.

Make sure you both know and agree how the model is to be built: Map Øe or Øt ? Map Net ? Map k or use K/Ø relationship

157

Log Evaluation Workflow Lithology Clay Volume Estimation Porosity Computation

Water Saturation Calculation

Fluid Zones Permeability Determination Net Pay / Net Reservoir Quantification

Reality check 158

Log Evaluation Workflow Lithology Clay Volume Estimation Porosity Computation

Reasons for iteration New Well data

Core calibration

Water Saturation Calculation Core derived parameters Comparisons with core Saturation-height

Fluid Zones

New Production data

New core data

Part or Total iterations

Fluids present Fluid contacts FWL

Permeability Determination

Inconsistencies seen in sense checks

Fluid samples Problems in 3-D modelling

Core derived predictors

Net Pay / Net Reservoir Quantification Reality checks Uncertainty Analysis

Problems in simulation

159

Log Evaluation Workflow: Reality Checks 1 Look for consistency: Between parameters from different data types. Different data types may not all tell the same story but any conflicts should be explained. Conclusion 1 shows and core data should be identified prior to Lithology, hydrocarbon log evaluation.

Lithology and Clay volume: Compare with clays and other minerals seen in core. Use core grain density as guide to main matrix material. Compare with core mineralogy (XRD, thin section).

Porosity Porosity: Differences or similarity of different log porosities. Log to core comparison or calibration. Sense check magnitude of porosity. 160

Log Evaluation Workflow: Reality Checks 2 Log derived water saturation should be compared with: Capillary pressure curves. Core fluid saturation measurements (Dean Stark). DST and WFT samples. Discrepancies may point to the need for modified interpretation.

Log derived permeability should be calibrated to core data. Compare cumulative log permeability with production log inflow profiles. Compare permeability-height (KH) from log permeability with KH from well tests.

Net Pay and Net Reservoir should be compared to permeability indicators and core if available. Effective formation evaluation is a process of integration of different data types in order to provide a robust interpretation.

161

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Section 5.2 Clay Volume and Lithology

Basic Interpretation Workflow Lithology Interpretation ├ The Gamma Ray log responds to natural radioactivity in rocks. Contrast between sand and shale.

├ Exceptions: Feldspathic (potassium feldspars), micaceous, or glauconitic sands will show an atypical, high gamma ray response. Source rock shales can have very high GR values – often a characteristic of the Kimmeridge Clay Formation in the North Sea. ├ Neutron and Density logs when run together are, by convention, displayed with the curves superimposed in the same log track, on standard scales such that curves overlay in water-bearing limestones. The curves shift according to lithology and porosity. ├ Some minerals have characteristic D/N responses and cross-plots can be used to determine these. ├ Calcite, Coal, Salt, Anhydrite, Gypsum etc ├ The photo-electric curve (PE or PEF) can also be used.

163

Typical Log Responses to Lithology and Gas

Density

Reservoir Rock

Neutron

Log response Decreases with Increasing Porosity Low High

Limestone (Reference)

2.71 g/cc

Sandstone

2.65 g/cc

Log response Increases with Increasing Porosity High Low ≈ 0%

Log response Decreases with Increasing Porosity High Low 47.5 us/ft ≈ 52.5 – 55.5 us/ft Variable with Compaction

≈ - 4%

2.83 to 2.87 g/cc

Dolomite

Sonic

≈ 42.5 us/ft

≈ 6 to 8 %

Non Reservoir Rock 2.98 g/cc

Anhydrite 2.33 g/cc

Gypsum

Salt

Shale

2.08 g/cc

≈ - (1 to 2) %

52 us/ft

48 %

67 us/ft

0%

Wide Range 2.3 – 2.7 g/cc Variable with Clay Density

≈ 50 us/ft

Reads High Increases with Clay Bound Water

≈ 130 – 175 us/ft Variable with Compaction

Hydrocarbon Gas Effect

Reads Low

Reads Low

Reads High

164

Lithology Example 1 Minerals Determined from D/N Cross-plot: 41/8-2

Scale : 1 : 1000

DEPTH (2300.FT - 3000.FT)

DB : IPDB (4)

Salt

ip:VWCL (Dec) 0.

Dolomite

1. ip:VSILT (Dec)

0.

1. ip:PHIE (Dec)

1.

Anhydrite

0. ip:VSALT (Dec)

0.

1. coalflag ()

Roter Saltzon

DEPTH raw :RD (OHMM) CAL ZDENds (G/CC) 0. 150. FT 0.2 2000. 6.16. 1.95 2.95 raw :SP (MV) raw :RMLL (OHMM) CNCds (dec) -200. 200. 0.2 2000. 0.45 -0.15 BIT (FT) ZCORds (G/CC) 5. 20. -1. 0.25 raw :CAL (INCH) ACds2 (US/F) 5. 20. 140. 40. rftp (psia) PEds (BARN) 1700. 2000. 0. 20.

07/03/2006 15:47

raw :GR (API)

0.

2.

3.

40 40 40

Leine Halite

Plattendolomit

10

20 10

SS 0

Halite StrassfurtDeckanhydrit

Basalanhydrit Hauptdolomit Werraanhydrit

2900

20

2.4

10

LS 0 2.8 DOL 0 (WA) Neutron Density Overlay, Rhofluid = 1.0 (Ch.6-42 1985) 3. -0.05 0.05 0.15 0.25 0.35 Neutron

2800

Hauptdolomit

30

20

2.6

2600

2700

30

Den sity

Plattendolomit

2500

2.2

Hauptanhydrit

2400

30

1319 points plotted out of 1334 Zone Depths (7) Leine Halite 2319.F - 2474.F (9) Plattendolomit 2485.F - 2611.F (10) Deckanhydrit 2611.F - 2654.F (11) Strassfurt Halite 2654.F - 2657.F (12) Basalanhydrit 2657.F - 2775.F (13) Hauptdolomit 2775.F - 2994.F

0.45

165

FMT Gradient = 0.474 psia/ft . Sample results similar to mud filtrate.

Lithology Example 2

41/8-2

Scale : 1 : 1000

DEPTH (4300.FT - 5050.FT)

DB : IPDB (4)

Minerals Determined from D/N Cross-plot:

07/03/2006 16:02

raw :GR (API)

DEPTH raw :RD (OHMM) CAL ZDENds (G/CC) 2000. 6.16. 1.95 2.95 FT 0.2 raw :SP (MV) raw :RMLL (OHMM) CNCds (dec) -200. 200. 0.2 2000. 0.45 -0.15 BIT (FT) ZCORds (G/CC) 5. 20. -1. 0.25 raw :CAL (INCH) ACds2 (US/F) 5. 20. 140. 40. rftp (psia) PEds (BARN) 1700. 2000. 0. 20. 0.

ip:VWCL (Dec)

150.

0.

1. ip:VSILT (Dec)

0.

1. ip:PHIE (Dec)

1.

0. ip:VSALT (Dec)

0.

Limestone

1. coalflag ()

0.

3.

Claystone-sandstone 4400

Interval : 4200. : 5100.

2.

raw :GR 150.

40 40

4500

135. 40 30 2.2

120.

30

4700

105.

30

20 20

2.4 Den sity

Undifferentiated Carboniferous

Undifferentiated Carboniferous

4600

10

90. 20

75.

10 2.6

60. SS 0

10

45.

LS 0

4800

2.8

30. DOL 0

4900

5000

(WA) Neutron Density Overlay, Rhofluid = 1.0 (Ch.6-42 1985) 3. -0.05 0.05 0.15 0.25 Neutron 1791 points plotted out of 1801 Well Depths 41/8-2 4200.F - 5100.F

15.

0.35

0.45

0.

166

Clay Volume Determination from Wire-line Logs

Clay Volume (Vclay) The clay content reflects the amount of clay minerals present in a rock. The term „SHALE‟ normally denotes assemblages of „clay grade‟ particle sizes which include clay minerals as well as other minerals such as quartz, mica etc. The proportion of clay in „shale‟ can range from 50 to 100%.

Clay volume is estimated to determine: Shale / Sand ratios. Shale corrections in porosity determination. Shale corrections to Sw . Log facies. Reservoir Delineation. 167

Clay Volume Determination from Wire-line Logs Commonly used Clay Indicators are: GR. SP. Resistivity (in hydrocarbon-bearing reservoir). Neutron-Density log Cross Plot.

Typically determine Vclay using several alternative methods and use either the minimum or average value of them Care required: If radioactive minerals (other than clays) occur in sands VclayGR is an overestimate. If hydrocarbon type is gas VclayDN is an underestimate.

The Vclay from logs should be calibrated or compared with core data where possible: Shale count observed in core. Thin section point count data. XRD data. 168

Clay Volume from Gamma Ray VclayGR

Normally shales contain radioactive minerals and sands do not. Sands may contain radioactive minerals e.g. Biotite, Potassium feldspars or Glauconite. Need corroboration with other clay indicators.

Select „clay‟ and „clean sand‟ lines. A linear relationship is normally assumed (non-linear versions Larinov or Clavier used in FSU for older rocks).

Vclay is obtained from the following equation:

Where,

VclayGR GRlog GRsand GRclay

VclayGR

= Clay volume from GR (v/v) = Log GR (GAPI) = GR in clean sand (GAPI) = GR in clay/shale (GAPI)

(GRlog GRsand ) (GRclay GRsand )

169

Clay Volume from Gamma Ray: Thin Beds

Heterogeneity – Thin Bed Problem In rock beds less than 2 feet thick, log resolution starts to have an impact by being strongly influenced by adjacent beds. Thinly laminated sand-shale sequences can have clean sands, which are not resolved and are interpreted as „shaley‟ sands or shales. Note: This problem is not limited to shale volume detection and the GR log. Similar effects with respect to nonresolution of thin beds also occur with porosity and resistivity tools. 170

Clay Volume from Gamma Ray – Plot illustrating picking sand and clay GR It is often difficult to decide which shales are characteristic of the clays dispersed in the sands: This will depend on the mode of deposition of sands and shales. Talk to the project geologist to get his insights!

Test Well

Scale : 1 : 750

DEPTH (8100.FT - 8400.FT)

GR Sand Line

Other considerations It is likely that different parameters will be required in different intervals in the well. Take care to note changes of hole diameter or presence of casing. Both will change the attenuation of the GR.

Parameters are chosen by one of several methods:

DEPTH 0. FT

GR (GAPI)

22/05/2004 15:02 VCLGR (DEC)

150. 0.

1.

GR Clay Line 8200

8300

By “eyeballing” sand and clay GR. Using sand and clay lines in a depth plot. Note: GRsand <= the smallest Log GRlog and GRclay< largest GRlog . 171

Clay Volume from Gamma Ray – Histogram illustrating picking sand and clay GR

Typically 5 and 95 percentile values of GR are adopted as GRsand and GRclay respectively.

Exercise Well GR (GAPI) Interval : 7450. : 8150. 100 10

80

Percen t o f T o tal

8

60 6

40 4

Cu m u lative F req u en cy

In some cases to render the process of choosing less subjective or to facilitate fast interpretation in a large number of intervals the parameters may use specified percentile points in histograms of GR.

20

2

0

0 0.

10.

20.

1347 points plotted out of 1401 Curv e Well GR All Zones

Exercise Well

30.

40.

50.

60.

70.

80.

90.

100.

Depths

Min

Max

Mean

Std Dev Mode

P5

P50

P95

7450.F - 8150.F

27.54

99.125

55.968

14.36

47.

39.95

51.459

86.126

27.54

99.125

55.968

14.36

47.

39.95

51.459

86.126

172

Clay Volume from SP VclaySP Responses in clay and sand – sand line and clay line. Select „clean‟ and „clay‟ lines (methods for choosing parameters are essentially the same as for GR).

Vclay calculated using the following equation:

VclaySP

Where,

VclaySP SPlog SPsand SPclay

( SPlog

SPsand )

( SPclay

SPsand )

= Clay volume from SP (v/v) = Log SP (mV) = SP in clean sand (mV) = SP in clay/shale (mV)

173

Clay Volume from SP

SPsand and SPclay are picked in a similar manner to the GR equivalents Considerations: SP deflection is suppressed (reduced) in hydrocarbon-bearing sands. SP deflection varies with Formation Water Salinity changes. Hence require different parameters in different zones of the well if formation (or mud-fluids) salinity changes.

SP is not effected by non-clay radioactive minerals. SP has poor vertical resolution – “lazy” response compared with GR.

174

Clay Volume from Neutron-Density VclayDN

Typically VClayDN is determined using Density-Neutron cross-plots: Choose appropriate lithology line by observation and hence select clean points. Choose a clay point as a “SE” point in the data distribution. Parameters are likely to vary by zone in a given well and between wells. Clay volume determined based on location of data points in the cross-plot.

175

Clay Volume from Neutron/Density Cross-plot Gas affected data: will lead to underestimate of Vcl from D/N cross-plot unless clean line is adjusted in gas zones.

May wish to place the Clay point at a position of greater data density; it should not be at the extreme edge of plotted data.

Callenish 1 CNC / ZDEN

2.

GR 100.

40 40

90. 40 30

2.2

80.

30

70.

30

20 20

Z DEN

2.4 10

60. 20

50.

Clay Point

10 2.6

40. SS 0 LS 0

10

30.

2.8

20. DOL 0

(SWS) Density Neutron(TNPH) overlay Rhofluid = 1.0 3. -0.05 0.05 0.15 0.25 0.35 CNC

10.

0.45

0.

176

VClay Comparison of Methods

Gamma Ray

SP

Density-Neutron

Pro‟s

Cons

Pro‟s

Cons

Pro‟s

Cons

Insensitive to borehole conditions

Radioactive Minerals in sands

Insensitive to borehole conditions

Requires water based mud

Not as sensitive to radioactive minerals as GR

Sensitive to Borehole Conditions

Available through casing

Radioactive Mineral variation in shales

Not affected by radioactive minerals

Poor Bed Resolution

Mineral typing

Sensitive to presence of gas

Not affected by hydrocarbons

Affected by Hydrocarbons

• Exercise 2 VClay

177

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Section 5.3 Porosity

Basic Petrophysical Properties: Porosity and measuring techniques Log and core Porosity Measurements Total Porosity, Sonic Log Total Porosity, Neutron Log Total Porosity, Density Log Absolute or Total Porosity

VSHALE Quartz

**

Oven-dried Core Porosity

Matrix

Clay Layers

**

Humidity-dried Core Porosity

Clay surfaces & Interlayers

Small Pores

Large Pores

Hydration or Bound Water

Capillary Water

Hydrocarbon Pore Volume

Isolated Pores

Structural Water

Irreducible or Immobile Water

** If sample is completely disaggregated (after Eslinger and Pevear, 1988)

179

Porosity from Sonic Wyllie Equation For much of the depth interval drilled in any well, the sonic log is likely to be the only means of deriving porosity. There are two equations (Wyllie time average and Raymer-Hunt-Gardner) In the Wyllie Equation, or the „Time Average‟ equation, porosity is assumed to be a linear function of the interval transit time:

s

Where, Øs = tlog = tma = tfl

=

Bcp

=

( t log

t ma )

( t fl

t ma )

*

1 Bcp

Sonic porosity (v/v) Interval transit time measured by the sonic log (μsec/ft) Matrix travel time (Sandstone 52-56, Limestone 49, Dolomite 44μsec/ft) Travel time of fluid contained in the formation (Brine or Water Based Mud 189, Oil Based Mud 200-220 μsec/ft) „Compaction factor‟ determined by comparison with core or regional experience. Often assumed to be 1.

180

Porosity from Sonic: Raymer-Hunt-Gardner Equation The Raymer-Hunt-Gardner relationship is an empirically-based Porosity solution using the comparison of sonic log transit times, core porosities and porosities from other logs. It provides more realistic values than the Wyllie equation particularly at high porosities and in poorly consolidated formations. In simplified form it is:

s

C. 1

Where, Øs

t ma tlog

=

Sonic porosity (v/v)

tlog

=

Interval transit time measured by the sonic log (μsec/ft)

tma

=

Matrix travel time (μsec/ft)

=

A constant (0.67 in liquid saturated rock, 0.6 in gas saturated rock)

C

This equation has the advantage that it does not require tfl as input.

