Datalifecycle_vstudent Con Titulos

12/18/2017

Data Life Cycle

Presenter Schlumberger Date 12th December 2016

 © 2016 Schlumberger. All rights reserved.

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Schlumberger-Private

 An asterisk is used throughout this presentation to denote a mark of Schlumberger. Other company, product, and service names are the properties of their respective owners.

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Objective   



Schlumberger-Private



Understand/review data categories Understand life cycle for each main datatype of the E&P industry, Get best practices managing Corporate/Project Data taking into account their position into the lifecycle Understand positive impacts taking into account the stage of the data into the lifecycle Understand negative impacts not considering the lifecycle

The final objective is to pass to you the knowledge required to better define data management processes and increase value of the data. Key to be successful in your future position

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Agenda  

 4

Schlumberger-Private



Introduction Definitions and Data Categories  Data versus Metadata  Physical Assets versus Digital/Electronic data  Corporate versus Project  Structured Data versus Unstructured Data  Raw data/Processed data/Result data Data Life Cycle  Presentation  Data Categories  Seismic Data  Well Data/Deviation Survey/Well Log Data  Lease/License Conclusion Copyright ©2001-2012 NExT. All rights reserved

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Schlumberger-Private

Introduction

Copyright ©2001-2012 NExT. All rights reserved

Oil and Gas Life Cycle - Upstream

Exploration & Appraisal Search for oil & gas deposits



Reservoir characterization



Reserve estimation



Plan wells and surface facilities



Abandonment

Production

Build surface and subsurface facilities to produce oil and gas 

Extract, process and export 

Plug wells



Remove surface facilities



Restore field initial state

Schlumberger-Private

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

Development

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Relevance of Data Management during Upstream

Production

Exploration Data

Production Data

Prospect Analysis and Definition of Development Options

Further Reservoir Characterization and Production Optimization

Best Investment Decisions

Maximize Return of Investment

[ref]

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Schlumberger-Private

Exploration

DQM Solution Workshop (SIS) by Luis Carlos Jaramillo

Copyright ©2001-2012 NExT. All rights reserved

Relevance of Data During Exploration

Exploration (Appraisal Phase) Schlumberger-Private

• Rock, Fluid Properties • 2/3D Seismic • Log Curves

GO Field Development

Exploration Data

Reserves Estimation Reservoir Characterization

Project Viability?

STOP Non Profitable

Plan Wells and Surface Facilities 8

Copyright ©2001-2012 NExT. All rights reserved

[ref]

DQM Solution Workshop (SIS) by Luis Carlos Jaramillo

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Relevance of Data During Production

Production (Development Phase)

Reservoir Simulation

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Economic Model

Wells and Surface Models

• Production Volumes • Reservoir Pressure, Temperature • Flow Rates

Reservoir Model Update

Production Optimization

[ref] Copyright ©2001-2012 NExT. All rights reserved

DQM Solution Workshop (SIS) by Luis Carlos Jaramillo

 Data versus Metadata

Schlumberger-Private

Definitions and Data Categories

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Schlumberger-Private

Production Data

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Data vs Metadata (Definitions) 

Data

“Factual information (as measurements or statistics) used as a basis for [1] reasoning, discussion, or calculation”

Metadata “Metadata is data that provides information about data” 

[1]

http://www.merriam-webster.com/dictionary/data

[2]

http://www.merriam-webster.com/dictionary/metadata

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Schlumberger-Private

“Information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to [1] be meaningful”

[2]

Copyright ©2001-2012 NExT. All rights reserved

Data vs Metadata (Example – Digital Camera)

Schlumberger-Private

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[ref]

Fundamentals of Data Quality Management – NEXT* training - Author: Nigel Corbin

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Data vs Metadata (Example – Digital Camera) 

Data – in this case a JPEG image – Acquired by a photographer with a digital camera Schlumberger-Private

– Metadata about the image  Captured by the Camera – Date taken – Camera model – Focal Length – ISO speed

 Captured by the process – Note Quality tag = star rating. – Title – Authors 13

Copyright ©2001-2012 NExT. All rights reserved

[orig ref]

Fundamentals of Data Quality Management – NEXT* training - Author: Nigel Corbin

Data vs Metadata (LAS 2.0 file example)

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Schlumberger-Private

- Identify Curve Data section. - Identify Metadata (related to curve data) - Any other Metadata ?

