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

New Articles

The book provides an overview of the key topics, but any printed material is always bound to go out of date. That is exactly why we feel it is necessary to make a number of new articles available here to update readers on the most recent thinking and advances. If you would like to submit an article we would be happy to hear from you, please contact us using the form below.

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Article 74
Counting Edwards: A precise definition overcomes unexpected complexities
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Article 73
Flying over Yugoslavia: Having an elegant data architecture will pay dividends in the long run
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Article 72
The slowest soldier: Why the handling of technical data is more important than your CEO thinks it is
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Article 71
CDO: Data or Digital?: What does CDO stand for, data or digital?
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Article 70
Core Competencies: Is Data Management a core competency for Oil Companies?
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Article 69
A mountain of pots: Failing to innovate to avoid impacting vested interests has been a theme for some time
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Article 68
Training Unicorns: If skills are not available perhaps we should put more effort into training our people?
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Article 67
Declaring ignorance: Admitting what we don't know can be hard for most people
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Article 66
Dreams of integration: If integration is such a good thing why is there so little of it in the upstream oil industry?
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Article 65
The right tool: Knowing which tool to select is an important skill,
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Article 64
Good leaders making stupid decisions: One reason rational executives sometimes make bad choices
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Article 63
Different levels of impact: Data and people have more impact than tools and processes
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Article 62
Model free data: Big data doesn't remove the need for thinking
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Article 61
The dragon of data quality: Data quality relies on many different concepts
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Article 60
The gap between intent and execution: Implementing things is often hard, so we should always document what was intended as well
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Article 59
How hard can it be...: Writing a workable data standard is really hard
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Article 58
The joy of posters: Thinking about complex topics in the form of a poster can give good insights
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Article 57
Pick a project, any project...: Selecting from different opportunities requires data
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Article 56
Investment's Impact: In the long term its easy to see what was important, how do we find out sooner?
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Article 55
A home of our own: Do we need a society to regulate data handling professionals?
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Article 54
Holding back the tide: The data environment is determined by the actions of data users
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Article 53
Data Management tools: The current data mess is a people issue, new technology won't ever solve it
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Article 52
Telling tales: Every presentation tells a story, there's no reason to be boring
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Article 51
Cost cutting versus investment: Should oil companies focus on expansion or costs?
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Article 50
Finding what isn't there: In order to see what is missing we must know what to expect
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Article 49
Body of Knowledge: What should every oil industry data manager know?
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Article 48
Size isn't everything: No single metric can every provide the level of detail needed to assess data handling
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Article 47
On groking mundanes (again): If you want communication to be clear you must worry about the words specialists use
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Article 46
Franchising TV shows: A body of knowledge must be extensive rather than brief
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Article 45
The professional's tale: What does it take to be professional?
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Article 44
I only changed one line...: When manipulating data paranoia is your friend
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Article 43
Modelling oil company data processes: The best models get into details
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Article 42
Corporate strategy models: Sometimes ommiting the details can improve a model
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Article 41
Decisions and decision trees: The 'value of information' process has limited application to working out overall business value
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Article 40
Why time budgets don't work: Basing a business case on the time saving when looking for data is questionable at best
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Article 39
Proving value: How can you prove the value data delivers?
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Article 38
Wasting time with pictures: Why you should worry more about perfecting pictures
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Article 37
Giant robots and small teams: Changing the sizes of robots, software teams and oil companies means changing the way things are done
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Article 36
Being certifiable: Just because there is no ethics committee doesn't mean we can reveal client details
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Article 35
Adopting standards: Getting a new standard adopted requires more than just proving it will improve things
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Article 34
Having your cake: Being able to describe things at different levels is an essential skill
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Article 33
Lumpers and Splitters: Do you mentally join things together or split them apart?
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Article 32
Planning and being wrong: Failure to plan because of fear of analysis is always a bad approach
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Article 31
The value of paella: Working out the costs and added value can be tricky even for simple things
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Article 30
Little prospects for big data?: How does the 'new' concept of big data apply to the upstream oil industry?
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Article 29
New paradigms: Learning new concepts, new ways of seeing the world, is almost always a good thing
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Article 28
Convergent Explanations: Understanding interactions is harder and more rewarding
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Article 27
Pursued by a bear...: How much is it worth to be slightly better than your competition?
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Article 26
IT or not IT, that is the question: Should data management be part of the IT group?
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Article 25
Which data is 'managed'?: Why does traditional data management focus on the least valuable data?
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Article 24
Self fixing data: Some data users have strange beliefs
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Article 23
T-Shaped People: Good data managers need to understand a wide range of topics
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Article 22
A bucket of crabs: Not everyone who blocks your change is being spiteful, but an experiment suggests how to deal with those that are
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Article 21
Better than average: Boasting about past success is part of the job
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Article 20
Good and Bad Hype: Overselling simple ideas is necessary to get new things built
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Article 19
Proving Yourself Wrong: Geological data is used forensically rather than assembled
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Article 18
Data, Information and Knowledge: Understanding the difference between data, information and knowledge
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Article 17
The maturity of Maturity: The concept of Maturity can now be considered mature
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Article 16
Spreading Unhappiness: Ensuring everyone is equally unhappy can deliver great results
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Article 15
Deal or No Deal: TV Game Shows can provide great lessons about risk
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Article 14
The treachery of data models: A data model of a thing is not the thing it describes
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Article 13
Bumps on the Head: Is Data Management a real topic?
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Article 12
Benchmarking: How can we compare data handling efficiency across companies?
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Article 11
Only what will be enforced: Should data managers change their whole outlook?
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Article 10
The Flood of Data: Is the deluge of data about to overwhealm us?
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Article 9
Test Yourself: A quick test of your oil industry data management knowledge
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Article 8
We need a Map: A good map helps understanding, even when its not perfect
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Article 7
A Tale of Long Ago...: Why are oil industry data standards so hard to define?
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Article 6
We need a Library: If we want interesting conferences we need to make old papers easy to find
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Article 5
Return of the Notorious Iceberg: Is it true that 70% of company data is unstructured?
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Article 4
The Joy of Checklists: Checklists make it easier to be consistent, why would you not use them?
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Article 3
The Perils of Flexibility: Is having flexible software always a good idea?
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Article 2
Competitive Advantage: Is it time to stop telling other oil companies how you are managing your data?
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Article 1
Data Quality, Fish and Ska music: A short article about data quality dimensions, and how they relate to fish and ska music

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20 May 2014

camiseta espaċ¸½a 2014

Greetings! Very useful advice in this particular post! It is the little changes that will make the greatest changes. Thanks for sharing