181

Porosity from Sonic (Imperial Units)

Chart Book sonic porosity chart comparing: Wyllie Time-Average equation __________ Raymer-Hunt-Gardner equation __________

182

Porosity from Density

The Density measurement is the most reliable means of deriving porosity from logs given: Good hole conditions Fairly constant grain density

Density porosity is calculated using:

b

ma

fl

ma

d Where, Ød

=

Density porosity (v/v)

b

=

Log bulk-density (gm/cc)

ma

=

Matrix density (Sandstone 2.65, Limestone 2.71, Dolomite 2.88 gm/cc)

fl

=

Apparent fluid density (Approximate using: Fresh water-based mud 1gm/cc, oil-based mud 0.85 gm/cc)

183

Water Density Variations with Temperature and Salinity

Densities of water and NaCl solutions at varying temperatures and pressures

184

Porosity from Density: Chart Book Nomograph

185

Porosity from Density-Neutron Combination

Neutron porosity is seldom used independently: However neutron porosity may be the only porosity log in some early wells. Usually used in combination with the density log. Weighted average porosity: n

d

Oil/water

nd

2 2

Gas

2

d nd

n

`

2

Density-Neutron Cross-plot porosity

Density-Neutron combined porosity is particularly useful in gas zones where Ød and Øs tend to be overestimates unless core is available to calibrate them. 186

Porosity from Density / Neutron Cross-plot in shaly sands Establish Dry Clay point Cl. Scale Clay volume parallel to clean sand line. Scale porosity parallel to Shale line.

187

Effective Porosity

Effective porosity:

e

t

Vcl

tcl

Where, Øe = Effective porosity (v/v) Øt = Total porosity (v/v) Øtcl = Total porosity of clay (v/v) Vcl = clay volume (v/v)

188

Porosity Equations - Summary

( t log

Sonic -Wylie

s

Sonic - Raymer Hunt Gardner

Density

d

*

t ma tlog

C. 1

b

ma

fl

ma

ØN = Neutron Log + Matrix Correction

Neutron / Density

Effective porosity

1 t ma ) Bcp

( t fl

s

Neutron

t ma )

n

d nd

e

t

Vcl

2

tcl

• Exercise 3 Porosity 189

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Section 5.4 Water Saturation

Basic Interpretation Workflow Water Saturation using Archie Equation

Formation water resistivity ( m)

Tortuosity constant Use a = 1 (or 0.62) unless core data suggests otherwise

Calculate in water-leg or use sample analyses

Water saturation (v/v)

Sw

a m

Rw Rt

1 n

Saturation exponent Determine from core data or use n=m

Porosity (v/v)

True formation resistivity ( m) Cementation exponent Determine from core data or use m = 1.8 - 2 in Sandstones; m=2 in Carbonates (Some!)

Use deep resistivity for Rt ILD or LLD corrected as necessary 191

Archie Equation & Formation Factor Rw

Ro

Water saturated Rock

Formation Brine

Resistivity of water saturated formation Ro depends on the resistivity of the water linearly with a scaling factor called the Formation Resistivity Factor (FR) because it accounts for the effect of the formation and is a function of the porosity and pore geometry of the rock

Archie Experiment 1

Ro

Each Plug was saturated with a series of brines of resistivity Rwi and Ro measured at each.

Slopes FRi

Log FR

Rw

FR = Formation Resistivity Factor

R0

1000

FR

Rw

Log ( FR )

Log (a) m Log ( )

1 0

Log Ø

1

FR a=F@Ø=1

a m

Ro Rw

m = Cementation Exponent a = Tortuosity constant

Archie a = 1, m = n = 2

192

Archie Equation & Resistivity Index Rw

Formation Brine

Ro

Rt

Water saturated Oil a & Water Rock saturated Rock

Resistivity Rt of a partially oil saturated formation is a multiple of that of the fully water saturated plug Ro. Rt increases as the path for flow of electrical current is progressively restricted by increasing So or Sg. Archie called the multiplying factor the Resistivity Index (I).

Rt

Archie Experiment

Multiple Saturations

Archie Experiment 2 Each Plug was first saturated with brine of resistivity Rw and progressively de-saturated by injection of oil. Rt was measured at each Sw

I Ro

I = Resistivity Index

n = 1.8-2

Log I

I Log ( I )

0

n Log ( S w )

Log Sw

1 Sw

n

Rt Ro

1 n = Saturation Exponent

193

The Archie Equation is obtained by combining the FR and I expressions:

Taking Formation Factor expression:

FR

a m

Ro Rw

Then Re-arranging The Resistivity Index expression to: And substituting this in the expression for Formation Factor

Which when rearranged is the Archie equation in familiar form :

R0

a m

Sw

Rt S w

n

Rt S w Rw a m

Rw Rt

n

1 n

194

Water Saturation - The Archie Equation Archie Equation

Sw

a

* m

Rw Rt

1 n

Six unknowns: True formation resistivity Rt is taken as the most suitable deep reading resistivity, environmentally corrected if necessary. Formation water resistivity Rw SP interpretation From Rwa in a water leg Pickett plots Water samples

Porosity: log total porosity Tortuosity constant (a), Cementation exponent (m) and Saturation exponent (n): Preferably determined from Core measured Formation Factor (FR) and Resistivity Index (I) respectively. In Sandstones if lacking core choose from: Archie Parameters a=1, m=n=2 Humble parameters a=0.62, m=2.15, n=2 parameters for sucrostic rocks Tixier parameters for sucrostic or granular rocks a=0.81, m=n=2 Check suitability of a and m using Pickett plot in the absence of core data

195

Rt Preferred ranges of Induction Logs and Laterologs In Oil-based mud wells have to use Induction logs. In wells drilled with Water-based mud use Chart to determine preferred log for Rt appropriate to Rmf

196

Sources of Formation Water Resistivity Rw for Sw calculation: Water Sample Analysis Gen-8

Formation water sample analysis should provide: A measurement of Rw at a specified temperature (often @ 60ºF). Must convert Rw to the resistivity @ reservoir temperature using the ARPs equation.

Alternatively we may only have the sample composition in terms of dissolved solids. The resistivity of the sample can then be determined on the basis of it‟s composition: Determine equivalent NaCl concentration using Gen-8 chart or equivalent. Use Gen-9 Chart or equivalent to determine Rw at formation temperature. Care is needed to decide if water samples are contaminated by Mud filtrate or Injection water. Sample chemistry should indicate whether it is contaminated. Such contamination can invalidate this source of Rw.

Example:

Sense check Rw by determining it‟s salinity equivalent and the formation water density using the chart reproduced in the density porosity section. The result can then be compared with the fluid-density determined from formation pressure data in the water leg.

Total solids is 22,000ppm

Sample analysis Ca 500 ppm, SO4 1,500 ppm and 20,000 ppm NaCl

Entering Gen-8 at this total concentration multipliers for the equivalent NaCl concentration of Ca and SO4 are determined as ~0.8 and ~0.46 respectively. Hence equivalent NaCl concentration: 500x0.8+1500x0.46+20000x1~ 21,000ppm Entering Gen-9 with 21,000ppm and a reservoir temperature of 100ºF Rw is found to be 0.21 ohm.m.

197

Sources of Rw for Sw calculation Rw sources: From logs SP Rwa in water leg where re-arranging Archie Rw=ØmxRt Pickett plots MDT water gradient

From water samples WFT samples Produced water Rw catalogues

Rw dependence on salinity and temperature is described by Chart Gen-9. Can determine Rw knowing salinity.

Rw variation with temperature is described by the ARPs Formula:

R2

R1

(T1 (T2

21.5) 21.5)

Where, R1 and R2 are resistivities at temperatures T1 and T2ºC

Allows conversion of Rw measured in lab to down-hole equivalent.

198

Determination of Rw and m from a Pickett Plot From the Archie equation:

Sw

a

* m

Rw Rt

1 n

Rearranging and substituting 1 I resistivity Index S n

w

Rt

a Rw I m a.Rw

Taking Logs Log ( Rt )

Log (a Rw )

Log ( I ) m Log ( )

This equation describes a family of parallel lines, in a plot of Ø versus Rt, for different resistivity indices whose slope is –m. The line for I=1 (and Sw=1) is the water line with an intercept a.Rw. A plot of Porosity versus Rt of this form is is called a Pickett Plot.

Rw at formation

temperature = 0.025 ohmm

Slope m = 1.9 Assumed: a = 1, n = 2

199

Formation Resistivity Factor Variation with Porosity

200

Derivation of Tortuosity Constant (a) and Cementation Exponent (m) from Formation Factor (FR) Data Measured on Core Formation Resistivity Factor is :

Log ( FR )

a m

a m Log ( )

Hence in a Log-Log plot of FR versus porosity for a set of SCAL measurements made on a number of core plugs:

100

Formation Resistivity Factor, F (Rt/Rw)

Taking Logs:

FR

FRF v. Porosity (a=1, Fixed Regression through 1)

10

SCAL Data

Regression through 1 y = x-1.888 Slope = m=1.888 1 0.01

0.1

1

y = x -1.888

Porosity (v/v)

The slope = -m

A forced fit, assuming a=1 is often used. FR is measured under reservoir equivalent stress conditions are preferred if available.

FRF v. Porosity (Free regression) 100

Formation Resistivity Factor, F (Rt/Rw)

In a free regression the intercept on the FR axis at Ø=0 is a.

SCAL Data

Free regression y = 2.5582x-1.4017 , logy=2.5582+logx(1.4 017) ----m=1.4017 ---a=antilog(2.558)=0.41 2

10

1 0.01

0.1

1

y = 2.5582x -1.4017

Porosity (v/v)

201

Derivation of Cementation Exponent (n) from Formation Resistivity Index (I) Data Measured on Core

Formation Resistivity Index is :

I

Resistivity Index v.Water Saturation

1 n Sw

100

Log ( I )

n Log ( S w )

Hence in a Log-Log plot of I versus Sw for a set of SCAL measurements made on a core plug:

Resistivity Index, RI

Taking Logs:

10

The slope = -n A forced fit assuming a=1 is appropriate.

y = x -1.6485 1

Data for all plugs representative of a given formation or facies would normally be averaged to determine n.

0.1 Core Plug 1

1 SW

Regression through fixed point ----n= slope=1.65

202

Nomograph for Estimating Water Saturation

• Exercise 4 Sw203

Basic Interpretation Workflow: Fluid Contacts and FWL Oil Water Contact - OWC Picked at the point where the water saturation reaches 100% in interpretation plot. Picking is often complicated by changes of lithology or reservoir quality. Contact may not be seen in a given well in which case an Oil-down-to (ODT) and Water-up-to WUT) are picked. The Free Water Level (FWL) is at (if the entry height is zero) or below the OWC in a water wet formation. In an oil-wet formation the OWC will be below the FWL.

Free Water Level - FWL The FWL is most easily picked from the intersection of the oil and water gradient in formation pressure data. This can however be in error in water-wet formations drilled with oil-based-mud. The FWL can also be estimated from Sw versus depth plots as the deepest OWC observed if the entry height tends to zero in some wells. Residual hydrocarbons, aspheltine or tarmats below the current OWC often confuse the picking of the FWL and contacts.

Gas Oil Contact - GOC Picked in logs from the change in the density/neutron separation. Picked as the intersection of the gas and oil gradients in formation pressure data versus depth plots. 204

Free Water Level from Formation Pressures

• Gradients are defined by the density of moveable fluid. • The intersection of water and hydrocarbon gradients defines the Free Water Level (FWL).

Formation Pressure v. Depth Pressure (psia) 2955 6640

2960

2965

2970

2975

2980

2985

2995

3000

3005

3010

• Frequent Problems

6650

• Supercharging

6660

• Plugging

6670

• Gauge Hysteresis

0.320 psi/ft Oil Gradient

Oil Gradient

6680

Depth (ft TVDSS)

2990

• Wettability effects in OBM

6690 6700 6710 6720 6730

Water Gradient 0.452 psi/ft

Water Gradient

6740 6750 6760

205

• Exercise 5 Fluid Contacts

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Section 5.5 Permeability

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Permeability Measurement, Estimation and Indication Measurement Core measurement (on core plugs or whole core). Probe permeametry (on slabbed core). Drill-Stem Test (DST).

Estimation Generic relationships. NMR log and relationships. Field specific relationships determined by regression analysis of core data. K/Ø relationships K = F(Ø, Vcl, Sw etc.)

Indicators of permeable intervals Mobility from formation pressure measuring tools. SP deflection. Resistivity Separation. Mud Cake build-up. 207

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Generic Porosity- Permeability Relationships

Use with caution and only when no core data is available.

Wyllie Rose:

C S wirr

k

3

2

Morris-Biggs 2

3

Oil Reservoir

Gas Reservoir

k

0.0625

S wirr 3

k

0.0025

2

S wirr

4.4

Timur Equation:

k

0.136

S wirr

2

Jorgensen equation (for water saturated coarse clastics):

k

84,105 (1 )2

Where, C = a constant, K = permeability (mD), Ø = porosity (v/v), Swirr = Irreducible Sw (v/v), m = cementation exponent.

m 2

208

Permeability from Logs Permeability prediction from logs without reference to core data is problematical. Best practice is to correlate core permeability to core porosity and apply the resulting expression using core calibrated log porosity as input. This process is discussed in a later section.

No log measures permeability directly: WFT logs measure mobility and give an indication of the order of magnitude of the permeability. NMR logs provide the best stand alone log derived permeability; however core calibration is still preferred.

If estimation of permeability without core data support is necessary apply the appropriate generic porosity permeability relationship but use with caution. 209

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Section 5.6 Net and Pay Determination

Basic Interpretation Workflow Net and Pay Definition Gross Rock: Comprises all rock in the evaluation interval.

Net Sand: Comprises those rocks which may have useful reservoir properties. Sand is a generic oilfield term for lithologically clean sedimentary rock. Determined using a Vclay cut-off.

Net Reservoir Comprises those rocks which do have useful reservoir properties. Determined using a porosity cut-off on Net sand.

Net Pay: Comprises the net sands that contain hydrocarbon. Determined using a water saturation cut-off on Net Reservoir 211

Net Reservoir Determination

Western Petroleum Industry Practice Traditionally adopts rules of thumb as cut-offs for the evaluation of net pay from cut-offs. The arbitrary nature of the cut-offs is recognised. Usually the cut-offs have been fixed permeability values: 0.1 mD for gas reservoirs 1.0 mD for oil reservoirs

These nominal cut-offs are still commonly used.

Because permeability is not measured by logs the normal practice is to relate core permeability to porosity and/or other log-derivable parameters. The precise type of permeability used to specify the cut-off is not defined. 212

Determining Net Sand cut-offs

Determine using a Vcl cut off. The cut off is generally arbitrary and of the form Vcl<= Cutoff. The sensitivity of Net Sand count to the cut-off is generally examined by determining the net-sand for a range of cut-offs. The cut off should be determined in an insensitive region of the sensitivity plot if possible. Cut-offs should be validated by comparison of resulting Net sand with that observed in core. If sands with laminations below log resolution are encountered it is possible no net reservoir will be resolved. In these cases cut-offs may not be appropriate

213

Determining Net Reservoir cut-offs

Determined by applying an additional cut-off to intervals that have passed the Net Sand critera. Determine cut-offs equivalent to appropriate permeability: Oil field k=1mD Gas field k=0.1mD

Usually use a porosity cut-off equivalent to the appropriate permeability cut-off in a cross-plot of core permeability versus core porosity. Permeability and porosity corrected to down-hole conditions should be used. Hence the Net Reservoir Criteria are of the form: Vcl<=Cut-off and Ø>=Cut-off.

The sensitivity of Net Reservoir count to the cut-off is generally examined by determining the Net Reservoir for a range of cut-offs. The cut off should be determined in an insensitive region of the sensitivity plot if possible (see next slide).

Where reservoir can easily be identified in core the net reservoir should be measured and compared with the log net reservoir to tune the cut-off(s). Core photographs in natural and UV light may assist the picking of net reservoir in the core. Variation of the Net sand Vcl cut-off may be useful to achieve this match.

If core data is not available it may be useful to plot Density–Versus GR. A transition to a shale density can sometimes be observed which serves to define a GR or clay volume cutoff. See cross-plot overleaf. Comparison of net picked from logs with the intervals seen to be flowing in the production profile from a PLT can also be used to validate the cut-offs adopted. Such comparison is not however definitive since factors other than reservoir quality influence which intervals will flow. 214

Net Cut-offs Useful Plots

Cut-off Sensitivity Plot

N/G

A

B

Determining GR cut-off in GR-Density Cross-plot

CUT-OFF

2.9 2.8 Density (gm/cc)

Plot indicates whether the cut-off adopted is in a sensitive (A) or insensitive region (B). B is preferable.