Copyright ©2001-2012 NExT. All rights reserved

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Data vs Metadata (LAS 2.0 file example) Log Curve Metadata

Schlumberger-Private

Log Curve Metadata

Log Curve Data

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 Physical Assets versus Digital/Electronic data

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Schlumberger-Private

Definitions and Data Categories

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Physical Assets (or Physical Object) Definition: “A thing that you can see or touch but that is not usually a living animal, plant, or person”[1]

– Cores, core plug, rock sample, fluid samples

[1]

Schlumberger-Private

Examples: – Books, Printed documents (reports, presentations, pictures) – Digital media (CD, DVD, USB portable disk, Tape)

http://dictionary.cambridge.org/dictionary/english/object

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Electronic data Definition: “Information recorded in a manner that requires a computer or other electronic device to display, interpret, and process it.”[1]

[1]

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Schlumberger-Private

Examples: – Text file, email, image file, video file – Word*, Excel*, PowerPoint* files – DLIS, LAS, SEGY*, UKOOA* P1/90, UKOOA* P7 files http://www.businessdictionary.com/definition/electronic-document.html

Copyright ©2001-2012 NExT. All rights reserved

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 Corporate versus Project

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Schlumberger-Private

Definitions and Data Categories

Copyright ©2001-2012 NExT. All rights reserved

Corporate v.s Project

Schlumberger-Private

 In many cases there exists some confusion as to the differences and relative usefulness of both Corporate and Project data stores. • What are these differences ? • Why do they exist ? • When is either one an appropriate solution?

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate Data Store

Schlumberger-Private

 A corporate data store is usually seen as an enterprise-wide repository for the long-term storage of approved and validated data.  This data may be either original acquisition data, the results of previous processing or interpretation.  A focus on sharing data.

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate Data Store Advantages

– Approved values – Preservation of corporate knowledge and data in an industry standard data

– Integration of distributed asset teams – Reduction in data search and validation times

Schlumberger-Private

management environment

– Better data security and access

[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate Data Store Advantages

– Strong reference constraints – Validation procedures and methodology

– Federation

Schlumberger-Private

– Backup

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate Data Store Disadvantages

Lower performance for specific data sets No opportunity to “play” with values Incomplete data Scale:  Costs more to create/ configure  More effort to maintain

Schlumberger-Private

– – – –

[original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Project Data Store Relatively small data storage system Accessed by a single person or small group of people Frequently not a database as such Can be a closed proprietary file store Data access may or may not be allowed via some form of application programming interface (API) – Accessed by a single application or family of related applications

Schlumberger-Private

– – – – –

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Project Data Store Advantages Sandbox for “What if” scenarios Incomplete data handling High performance Offline availability Highly optimised data model

Schlumberger-Private

– – – – –

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Project Data Store Disadvantages Intermediate or indeterminate data quality Data duplications across multiple projects Different versions of the “same” data Indeterminate reference data integrity Indeterminate meta-data integrity Poor performance for large data sets No storage of original data Poor or non-existent security Recoverability of older archived data

Schlumberger-Private

– – – – – – – – –

[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate v.s Project - Metaphor - transport Shared resource High cost Fixed location Specialists required Large capacity Long term investment

– – – – – –

Personal resource Low cost Flexible route Anyone can maintain Limited capacity Rapid payback

Schlumberger-Private

– – – – – –

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate or Project

Schlumberger-Private

– Corporate and Project stores may have similar functions and data but serve radically different purposes – The Corporate store is a central repository of high quality, validated data with a stringent set of consistency rules and accompanying policies and procedures – It is expected to store data long-term, in a non-varying form – Much of the data may be of benefit over a large area

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Corporate or Project – – – –

