Data Integration Challenges in the Oil and Gas Industry

The oil and gas industry has been a hotbed of activity in information integration over the past five years.  The activity began to come into focus in 2008, when the W3C organized a workshop [1] hosted by Chevron on data integration challenges in the oil and gas industry.

In many ways, the data integration challenges of the oil and gas industry are similar to those in many other industries – large disparate datasets that have to be combined to provide industrial-quality insight.  The W3C Oil Gas and Chemicals Business Group even modeled its organization to some extent on that of the Health Care and Life Sciences group, because of perceived similarities in those industries [2].

The oil and gas industry has galvanized around a single industry standards effort, ISO 15926 [3]. This standard has the lofty goal of providing a lingua franca for computer systems to exchange data.  ISO 15926 has a long history and a complex structure.  Part 8 of the standard is currently under development, and is based on the W3C OWL standard.

While W3C working groups and standards efforts are a good indication of the direction an industry is taking, a more concrete way to see what is really happening is to look at projects that are actually underway, and how they are being approached.

The major efforts have been applied in the North Sea oil fields.  The North Sea provides a particularly good application ground for application of information management innovation; the discoveries of oil resources in the North Sea are relatively recent in comparison to other areas of the world, and the geography provides novel challenges to distribution of information.

It is therefore no surprise that a major effort in oil and gas information sharing is the Integrated Operations in the High North joint industry project (IOHN).  The IOHN identifies several data integration challenges in the North Sea oil and gas operations.

The information that has to be integrated is multi-disciplinary, including that of geology, hydraulics, electronics, navigation, etc.  Furthermore, it comes in huge volumes, in which the relevant information for any particular decision makes up a small fraction—a proverbial needle-in-a-haystack problem.  The cross-disciplinary nature of the data means that it is difficult to discern its meaning in isolation; each dataset requires some specialized knowledge to make it comprehensible and usable.

The cross-disciplinary nature of the North Sea oil effort also has an impact on the utilization of the data. It is difficult to foresee how information will be used.  This means that the data integration has to be able to support novel query modes, based on interest from many different sources.

IOHN has made great strides in moving forward on these fronts.  Following projections made by Price Waterhouse Coopers [4] that show that a linked data approach results in lower integration costs for large numbers of data sources, IOHN has created an integration platform based on linked data principles using on semantic technology. But more about that in my next blog, where I’ll explain why the semantic model was indispensable to meet the data integration challenge for oil and gas.
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[1] http://www.w3.org/2008/12/ogws-report.html
[2] http://www.w3.org/community/oilgaschem/wiki/Why_Work_in_This_Venue
[3] http://en.wikipedia.org/wiki/ISO_15926
[4] PricewaterhouseCoopers (PwC). Pwc technology forecast: Semantic web in the enterprise, 2009. Available at http://www.pwc.com/us/en/technology-forecast/spring2009/index.jhtml.

Dean Allemang, co-author of the bestselling book, Semantic Web for the Working Ontologist, is a consultant, thought-leader, and entrepreneur focusing on industrial applications of distributed data technology. He served nearly a decade as Chief Scientist at TopQuadrant, the world’s leading provider of Semantic Web development tools, producing enterprise solutions for a variety of industries. As part of his drive to see the Semantic Web become an industrial success, he is particularly interested in innovations that move forward the state of the art in distributed data technology. Dean’s current work is concentrated on the life sciences and finance industries, where he currently sees the most promising industrial interest in this technology.

Dean Allemang

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