3

2.7

Model

2.6

GRsand

2.5

GRclay

2.4

Log Data

2.3

Cut-off Point

2.2 2.1 2 0

50

100

150

GR (GAPI)

215

Determination of Net Cut-off using Porosity/Permeability cross-plot

Determination of porosity cut-off equivalent to a 1mD permeability cut-off in an oil reservoir.

216

Determination of Net Pay Net Pay is determined by the addition of a water saturation cut off to the Net Reservoir Criteria: Vcl<=Cut-off and Ø>=Cut-off. Net Pay defines the potentially productive portion of the reservoir.

The cut off Sw is in most cases largely arbitrary (typically 50% - 60%).

Relative permeability curves can be used to inform the choice of Sw cut-off ~ Sw Critical. Net Reservoir and Net Pay are used to determine Reservoir summary zonal averages. Versions of the log interpreted curves, set to null outside the net sands, are often generated. Numerical Flags are usually created for Net Sand, Net Reservoir and Net Pay.

• Exercise 6 Net and Pay

217

Net and Pay The Role of Cut-offs in Integrated Reservoir Studies, P.F. Worthington, SPE 84387, 2003. Impacts of Petrophysical Cut-Offs in Reservoir Models, B.J.P. Lalanne, G. J. Massonnat, SPE 91040, 2004. A Review of the Concepts and Methodology of Determining “Net Pay”, R. H. Snyder, SPE 3609, 1971.

218

Course Outline and Timetable Day 2 Module 4: Petrophysics Data Types 2 Section 4.1: Logging While Drilling Section 4.2: Conventional and Sidewall Core Data Section 4.3: Mud Logging Data

Module 5: Basic Deterministic Interpretation Section 5.1: ….. Preparation for Interpretation Section 5.2:…… Clay Volume and Lithology Section 5.3:……Porosity Section 5.4:……Water Saturation Section 5.5:……Permeability Section 5.5:……Net and Pay Exercises 2-6

Module 6: Reporting and Pitfalls Section 6.1: …..Petrophysical Report Writing Section 6.2:……Hints, Tips and Pitfalls

Module 7: Water Saturation in Shaly Sands

219

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Module 6:

Petrophysics: Report Writing, Pitfalls and Uncertainty

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Section 6.1 Petrophysical Report Writing

Petrophysical Reporting: Objectives

Good reporting is important: Communicates your results clearly to the users of your data. Is an aid to clear thinking and planning during the study. Without good reporting the study may well need repeating!

Regardless of the type of report being written the objectives remain the same: To provide a clear concise summary of the reservoir properties. Document the data available, methods used, assumptions made and parameters adopted such that another petrophysicist could reproduce your results.

Make clear the uncertainties and scope for improvement in the interpretation including any future data gathering opportunities in additional wells to be drilled. Document the petrophysical database to allow its future use if necessary by a different petrophysicist.

222

Reporting a Petrophysical Study

Executive Summary Introduction Data Interpretation Summary Uncertainty Conclusions Recommendations Appendix: Database Description 223

Study Reporting: Introduction

Executive Summary Top level overview The field or prospect Why the work reported was done Results

Introduction. The field prospect or well being studied To include: Ownership, Size and number of wells, Reservoir type, special characteristics etc.

Objectives of the study. New 3-D modelling exercise and simulation? New data from recent wells changed perceptions? Problems with old model need to be addressed….

Data Data Available

Timing – why was this study done now? Brief description of what the study consisted of. 224

Study Reporting: Data

Document all data available Logs, Conventional Core Data, Special Core Data, Fluid Samples, Formation Tops etc.

Logs Tabulate Logs available Comment on log quality Describe Environmental corrections made Record any depth shifts made

Core Tabulate core cut and core data available Describe core data quality and corrections Record depth shifts

Fluid samples Tabulate details of water or hydrocarbon samples

Tops Detail formation tops used and their origin

225

Study Reporting: Tabulate Data Available

General Well Data Log Data

Log Header Data

226

Study Reporting: Interpretation General

Report the interpretation process sequentially, typically: Lithology Clay Volume Porosity Water Saturation Permeability Fluid Contacts Net and Pay Reservoir Summaries Uncertainty For each section document: Method used and why Equation used in correct mathematical form Description of inputs to the equation including units Parameters used and how they were derived Support with plots or histograms used to derive parameters. Support with data and references (for example if Rw is based on water samples provide a copy of the analysis and reference to its origin) Comparison with core data if appropriate Depth plot comparisons with core data (porosity, permeability, Sw) Histograms of log derived and core data (porosity, permeability, Sw) Cross-plots Tabulated Statistics

227

Study Reporting: Interpretation Detail 1 Lithology and Shale Volume Report Methods Tabulate Parameters used by Well and Zone If thin-section clay volumes are available compare with log

Porosity Report Methods used Logic used if multiple methods are adopted Origin of parameters Clearly state type of porosity Øe or Øt

Water Saturation Record equation used Explain why shaly-sand or clean sand approach is adopted Parameters used and their origin Compare with Saturation-Height Function from Pc Compare with Dean Stark Sw 228

Study Reporting: Porosity Graphical Support

The report should include depth plots of all wells on both MD and TVD bases showing all interpreted logs, the logs used in the interpretation, the core data used in calibration and Net and Pay Flags. Such plots are often referred to as CPIs (Computer Processed Interpretations)

Core Calibration Plots Reproduce the histograms used to determine matrix parameters and cross-plots used to derive fluid parameters.

Depth Plots On the main depth plot for each well compare log porosity with compaction corrected core porosity, even if calibration to core was not made.

Cross-plots Cross plot compaction corrected core porosity versus log porosity

Histograms Histogram log and core porosity over the same intervals Separate histograms of both all log data and only that corresponding to depths with core data.

229

Study Reporting: Porosity Graphical Support

Mean grain density

Defines fixed point

2.65 gm/cc

230

Study Reporting: Interpretation Detail 2

Permeability Document Log k predictors used and reproduce graphically. Demonstrate performance of predictors in Histograms, cross-plots and depth plots

Saturation-Height Function Compare the function with Pc data from which derived in a cross-plot (Pc converted to depth). Compare Sw generated using the function with log and core Sw in depth plots. If the free water level (FWL) is uncertain may need to show sensitivity of Sw from the function to the height of the FWL.

231

Study Reporting: Permeability Graphical Support

Core Calibration Plots Reproduce the cross-plots and regression lines used to derive the log permeability predictors.

Depth Plots On the main depth plot for each well compare log permeability with compaction corrected core permeability. In most cases permeability comparisons are made on a log scale- in some cases it is informative to also plot it on a linear scale. Cross-plots Cross plot compaction corrected core permeability versus log permeability. As in the case of depth plots it is most common to use Log scales but can be useful to also use linear scales. Histograms Histogram log and core permeability over the same intervals Separate cross-plots of both all log data and only that corresponding to core data. Display statistics on the histogram (automatic in most petrophysical software).

232

Study Reporting: Permeability Graphical Support

Core k

Log k1

Log k2

Regression Plot from which K predictors were derived

233

Study Reporting: Fluid Contacts Graphical Support

Compare Fluid contacts determined by observation in depth plots with the FWL from Formation Pressure Data and with Fluid Samples

Reproduce depth plots used to pick OWC or ODT/WUT. In this case formation pressure data used to determine a FWL is also shown for comparison.

234

Study Reporting: Interpretation Detail 4 Net and Pay Document the cut-offs used and how they were selected Reproduce sensitivity plots Compare net from logs and core graphically

Uncertainty Provide estimates of uncertainty Porosity Water Saturation Contacts Net

Flag the major contributors to uncertainty

Conclusions General discussion of the results

Recommendations If there are particular areas of concern flag them Provides a starting point for further studies May provide guidance to logging or coring requirements in new wells May point to the need for fluid sampling or new core analysis 235

Study Reporting: Reservoir Summary

236

Study Reporting: Summary Present your results in the form of Reservoir Summary Tables for Each well. Broadly describe the results Method used and why Parameters used and how they were derived Comparison with core data if appropriate Support comparisons with Depth Plots, Cross-plots, Histograms and statistics as appropriate.

237

Well Interpretation Depth Plot or CPI

238

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Section 6.2 Hints, Tips and Pitfalls

Curve Tidy up or Cosmetics Before making a reservoir summary and interpretation depth plots:

Ensure spurious raw and interpreted data has been removed At Casing shoe Log pick-up Washouts Coals

Ensure that the interpreted parameters all have physically real values. Some software may generate negative values! Porosity >0 and < Upper limit depending on the environment. 0<Sw<1 0<= Permeability<= Largest value seen in core.

240

Using User Programs and Checking Correct Use of Interpretation Equations Make use of user programmes: To tailor the interpretation to your own requirements. To check “hard-wired” interpretation modules.

Use a spreadsheet (Excel) To confirm that you understand the intended form of functions picked up from previous studies. For example check the permeability predictors by using them to calculate permeability at several arbitrary porosities. To confirm you understand the input requirements for hard-wired functions in PP software.

Take care with inherited functions with no units specified. Porosity used in regression of core data is often in %! Check that equations work and produce sensible results in Excel.

Take care when using inherited functions containing Logarithms: Loose annotation; should specify Log10 or Loge or Ln If an equation uses simply Log try it out in a spreadsheet and see if it produces sensible results. I have witnessed this problem precipitate an equity redetermination! 241

Regression Analysis To derive a predictor in the form y = F(x) from regression in a cross-plot of y versus x. Use y-on-x regression because this form minimises the squares of the y distances of the regression line ( yi)2. Hence this line minimises the differences between the predictions of your regression line and the actual y values!

Do not use x-on-y regression or RMA regression for prediction.

y

yi

x

242

Care with Reservoir Summaries

Each depth in the data is considered a discrete interval, with the recorded depth at it‟s centre.

i n i

xhi

i 1 i n

av

hi

Averages over an interval therefore include only half of the top and bottom increment.

i 1

Some soft-wares get this wrong! Particularly significant in its effect if there are many thin segments of net!

Average Porosity Net weighted average

Average SH Net and Porosity Weighted Average. Some soft-wares get this wrong! i n

If in doubt about the correct functioning of your reservoir summary module, Download data to a spreadsheet and check your results against results of direct application of the correct equations.

i

S Hav

1

xhi x(1 S wi )

i 1 i n i

xhi

i 1

243

Delivering Permeability to the 3-D model

Modelled permeability may be made available to the modellers either: As a poro-perm relationship to be used in the 3-D model. using model Ø as input. As logs supplied to the 3-D modeler for “mapping” – not - recommended because of the lognormal distribution of k. As zonal permeability averages.

If permeability averaging is used the type of average used should be appropriate to the distribution of permeability in the reservoir. Discuss with the Geologist and decide which is appropriate: Homogeneous distribution – Arithmetic Averaging. Heterogeneous distribution – Geometric Averaging. Very Heterogeneous – Harmonic Averaging.

Harmonic averaging of horizontal permeability is often used as proxy for Kv. Permeability is scale dependent hence inter-scale comparisons (for example plug to log or log to DST) are problematic. There may be good reason that the log permeability does not match core or DST permeability. Upscaling is usually necessary before making comparisons.

244

Permeability Averaging Well to Field: Arithmetic If the permeabilities in a homogeneous matrix are parallel to the flow direction, an arithmetic average of permeability is appropriate.

Schematic fluid flow and k distribution

Formula for Arithmetic Average

n

kA

i 1

ki

n

Layers of equal thickness l

kA

i 1

ki ti l

Typical Geological Fabric

Layered or Laminated fabric calculating permeability for horizontal flow. Parallel to layering.

t

i 1 i

For variable thickness t

245

Permeability Averaging Well to Field: Geometric If the permeability is distributed randomly to a flow direction i.e. in a heterogeneous, unstructured reservoir, horizontal permeability is approximated by the geometric average. This average is dominated by the lower permeabilities and misses potentially significant high k streaks.

Alternative Formulae for Geometric Average

Schematic fluid flow and k distribution

Typical Geological Fabric

n

kG

ki

n

Bioturbated sediments

i 1

kG kG

(k1 k 2 k 3 ....k n ) n

k1

1 n

k 2 k 3 ...k n

246

Permeability Averaging Well to Field: Harmonic Harmonic averaging is used to approximate vertical permeability. This method is influenced by the lowest permeabilities of a dataset and assumes full continuity of each layer.

Schematic fluid flow and k distribution

Formula for Arithmetic Average

kH

n n i

1 1 ki

Layers of equal thickness

ti 1 ki

l i

kH

l

Typical Geological Fabric

Layered or Laminated fabric calculating permeability for vertical flow relative to layering.

t

i 1 i

For variable thickness t

247

Section 6.3 Petrophysical Uncertainty

248

Why Estimate Uncertainties in Petrophysical Data? To provide input to estimates of uncertainty in the petrophysical parameters used in STOIIP calculations. Since three of the inputs to the calculation of STOIIP are provided by Petrophysics:

STOIIP Where,

STOIIP GRV Net Gross Ø Sw B0

= = = = = = =

GRV

N G

1 (1 S w ) B0

Stock tank oil initially in place. Gross rock volume. Net Reservoir Gross Reservoir Porosity Water Saturation Formation Volume Factor

To highlight where the origins of the most significant errors occur and pinpoint where new data or improved interpretation can have most effect. 249

Spider Plot Evaluation of the Effect of Uncertainties in Petrophysical Interpretation Provides a mathematically simple way to evaluate the relative significance of the errors in the inputs to the petrophysical interpretation. Use the interpretation equations used in the interpretation as the basis. For instance the Waxman-Smits Equation for Sw: 1 Rt

m*

S wt t a * Rw

n*

1 B Qv

Rw S wt

Vary the magnitude of each of the inputs to the equation, over a range +/- 50% about the base value (while retaining the rest of the parameters at the value used in your interpretation). Calculate the % change in Sw resulting from the variation of each parameter and plot as a “spider” diagram as overleaf. The Plot is readily produced in a PC spreadsheet. 250

Spider Plot Evaluation of the Effect of Uncertainties in Petrophysical Interpretation The resulting plot indicates clearly the relative contributions of the errors in the parameters to the calculated property.

STOIIP SENSITIVITY TO PETROPHYSICAL PARAMETER ERRORS

phi 45

Hence in the example plot it is clear that the biggest source of error in this case is the porosity.

n*

35

Rw

30

Rt

25

Qv

20 15 STOIIP Error (%)

Rw ,Rt and Qv have relatively small effects while m* and n* have significant ones.

m*

40

10 5 0 -5-60

-40

-20

0

20

40

60

-10

Places to concentrate to reduce uncertainty:

-15

Improved porosity calibration

-30

-20 -25 -35 -40

More SCAL to reduce errors in m* and n*

-45 PARAMETER ERROR (%)

251

Estimation of Uncertainty Using Partial Derivatives

The Zonal average petrophysical properties determined for a field effectively represent the results of a series of physical experiments. The errors in the determination of each property result from a combination of systematic and random errors in those “experiments”. Systematic errors arise from the individual uncertainties in the input parameters. These can arise from mis-calibration of tools, experimental errors in core measurements etc. Random errors arise from the statistical nature of making multiple measurements.

Both types of uncertainties can be estimated for each parameter and then combined using the expression: 2

Where, Total Stat Sys

Total

= Total Uncertainty, = Statistical Uncertainty = Systematic Uncertainty

Stat

2 Sys

252

Statistical Uncertainty

The statistical uncertainty in a parameter can be determined from the standard deviation of the distribution of outcomes about the mean and the number of samples:

( x) T Stat

Where,

Stat

n T

= = = =

n Statistical Uncertainty in the mean of x. The standard deviation of individual x values. The number of values of x. For small samples, a function of n and required confidence level. 253

Statistical Uncertainty in Porosity and SW

The statistical errors in both Ø and Sw can be determined using the mean values from a given zone in individual wells together with the number of occurrences of that zone in the expression on the previous slide.

254

Systematic Uncertainties in Density Porosity

The density porosity is calculated using the expression:

b

ma

fl

ma

d

The total systematic error in density porosity can be estimated using the expression, derived by partial differentiation at right:

2

x

systematic

x

b

b

Where the partial derivatives with respect to b, ma and f are given by the expressions at right:

2

2

x

ma

ma

f

f

1 b

(

m

f

)

1 m

(

m

f

)2

1 f

(

m

2 f )

x

x

m

f

m

m

b

b

255

Estimation of Systematic Uncertainties in Density Porosity The Systematic Error in each of the input parameters to the density porosity equation must first be estimated. b Often assumed to be zero given the large number of measurements made in a

given reservoir interval. Could alternatively use tool resolution quoted by logging contractor. ma Determined from the statistics of the core matrix density ( / n) if that is the

origin of the value used. Otherwise estimate depending on uncertainty in matrix type and degree of cementation.

