Ln Lithology Print

Formation Evaluation: Lithology Assessment PETE 321, Sections 501 506 Spring 2014

Instructor: Zoya Heidari, Ph.D. Assistant Professor, Chevron Corporation Faculty Fellow in Petroleum Engineering Texas A&M University

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PETE 321 Outline Background on Petrophysics and Geology What is Well Logging? Definitions and Overview Mud Filtrate Invasion

Measurement Environment

Anatomy of Well Logs

Reading Logs and Header Core Data

Quick Look Interpretation

Caliper Logs Tension Logs Temperature Logs

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

Data Quality Control

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PETE 321 Outline Elastic Properties GR Logs Invasion

Spontaneous Potential (SP) Logs

Fluid Saturations

Electrical Resistivity Logs

Type of Fluids Estimate

Density Logs Active Nuclear Logging

Photoelectric Factor (PEF) Logs Well Log Interpretation Techniques

Neutron Porosity Logs Sonic Logs

• • • •

Picket Plot Using Cross Plots Thomas Steiber Diagram ...

Petrophysical Interpretation of Well Logs in Clean and Shaly Sand Formations Brief Introduction to Formation Evaluation of Organic Shale PETE 321: Formation Evaluation, Spring 2014

Porosity

Zoya Heidari, Ph.D.

Lithology Volumetric Concentration of Shale Permeability Capillary Pressure Movable/Trapped Hydrocarbon Saturation Net Pay

Density Measurements How can I use well logs to distinguish these rocks?

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PETE 321: Formation Evaluation, Spring 2014

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Zoya Heidari, Ph.D.

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Physical Properties of Minerals/Fluids Material

Coal Hydrocarbon Water

Chemical Formula

(g/cm3)

e

(b/e)

Quartz

SiO2

2.65

1.806

Calcite

CaCO3

2.71

5.084

Dolomite

CaMg(CO3)2

2.87

3.142

Montmorillonite (Smectite)

(Na,Ca)0.33(Al,Mg)2Si4O10(OH)2×nH2O

2.06

2.04

Illite

KAl4(Si,Al)8O20(OH)4(O,OH)10

2.64

3.45

Kaolinite

Al2O3×2SiO2×2H2O

2.59

1.83

Chlorite

Mg5(Al,Fe)(OH)8(Al,Si)4O10

2.88

6.30

K Feldspar

KAlSi3O8

2.56

2.86

Plagioclase (Na)

NaAlSi3O8

2.62

1.68

Plagioclase (Ca)

CaAl2Si2O8

2.76

3.13

Barite

BaSO4

4.48

266.8

Siderite

FeCO3

3.94

14.69

Pyrite

FeS2

5.01

16.97

Anhydrite

CaSO4

2.96

5.05

Gypsum

CaSO4×2H2O

2.31

3.420

Halite

NaCl

2.165

4.65

Sylvite

KCl

1.99

8.510

Anthracite

C720H258N6O16

1.60

0.161

Bituminous

C532H418N8O41

1.35

0.180

Lignite

C480H412N7O101

1.10

Oil (medium gravity)

n(CH2)

0.80

0.125

Gas (160°F, 5,000 psia)

CnH2n+2 (n=1–6)

0.20

0.119

Fresh Water

H2O (fresh)

1.00

0.358

Saline Water

120,000 ppm NaCl

1.086

0.807

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

t (us/ft)

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Applications • Why is it important to estimate lithology? – Reliable porosity assessment – Reliable assessment of fluid saturations – Reliable rock typing – Detect zones for perforation and completion jobs – Detect zones for fracturing jobs – Predict performance of stimulation jobs PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Assessment • Well logs are sensitive to lithology – GR, PEF, Density, Neutron porosity, Acoustic, ECS

• How do we combine these measurements to estimate volumetric/weight concentrations of minerals? – Cross plots (e.g., N D, N t, D t, …) – The M N plot – The MID plot – Quantitative linear inversion techniques – Quantitative nonlinear inversion techniques PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Assessment

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Assessment

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology and Porosity Assessment

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology and Porosity Assessment

Example

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology and Porosity Assessment Neutron sonic cross plot

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology and Porosity Assessment Density sonic cross plot

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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The M N Plot

M

tf b

N

N,f b

t log

0.01

f

N f

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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The MID Plot

D , Lime x

2 b

maa

t maa

N , Lime

x

1 t

f x

tf

x

1

x

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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The MID Plot

Courtesy of Schlumberger

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Identification

U maa

Pe 1

b x

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Identification

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Indicators

Source: Bateman, R. M., 2012, Openhole Log Analysis and Formation Evaluation.

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Lithology Indicators

Source: Bateman, R. M., 2012, Openhole Log Analysis and Formation Evaluation.

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Reliability of Cross Plots for Lithology Assessment

• The cross plots are accurate if there is less than two minerals in the matrix. – For example in the cases where the rock consists of one clay type and quartz

• Unreliable results in the presence of – Gas in the formation – Barite in the mud

• What should we do if the matrix consists of more than two minerals? – Example: in the complex carbonate cases or organic shale PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

Linear Mineral Solvers • Linear/semi linear system of equations: put all of your known parameters into matrix A

Concentration s

Unknown s

measured well logs

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

you can do this in excel or just rref(Ax-b)

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Linear Mineral Solvers

• How many unknowns do we have? If you have more unknowns that well logs then you have un-uniqueness of results. Underdetermined! this is a big problem in shale formations/unconventionals.

• How many known parameters do we have? • Is there a unique answer to this inverse problem?

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Uniqueness of the Solution • Over determined

• Even determined

• Under determined

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Decreasing Non Uniqueness of the Results • Increasing the number of input parameters: – Increasing the number of well logs – Using core data

• Grouping minerals • Adding constraints

what can you do to solve the problem of non-uniqueness? Says Zoya…

– Unity constraint tells you that you have had experience for example you know C1+C2+blah blah blah=1 – Constraints basedthaton core data –… use the linear relationship between quartz and plagioclase to provide more knowns.

Examples PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

Limitations of Linear Techniques • Where can I assume that the linear correlations are reliable? In what conditions do they fail? • Can I assume that the linear correlation is valid for all the well logs? • What other parameters should we take into account? PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Nonlinear Numerical Methods

PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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Complementary References • Bateman, R. M., 2012, Openhole Log Analysis and Formation Evaluation, Chapter 24 • Ellis, D. V. and Singer, J. M., 2007, Well Logging for Earth Scientists, Chapter 22 • Bassiouni, Z., 1994, Theory, Measurement, and Interpretation of Well Logs. SPE Textbook Series Vol. 4., Chapter 14 • Suggested references in the syllabus PETE 321: Formation Evaluation, Spring 2014

Zoya Heidari, Ph.D.

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