Project stores allow rapid data change with multiple copies. Data access is designed specially to deliver to an application Poor stores for business reporting Often only look at small regions Schlumberger-Private

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Summary

Schlumberger-Private

– Corporate and Project data stores both have their own strengths and weaknesses – These are a direct outcome of the purpose for which they are used – The project store tends to be small and agile, concerned with a sub-set of data and with generating results in the most efficient manner – The Corporate store is large and monolithic, but more capable of handling large amounts of high quality data over a long period of time

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Summary – The concept of the Corporate and Project stores has arisen because one single data store cannot perform both roles – In order to implement a consistent data management strategy there is a need for both the Corporate and Project data store for the foreseeable future Schlumberger-Private

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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 Structured Data versus Unstructured Data

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Schlumberger-Private

Definitions and Data Categories

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Structured data  Structured data is data that can be read by a machine system without loss of meaning:

Schlumberger-Private

– Tabular data (in databases) – XML or similar – Text structures such as CSV files

[orig ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Unstructured Data  “Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner.”

– Voicemails, Audio transcripts, Videos, Pictures.

Schlumberger-Private

Readable by a person but not easily by a machine – Emails, text message, letters, reports, presentations and all kinds of documents.

– Social media content, online forums.

Most data is unstructured 80 to 90% (according to experts) 35

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 Raw data/Processed data/Result data

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Definitions and Data Categories

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Raw data Raw data is data which has not been processed since acquisition either manually or by using computer based processing.

 Instrument reading.

Schlumberger-Private

This data is collected from different sources which can be for example:  Sensor recording. Raw data is also know as primary data or source data. 37

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Processed data Processed Data is data which has been modified manually of by using computer based tools to become meaningful. Schlumberger-Private

These operations are originally done on raw data but can also be applied on already processed data (coming from previous processing phase) Examples:  Verify (remove outliers or wrong data)  Calculation (like signal processing)  Merge 38

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Raw Data or Processed Data in previous cycle

Data Processing Cycle

Data Collection

1

Input (Data)

2

3

Process Schlumberger-Private

5

4

Output (Information)

Storage

Processed Data 39

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Result data Result Data is Processed data which has been interpreted by using computer tools or manually.

Schlumberger-Private

“Interpretation is the process by which sense and meaning are made in data gathered.”[1]

RAW PROC

RESULT

RAW

[1] https://www.aqr.org.uk/glossary/analysis-and-interpretation

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Data Life Cycle Schlumberger-Private

 Presentation

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Data Lifecycle Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

Data also has a lifecycle: The time scale here may span a few minutes, the lifetime of a field, or more



The various stages may relate to many activities in the oilfield



Each stage has its own risks and costs



Data is only of value when it is used

Schlumberger-Private



[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Planning for Acquisition Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

Archive & restore

Purge or Dispose

 Most (all?) activities are planned Schlumberger-Private

– Some may be unplanned – disaster recovery  But we still should have a contingency plan

– Information (data) is used to develop the plan – A business case is developed  With documents, contracts, and presentations to match. – So we have a lot of new unstructured data

– That plan may include more data acquisition

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Acquisition Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

 Data acquisition may be a small part of the oilfield activity. Schlumberger-Private

– Seismic acquisition is focussed on acquiring seismic data – Drilling a well is focussed on constructing the well as planned, and acquiring data within the process – Installing gauges in a well provides  Metadata about the gauges (where are they, what are they?)  The data they acquire - may be continuously acquired and transmitted for years later

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Maintain Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

 Data acquired needs to be kept and made available to be used. Schlumberger-Private

– If it is not used, it is of little value – Work needs to be done to maintain it  Some physical media needs care, or it becomes unreadable. – Sticky and unreadable magnetic tapes – Lost media – cannot be found or used

 Digital data on disk may be lost or corrupted – So we need backup processes, disaster recovery (etc..)