Estimate depending on method used to determine this parameter. For example use the reservoir fluid densities and mud filtrate densities together with estimates of the degree of invasion to estimate the possible range of f and use this range to estimate the systematic error in it. f

Example: North Sea sandstone reservoir: Mean Porosity 30%: bsys 0

gm/cc;

masys +/-0.01gm/cc;

fsys

+/-0.2 gm/cc

Resulting systematic error in Porosity ~1.7%. 256

Systematic Uncertainties in Water Saturation from Archie Equation: The Archie Equation can be partially differentiated to give the following expression for the systematic error in Sw: Sw

systematic

Sw x a a

2

2

Sw x m m

Sw x n n

2

Where the partial derivatives with respect to each of the input parameters a, m, n, Rw, Rt and Ø are given by the expressions at right and below: Sw Rw Sw

1 n

a Rt 1 n

m n

1 n

1 n

m

2

Sw x Rw Rw Sw a

n 1 Rw 1 n

a Rt Rw mn

1

Sw n m 1 n

2

Sw x Rt Rt 1 n

Rw m Rt

Sw m 1 1 n

Rw Rt

a

Sw

1 n

1

n a

Sw

2

x

1 1 n

S w n 1 ln

n

Sw Rt

2

1 n

Rw Rt

a m

1 n

a Rw

ln 1 n

m n

a m

1

n Rt

Rw Rt 1 1 n

257

Estimation of Systematic Uncertainties in Archie Saturation As in the case of porosity the systematic errors in each input parameter must be estimated. There is no standard procedure for this and the methods adopted will depend on the origins of the values used. Rt Estimate using logging contractor specifications or (say) 10% of the measured value (due to tool calibration errors etc).

Rw Method of estimation depends on origin of value used, samples, Pickett plot etc. If multiple methods give different values use the differences between them as estimates of uncertainty. Ø Use the value determined in the previous exercise. a Often assumed to be 1 in which case errors associated can be assumed to be addressed by the error in m. m Determine by error analysis of the SCAL data used to derive it. If there is no SCAL estimate based on the known range in analogous fields. n Similar methods to m. 258

Estimation of Systematic Uncertainties in Archie Saturation Example North Sea Field with Mean Porosity 30%, Mean Sw 19%: Rtsys +/- 0.71ohm.m Rwsys +/- 0.005 ohm.m (Rw =0.035 Ohm.m) Øsys +/- 0.02 asys 0 msys +/-0.08 (m=1.9) nsys +/-0.22 (n=2.4) Resulting systematic error in Sw 3.3% 259

Tornado Plot of Error Contributions

A tornado plot provides an indication of the relative importance of the errors in the various input parameters to the petrophysical interpretation.

Shows which parameters merit most attention for improved interpretation. Most petrophysical applications now include a Monte-Carlo module which allows the effects of uncertainties on interpreted petrophysical parameters to be evaluated and presented graphically.

Anonymous Field : Log derived Hydrocarbon Saturation Uncertainty Contributions Saturation Units (Fractional) -0.1

-0.05

0

0.05

0.1

n

Rt

Rw

Por

m

a

260

Course Outline and Timetable Day 2 Module 4: Petrophysics Data Types 2 Section 4.1: Logging While Drilling Section 4.2: Conventional and Sidewall Core Data Section 4.3: Mud Logging Data

Module 5: Basic Deterministic Interpretation Section 5.1: ….. Preparation for Interpretation Section 5.2:…… Clay Volume and Lithology Section 5.3:……Porosity Section 5.4:……Water Saturation Section 5.5:……Permeability Section 5.5:……Net and Pay Exercises 2-6

Module 6: Reporting and Pitfalls Section 6.1: …..Petrophysical Report Writing Section 6.2:……Hints, Tips and Pitfalls

Module 7: Water Saturation in Shaly Sands

261

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Module 7: Water Saturation in Shaly Sands

The effect of Shale on the conductivity

The additional conductive path reduces the resistivity of the formation.

The negatively charged clay surfaces provide an additional conductive path.

If this effect is not taken into account this has the effect of increasing the calculated water saturation above it‟s real value. Shaly sand interpretation corrects for this effect to calculate Sw. 263

Clay and the nature of Bound Water

Cations (Na+) are surrounded by water molecules because of the dipolar nature of the latter.

264

Thickness of the Clay-bound Water Layer as a Function of Brine Salinity

265

Clays and the Difference between Total Porosity and Effective Porosity Non-Clay Solids Formation Water Clay and Claybound water & Structural Water

Oil / Gas

Clean Matrix

Dry Clay

Struc Wtr.

Bound Water

Water

Oil / Gas

Wet Clay Volume (VWCL) Total Porosity (PhiT)

Sw = WaterVol / Effective Porosity Effective Porosity (Phie)

SwT = WaterVol / Total Porosity WaterVol

HydVol

266

Comparison of Clean Sands and Shaly Sands Porosity Segregation and Formation Factor Characteristics

“Clean” Sands No Clays or Claybound water

“Shaly” Sands Øt includes Clay-bound water

267

Measures of Shaliness

The number of positive ions (cations) attracted to the clay surface depends on the amount of clay and the type of clay. The number is called the Cation Exchange capacity (CEC), also denoted by Qv. CEC is expressed in milli-equivalent of exchangeable ions per hundred grams (meq/100gm). Qv is expressed in milli-equivalent per milli-litre (=cc) pore volume The conversion between the two is: (1 ) Qv

CEC

t

g

100

t

The Qv is indicative of the degree of shaliness of a formation: Qv<0.1 0.10.5

Clean sands Slightly shaly sands Moderately shaly sands Shaly sands Very shaly sands

Clays vary in their electrical activity as indicated by their CEC: Kaolinite 3-15 meq/100gm Illite and Chlorite 10-40 meq/100gm Montmorillinite 80-150 meq/100gm The GR is not a good indicator of CEC , for instance montmorillinite contains no potassium and hence has a low GR response but high CEC. 268

Shaly sands The Archie equation assumes that the matrix is non-conducting.

In shaly sands the resistivity is lower than in clean sands for the same Ø and Sw. This is caused by the additional electrical conductivity of the clay. Hence use of the Archie equation in shaly sands will result in too low a hydrocarbon saturation.

There are a large number of shaly-sand Sw equations. All have the basic Archie form with an additional term to account for the extra conductivity of the clay.

The clay-distributions for which the equations are intended are not always clear. Two equations will be described here: The Indonesia Equation – well adapted for application without supporting core analysis data. The Waxman-Smits equation – which is intended for application where the clays coat the matrix grains (dispersed shale). This equation performs well when core measurements of the clay properties are available. 269

Alternative Shaly Sands Water Saturation Equations

Many alternative equations: Adapted to different clay distributions Local conditions Approaches

Indonesian Simandoux Waxman Smits Dual water Poupon Modified Simandoux 270

Alternative Shaly Sands Water Saturation Equations Comparison Several equations are shown at right in conductivity form which facilitates comparison. The similarities and differences between equations are apparent.

271

When do I Need to use a Shaly Sand Interpretation? If possible treat sands as “clean” – non-shaly because it is much simpler to do so! In that case Øt = Øe and the Archie equation can be used to determine Sw. How can you tell if you need to use a shaly sand approach or not? If the formation has high shale volumes as seen in core. If CEC or Qv measurements on core indicate high values. Compare wetting phase saturations from air-mercury and air-brine Pc data. If the latter are significantly larger than the former then the difference is due to clays (which do not influence air/mercury saturations) and the need for shaly sand interpretation is indicated.

The fresher the formation water the more significant will be the effect of shale content. At high salinity (100‟s of kppm) shale effects become negligible even with substantial clay content. Examine the formation resistivity in sands; if it shows a dependence on shale volume you need to use Shaly sand interpretation. If in doubt as to the significance of shales calculate Sw using the Archie equation and a simple shaly sand equation (suggest the Indonesia equation) and see how much difference the two approaches make to Sw (and Sh) 272

Indonesia Equation

Has the advantage that it can be used without core derived parameters (although core derived m and n are preferred). Equation developed by Poupon & Leveaux) 1 Rt Where,

Swe Øe a m n Rw Rcl

= = = = = = =

m e

a Rw

Vcl

(1 (

Vcl )) 2

Rcl

S we

n 2

Effective water saturation (v/v) Effective porosity (v/v) Tortuosity constant Cementation exponent Saturation exponent Formation water resistivity (ohm.m) Clay resistivity (ohm.m) 273

Use of Indonesia Equation

Calculate Vcl from logs. Use conventional methods for Vcl (typically GR and D/N) Calculate Øe from logs. Effective porosity from density or sonic log:

e

t

Vcl

cl

Øcl can be determined as Øt as Vcl tends to 1 in a cross-plot.

Cross-plot Rt versus Vcl to determine Rcl. Determine Rcl as the value of Rt as Vcl tends to 1. Investigate the need for Rcl variation by zone.

Compare saturations with Swirr from Pc data and Dean-Stark saturations if available. Tune parameters as necessary. 274

Waxman Smits Equation

Has the advantage that it does not require Vcl as input and uses Øt rather than Øe. However it is best applied when core measurements of Cation Exchange Capacity (CEC) or Qv are available. Equation developed by Waxman & Smits 1 Rt Where,

Swt Øt a* m* n* Rw B Qv

m*

t

S wt a * Rw

= = = = = = = =

n*

1 B Qv

Rw S wt

Total water saturation (v/v) Total porosity (v/v) WS Tortuosity constant WS Cementation exponent WS Saturation exponent Formation water resistivity (ohm.m) Cation Mobility (mho cm2/meq) Cation Exchange Capacity (meq/ml) 275

Use of Waxman Smits Equation

Calculate Øt from logs. Calculate B using the Thomas equation: B

Where,

B T Rw

1.28 0.225T 1 Rw

= = =

1.23

0.0004059T 2

(0.045T

0.27)

Cation Mobility (mho cm2/meq) Formation temperature (ºC) Formation water resistivity @ T (ohm.m)

Obtain a relationship between Qv and Øt using special core analysis data.

a* m* and n* are best determined from SCAL.

276

Comparison of Total and Effective Saturations

If saturations are determined by a number of different methods are to be compared care is needed if water saturation is calculated with reference to total porosity Swt is to be compared with that calculated relative to effective porosity, Swe. Conversion from Swe and Swt is achieved by: (1 S wt )

t

(1 S we )

e

277

Example of the Effect of Shaly Sand Analysis Qv as a Function of Porosity

Water salinity 11,000ppm; Rw 0.2 ohm.m @ 200ºF

0.4 0.35

Hence from Thomas equation B = 10.5

Qv (meq/ml

0.3 0.25 0.2 0.15 0.1 0.05

a*=1, m*=1.78, n*=1.33

0 0

0.05

0.1

0.15

0.2

0.25

0.3

Porosity(v/v)

Qv=-2.086*Ø+0.55 Moderately shaly formation but relatively fresh water. Hence treat as shaly sand.

Comparison of log derived Sw with Sw/Height Function and Dean-Stark data much improved. 278

References: Shaly Sand Interpretation SPWLA reprint volume “Shaly Sand”, July 1982 Contains many key articles, e.g. the ones by Waxman & Smits, Juhasz, Hill /Shirley / Klein, and many others.

M.H. Waxman and L.J.M. Smits, Electrical conductivities in oil bearing shaly sands, SPE Journal, June 1968. The original article.

I. Juhasz, Normalised Qv - The key to shaly sand evaluation using the Waxman-Smits equation in the absence of core data, SPWLA 22nd Annual logging symposium, June 23-26, 1981. Archie III: Electrical conduction in shaly sands, Oilfield Review, Vol. 1, Number 3, October 1989. Description of history and some alternative Schlumberger models.

Worthington, P.F., The evolution of shaly sand concepts in reservoir evaluation, Log Analyst, Jan.-Feb. 1985, pp. 23 – 40. History and discussion of alternative models to the Waxman-Smits equation.

Hill H.J., Shirley, O.J. and Klein, G.E., Bound water in shaly sands – Its relation to Qv and other formation properties, Log Analyst XX, no. 3, 1979 279

Course Outline and Timetable Day 3 Module 8: Integration of Core data

Example Field Anonymous Sands: Lambda Function Compared with Stress Corrected MICP data

800

700

600

500

HAFWL (Ft)

Section 8.1: Porosity calibration to core Section 8.2: Permeability/porosity relationships Section 8.3: Interpretation of Capillary Pressure Data Exercise 7 Section 8.4: Log and core databases

12A 18 27B 22B 3 6 A B C 24 D 30 E 34 41 42 49 53 Porosity 0.15 Porosity 0.2 Porosity 0.25 Porosity 0.3

400

300

Module 9: Specialist Petrophysical Techniques

200

100

Section 9.1 Petrophysics and Geomechanics Section 9.2 Petrophysics in Carbonates Exercise 8

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sw (v/v)

Module 10: Data Acquisition Planning Section 10.1 Formation Evaluation Value of Information Section 10.2 Planning Logging Programmes 280

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Module 8

Integration of Core Data

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Section 8.1

Porosity Calibration to Core

Conventional Core Porosity Correction to Overburden Stress Conditions Conventional core porosity measurements are made at laboratory stress conditions. The measured porosity is hence higher than it would be under reservoir stress conditions. The core porosity needs to be corrected to reservoir stress conditions before use in calibrating log porosity. Correction factors are typically in the range 0.9-0.98. Special core analysis measurements of porosity at a series of applied stresses (covering a range to include reservoir stress) are required to allow determination of the correction factor in a specific reservoir. If routine data is based on ambient helium porosity then SCAL tests must also measure ambient porosity under identical conditions. Formation factor and cementation exponents at overburden can also be measured during porosity compaction measurements. The applied stress is isostatic

v

=

H

=

h.

The stress regime in the reservoir is not isostatic since the vertical stress is in most cases much larger than the horizontal stresses. Must determine the effective isostatic pressure isoeff, equivalent to the reservoir stress, at which the laboratory measurements made under isostatic conditions should be interpolated. 283

Overburden Porosity Test Equipment

Plug sample stressed isostatically v

=

H

=

h

284

Effective Isostatic Stress

Effective isostatic stress v isoeff

H

h

3

.Ppore

Where, isoeff v H h

Ppore

= = = = = =

Effective isostatic stress (psi) Vertical stress (psi) Major horizontal stress (psi) Major horizontal stress (psi)~ Pore pressure (psi) Biot‟s constant

Vertical stress is determined either: By integrating the density log from the surface to reservoir reference true vertical depth. Or by assuming an average overburden of 1 psi/ft and multiplying by the reference true vertical depth

Pore pressure is determined from WFT formation pressure data, projected if necessary to the reference depth. 285

Effective Isostatic Stress In Low horizontal stress regime: In tectonically passive areas assume zero horizontal deformation or uniaxial compaction then H = h

Which allows simplification to :

1 3 (1

isoeff

Where,

=

Making the assumptions; equation:

)

(

v

.Ppore )

Poissons Ratio = 0.3 and

isoeff

= 1 leads to the Teeuw

0.62 (

v

Ppore ) 286

Example: Effective Isostatic Stress and Porosity Compaction Factor Determination Reservoir with reference depth 6,500 Ft TVDSS with a formation pressure of 2,800 psi at that depth Estimated vertical stress 6,500 psi Effective isostatic stress = 0.62x(6,500-2,800) = 2,294 psi. For each core plug, in a plot of core porosity versus isostatic stress interpolate at isoeff to determine porosity at reservoir overburden stress. Plot interpolated porosities at isoeff (y-axis) versus porosity at laboratory conditions (x-axis) and perform forced y-on-x regression through the origin. The slope of the line gives the compaction factor. Illustrated in next slide.

Multiply the conventional core porosities by the compaction factor (0.947 in this case) before calibrating log porosity to core data. 287

Schematic of porosity Compaction factor Determination Cross-plot Core Porosity at Effective Isostatic Stress equivalent to reservoir OB Stress versus that at Laboratory Conditions and use regression analysis to determine the Porosity Compaction Factor.

Core Porosity versus Isostatic Stress 0.34 Plug 2

0.3

Plug 3 Linear (Plug 3)

0.28

Linear (Plug 1) Linear (Plug 2)

0.26 0.24 0.22 0.2 0

500

1000

1500

2000

2500

3000

3500

4000

4500

Isostatic Stress (psi)

Interpolate core porosity versus Isostatic stress data at Effective Isostatic Stress equivalent to reservoir OB Stress to determine porosity at OB for each core plug.

