The World Wide Web Consortium (W3C) has published its report from its December workshop on the semantic web’s role in the oil and gas industry. Expectations are that the semantic web has enormous potential in this sector, given the huge amount of data it produces on a daily basis thanks to its work in drilling, exploration and production, reservoir management, major capital projects, and facility and upstream and downstream operations.

In fact, according to a presentation at the workshop given by Jim Crompton, Chevron manager of technology and architecture, “Growth in data volume is increasing, especially with field automation on new major capital projects and with digital oil field programs.”

Chevron, he notes in the presentation slides, has more than 6,000 terabytes of data, and counting. It’s seen an 80 percent annual growth rate of technical data in 2007, and a 60 percent compound annual growth rate of business (unstructured) data for the last two years.

“This means we will have 10x the data in 5 years, 100x in 10 years and 1000x in 15 years,” according to the presentation slides.

Yet enterprise-wide information management successes have been few, with consequences on a number of fronts. A few cited in the presentation slide deck are that a significant amount of time (30 percent to 70 percent) is spent looking for and assessing the quality of the data found; that it is hard to trust the data found because of issues around a lack of consistent data definitions, for one thing; and that this has an impact on the ability to share data with other team members, offsite experts, partners, and regulators and landlords.

“Our successes in IM have mostly been in restricted domains, often involving descriptions and organizations peculiar to those domains, and the isolation of these domain-specific efforts has been worrisome,” the presentation notes.

Semantic web standards and tools can have a role in increasing the value of information by improving data quality, making data more available for decision-making, enhancing data integration and interoperability, and enabling effective operation of data management systems. The presentation notes that there has been some success with XML-based data protocols to exchange data, and underscores the potential that an understanding of data relationships between functional taxonomies can unlock integration opportunities.

An organizing mechanism

Those ideas contribute to the high-level goal of this workshop, which, according to the W3C, was to gather and share possible use cases and/or case studies for semantic web in the industry. Talks also focused on the general methodology for creating and managing ontologies used in semantic web applications, according to the W3C, with three general types of potentially useful ontologies coming to the fore.

The W3C lists them as: upper ontologies that express concepts not specific to the industry (such as location information); domain ontologies that express concepts specific to, or heavily used, in the industry (such as geology, reservoir characteristics, and production volumes); and application ontologies that express information used in a particular project or experts’ experience in industry activities (such as information about geological interpretations or reservoir simulations).

According to a paper from academics at the University of Texas, Austin, Enhanced Oil Recovery (EOR) has become an increasingly important aspect of hydrocarbon production, given the volatility of oil prices and the fact that 65 percent of oil still remains in place after secondary recovery. An ontology-driven EOR decision support system can be critical in selecting and executing a plan for EOR for a particular reservoir, the paper argues.

“Ontology provides an organizing mechanism such that knowledge is managed to be accessible to software agents as well as humans. It also helps bridge the gap among disciplines and maximizes reusability. In addition, it provides the basis for automatic inferences,” the authors write. “The idea is to capture all the workflows dealing with different aspects of EOR, create workflow-driven ontologies, and combine them together into a comprehensive decision-support system” that will be able to address questions ranging from what EOR Methods should be considered for a reservoir, to how much uncertainty is associated with the prediction of performance in the field, to calculating the value of doing more lab work before going into production with an EOR method.

The authors note they are using the Stanford-developed and -maintained Protégé-OWL to build all ontologies –- including to date a pilot EOR screening ontology, a simplified recovery calculation ontology, and a risk management ontology -- as it provides a very flexible environment for ontology development with many plug-ins and APIs for extensibility.