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Use Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

 Data is typically stored in a number of different repositories

 We call this “interpretation”  Some is structured, some is unstructured

Schlumberger-Private

– These may be physical warehouses, or databases, or a cabinet full of paper – Users typically generate new information from old

– First step is always to find the old data

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Share and Publish Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

 For data to be valuable to the organisation (as opposed to the individual) it has to be shared Schlumberger-Private

– Ideally shared data should be:  Public, Precise & Unique

– Where is the "definitive" version to be found? – Who reviews the data? – How is the data's history tracked?

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Activities – Archival and Disposal Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

 Some data has a very short lifecycle, and we can archive it quickly  Legal constraints, regulations …  We may need to restore the data …  Eventually, we can dispose of it properly and securely

Schlumberger-Private

– We may need to maintain that archive for a long time

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Generating Value Plan

Specify

Enable

Acquire

Maintain & Use

Share and Publish

Archive & restore

Purge or Dispose

Schlumberger-Private

 Data only generates value for the organisation when it is being used

 And the cost of the data handling activities must be justified by the value that comes from the data… [ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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 Data Categories

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Schlumberger-Private

Data Life Cycle

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Lifecycle and Data Categories

Exploration

Appraisal

Development

Aban donm ent

Production (Maturity)

Schlumberger-Private

Life cycle duration: 15 to 30 years from first oil to abandonment. (can be 50 years or more on largest deposits)

Production Well Reservoir Seismic Spatial [original ref] ] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Data Categories Well

Spatial Culture



Boundaries / Licenses / Leases



Quadrants / Blocks



License History



Bathymetry/ Topography



Surface Images



Gravity & Magnetics



Coordinate Systems



Stratigraphic Columns

Identity

Headers

Seismic

Directional

Drilling

Planning Drilling Completion Events

Configuration

Formation

Network

Geochemistry

Surface

Surface Picks

Sub-Surface

Logs

Seismic Interpretation Interpretation Studies

Regular

Horizon Time Grid Horizon Depth Grid

Pump Data

Geologic Models

Other Measurements

Simulation Models

3D Outlines

Curves - Preliminary

2D Seismic Trace

Curves - Final Processed

Operational

3D Seismic Trace

Curves - Final Composite

Allocated Volumes

Acquisition Parameters

Petrophysical Parameters

Processing Parameters

Zoned Properties

Planned Events

Velocities

Checkshots

Unplanned Events

VSP

Samples

Synthetic Seismograms

Well Tests

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Reserves Prospects

Measured Volumes

Pressures

3D Navigation

External Network

Core Description

Intervals

2D Navigation

Reservoir

Production

Drilling/ W-O Treatment

Schlumberger-Private



Indexes Occasional

Physical Assets Scanned Documents Electronic Documents

Copyright ©2001-2012 NExT. All rights reserved

[original ref] ] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Data Classification – There are a number of different ways to classify the data being used for E&P  by domain - geophysics, petrophysics, drilling  by business lifecycle - explore, develop, produce etc..  by identifier – categories like maps, wells, seismic etc.. Schlumberger-Private

– There are always more choices to be made  Too broad - confusion between categories  Too much detail - endless discussions

[ref] ] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Seismic Data

Seismic 2D Navigation 3D Navigation 3D Outlines 2D Seismic Trace 3D Seismic Trace Acquisition Parameters

Schlumberger-Private

 Acoustic waves are generated, at the surface of the earth or ocean. – Propagate into the subsurface – Reflect from rock boundaries where properties change – Reflections are recorded at the surface. – Several different techniques, depending on location and equipment used

Processing Parameters Velocities

From http://www.glossary.oilfield.slb.com/Terms/s/seismic_acquisition.aspx

[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Key attributes  Acoustic trace data

– Survey/Activity name  navigation

– Amplitude v. time traces – Can be very high volume.

– Coordinate systems – Acquisition systems and geometry

Schlumberger-Private

 Spatial data

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Seismic Processing



Ex: Processing History Stanza

[2]

(extended textual header of SEG-Y rev 1 data exchange format).

[1]

Thanks to C.Martinez from WesternGeco* for Processing screenshots

[2]

From SE-Y rev 1 Data Exchange Format – © 2001, Society of Exploration Geophysicist.

