State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey
- César E. Bravo (Halliburton) | Luigi Saputelli (Frontender Corporation) | Francklin Rivas (Universidad de los Andes) | Anna G. Pérez (Universidad de los Andes) | Michael Nickolaou (University of Houston) | Georg Zangl (Fractured Reservoir Dynamics) | Neil De Guzmán (Intelligent Agent Corp) | Shahab Dean Mohaghegh (West Virginia University) | Gustavo Nunez (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- August 2014
- Document Type
- Journal Paper
- 547 - 563
- 2013. Society of Petroleum Engineers
- 7.6.6 Artificial Intelligence, 3.3.6 Integrated Modeling, 7.2.3 Decision-making Processes, 7.6.4 Data Mining
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- 1,048 since 2007
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Artificial intelligence (AI) has been used for more than 2 decades as adevelopment tool for solutions in several areas of the exploration andproduction (E&P) industry: virtual sensing, production control andoptimization, forecasting, and simulation, among many others. Nevertheless, AIapplications have not been consolidated as standard solutions in the industry,and most common applications of AI still are case studies and pilotprojects.
In this work, an analysis of a survey conducted on a broad group ofprofessionals related to several E&P operations and service companies ispresented. This survey captures the level of AI knowledge in the industry, themost common application areas, and the expectations of the users from AI-basedsolutions. It also includes a literature review of technical papers related toAI applications and trends in the market and in research and development.
The survey helped to verify that (a) data mining and neural networks are byfar the most popular AI technologies used in the industry; (b) approximately50% of respondents declared they were somehow engaged in applying workflowautomation, automatic process control, rule-based case reasoning, data mining,proxy models, and virtual environments; (c) production is the area mostaffected by the applications of AI technologies; (d) the perceived level ofavailable literature and public knowledge of AI technologies is generally low;and (e) although availability of information is generally low, it is notperceived equally among different roles.
This work aims to be a guide for personnel responsible for production andasset management on how AI-based applications can add more value and improvetheir decision making. The results of the survey offer a guideline on whichtools to consider for each particular oil and gas challenge. It alsoillustrates how AI techniques will play an important role in futuredevelopments of information-technology (IT) solutions in the E&Pindustry.
|File Size||742 KB||Number of Pages||17|
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