The Management of Oil Industry Exploration & Production Data

Who are we? Purchase Options - B&W USA/World - B&W UK - B&W Germany - B&W France - Color USA/World - Color UK - Color Germany - Color France
1 Introduction 12 Physical data
2 Value of data 13 Documents
3 Subsurface data 14 Auditing
4 Current practice 15 Quality
5 DMBoK 16 Other elements
6 Governance 17 Assessing
7 Architecture 18 Glossary
8 Development 19 Figures
9 Operations 20 Bibliography
10 Security 21 Index
11 Corporate data 22 Further info
Upcoming events New articles Extra material Links
Sample chapter Figures Bibliography Extra material Historical Papers

Extra material

The Bibliography lists the papers and presentations that were available at the time the book was written. This page lists additional material that has since become avaiable and is likely to be helpful.

If you would like to suggest (or provide) some more articles, presentations or downloads that you think would be of value to those looking at the site please contact us using the form at the bottom of this page.

Chapter 1 Introduction

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Are you spending too little on data?
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There are many ways to understand and improve complex systems, knowing what they are helps
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What are the things a data manager in the oil industry should know?
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What are data, information and knowledge?

Chapter 2 The value of data to oil companies

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Data management should be considered a core competency for all oil companies
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Should oil companies be expanding or cost saving?
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Noah Consulting's 'Industry Insight: IM Benchmarking' event April 2013
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A paper from the Molten group about the value of good sub-surface data management
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A comparison of how different industries manage their data, from Oracle
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Detailed models often deliver the most accurate results
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Sometimes a strategic model delivers better results than a detailed one
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Why using decision trees has limited strategic application
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Using a time budget approach to showing value is of limited utility
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Five different models that have the potential to demonstrate data's value
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Changing scale means doing things differently
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Eggert, J., Tidemann, S. & Kozman, J. (2011) "Communicating the Value of Data Management to Non-Technical Stakeholders"
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How do you work out the value of a single process?
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The benefits of being just a little bit better than the next guy
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Short introduction to data, information and knowledge
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The lessons that a TV Gameshow teaches about risk
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Should you be telling other oil companies about your data handling successes?

Chapter 3 E&P data for the non-specialist

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Carlos Damski has written a book about Drilling from a data perspective, worth looking at
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Randy Clark (of Noah) on Well Files, Coffee Stains and Cigarette Burns
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Randy Clark (of Noah) explains the keys to managing Well Files
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Introduction to wellbore positioning data
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If the strucural model is valuable, shouldn't it be kept for posterity?
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Building a geological model is not just assembly of pieces

Chapter 4 Current practice

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Contrasing the impact of people, process, tools and data helps clarify current issues
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Jane McConnell's list of 10 things she hates about current data management
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The data landscape emerges from the decisions of data users
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All good data managers know that paranoia is a good thing
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A duty of confidentiality holds for all the work data managers do
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The concept of Maturity for data management is now fairly mature
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The rapid increase in data volumes is a sign of past success, not future problems
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Why defining standards is complex the Petris view
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Checklists make it easier to be consistent, why would you not use them?
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Nigel Corbin's original introduction of the Data Management roundabout
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This is intended just for fun, a cutout and make version of Nigel Corbin's Data Management roundabout

Chapter 5 The Data Management Body of Knowledge

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Should we have a formal group to coordinate the topic?
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You have to have a theory in order to see what isn't there
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What would be in the oil industry Body of Knowledge?
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Finding a new way to seeing the world is always a good thing
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Could this whole approach be wrong? What to ask a sceptic
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A short piece on why even a distorted map can help understand complex territory

Chapter 6 Data Governance

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'Fear has Replaced Apathy as the Number One Enemy of Data' by Thomas Redman
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Writing good data standards is harder than one might think
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Its not new tools we need, its a change in approach
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Dave Blosser (Chevron) and Paul Haines (Noah) on Data Governance at Chevron GOM
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Without an idea of what should be there how can we see what isn't
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If you want to be correctly understood you have to be paranoid about words
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Brevity is important in its place, but sometimes you have to be comprehensive instead
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Failing to plan is planning to fail, failing to analyse is stupid even when you know you're wrong
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Changing our outlook would make delivering data management a whole lot easier
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Data Governance at Devon available from the Noah Consulting site
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'Data Governance: We Know We Want It, But What Is It?' presented at PNEC17 Houston 2013

Chapter 7 Data Management Architecture

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Precise definitions overcome unexpected complexities
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Elegant data architecture pays dividends in the long run
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Jim Crompton (of Noah) on defining Enterprise Architecture
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Pick levels of detail that match your audience not the situation
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It's understanding the interactions that makes business processes interesting
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A presentation from the 2007 ECIM conference reviewing different types of architecture pictures

Chapter 8 Data Development

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Why is there so little integration in exploration and production data?
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Data Integration Technology report from 2000 (for those interested in history)
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Adopting new standards can be a difficult change to implement
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It can be hard to empathise with other people's reaction to change, but publicity can stop them being nasty
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Just because two people use the same name doesn't mean they are talking about the same thing
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A short explination of what really makes E&P data standards hard to define

Chapter 9 Data Operations

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Should data management be part of IT?
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Boasting about the previous success of data initiatives is an essential part of the job
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A short piece on why contrasting the efficiency of data handling services needs careful thought

Chapter 11 Corporate and project data

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Too much flexibility is not always a good thing

Chapter 13 Document Management

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'Intertwingled' is a high level overview of the discipline of 'Information Architecture'
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When defining tags the goal is to ensure everyone ends up equally unhappy
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The division between structured and unstructured data is fuzzy
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An alternate view of the key E&P aspects defined by a PPDM workgroup

Chapter 15 Data Quality

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Doing a good job with data quality requires a wide range of concepts
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When documenting quality rules both the intent and the implementation should be available
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ETL article on Data Quality in Oil and Gas
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Gartner study on measuring the impact of data quality
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AHIMA (2012) "Data Quality Management Model (Updated)" Journal of AHIMA 83, no.7 (July 2012)
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Once data is corrupted it can only be fixed if the system has redundancy
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A review of data quality dimensions as defined by various authors
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A short article about data quality dimensions, and how they relate to fish and ska music

Chapter 16 Other elements

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Making the story interesting is an essential skill
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Is overselling simple concepts required to get innovative tools funded?

Chapter 17 Assessing Data Management

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One good assessment question is to ask about people, process, tools and data
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Jezz Kozman and Ed Evans employ maturity metrics to define business strategy
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Seven different types of stories that can be told with data
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Showing results in poster form has all sorts of advantages
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A2 paper sized version of the BigOil poster (6Mb file) showing more detail
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Maturity is a good metric, but it is not enough
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The concept of Maturity for data management is now fairly mature
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'Putting the Head Back on the Chicken' by Jess Kozman presented at PPDM Perth DM Symposium Aug 2012
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The standards provide some great checklists when assessing data managment

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