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Schlumberger-Private

– Data acquired has to be processed [1] – Processing information and processing workflows must be properly tracked and kept linked to processed data.

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Seismic Processing – It may be possible or necessary to re-process old data  New techniques => better information  4D Seismic (Time-Lapse Seismic) => to be able to compare on a given time lapse. Schlumberger-Private

From Schlumberger Oilfield Glossary: http://www.glossary.oilfield.slb.com/Terms/t/time_lapse_seismic_data.aspx

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[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

Data life cycle – Seismic (2D/3D) UKOOA P1/90 OGP P1/11

RAW

ACQUISITION PARAMETERS

ACQUISITION

SEG-D DOCUMENTS

PRE-PROCESSING PROCESSING PARAMETERS

PROCESSING

UKOOA P1/90 OGP P1/11 INTERNAL FORMAT and finally SEG-Y DOCUMENTS

Schlumberger-Private

PROC

INT

INTERNAL FORMAT INTERPRETATION PARAMETERS

INTERPRETATION

GEOLOGICAL MODEL DOCUMENTS

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Seismic Data and Navigation formats – Seismic data recording formats:  SEG-D rev 3.1 (2009, updated 2015)  SEG-Y rev 1. (2002)  SEG-P1, … (1983)  SEG-C, … (1972)  SEG-A,B… (1967)





UKOOA (now Oil & Gas UK)) –

P1/90 is similar to SEG-P1 (www.iogp.org/Exchange/P1.pdf)

–

P2/94 - Raw marine positioning data (http://www.iogp.org/Exchange/P2.pdf)

–

P6/98 - 3D Seismic binning grids (http://www.iogp.org/Exchange/P6.pdf)

Schlumberger-Private

 http://www.seg.org/resources/publications/misc/technical-standards

IOGP (Association of Oil & Gas producers) –

OGP P1/11 (replacement of UKOOA P1/90 and SEG-P1)

–

OGP P2/11 (replacement of UKOOA P2/94)

–

OGP P6/11 (replacement of UKOOA P6/98)

http://www.iogp.org/Geomatics#2521696-geophysical-operations 59

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[Orig ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

Standards Bodies in Geophysics

Schlumberger-Private

EPSG = European Petroleum Survey Group (absorbed by IOGP in 2005) IOGP = International Association of Oil & Gas Producers (formerly known as OGP) OIL & GAS UK = Representative body for the UK offshore Oil and Gas industry (formerly known as UKOOA) SEG = Society of Exploration Geophysicists

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[Orig ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Data life cycle – Well Data

Well Data represents the greatest variety of data categories.

Schlumberger-Private

– Data which can be measured during the life of a well – Acquired during operations related to the well (Drilling, Directional survey, Wireline Logging, Completion…)

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Well – Key Attributes What is a well?

– Surface Location – Identification  Name, UWI, Number…  “Active”, “Plugged an Abandoned”, “Inactive”

– Classification  “Onshore”, “Offshore” – Plan – Owner and Operator (legal) – Changes during life cycle (status, operator,….)

Schlumberger-Private

– Status

https://ppdm.org/ppdm-standards/ what-is-a-well-definitions

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Drilling

[1]

[1]

63

Schlumberger-Private

 One or more boreholes (or wellbores) are drilled from a given well – Possibly at different times. – Drilling processes produce: Directional survey, well path – spatial location, total depth Measurements While Drilling (MWD) Cores recovered Mud/Fluid samples – the Mud Log. Reports and operational records. Pressure tests from NPD guidelines for designation of wells and wellbores

http://www.npd.no/Global/Norsk/5-Regelverk/Tematiske-veiledninger/Bronner_betegnelser_og_klassifisering_e.pdf

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[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

Borehole (Wellbore) – Key Attributes – Bottom Location – Identification

What is a well?

