Semantic Web Technologies for Smart Oil Field Applications

Abstract

In model based oil field operations, engineers rely on simulations (and hence simulation models) to make important operational decisions on a daily basis. Three problems that are commonly encountered in such operations are: on-demand access to information, integrated view of information, and knowledge management. The first two problems of on-demand access and information integration arise because a number of different kinds of simulation models are created and used. Since these models are created by different processes and people, the same information could be represented differently across models. A unified view of the models and their simulations is desirable for decision making, and thus the necessity for information integration. Knowledge management refers to a systematic way to capture the rationale (knowledge) behind the various analyses performed by an engineer and decisions taken based on the analyses. It is critical to capture this knowledge for auditing, archiving, and training purposes. In this paper, we propose the application of semantic web technologies to address these problems. The key elements of the semantic web approach are the ontologies or the information schemas that model various elements from the domain, and a knowledge base (KB) which is a central repository of the instance information in the system. We present a modular approach for organizing the ontologies and outline the process that was followed to define the ontologies. We also describe the workflow that was used to populate the KB and briefly discuss some of our prototype applications that address the problems mentioned above. Based on our experience, semantic web technologies appear to be a highly promising approach to deal with these information management issues in the oilfield domain, although performance and tool support remain the key areas of concern at this stage.

Detecting Dirty Queries during Iterative Development of OWL Based Applications

“…The third benchmark we have used (called CiSoft) is based on a real application that we have built for an oil company [23]. The schema for LUBM is relatively simple-it has about 40 classes.…”

Semiautomatic, Semantic Assistance to Manual Curation of Data in Smart Oil Fields

“…Semantic web technologies are increasingly being identified as key enabling technology for integrated asset management and smart oil field applications [29]. Among the proposed data curation approaches also, several proposals explored the semantic web technology at varying levels.…”

An implementation of a distributed artificial intelligence architecture to the integrated production management

“…Following the approach presented in (Soma et al, 2008), the general purpose concepts to use in the semantic layer will be the concepts proposed in the Semantic Web for Earth and Environmental Terminology (SWEET) developed by the California Institute of Technology Jet Propulsion Laboratory and the NASA (NASA, CalTech, 2009). These concepts will be the base to the Oil Production Ontology construction.…”

“…Following this approach, several oil & gas production enterprises are developing technological projects related with Integrated Reservoir Management (IRM). Some of the most relevant projects are the High North Integrated Operations (POSC, 2009), developed by Statoil Ò and the Norway Scientific Research Council, the i-Field (Soma et al, 2008;Sankaran et al, 2010) the Smart Field developed by Shell Ò (Potters and Kapteijn, 2005) and the Field of the Future (Sisk et al, 2007) developed by BP Ò . These kinds of models approach the automation as a mean for the production planning and operation, and not as an end in itself.…”