Core Porosity at Effective Isostatic Stress versus that at Laboratory Conditions 30 Porosity at Effective Isostatic Stress (%)

Porosity (%)

Plug 1 0.32

y = 0.9471x R2 = 0.9969

25 20 15 10 5 0 0

5

10

15

20

Porosity at Laboratory Conditions (%)

25

30

288

Calibration of Log Porosity to Compaction Corrected Core Porosity Most often used to calibrate density porosity but similar method is readily applied to sonic porosity. Core data should be depth shifted to logs prior to use in calibration. Treat intervals with different mud-types, reservoir fluids, lithologies etc. separately. Determine mean core grain density (in some instances the mode may be appropriate) and adopt as ma. Note that if the grain density shows a high degree of variability this method may not be applicable. Cross-plot compaction corrected core porosity (y-axis) versus log bulk density and perform forced y-on-x regression through the matrix point ( ma,0). The equation of the line is: 1 ma

b

Hence, the slope of the line:

m

(

ma

)

fl

(

1 (

ma

fl

)

ma

fl

)

and rearranging:

fl

1 m

ma

Note: that one can use the same cross-plot of log porosity versus bulk density to determine fluid density used in inherited projects. 289

Example of Density Porosity Calibration to Core Hence: Slope of regression = 54.66 (%)/(gm/cc). Converted to (v/v)/(gm/cc) =0.5466 and fl = 2.65-(1/(0.5466)) = 0.8 gm/cc

Illustrate quality of match in report using: Depth Plots Cross-plot Log versus core porosity Histograms of log and core porosity

Mean grain density

Defines fixed point

2.65 gm/cc

290

References: Overburden Correction

J.A. Nieto, D.P. Yale and R.J. Evans (1990). Core compaction correction – a different approach. Advances in core evaluation: accuracy and precision in reserves estimation, pp. 139-156, Gordon and Breach, London, 1990. Worthington, Daines, Bratli and Nicolayson (1997). Comparative evaluation of core compaction corrections for clastic reservoirs. The Log Analyst, Sept-Oct. 1997. M.H.H. Hettema, P.M.T.M. Schutjens, B.J.M. Verboom and H.J. Gussinklo (2000). Production-induced compaction of a sandstone reservoir: the strong influence of stress path. SPE reservoir evaluation and engineering, August 2000.

291

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Section 8.2

Permeability-Porosity Relationships

Core Measurements of permeability

Determined on core by: Measuring rate of fluid flow through cylindrical core sample of known length and cross sectional area subjected to a pressure gradient.

Core plugs or whole core. Plugs are cut horizontal or normal to bedding plane to measure KH. A smaller number of Kv measurements are also usually made.

If the flowing fluid is gas, need to correct for gas slippage or Klinkenberg effect. Need to correct to reservoir stress and fluid conditions. Or make measurements using reservoir fluid in samples subjected to reservoir stress. 293

Air Permeability: Steady-State Measurement

Confining Pressure

P1

P2 Pa

In

Plug Qa

Regulator

Regulator Coreholder

Manometer 294

Permeability, Klinkenberg Corrections examples Air permeability KHa measured at a series of mean pressures.

Klinkenberg Plot 60

Determine KHc by extrapolating to permeability at infinite mean pressure.

Permeability mD

50 40 30 20 10 0

KHc is the absolute horizontal permeability.

0

5

10

15

20

25

30

35

Reciprocal Mean Pressure 1/Pm

295

Ambient Permeability

Absolute Permeability Air (N2) Permeability cheap and convenient 1 per 1 ft (30 cm) - “horizontal samples” 1 per 5 ft or 10 ft - “vertical samples” Does not relate to reservoir fluids nor pressure Confining pressure up to a few hundred psi

Klinkenberg Permeability Pseudo-liquid permeability selected samples only (SCAL)

Brine Permeability selected samples only (SCAL)

296

Permeability Modelling Overview

Core data should be depth shifted to logs prior to use in calibration. Correct core permeability to reservoir in situ stress conditions either: Using measurements of air or brine (preferred) permeability at a series of isostatic stresses.

Using the Juhasz formula for shaly sands.

Establish permeability predictors: In most cases k/Ø relations determined by regression analysis using overburden corrected k and Ø. More complex relations using for instance resistivity, Sw or Vsh as input sometimes used.

Calculate permeability log. Determine zonal averages per reservoir Unit. Use appropriate averaging method depending on heterogeneity. 297

Correction of Air Permeability at Lab Conditions to Liquid permeability in situ Stress

Determine effective isostatic stress isoeff as described in the section on porosity correction to in-situ conditions. If either Klinkenberg or Brine permeability measurements at overburden stress are available: Interpolate the data for each individual plug in a plot of k versus isostatic stress at isoeff to determine kOB. In a log-log plot of kOB (y-axis) versus kair (x-axis) for all of the SCAL plugs perform y-on-x regression to determine the conversion from kair to kOB. Correct routine core analysis Kair to kOB. Correction is generally more severe at low permeability than at high.

298

Correction of Air Permeability at Lab Conditions to Liquid permeability in situ Stress If measurements of permeability under stressed conditions are not available a method based on an extensive shaly carbonate and clastic data set by Juhasz can be used to correct Kair to kbrineOB: 3

kb

For kair>660mD

k air

e t

3.045

For 160
kb

0.28 k

1.194 air

e t

C

For kair<160mD

kb

A k air

B

e t

With

A

Where, Øt Øe S Qv

4.14

0.39

= = = = =

;B

0.8

0.058

;C

2.04

0.058;

e t

1 (0.6425 S

0.5

0.22) Qv

Effective isostatic stress at in-situ conditions (psi) Total porosity (v/v) Effective porosity (v/v) Formation water salinity (gm/l[=kppm for dilute aqueous solution]) Cation exchange capacity/unit pore volume (meq/cc)

To correct to stressed Klinkenberg permeability only (or if no Qv data is available) set the ratio Øe/ Øt to 1.

299

Core Based Permeability Predictors

Perform regression using:

North Sea Clastic Reservoir

x-axis: compaction corrected core porosity. y-axis: kOB (Kair corrected to Klinkenberg or Kbrine at isoeff). Usually in a Log-Linear cross-plot.

Segment the data set as required to improve the correlations; By zone By facies Etc.

Wells

Resulting regression equations take the form:

Or rearranged:

k 10( A

Log10 (k )

A

B

B)

Other forms may be necessary depending on data set – see example at right.

k

A

B

300

Correction of Permeability Predictors to Arithmetic Averaging Permeability k has a log-normal distribution while the Log (k) used in regression is normally distributed. Regression as previously described predicts the expectation or mean value of Log (k). This mean is not equal to the mean of k which will be larger. The mean of Log (k) is in fact equivalent to the geometric mean of k. This effect is illustrated below where the Log (k) is normally distributed with a mean of 2. Converting to a linear scale it is clear that mean k is not 100 but 194.

The permeability predictors determined by regression using Log (k) can be corrected to predict the equivalent of the arithmetic mean of k. 0.9

0.9

Mean = 2

0.8

Mean = 194

0.8

0.7

0.7

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

log(k)

0.1

k

0.1

0

0 0

0.5

1

1.5

2

2.5

3

3.5

4

0

500

1000

1500

2000

2500

301

Correction of Permeability Predictors to Arithmetic Averaging For a permeability predictor of the form:

Log10 (k )

A

B

The predictor can be corrected to the k arithmetic averaging equivalent using:

Log 10 (k arith ) Where, Kregression Karith StDev Log10(Kcore) R2

Log 10 (k regression ) 1.151 ( StDev Log10 ( Kcore) ) 2 (1 R 2 )

= Permeability from Y on X regression in Log-linear K/Ø plot. = Permeability corrected to arithmetic averaging. = Standard deviation of Log10 of core permeability data used in the regression. = Coefficient of determination of Log10 (K) versus .

Since the correction term is added to the Log expression for k it has the effect of multiplying the permeability determined from y on x regression by the factor: 1.151 ( StDev Log10 ( Kcore) ) 2 (1 R 2 )

Hence the relationship corrected to arithmetic averaging is: log10 (k ) Where:

B'

B

1.151 ( StDevLog10 ( Kcore) ) 2

(1

A

B'

R2 ) 302

Correction of Permeability Predictors to Arithmetic Averaging: Example

Regression line:

log10 (k )

0.21494

Correction Term

Standard deviation of Log10(k)

2.511

R

1.151 1.234 2 (1 0.867 2 )

Hence:

B' B 0.437

Regression line corrected to Arithmetic averaging:

0.437

2.511 0.437 log10 (k )

0.2194

2.074 2.075

303

References: Permeability Predictors Literature

Novel approaches to permeability averaging: Experimental Tests of a Simple Permeability Composition Formula (1991), B. Noetinger and C. Jacquin, SPE 22841

Upscaling from core to log resolution: The effect of scale on the petrophysical estimation of intergranular permeability (2003), Paul F Worthington, SPWLA 44th Annual Logging Symposium

304

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Section 8.3

Interpretation of Capillary Pressure Data

Capillary Pressure

Capillary Pressure Data Applications: Reservoir fluid displacement processes. Saturation-height models. Original saturations in depleted zones. Reconciling problems with resistivity logs (log Sw). Swirr and Sro determinations. Derivation of free water level (FWL). Differences in contacts between wells. Pore size distribution. Mud solids size selection.

Overview Capillary pressure physics & controls. Data acquisition methods. Data corrections. Saturation-Height models.

306

Basic Physics Capillary Pressure is balance of Interfacial Tension Dependant on: Surface tension ( ) of the fluids present The wettability of solid surfaces present (contact angle q) Capillary radii or pore size

Gravity/buoyancy Dependant on: Density of the fluids present Height above the free water (FWL)

Wettability Is the tendency for one fluid to spread or adhere to a solid surface in the presence of a second fluid. Characterised by the contact angle q.

At

so

sw

q

ow cos 307

Fluids in Capillaries Analogous to Fluids in Reservoir Rock : Water Wet Case Pc = Poil-Pwater = Force/Area = 2 r. .Cos( )/ r2 = 2. .Cos( )/r

Behaviour of oil and water in the pores in rock are analogous to that in a series of capillary tubes.

.Cos( )

For a water-wet system:

Interfacial tension Contact angle Capillary radii- Pore size distribution Fluid density contrast

Oil

Determining parameters:

Water

The smaller the tube diameter the higher the capillary rise.

Glass Plate

Water is held above the free water level.

Po Oil

Pw R

Water 308

Fluids in Capillaries Analogous to Fluids in Reservoir Rock : Oil Wet Case

For an oil-wet system: Oil is held below the free water level

Oil

OWC is below FWL

FWL h OWC

Water 309

Basic Petrophysical Properties: Capillary Pressure Water rises until the capillary force is balanced by the weight of the water column.

Depth Pw-Poil

C1 C1

B1 Oil Gradient

B1

Water Gradient

A1

h

A1

Free Water Level

Oil

H = 0; Pc = 0

Water

A

B

Water Zone

Poil-Pwater = 2. .Cos( ) r

C Water Zone

Pressure Poil-Pwater = (ρw – ρH).g.h

310

Capillarity, the drainage process in a Core Capillary Pressure Measurement Oil replaces water Water

Oil

Po

Pw

So

Drainage Oil enters largest pores: Pentry

Po-Pw 350

Matrix The higher the pressure within the oil Po the higher the curvature of oil/water interface and the smaller are the pores penetrated by oil.

Swirr

30

250

25

200

20

150

15

100

10

50

Capillary Pressure psi

Pw

Height above FWL ft

Oil

Po

300

35

5

Pentry

0

0 0

0.2

0.4

0.6 SW

0.8

1

311

Effect of Pore Size on Water Saturation versus Height above Free Water Level Smaller Pore Size : Higher capillary pressure. Higher water saturations. Higher entry pressure and hence height. Longer Transition Zone.

312

Effect of Fluid Density on Water Saturation versus Height above Free Water Level

Swirr GAS

Oil 0.3 gm/cc

Oil 0.5 gm/cc

Oil 0.8 gm/cc

Transition Zone

313

Effect of Permeability and Porosity on Water Saturation versus Height above Free Water Level

Increasing Porosity / Permeability

314

Saturation History in a Capillary Pressure Measurement

Drainage (of the wetting-phase) represents hydrocarbon accumulation. Drainage Pc data is used to establish the initial hydrocarbon distribution for volumetrics and initiation of simulation.

Ultimate Sro

315

Capillary Pressure Test Methods

All methods consist of: Saturating a sample with a fluid. Causing the first fluid to be displaced by a second fluid at a series of applied pressures. The applied pressures and sample fluid saturations are measured at each step and these paired data constitute the Pc data for a single sample.

Mercury Injection (air-mercury) Capillary Pressure Good for low permeability & clean sands Not strictly capillary pressure (no irreducible Swet) Centrifuge (oil-water, gas-water and gas-oil) Good for most rock types Data need to be corrected Overburden and reservoir conditions

Porous plate (oil-water and gas-water) Only “good” for high permeability Takes a long time (months) Imbibition possible but difficult Grain loss errors

316

Capillary Pressure Method Comparison

Porous Plate

Mercury Injection

Centrifuge (Standard)

Time

Slow (5 weeks +)

Fast (1 day)

Fast (3 days)

100 ft gas

21,000 ft gas

250 ft gas

200 ft oil

51,000 ft oil

500 ft oil

Overburden

Yes

Yes

Yes

Equilibrium

Yes (?)

Yes

Nearly

Destructive Test

No

Yes

No

Costs

Expensive

Cheap

Medium

Other Info

Imbibition

Imbibition

Imbibition

Resistivity Index

Pore Size

Wettability

(50 weeks oilwater) Max. Pc

317

Conversion of Capillary Pressure between fluid systems and measuring conditions Pc data can be converted from one fluid pair & conditions system to another using the expression: Pc 1 cos( )1 Pc 2 cos( ) 2 Where,

Pc1 = Capillary pressure measured using fluids & conditions 1. Pc2 = Capillary pressure measured using fluids & conditions 2. 1, 2 = Interfacial tensions for fluids & conditions 1 and 2. 1, 1 = Contact angles for fluids & conditions 1 and 2.

This method is used to combine Pc data measured using different fluids to a common system for interpretation in a single set. For instance conversion of air-mercury data to the oil-brine system:

Pc ob

Pc am

cos( ) ob cos( ) am

Also used to convert from laboratory conditions to reservoir conditions.

Pc ob RES

Pc amLAB

cos( ) obRES cos( ) amLAB

318

“Typical” Interfacial Tension ( ) and Contact Angle ( ) values

System Laboratory

Reservoir*

Air/brine Oil/brine Mercury/air Air/oil Oil/brine gas/brine

Cos 1.0 0.866 0.765 1.0 0.866 1.0

(mN/m) 72 48 480 24 30 50

cos (mN/m) 72 42 367 24 26 50

Note: lab values reasonably reliable real reservoir

cos values vary considerably

319

Relationship between Pc and HAFWL Capillary Force Pc

2

cos( ) / r

Buoyancy Force

Pb

g HAFWL

Hence can calculate Pc in the reservoir from:

Pcres

(

water

hyd

) g HAFWL

Where, HAFWL = Height above free water level (M) Pcres = Capillary pressure in the reservoir (Pa) -3 water = Density of formation water (Kg.M ) -3 hyd = Density of hydrocarbon (Kg.M ) g = Acceleration due to gravity (9.81 MS-2)

If working in psi and feet a simplified equation can be used:

Pcres

(Grad water Gradhyd ) HAFWL

Where, Gradwater and Gradhyd are water and hydrocarbon gradients (psi/ft).

320

Pc Data Format Pc data: Paired Pc and Saturation Sample parameters: Depth Ø K

Swirr can be estimated immediately from the data by eye. Care is needed with MICP data, convert Pc to HAFWL and use data only to height of hydrocarbon column expected in the reservoir MICP Swirr is an underestimate.

