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

by Steve Hawtin
6 Jul 2013


The topic of "Information Management Benchmarks" comes up fairly regularly, and it's easy to see why. A newly appointed Chief Information Officer quickly discovers that their background in IT has not prepared them for the complexities of E&P technical data handling. They know that keeping hold of experienced data handlers is expensive, but they have trouble getting those guys to explain exactly what it is they do all day. So fairly quickly the new CIO is thinking "this data management stuff is really expensive, surely I can cut costs here. But, if I sack all the data guys and the following week a disaster happens then I'll be the scapegoat. I know, I'll do some measurements, if our data handling turns out to be in line with our competitors then I can't be criticised for wasting money on it, if it turns out that our spend is on the high side I can justify why we need to be more efficient and cut some costs".

Apples and Oranges

So how can you contrast what your organisation is spending on data handling with a peer company, or even better with industry averages? To provide such a benchmark we need to measure both the costs of handling data and the benefit that doing so delivers.

Each side of that equation is a challenge, first costs, one way would be to total up the wage bill for every staff member with the word "data" in their title. But that doesn't work because much of the key data handling activities are carried out by the geoscientists. Indeed there are many crucial categories of data, like simulation models, that the "Data Management" department (assuming such a thing exists) never even touch. What one organisation considers a key "Data Management" responsibility another one might not even do. There's also an issue with data volumes, looking at file sizes just makes the management of pre-stack 3D seismic seem incredibly efficient. A better approach would be to normalise the calculated costs by looking at the average cost of managing, say, a "standard chunk" of data, the data from 1,000 wells for example. But, leaving aside the assumption that everyone has agreed exactly what a well is, a horizontal off-shore exploration well has a few orders of magnitude more data that a vertical on-shore in-fill one does, contrasting the costs for these two makes little sense. Maybe looking at "service activities" would make more sense, if only there was some standard definition of what services the data management function delivered. As to benefits, there have been many attempts to document the additional value that oil companies gain by having better managed data. While the answer clearly is "a lot" getting to a more precise value requires an extensive insight into a particular company's activities. Measuring such things internally has to be classed as at least "interesting", estimating those figures for competitors is currently beyond our capabilities.

The business reasons for wanting "Industry Benchmarks" are just as compelling as they ever were. The fact that this is a challenging exercise shouldn't deter us from trying to achieve it, but, at the moment the territory is not well defined enough for results to be meaningful.

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