 Name, UBHI, Number… Schlumberger-Private

– Identification of the Well – Type of trajectory  “Vertical”, “Horizontal”, “Directional”

– Status  “Drilled”, “Completed”, “Producing”, “Plugged”, “Injector”, …

– Changes during life cycle (status)

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https://ppdm.org/ppdm-standards/ what-is-a-well-definitions

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Wells/Wellbores Identification

– Many identification standards exist.  US Well Number (replacement of API Well Number) http://ppdm.org/ppdm/PPDM/Standards/Well_Identification/US_Well_Number_Standard/PPDM/US_Well_Number_Standard.aspx Schlumberger-Private

 CWIS Standard (Canadian Well Identification System) http://ppdm.org/ppdm/PPDM/Standards/Well_Identification/Canadian_Well_Id_System_Standard_/PPDM/CWIS_Standard.aspx

 NPD designation for Wells and wellbores (Norwegian Petroleum Directorate) http://www.npd.no/Global/Norsk/5-Regelverk/Tematiske-veiledninger/Bronner_betegnelser_og_klassifisering_e.pdf

– Some companies are also creating their own internal identifiers

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Data life cycle – Deviation survey (Wellbore path computation)  Survey performed after or during drilling (MWD) to calculate 3D wellbore path. – Type of survey instruments: – Measured parameters

– Other parameters

DX

S

E

DY

TVD MD AZIMUTH

INCLINATION

Schlumberger-Private

 MD (Measured Depth)  Azimuth: 0° to 360° (clockwise from North Reference)  Inclination (from vertical) (0°= vertical, 90° = Horizontal)

N

W

 Gyroscopic, Magnetic (used in non magnetic environment)

 North Reference (True North, Magnetic North, Grid North)  Declination (when north reference is Magnetic North)  Projected Coordinate System (when north reference is Grid North)

– Computed wellbore path (usually by using Minimum Curvature method):  TVD: True Vertical Depth (MD ≥ TVD in all cases)  Easting Offset (DX)  Northing Offset (DY) 66

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Data life cycle – Deviation survey (Wellbore path computation)  Example of 3D wellbore path (from Petrel* Software).

Schlumberger-Private

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Well Logs

– Complex tools measuring usually against depth  But sometimes time (flow rates, …)

 Many tools  Much naming and standardisation

Schlumberger-Private

– Operational records – activities – Many contractors

 Ex: Curve Mnemonic Dictionary http://www.apps.slb.com/cmd/

[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Data life cycle – Well Logs RAW

ASCII

ACQUISITION PARAMETERS

ACQUISITION

DLIS/LIS/SEGY DOCUMENTS DLIS/LAS/LIS/ASCII

PRE-PROCESSING

PROCESSING PARAMETERS

INTERNAL FORMAT PROCESSING

DOCUMENTS

Schlumberger-Private

PROC

LAS/ASCII

INT

INTERPRETATION PARAMETERS

INTERPRETATION

ROCKS PHYSICAL PROPERTIES DOCUMENTS

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Well Log Standards  LIS – Log Information Standard (SLB[1] 1974, 1986)  Binary, tape based, need specialised tools to access  http://w3.energistics.org/LIS/lis-79.pdf

 DLIS – Digital Log Interchange Standard (API[2]) (V1.0 in 1991, V2.0 in 1996)  Binary, file based, built on RP66  http://www.energistics.org/geosciences/geology-standards

 LAS – Log ASCII Standard, CWLS[3]

Schlumberger-Private



 ASCII  Versions 1.2 (1990), 2.0 (1991-2014), 3.0 (2000)  http://www.cwls.org/las [1] [2] [3]

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Schlumberger American Petroleum Institute Canadian Well Logging Company Copyright ©2001-2012 NExT. All rights reserved

[Orig ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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LAS files – pros and cons  PRO:

 CON:

– LAS files are simple text files and can be edited with a simple tool like Notepad

– LAS files are simple text files and can be edited with a simple tool like Notepad Schlumberger-Private

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Log Images  Historically – logs printed to paper or film – So a kind of document – Can be handled as such  Or digitised back to digital data  May have useful metadata

– In Calgary log images freely available for old wells (Alberta Gov.)

Schlumberger-Private

 Can be a scanned image and a LAS file (e.g.)

[ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Data life cycle – Lease/License

Schlumberger-Private

 Represents legal limits on rights to explore and operate (can be onshore or offshore) – Licenses are usually distributed during a licensing round. – Grants are provided for a given period of time (depending on the type of license and country regulations) – Owned by a Joint Venture or by a Company – This procedure can be different from one country to another. Bid System  Best offer will obtain the license. Grant System  Highest involvement, interest, experience, investment plan. – License Fee to be payed. From: https://en.wikipedia.org/wiki/Petroleum_licensing 73

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Lease/License - Boundaries Culture

Spatial

Boundaries / Licenses / Leases Quadrants / Blocks License History

Surface Images Gravity & Magnetics Coordinate Systems Stratigraphic Columns

Schlumberger-Private

 Geographically delimited – National Boundaries And sub-divisions – Regulatory divisions Quadrant, blocks, lease, license – Contractual limits Agreed area for a Joint-Venture (JV)

Bathymetry/ Topography

Example of License, Quadrant and Block (From Norwegian Petroleum Directorate) [Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

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Lease/License - Key Attributes

[1]

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[1]

Schlumberger-Private

– Name – Status (ex: Active, Inactive) – Effective Date Example of Licensing Arrangements and regulation – Expiry Date – Related contracts, maps, etc.. – ……… – Changes during lifecycle (status, extended expiry date, area,….) Ex: http://factpages.npd.no/factpages/ License tab: Production license 248.

http://cdal.com/wp-content/uploads/2015/09/UKCS-Petroleum-Exploration-Licensing-Arrangements-and-Regulation-2006.doc Copyright ©2001-2012 NExT. All rights reserved

[Original ref] Introduction to Information Management in Petroleum Industry - NEXT* Training - Author: Nigel Corbin

Conclusion Schlumberger-Private

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Conclusion

 Qualifying data inside its lifecycle will be key for its quality  Data without its complete context has a very limited value and can impact dramatically all processes related to its usage  If data is not used, it has also a very low value. 77

Schlumberger-Private

– You have now the knowledge to better manage data generated by the E&P Industry – You now understand that:

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Bibliography Schlumberger-Private

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Bibliography and References • National Information Standards Organization (NISO) (2001). http://www.niso.org/publications/press/UnderstandingMetadata.pdf (NISO Press. ISBN 1-880124-62-9) • Geophysical Technical Standards: http://seg.org/Publications/SEG-Technical-Standards • Geology Standards: http://www.energistics.org/geosciences/geology-standards • PPDM (Professional Petroleum Data Management Association): https://ppdm.org • Well Logs Reference guide: https://wiki.ppdm.org/Well_Logs_Reference_guide • Log ASCII Standard: http://www.cwls.org/las • Data Processing: https://en.wikipedia.org/wiki/Data_processing • Schlumberger Curve Mnemonic Dictionary: http://www.apps.slb.com/cmd/ • Schlumberger Oilfield Glossary: http://www.glossary.oilfield.slb.com/ • Wikipedia: https://en.wikipedia.org/wiki/Oil_and_gas_law_in_the_United_States

Schlumberger-Private

• Raw Data definition: https://en.wikipedia.org/wiki/Raw_data

• Licensing UKCS: http://cdal.com/wp-content/uploads/2015/09/UKCS-Petroleum-Exploration-Licensing-Arrangements-and-Regulation-2006.doc • Data Management Value Study (CDA UK): http://cdal.com/wp-content/uploads/2015/09/Data-Management-Value-Study-Final-Report.pdf • Norwegian Petroleum Directorate (NPD): http://www.npd.no/en/ • Norwegian Licensing System: http://www.norskpetroleum.no/en/exploration/exploration-policy/ • NPD guidelines for designation of wells and wellbores: http://www.npd.no/Global/Norsk/5-Regelverk/Tematiskeveiledninger/Bronner_betegnelser_og_klassifisering_e.pdf • NPD Fact Page: http://factpages.npd.no/factpages/ • Schlumberger Seabed Data Model: http://seabed.software.slb.com/

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