Pc Data Depth XX30.56 80.000 70.000

In order to examine the length of the transition zone and entry heights Convert PcLAB to PcRES Convert PcRES to HAFWL Plot Sw versus HAFWL

Pc (psi)

60.000

Swirr

50.000 40.000 30.000 20.000 10.000 0.000 0.000

0.200

0.400

0.600

0.800

1.000

1.200

Sw (v/v)

321

Pc Data Conversion & Interpretation Convert Pc data from laboratory fluids and conditions to reservoir fluids and conditions: PcOWres

PcOWlab

cos cos

OWres OWlab

Convert Pc to HAFWL: Pc = HAFWL*(Gradw- Grado)

Pc Data Depth XX30.56 400.00 350.00

HAFWL = Pc/(Gradw- Grado) Where, Pc = Capillary pressure (psi) Gradw = Formation Water gradient (psi/Ft) Grado = Oil gradient (psi/Ft)

HAFWL (Ft)

300.00

Swirr Length of Transition Zone

250.00 200.00 150.00 100.00 50.00 0.00 0.000

Entry Height ~0 0.200

0.400

0.600

0.800

1.000

1.200

Sw (v/v)

• Exercise 7 Pc Data Conversion

322

Why derive Saturation-height functions from Pc Data? To establish the initial fluid distribution between wells for volumetrics and initiation of simulation. To provide insights into fluid distribution: Length of Transition Zone Swirr Entry Heights FWL/OWC Allows consistency checks with Log Sw to validate the choice of methods and parameters. Allows accurate Sw determination in thin beds below resistivity log resolution. Allows calculation of the initial hydrocarbon in place in depleted wells.

323

Saturation-Height Functions Determined from Pc Data: Key Steps Construct a Pc database Apply corrections to the Pc data. Convert Pc data to a common fluid system. Ensure that poro-perm distributions represented in Pc data are representative of reservoir. Examine the Pc data to establish whether it belongs to a single class or requires segmentation. Application of Leverett J normalisation may clarify whether all data has similar poro-perm character. If necessary split the data into sets demonstrating similar behaviour and produce several saturation-height functions

Fit Functions that model the individual Pc curves. Compare predicted Pc from model with measured Pc. Hence have Pc = F (Sw, Ø and/or k)

Determine reservoir conversion Pc to Hafwl Substitute for Pc Convert Pc = F (Sw, Ø and k) to Sw = F(HAFWL, Ø and k)

Quality assure the fitted function by comparison with the Pc data on which it is based. Reconcile core Sw-Height Function with log Sw Log Sw method and parameters. Bed effects/ vertical resolution of resistivity logs. If FWL is poorly determined may need to optimise. Compare with core saturations.

324

Closure Effects in Capillary Pressure Measurements Closure effects are artefacts of sample surface imperfections being filled by the injected fluid in a Pc measurement.

160

140

If a large data set is available, most of which do not show closure effects, it is probably better to exclude the measurements thus affected rather than attempt to correct them.

120

Air Brine Capillary Pressure (psig)

Corrections should be applied by the core analysis labs before receipt by clients.

100

80

Closure Correction

60

40

20

0 0

0.2

0.4

0.6

0.8

1

Water Saturation (-)

325

Stress Correction to Pc data

Ambient test data Assume Pc behaviour same at ambient and overburden k- distributions similar

Juhasz Method

Snw*

Snw

res lab 0.5

Pc *

Pc

res lab

326

CBW Correction to Pc Data Mercury Injection Data Only Accounts for clay CBW which is not present in air-mercury tests. If the CBW is not accounted for: Sw from MICP data will be too low.

Hill, Shirley and Klein Method: 0.5

Pc *

Pc

e

SHg *

SHg

t

t

Where:

e

e

1

0.6425 * Sal

0.5

0.22 * Qv

t 327

Saturation Height Function Fitting alternative approaches: 1.Using Pc data normalised using the Leverett J-Function

Using Leverett-J function normalisation Normalise Pc data:

J

Pclab lab

k/ Cos( ) lab

B

J A Sw A function of the form analysis in a log-log plot of J versus Sw.

Rearranging this equation:

Sw

is fitted by regression J A

1 B

This can then converted to a function of height above the FWL by k/ substituting: J

Pc res

res

Cos( ) res

Where Pc in the reservoir is calculated using:

Pc res

h (

w

h

) g

Or more simply if fluid gradients in psi/ft and height in feet are used:

Pcres

HAFWL (Grad w Grad h )

328

2.Fitting Pc using a function defined by parameters dependant on sample characteristics (Ø and K)

Convert the Pclab data to Pcres using the expression:

Pc res

Pclab

cos cos

res lab

Fit functions of the form Sw = F(Pcres) to the individual Pc curves. A number of alternative functions can be used (see list at right) ; the Lambda Function is used here for illustration.

Sw a Pc

b

The fitting parameters a, b and of the equations for the individual plugs are then fitted as functions of either Ø or Ø and k.

a = Fa(Ø, (k)); b = Fb(Ø, (k));

= F (Ø, (k))

Lambda Function

Thomeer J Function Johnson Power Function

FOIL Function Skelt-Harrison

It is worth remembering that the variables in the functions for the parameters must be capable of being mapped for the function to be used in a 3-D or simulation model. Pcres for input to the function is calculated using: Pcres

HAFWL (Grad w Grad h )

329

Quality Assurance of Saturation-Height Function: Provide a good fit to the measured data Pc data: Swirr Length of transition zone Entry heights For the rock property range observed

Must be able to be easily integrated into 3-D models: Ensure that the input parameters used are available in the 3-D model or can be predicted from them: Height above free water level Porosity  Be clear whether ØT or ØE is being modelled

Permeability ? Unlikely to be mapped Often difficult to predict from log Ø Be clear which permeability will be modelled (KHA, KHAOB, KBRINEOB etc)

Other parameters ?

Requires discussion by Petrophysicist/Geologist/Reservoir Engineer Before starting modelling and derivation of Sw-height function

Ensure that functions are stable at extremes of the range of application:

Close to the FWL. At low porosity/permeability. “Wrap” the function in a logical statement to ensure Sw remains in the range 0-1.

330

Saturation Height Function Fitting alternative approaches: Example of Fitting Pc using parameters F (k and Ø)

Lambda Function: Sw

a. Pc

b

Where, Sw Pc a b

= = = = =

Pc

Water saturation (v/v) Capillary Pressure (psi) A fitting parameter A fitting parameter A fitting parameter

PcRES

HAFWL (

water

oil

)0.433

Where, HAFWL ρwater ρoil 0.433

= = = =

Height above free water level (ft) Formation water density 1.02 (gm/cc) Oil density 0.826 (gm/cc) Conversion of gm/cc to psi/ft

The parameters are: a = 1.322x(Ø) b = 0.1096 = 0.173+2.7892x(Ø)

331

Saturation Height Function Fitting: Quality Assurance of Function by comparison with Pc data

Generate the function for a series of porosities equivalent to those seen in the core samples used.

Example Field Anonymous Sands: Lambda Function Compared with Stress Corrected MICP data

800 12A 18 27B 22B 3 6 A B C 24 D 30 E 34 41 42 49 53 Porosity 0.15 Porosity 0.2 Porosity 0.25 Porosity 0.3

700

600

Compare measured data with function: Range of Swirr Length of transition zone Entry height

500

HAFWL (Ft)

Plot with the Pc data converted to HAFWL as at right.

400

300

200

100

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sw (v/v)

332

Saturation Height Function Fitting: Comparison of resulting Sw with log and Dean-Stark Data

The function compares well with Dean Stark data.

Comparison of Function with Log and Dean Stark Sw

The function predicts lower Sw than log values in thick sands. Function predicts much lower Sw in some zones than the log Sw and may indicate thinly bedded sands/low resistivity pay. Alternatively in a producing field this would indicate swept zones. 333

Saturation Height Function Fitting alternative approaches: Example Saturation Height Function Compared with the Mercury-Injection Pc Used

Mercury Injection data indicates unrealistically low Swirr. A large range of entry heights is seen in both the function and Pc.

The transition zone length and form match reasonably well.

334

Saturation Height Functions determined by fitting Log data: Why Derive Sw height Functions from Log Data? There may be no Pc data or only poor Pc data. Pc data may poorly represent the reservoir. The resulting functions do not require a permeability input, those derived from Pc may. The disadvantages of log derived functions as compared with those derived from Pc data:

They do not provide an independent check on Sw and the parameters used to calculate it. The Sw height trend may be poorly represented depending on the distribution of reservoir sands.

335

Saturation Height Functions Determined by Fitting Log Data: Data issues

Multi-Well Depth plot of Log Sw

High saturations reflect bed boundaries or tighter intervals

Transition zone artifacts due to deterioration of reservoir quality with depth and tarmats

Residual hydrocarbon makes picking FWL from logderived Sw difficult 336

Method of Derivation It may be necessary to segment the data in order to produce a fit: Divide data into porosity classes. Treat different lithologies separately.

Select the log data to be interpreted: Generally use data from Net sand only. It may be necessary to exclude data close to bed-boundaries because of their effect on Rt and hence Sw.

Curve fit a suitable function or functions of interpreted or raw log. Evaluating Function Performance: Cross-plot predicted and log Sw Plot residuals (Swlog-Swfunction versus Sw) Compare with Sw height functions from Pc Compare with core saturations 337

Alternative function of Bulk Volume Water

A simple log Sw based equation which often works well is the FOIL function which is determined by fitting bulk volume water ( x Sw) as a function of height above the free water level: BVW = A x HafwlB

The function is fitted by y-on-x regression analysis in plot of Log (BVW) versus Log (Hafwl). Where: BVW = x Sw Sw = Water saturation = Porosity Hafwl = height above free water level A and B are constants

338

References: Saturation-Height Functions

A simple, convincing model for calculating water saturations in Southern North Sea gas fields. S. Cuddy and R. Steele, SPWLA 34th Annual Logging Symposium, 1993. Describes use of log Sw based FOIL functions.

339

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Section 8.4:

Log and Core Databases

Log Database Store raw logs in a set RAW (say) Retain Logging Contractor curve names Take care with units particularly Neutron (v/v or %)

Copy to a set LQC (say) after quality checks. Make copies of curves and rename to company standard names if used. LLD & ILD > RDEP LLS & ILM > RMED CGR & GR > GAMM RHOZ & ROBB > DEN

Document curve renaming Apply depth shifts and name curves to reflect this. (add _S) Make environmental corrections and name curves to reflect this (add_SE)

Copy the LQC curves required into set INTERP (say) Make the interpretation in set INTERP Use curve names which clearly describe the data type and document! Do not simply append your name to a curve – the meaning will quickly be forgotten even by you. 341

Conventional Core Database 1

Set up a conventional core data set for each well CCA (say).

Save all conventional and non array data to the same set. Drill Depth Sample Number Porosity, Kha, Kva, Grain Density, saturations etc. Use flags to indicate fractured plugs

Core GR should also be stored in the database if it is available. Core data curve names should clearly indicate their origin and type: CHEPOR CKHA CKHV CGDEN

Sample numbers and original or drillers depths should be treated in the same way as all other core data: Allows individual plug data to be identified for quality assurance or audit trail. Provides clear documentation of depth shifts. 342

Conventional Core Database 2

Store data in such a way that the plug depths are preserved. If the core data is saved to a wire-line data set each plug‟s data will be stored at the nearest depth increment to the drillers depth.

Preserve the drillers depth by storing the core data either: As irregularly sampled point data. In a set with a depth increment equivalent to the smallest core plug depth increment.

343

Conventional Core Data Base 3: Depth Shifting Depth Shifting Core should be depth shifted to correspond to log-depth so that log-core comparisons and calibrations can be made. When the data is depth shifted the shifted data should be saved in a new data set CCA_SFT (say) and the un-shifted data preserved unchanged – shifts may need to be revisited. The shifted status should also be reflected in the core data curve names: DRILL_DEPTH_SFT, SAMPLE_NO_SFT, CHEPOR_SFT, CKHA_SFT etc Drillers Depth and Sample Number should be shifted together with the other core data. Core depth shifts should be stored and documented in any report. Depth shifts are best established initially by comparing core and log GR logs If the core GR is not available comparison of core porosity with log bulk density usually provides a good alternative. In some cases the core porosity/density comparison works better for depth shifting than GR correlation because the density tool has better vertical resolution than the GR.

344

Conventional Core Data Base 4: Core Calibration Before performing calibration to core data the data will usually need to be corrected to reservoir conditions. The uncorrected data should be retained. Corrected data should be differentiated from the corrected data by curve name: CHEPOR_SFT_C, CKHA_SFT_C, etc

Calibration of log interpretation requires cross-plots of core data versus log data. Cross-plots should be set up in such a way that the log data is interpolated at core depths rather than the reverse. The means to accomplish this will vary depending on the software used. 345

Course Outline and Timetable Day 3 Module 8: Integration of Core data

Example Field Anonymous Sands: Lambda Function Compared with Stress Corrected MICP data

800

700

600

500

HAFWL (Ft)

Section 8.1: Porosity calibration to core Section 8.2: Permeability/porosity relationships Section 8.3: Interpretation of Capillary Pressure Data Exercise 7 Section 8.4: Log and core databases

12A 18 27B 22B 3 6 A B C 24 D 30 E 34 41 42 49 53 Porosity 0.15 Porosity 0.2 Porosity 0.25 Porosity 0.3

400

300

Module 9: Specialist Petrophysical Techniques

200

100

Section 9.1 Petrophysics and Geomechanics Section 9.2 Petrophysics in Carbonates Exercise 8

0 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Sw (v/v)

Module 10: Data Acquisition Planning Section 10.1 Formation Evaluation Value of Information Section 10.2 Planning Logging Programmes 346

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Module 9: Specialist Petrophysical Techniques

Section 9.1 Petrophysics And Geomechanics 348

What does understanding Geomechanics do for you?

Sand Failure Prediction. Well integrity during Drilling. Pore Pressure /Overpressure.

349

Geomechanics and Rock Properties from Logs Select logs sensitive to strength RHOB, PHI, DT, DTS, DTCO Calculate dynamic moduli using logs as input: 1 2

Poisson‟s Ratio,

t s / tc Shear Modulus, G (psi)

1.34 x1010

Young‟s Modulus, E (psi)

2G 1

1.34 x1010

Bulk Modulus, Kb (psi)

2

t s / tc

b

2

1 1

b 2 s

t

1 tc2

4 3 t s2

Where,

Bulk Compressibility,

(psi-1)

1 Kb

b

= log bulk density (gm/cc)

tc = log sonic compressional transit time ( secs/ft) Dts = log sonic shear transit time (msecs/ft) 350

Scaling Dynamic and Static Moduli Dynamic moduli (from logs): Elastic and perfectly reversible.

Static (measured on core): Large strains. Irreversible.

Scaling Static < dynamic

Esta = 0.3 - 0.5 Edyn sta

= 1.2 - 1.5

dyn 351

Generic log-derived strength models to predict Unconfined Compressive Strength (UCS or C0 ) from logs

Sands Sarda1 (General) Porosity > 30%

Co ( MPa ) 115e

11.6

Co ( MPa )

9

Porosity < 30% Formel2 (N Sea) Sonic 140 12 max Porosity

max

0.10

c

43 8.0

258e

c

2 c

0.10

2.1 tc 2 c

140 [

0.0083 tc2

0.063

c

tc

63 2 11 c max =C0 for zero confining stress]

Vernick3 (Clean consolidated sandstones)

UCS Bruce4 (Schlumberger) C0

Shales Horsrud5

Global Horsrud5 North Sea

254(1 2.7 ) 2

0.087 x10 6 EK b (0.0087Vsh UCS

304.8 1.35 t

0.0045(1 Vsh ))

2.6

UCS

304.8 0.77 t

2..93

352

Comparison and Calibration of Generic Models to Eskdale-2 Interval: 2808.6 - 2843.6 m Eskdale Sand Core Measurements Eskdale-2 Interval: 2808.6 - 2843.6 m Eskdale Sand

UCS (psi) 0 1000 2000 Generic models have been developed 2805basins and usually for specific fields and have limits of applicability.

3000 0

UCS (psi)

1000

4000 2000

50004000

3000

5000

2805

2810

Models involving elastic moduli (e.g. E) require DSI logs to2810 be run:

2815

Limited application in development wells where only LWD is available.

2820

Shales Few core tests made due to limited availability of preserved core and 2820 difficulties of testing.

Depth (m RKB)

2815 Vernick 1

2825

Vernick 2 Formel DT Global Gdyn Model Offset Ec Model Core UCS

2830

GOC

2835

Depth (m RKB)

Most of the models are not sufficiently generic to fit all the data 2825 all of the time hence…

2840

Vernick 1 Vernick 2 Formel DT

Field calibration is essential! 2830

2845

Global Gdyn Model Offset Ec Model Core UCS

2850

GOC

353

Log-Core Calibration: Depth Shift

Accurate core depth shifting is essential to ensure that the sparse core measurements are compared with the right log responses!

G am m a R ay (AP I) 0

20

40

60

80

100

17500

17510

17520

17530

Core-Log depth shifting D ep th (ft M D )

correct core depths to log depths core gamma ray (continuous) and core porosity are key tools

17540

17550

Log C ore

17560

17570

core 6 ft higher

17580

Block or stretch shifts compress or stretch core to account for depth inconsistencies in logs

17590

17600

17610

Figure 29: C ore and Log G am m a R ay A ctivity

354

Log-Core Calibration: Methods

Single-variate analysis often not robust. Can be improved by using dynamic moduli or the Ec modulus as they exploit two independent tool responses (DT and RHOBC).

Multiple linear-regression Exploits more than one log variable. Ensure multiple correlations are intuitive.

Neural nets Requires large database.

Fuzzy logic Requires large database. Sensitive to binning method. 355

Gas effects Geomechanical Log Intepretation WELL: GAJAH_BARU-1 VERTICAL SCALE: 1:50 DRHO

-0.4 G/C3

0.1

PEF 0

B/E

tcmr 10

0.5

-0.15

0.5

NPHI

SGR 0

GAPI

G/C3

DT4P 2.95 240

-

40

2.95 440

US/F

V/V

0

0

CPOR_OB_1 50

DT2

CGDEN 200 1.95

US/F

FEET

18 1.95

FEET

IN

V/V

PHIE_1

RHOB

CALI 2

V/V

TVDSS

0.45

DEPTH

In high porosity formations, gas will increase the sonic transit time compared to the same rock saturated with water.

Well: GAJAH_BARU-1

YME_PSI_2 0

CPOR_1 50

40

-

0

CKHA_OB_1 0 0.1

-

V/V

V/V

4761.7 4669.7

Gas lowers the rock rigidity more than its density and decreases sonic velocity. In theory, gas effects can be accounted for by applying fluid substitutions to obtain the properties for the dry frame.

4700

4800

In practice, fluid substitution can be uncertain and may not be warranted.

As the CPI porosity interpretation essentially accounts for gas effects on the log responses, a porosity-based strength model is often preferred over a sonic-based model in high porosity gas sands.

5

EC_2 10000 0

4750

4850

4863.1 4771.1

Use data from water leg where possible!

356

5

Model Applications Geomechanical Log Intepretation WELL: 211_18A-A33 VERTICAL SCALE: 1:500 DRHO -0.4 G/C3

Zone11 Etive Zone P10 = 3722 psi

KHL_1 0.1

0.1

pef

Well: 211_18A-A33

Histogram of GEOMECH.TWC_PHIECOMBO Well: 211_18A-A31 211_18A-A33 TOP_ETIVE_FM to TOP_RANNOCH_FM Filter: BEST.VSH<0.3

0

0

IN

22 1.95

IN

0

GAPI

22 240

GR 0.10 0.8

G/C3

2.95

0.5

US/F

V/V

V/V

0 0.1

40

0.5

0FRAC2 0.5

V/V

MD

10000

MD

10000

TWC_2 10000 0

kair 0 0.1

CPOR_1 V/V

MD

kha 0 0.1

CPOR_CORE_3 NET_1

2.95

V/V

10000

khlr 0 0.1

PHIE_1

cgden 150 1.95

V/V

MD

khor

phie_limit

FEET

2

0.5

FEET

1.0

0.5

DT

cali 0.12

10

-0.15

RHOB

cali 2

V/V

TVDSS

0

22 0.45

DEPTH

319 319

IN

B/E

NPHI

cali 2

PHIT_1

0 0.1

MD

PSI

BVW_1 10000 1

twc_formelporgeneric 10000 0

PSI

10000 1

CKHA_CORE_1

TWC_PHIECOMBO_1

MD

PSI

10000 0

10000 0

V/V

0

PHIE_1 V/V

0

VSH_2 V/V

1

10223.08742.9 10250

0.08 0.6

10300

8800

10350

8850

0.06 0.4 0.04

10400 0.2

8900

0.02

10450

Statistics:

9000

10000

8000

7000

6000

Wells:

Possible values Missing values Minimum value Maximum value Range

319 0 3161.43994 5609.07910 2447.63916

Mean Geometric Mean Harmonic Mean

4146.87480 4130.69890 4114.90581

Variance Standard Deviation Skewness Kurtosis Median Mode

5000

4000

3000

2000

1000

0.0 0

0.00

8950 10500 10550

9000

10600

9050

top

1. 211_18A-A31 2. 211_18A-A33 Percentiles: 10% 50% 90%

3721.94444 4112.50000 4619.09091

138187.70856 371.73607 0.62798 3.66405 4112.50000 3950.00000

10650 9100 10700 9150 10750 10800.09203.9

Model developed for net sand from appraisal wells is used to build probabilistic strength model for sands that will be encountered in development wells. Defining a “critical” rock strength for any production/depletion condition enables a risked assessment of the magnitude of sand production.

Geomechanical log – appraisal well

357

Model Outputs Initial Pres

Geomechanical Log Intepretation WELL: 211_18A-A10 VERTICAL SCALE: 1:100 DRHO -0.4

0.1

pef

Well: 211_18A-A10 0

IN

22 0.6

IN

GR 0

GAPI

cgden 150 1.95

V/V

NET_1 0 FEET 2

2.95

11447.0 8611.4

Failure curves are generated as a function of: Perforation orientation Depth Reservoir depletion

TW C_NET_1 0

PSI

10000

3000 psi Depletion

CDP90 Initial PRES

5000 0

CDP90 Current Max Depletion

5000 0

CDP90 3000 psi Depletion

5000

0

cdp60 Initial PRES

5000 0

CDP60 Current Max Depletion

5000 0

CDP60 3000 psi Depletion

5000

0

CDP30 Initial PRES

5000 0

CDP30 Current Max Depletion

5000 0

CDP30 3000 psi Depletion

5000

0

CDP0 Initial PRES

5000 0

CDP0 Current Max Depletion

5000 0

CDP0 3000 psi Depletion

5000

0

40

Current Pres

0

10

2.95

dt

PERF_2 Excel

US/F

FEET

IN

FEET

G/C3

22 240

cali 2

V/V

RHOB 22 1.95

TVDSS

cali 2

B/E

NPHI

cali 2

DEPTH

Sanding evaluation log

G/C3

BVW _1 1

V/V

0

PHIE_1 1

V/V

0

VSH_1 0

V/V

1

top_brent_gp

top_t1

11500

8650

top_ness_fm

11550

8700

top_nu1

11600

top_nl2 8750 11650

Identify the extent of potential failure zone for current or future well operating conditions.

top_nl1

11700

8800

top_etive_fm top_e1 11750

8850

11800

top_rannoch_fm 8900 11850

Evaluate selective perforation, shut-off opportunities.

top_r4

11900

top_r3 8950

top_r2

11950

9000

12000

HUD 11532 ft MD (08/2005) top_r1

358

Estimating Vertical Stress,

v

Integrate density logs:

0.4335

v

h

bi i

Include sea water. Need density log data to sea bed.

0.23V p

3.50

3.25

3.00

2.75

2.50

2.25

2.00

1.75

1.50

4000

6000

6000

8000

8000

16000

16000

18000

18000

20000

20000

0.25

3.50

14000

3.25

14000

3.00

12000

2.75

12000

2.50

10000

2.25

10000

1.00

Use sonic data (e.g. Gardner)

4000

2.00

Fit regression model and extrapolate to mud line.

2000

1.75

Often no shallow density data:

2000

1.50

A0

0 16205 16861 0

0

1.25

A1Z

b

1.25

1.00

0

REFERENCE.TVDSS (FEET)

b

A2 Z

2

20

Usually model has form …

636

REFERENCE.TVDSS vs. XWIRE.RHOB Crossplot Well: 44_11-2 44_12-1 44_12-2 Range: All of Well Filter:

XWIRE.RHOB (G/C3) Wells:

44_11-2

44_12-1

44_12-2

359

Identifying Failure - Image Logs

Ultrasonic (e.g. UBI)

Microresistivity (e.g. FMI) Shear Failure Wide Breakout Tensile Failure Drilling induced

Shear Failure Shallow Knockout

STARTM

360

Image log identification Breakouts: Breakout segment can exceed the width of the measurement pad and return a defocused response. Breakouts should be seen as paired strips parallel to the hole axis and set 180° apart. Possible breakout in the image may not correspond with the caliper response (if breakouts are small).

Drilling-induced fractures: Often subtle as they are thin and discontinuous around the wellbore. Open and thus have the conductive/resistive character of the mud system. DIFs should be seen as continuous conductive/resistive fractures apparent as either borehole-parallel traces (tram-lines) set 180° apart around the well-bore or as enechelon limb segments of a sine-curve centred about the point of inflexion of the limbs.

Assign confidence level (A – high: E – low)

361

Shear Wave Anisotropy

Anisotropy in sonic response may arise from aligned fractures or layering, or from unequal stresses. If the formation is anisotropic, the shear wave splits into two components: one polarized along the stiff (fast) direction, the other along the compliant (slow direction). Shear sonic imaging tools can measure the components of shear slowness in any direction in a plane perpendicular to the borehole axis.

SPE93182

362

After Drilling – Sonic Log

GR (API) 0

50

100

150

200

250

300

0

8500

8500

8600

8600

8700

8700

8800

8800

8900

8900

9000

9000

9100

Crommer Fm

9100

9200

Depth feet TVDSS

9300 9400 9500

Kimmeridge Fm

Depth feet TVDSS

9200 9300

DTC (m s/feet) 100

150

200

Abnormal pore pressure indicator: Inversion of the sonic velocity trend

9400 9500

9600

9600

9700

9700

9800

9800

9900

50

9900

Heather Fm 10000

10000

10100

10100

10200

10200

10300

10300

10400

10400

10500

10500

363

After Drilling - Porosity

A trend line is drawn through the normal pressure, averaging the data points, and extended below the deviation indicating the transition to abnormal pressure.

Various methods of correlating the deviation from the trend with pore pressure are used, including transparent overlays, correlation graphs, and calculations. 364

Points of Note

When using such correlations, keep the following points in mind: trend lines and pressure correlation vary from area to area and in some cases with the geological age of the formations. analysis assumes consistent shale properties – not always true. resistivity tools are affected by changes in formation water salinity as well as porosity. Corrected d-exponent calculations are widely used but are not absolute, and can give false indications or lead to confusing interpretations.

Shale density, if carefully measured, can be useful in pressure prediction. Flow-line temperature measurements are uncertain - the transition zone may be deeply penetrated before the change in temperature gradient significantly affects the flow-line temperature. Increases in background gas, connection gas, or trip gas are significant. Formation tests can be helpful in validating or correcting pore pressure predictions.

365

Geomechanics References 1. 2. 3.

4. 5. 366

Section 9.2 Petrophysics In Carbonates

367

Contrasting Properties of Sandstones and Carbonates Sandstone A clastic sedimentary rock with predominantly quartz [SiO2] matrix although sandstones often contain feldspar, mica and other minerals held together by silica or other mineral cement. Clastic rocks are composed of fragments or clasts of pre-existing rocks transported to a new location and re-deposited to form another rock.

Carbonates Minerals Calcite [CaCO3] and/or Dolomite [CaMg(CO3)2] Can be clastic but are more commonly formed by: Precipitation Organic activity Often occur with evaporite minerals (halite, anhydrite, gypsum)

368

Carbonates Dominant feature is fabric (as opposed to clastics where grain size and clay content are dominant). Prone to diagenesis, original grains and fabric are often destroyed. Porosity creation by dissolution. Porosity destruction by deposition. Mineral alteration. Silicification. Dolomitization can increase porosity.

Intergranular, vug and fracture porosity have very different impacts on permeability. Carbonate evaluation require both porosity and porosity type to be understood. 369

Carbonate Petrophysics Porosity formed by dissolution. Limestone Natural Arch, Dovedale, Derbyshire, England.

370

Carbonate Petrophysics Caves in limestone Dovedale, Derbyshire, England. Vugs come in a range of sizes!

371

Carbonate Petrophysics Living Stromatolites in the Pinnacles Desert, Western Australia. Stromatolites are bacterial communities which form dome or column structures. Fossil stromatolites commonly occur in limestones.

372

Carbonate Petrophysics Reefal Limestone, Cottesloe Beach, Perth, Western Australia. Note the variation of texture, pore sizes and connectivity.

373

Limestone Fabrics Grain supported no mud

Ø 25% k= 5.2 md

Grain supported with mud

Ø 25% k= 1,500 md Grainstone Ø 18% k= 4 md

Mud Dominated Packstone

Grain Dominated Packstone Grain supported with mud

Ø 33% k= 9 md

Wackestone

Mud supported with <10% grains

374

Carbonate Rock fabric Petrophysical Classes [After Lucia] Mud-dominated Fabric Packstone Wackestone

Mudstone

Class 3 LIMESTONE LIMESTONE

Grain-dominated Fabric Packstone Grainstone

Class 2

DOLOMITE DOLOMITE Crystal Size

Class 1

< 20 m

LIMESTONE

Crystal Size

DOLOMITE

20-100 m

Crystal Size

Bar = 100 m

< 100 m

Crystal

Crystal

Size

Size

> 100 m

> 100 m

375

Pore Structure and Permeability

Carbonate Pore Structure Effect on Permeability

Vu ,t ou ch in g

100

ra

ct

ur

es

10

1

•I

•F

Permeability mD

gs

1000

nt

g er

ra

l nu

a

o P r

r

i os

ty

• Vu

g

os i r o P

ty

0.1 0

0.05

0.1

0.15

0.2

0.25

0.3

Porosity v/v 376

Limestone Rock-Fabric Petrophysical Class PorosityPermeability Relationships [After Lucia]

Class 1: Grainstone Class 2: Grain Dominated Packstone Class 3: Mud Dominated fabric

377

Carbonate Properties relative to Clastics

Extremely low and high porosities. Permeability is less related to porosity than is the case in clastics. Pore Throat Size Distribution is wider and often extreme. Higher m‟s and n‟s, often >2. Fractures are more common. More often Oil Wet.

378

Saturation exponent (n) in Carbonates:

The saturation exponent n is dependent on the wettability of the rock.

Oil wet carbonates:

Water Wet

n is increased in the range 8-11. Oil Wet

Core cleaning can modify wettabilty from oil to water wet.

Water wet: n 1.5-3 observed

In the absence of core derived values use n=2 initially.

379

Genetic Classification of Porosity Type Correlated with Rock Types [After Focke & Dunn]

Intergranular

Intercrystalline

Limestone & Dolomite Grainstones

Sucrostic Dolomites

Moldic Moldic oolitic Limestone & Dolomite Grainstones Moldic or Vuggy in addition to matrix Vuggy Packstones and Wackestones

Matrix or Chalky Mudstones, Chalks

Fracture or Fissure Porosity 380

Cementation exponent (m) Related to Rock & Porosity Types [After Focke & Dunn]:

Based on core measurements of Formation Factor on a large number of core samples.

Grainstones & Sucrostic Rock m ~2.

Matrix or Chalky m~2.

Fracture m < 2 and tends towards 1.

381

Cementation exponent (m) Related to Rock & Porosity Types [After Focke & Dunn]:

Moldic or Vuggy increases m to greater than 2 ranging up to @ 4.5.

Lucia investigated m as a function of ØVuggy/ØTotal: M increases as this ratio increases. Focke & Dunn extended the Lucia plot to include moldic porosity as end-points.

The ratio ØVuggy/ØTotal can, under some circumstances be estimated by comparing sonic and density/neutron or density porosity.

382

Cementation exponent (m) Related to Rock & Porosity Types: Moldic oolitic Limestone & Dolomite Grainstones

Moldic Limestone: m is a function of porosity; the range of m relates to the permeability of the rock.

Moldic Dolomite: m is similarly a function of porosity and permeability but over more limited ranges. Insufficient data was collected to derive relationships. Mean m was 2.4. 383

Clastic Versus Carbonates - Matrix Differences 10,000 Typical Carbonates: m =2.10 Typical Clastics

Clastics Typical Carbonates Carbonates

F = Ro / Rw →

Permeability (mD)

Fractures

Chalks Typical Fractures: m =1.30

0.01 0

Porosity (V/V)

0.40

- Pore Throat Size Distribution (PTSD) +

0

Typical Clastics: m =1.90

Porosity (V/V)

1.0

384

Carbonate Petrophysics: Simple Workflow

Look at the cuttings descriptions!! Flag non-reservoir minerals and exclude from the interpretation. Determine carbonate mineral type Limestone/Dolomite Using D/N cross-plot and PEF

Porosity: Calculate D, D/N and Sonic porosities. Assume matrix properties based on dominant carbonate type. Assume that the logging tools only see the invaded zone. If ØS<ØD or ØDN this may be an indicator of vuggy or fracture porosity.

Water Saturation: If possible determine Rw (and m) from a Pickett plot in water leg. Assume n =2 Start by using the Archie equation since many carbonates have low Clay volume. 385

Simple Lithology Flags Depending on the environment a number of non-reservoir minerals may be present: Coal Salt Anhydrite These minerals can be dealt with in quick-look and simple deterministic interpretation by: Calculating “flags” using a few log cut-off criteria. Excluding the flagged intervals from the interpretation by setting Ø to zero.

Recognising coal: High resistivity, low GR, low Density, high Neutron Flag_Coal if RT>cutoff & GR<cutoff & Density<cutoff & Neutron>cutoff

Recognising Salt: High Resistivity, very low GR, Low Density @ 2.04 gm/cc, Low Neutron (Very large sand type D/N Separation) Flag_Salt if RT>cutoff & GR<cutoff & Density<cutoff & Neutron<cutoff

Recognising Anhydrite: High Resistivity, very low GR, High Density @ 2.98 gm/cc, Low Neutron Flag_Anhydrite if RT>cutoff & GR<cutoff & Density>cutoff & Neutron<cutoff

Exercise 8 Carbonate Interpretation 386

Carbonate Petrophysics References

Cementation Exponents in Middle Eastern Carbonate Reservoirs, J.W. Focke and D. Munn, SPE 13735 June 1987.

Geological Nomenclature and Classification of Porosity in Sedimentary Carbonates, P.W. Choquette and L.C. Pray, Bulletin of AAPG (1970) 54, 207-50.

Effect of spherical Pores on Sonic and Resistivity Measurements,A. Brie, D.L. Johnson and R.D. Nurmi, Transactions of SPWLA Logging Symposium, Dallas, (1985) 1, Paper , 1-20.

Petrophysical Parameters Estimated from Visual Descriptions of Carbonate Rocks, a Field Classification of Carbonate Pore Space, F.G. Lucia, JPT (March 1983) 629-37.

387

Carbonate Properties relative to Clastics

Extremely low and high porosities. Permeability is less related to porosity than is the case in clastics. Pore Throat Size Distribution is wider and often extreme. Higher m‟s and n‟s, often >2. Fractures are more common. More often Oil Wet.

388

Module 10

Data Acquisition Planning

389

Section 10.1

Formation Evaluation Value of Information (VOI)

390

Need to Establish Value of Information The costs (including data acquisition time and risk) of obtaining data in a well are substantial and vary considerably depending on the types of data acquired: Mud Logging Wire-line Logs &/or LWD logs. Minimum – Additional Logs NMR, Image Logs etc. Core VSP Formation Pressures Formation Fluid Samples During the well planning phase it is useful, and in some companies mandatory, to establish the value of elements of the data acquisition programme (or indeed the whole well), in terms of the “Value of information” (VOI) gained. An understanding of the VOI of elements of the data acquisition programme in any case helps: Make the case for inclusion of data acquisition above the base case. Set data acquisition priorities. Make contingency plans (e.g. If the LWD density log fails do we need to trip to replace it?). Aids discussion with other stake-holders in the planning process (e.g. In a hole section where well-bore stability problems have been experienced is the VOI associated with running the D/N sufficient to justify the risk to the well involved in using radioactive sources ?). 391

Value of Information (VOI) Definition

The concept of value of information (VOI) may be used to help in evaluating the value of components of data acquisition. The VOI regarding an item of data in a project can be defined as the difference between the value of the project with the information and its value without the information. The acquisition of data can be justified if the VOI exceeds the costs of getting it.

392

Value of Information (VOI) Illustration of Concept

A couple has won a prize in a TV show. They can either get a cheque of £8,000 or pull one of three curtains:

Behind one of these curtains a brand-new car worth £21,000 is hidden. Behind the other two curtains are consolation prizes: Theatre tickets. Two cases of beer.

393

Value of Information (VOI) Illustration of Concept It is clear from the decision tree that the logical decision in absence of additional information would be to accept the cash offer. The value of the prize in this situation therefore amounts to £ 8,000. £8,000

Take Cash or Gamble

2 cases of Beer £ 150

1/3

£ 8,000 Gamble

Result ? 1/3

Theatre Tickets £ 300

£ 7,150 1/3

Car £ 21,000! 394

Value of Information (VOI) Illustration of Concept

If somehow the winning couple could obtain information on which prize was behind the first curtain, their decision would probably be different. As indicated in the decision tree on the next slide, the value of this information depends on what is hidden behind the first curtain. If the car is behind the first curtain, the couple is relieved of the necessity to choose and the value of the prize has increased to £ 21,000. The value of this certain information is therefore £ 21,000 - £ 8,000 = £ 13,000.

If, however, the car is not behind the first curtain, the couple can still take the cheque or gamble between the two remaining curtains. The gambling option in this situation has a higher value than the cash option although a cautious couple may still opt for the cheque! INFORMATION CAN INCREASES THE VALUE OF PRIZE !

395

Value of Information (VOI) Illustration Decision Tree

Cash £ 8,000

Beer 1/3

Cash or Gamble ?

Theatre 1/2

£ 10,650 Gamble

£ 300

Result ?

£ 10,650 What is behind first Curtain

1/2 Car 1/3

Take Car !

Car ! £ 21, 000

£ 21,000!

£ 21,000

£ 14,075 Cash £ 8,000 1/3 Theatre £ 10,575

Cash or Gamble ?

1/2

£ 150

Gamble £ 10,575

Beer

Result ? 1/2

Car ! £ 21, 000 396

Value of Information (VOI) Illustration Decision Tree – Cautious Approach

Beer 1/3

What is behind first Curtain

£ 10,650

Car 1/3

Cash £ 8,000

£ 21,000!

£ 21,000

£ 12,333 1/3 Theatre £ 10,575

Cash £ 8,000

397

Value of Information (VOI) Illustration of Concept

It is not known beforehand what is hidden behind the first curtain and the value of the prize is £8,000. After finding out what is behind the first curtain the value of the prize is the average value of the three cases, £14,075. £12,333 for the cautious couple.

The VOI on the prize behind the first curtain is therefore £14,075 - £. 8,000 = £6,075. £4,333 for the cautious couple.

The value of information depends on what action you will take in light of it. 398

VOI Real World Example A well in old development was to be abandoned. This requires that the 95/8” casing be pulled and two cement plugs set above the reservoir interval. In the overburden, where only a GR/DIL/Sonic log was run the GR log character indicated the possible occurrence of a sand while a slight positive kick on the resistivity indicated that it might contain hydrocarbon.

If this interval was indeed hydrocarbon bearing and permeable the rules of abandonment would require the setting of two additional cement plugs at cost of £750,000 Question: Could a through casing MDT prove that the interval was impermeable or if permeable water saturated and what is the value of this information? 399

VOI Real World Example

1/3

Good 1/3

No

Save £750K Water

Permeable? 1/3

£416K 2/3

Yes

1/3

Fluid ?

£ 250K

Save £750K ?£0 Oil

1/3

Is the cement job Good ?

Since a conservative estimate of the cost of running the MDT was

£0

£250,000 the proposal to run it

VOI =£139k

was quickly shown to be nonviable. 2/3

Poor £ 0k

Note the importance of the probabilities of alternative outcomes in the process! These are very difficult to access in practice and require multi-disciplinary input if the exercise is to be useful!

400

VOI: Real World Use The approach can be useful in determining priorities and evaluating the likely usefulness of data which at first sight appears to have high value. The last example also illustrates the importance of not being too constrained by logging to address the “known unknowns”. If in the original open-hole logging a D/N had been run in the subject interval it could have identified the interval as a tight stringer and removed the need for the more expensive abandonment! The method can provide clear outcomes if the action resulting from information carries high value. For example: If you could confirm that another development well is unlikely to be needed on your small sub-sea development saving say £20Million. By, for example running a dual probe MDT, at a cost of £0.5 Million proving that vertical permeability in the reservoir is high. In this case even if the data proved that the reservoir had poor Kv this knowledge is still valuable for the future development. 401

Section 10.2

Data Acquisition: Planning Logging Programmes and other data acquisition

402

www.senergyltd.com/training

Module 10

Data Acquisition: Planning Logging Programmes and other data acquisition

Formation Evaluation Requirements to be Considered During Planning Data Acquisition In all wells: Safety Well safety/integrity Value of Information (VOI) Redundancy of Data Hole Conditions Logging Environment (HT/HP) Mud Type Depositional Environment Costs Additionally Factors Logging Contractors Available in Country Type of Development Sub-sea moderate size Sub-sea Large Onshore

Stage of Development of Field/Prospect. Exploration wildcat Appraisal Early development Late development Pre-abandonment Nature of the field or prospect: Fluids present. Unknown Oil Gas Condensate Single/multiple reservoirs Isolated fault blocks “Tank of sand” Homogeneous/Heterogeneous Location Offshore Floater Jack-up Platform/Subsea Onshore Remote Accessible 404

Data Acquisition (FE requirements)

Exploration (on/off shore) LWD (GR-Res) – in top hole LWD GR-Res-DN WL Sonic / VSP WL Pressure data WL Percussion side wall cores

Appraisal a/a WL NMR / Image / Geochemical Full Bore Core Mechanical side wall cores

In all logging jobs consideration should be given to: Well safety Value of Information (VOI) Redundancy of Data Hole Conditions Logging Environment (HT/HP) Mud Type Depositional Environment Costs

Development LWD (GR-Res) 405

Planning Logging and Other Well Data Acquisition: Log the Well on Paper Log the Well on Paper Asset Team to review Logging Programme with Third Party Services parties (Logging Engineer / Well site Geologist / Operations team) Cover all aspects of the programme in detail Formation Evaluation Objectives Planned Formation Evaluation Summary Communication Strategy Contingencies General Logging Guidelines Detailed FEWD Logging Programme Objectives Guidelines

Detailed Wire-line Logging Programme Objectives Guidelines Asset Personnel Contacts Responsibilities Work Scope Deliverables Procedures Technical Limits Tool set up sheets 406

Data Acquisition: During Phases Field Development Exploration

Abandonment Appraisal

Primary Development

Further Development

Production

MAXIMISE

NPV

+ Cased Hole Logging

0

Time

-

Benefit

Data Acquisition

VOI Cost 407

LWD Characteristics Acquires data during the drilling process: Real time: limited sampling and quality. Memory: good sampling and quality.

Tool failure requires a bit trip to replace – high rig time penalty. Tools need to rotate for logging.

Cost basis: Tool rental (throughout drilling) Crew Costs (throughout drilling) Footage charges

Risk of losing tools in hole is small. Rig time attributed to log data acquisition small (tool make-up and break-down).

Limited range of logging tools: Range is rapidly expanding. Sonic Formation Pressure

Quality of logs regarded as poorer than wire-line? But improving with new tool developments.

Can log regardless of hole angle. Acquire data before significant invasion. Can re-log the hole several times during logging

408

Wire-line Logging Characteristics Acquired after drilling a hole section.

In difficult wells may not be able to log all services.

Well logged only once, after invasion has occurred.

Sticky hole or lost circulation may be reluctance to use nuclear tools (risk of loss of radioactive sources leading to well abandonment).

All logging services available (assuming tool availability).

Pad tools (D, MSFL, WFT) may be too risky in poor hole.

Log quality is as good as it can be (with some reservations wrt invasion) Cost Basis: Tool Rental Depth charges Logged interval charges Crew Charges

Can not log high angle or horizontal wells without modified delivery system: Logging on Drill-pipe (Open-hole) Tractor Logging (Cased Hole) Logging on coiled tubing Logging on drill-pipe carries high risk and takes a long time (Rig Costs)

Rig time spent logging is significant : Crew and tools called out when needed as each hole section is drilled to TD. Tool failure requires tool to be POOH and replaced. Less time than is required for a bit trip if LWD tools fail.

Typically 6+ hours per log Not unusual to spend 24-36 hours logging at each TD.

Need to fish for lost or stuck tools: Cut-and thread Overshot on drill-pipe

Can readily change tool configurations or logs run in response to well conditions or interpretation requirements. 409

In the “Real World” the Nature of the Reservoir May Require Specialised Logging

The Real World Complex mineralogy e.g. Pyrite/Carbonate Use PEF/D-N

Thin Beds FMI/High resolution density

Low resistivity FMI/High resolution density/NMR

Mixed Significant Clay types Petrology / D-N / SGR

Hot sands D-N/SGR

Variable Rw NMR/RST in C/O mode

Unknown Rw Formation water sampling 410

Example 1: Exploration Wildcat Well Onshore Well to be drilled in a remote location. Rank wildcat. A succession of Mixed carbonate and evaporite sequences are expected. Reservoir may

be tight with production dependant on fracture permeability. Well to be drilled vertically with water based mud in three hole sections in 17½, 12¼ (Primary Target) and 8½” (Secondary Target) hole.

Proposed Logging Programme: LWD GR/Resistivity in all hole sections.

17½” TD GR/SP/DLL/BHC (sonic) Run 1A SGR/D/N/MSFL – conditional on hydrocarbon shows Run 1B Sidewall percussion cores (CSTs) Run 1C

12¼” TD GR/SP/DLL/BHC (sonic) Run 2A SGR/D/N/MSFL Run 2B GR/FMI (Image Log) – conditional on hydrocarbon shows Run 2C GR/WFT– conditional on hydrocarbon shows Run 2D CSTs Run 2E

8½” TD As 12¼” TD subject to the requirement that testing the 12¼” section after plugging back is not compromised. Runs 3A-3F VSP Run 3E to be run prior to CSTs 411

Example 2: Appraisal Well Offshore Small Accumulation Well to be drilled in UKCS. Appraisal well on a small prospect which is likely to be economic only if developed using sub-sea horizontal wells (4 development wells expected if prospect matures) . The discovery well Found an oil column but no clear water-leg and no core was cut in that well.

The appraisal well is to be drilled vertically using oil-based mud in a location that is expected to locate the OWC and will be suspended for conversion to a producer if the Development proves economic.

Proposed Logging Programme: Top Hole Section 17½” and 12¼” hole LWD GR/Resistivity

8½” Hole LWD GR/D/N/Resistivity/Resistivity at bit (RAB) Core (90 feet) to be acquired from top reservoir as indicated by RAB. Wire-line SGR/Dipole Sonic Wire-line NMR Wire-line Formation Pressure data Wire-line VSP Wire-line Percussion sidewall cores 412

Example 3: Development Well Offshore Small Accumulation Well to be drilled in UKCS. Second development well on a small prospect which is being developed using sub-sea horizontal wells (2 to follow this one) . The first well was interpreted as crossing two faults which may seal separate reservoir compartments.

The new well is to be drilled using oil-based mud, the reservoir section is horizontal And the well TD is 18,000 MDRKB.

Proposed Logging Programme: Top Hole Section 17½” and 12¼” hole LWD GR/Resistivity

8½” Hole LWD GR/D/N/Resistivity LWD Formation Pressures

413

Example 4: Development Well Offshore Large Platform Development Late in Field Life Well to be drilled in UKCS. Field has been in production for 25 years and was developed using water injection to maintain reservoir pressure. Including sidetracks 200 production wells and 40 water injectors have previously been drilled. Pressure maintenance is in decline in the main fault block but targets remain in small multiple fault blocks on the edge of the field. A high angle development well (>70 degrees) is to be drilled through several fault blocks to target remaining oil, the trajectory has also to penetrate depleted sands due to well geometry. Gas sands may also be encountered.

Proposed Logging Programme: Top Hole Section 17½” and 12¼” hole LWD GR/Resistivity

8½” Hole LWD GR/D/N/Resistivity Contingency – if drilling problems are encountered (lost circulation or differential sticking – D/N will be removed from the BHA 414

Logging Programme

Must detail: The logs to be run, intervals to be logged and logging modes. The purpose of running each log. Risks associated with the logs. Mitigation and Contingency Plans.

Hence in the Logging Programme of Example 4: Purpose of logs GR, SP Correlation and Lithology D/N, Porosity, Fluid Typing, Lithology Resistivity, Hydrocarbon saturation

Risks Loss in hole of the D/N with associated sources could lead to well abandonment or at least a shallow sidetrack.

Mitigation and Contingency Plans In the event that significant mud-losses occur or over-pulls are experienced the BHA will be pulled and the D/N removed before drilling ahead. 415