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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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|
Biniwale, S., Trivedi, R., Zangl, G. et al. 2010. Streamlined ProductionWorkflows and Integrated Data Management--A Leap Forward in Managing CSGAssets. Paper SPE 133831 presented at the SPE Asia Pacific Oil and GasConference and Exhibition, Brisbane, Queensland, Australia, 18-20 October. http://dx.doi.org/10.2118/133831-MS.
Courteille, J. M., Fabre, M., Hollander, C. R., Teknowledge. 1986. AnAdvanced Solution: The Drilling Adviser. J Pet Tech 38 (8):899-904. http://dx.doi.org/10.2118/12072-PA.
Hajizadeh, Y. 2010. Ants Can Do History Matching. Paper SPE 141137 presentedat the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19-22September. http://dx.doi.org/10.2118/141137-STU.
Landre, E., Ølmheim, J., Waersland, G., et al. 2006. Software Agents - AnEmergent Technology that Enables Us to Build More Dyanamic, Adaptable, andRobust Systems. Paper SPE 103354 presented at the SPE Annual TechnicalConference and Exhibition, San Antonio, Texas, 24-27 September. http://dx.doi.org/10.2118/103354-MS.
Levart, L., Morineau, A. andWarwick, K. 1984. Multivariate DescriptiveStatistical Analysis: Correspondence Analysis and Related Techniques for LargeMatrices. New York, New York: John Wiley & Sons.
Marik, V. and Vrba, P. 2005. Simulation in Agent-Based Control Systems: MASTCase Study. Proc., 16th IFAC World Congress, Prague, Czech Republic, Vol. 16,Part 1.
Mohaghegh, S. D. 2012. Application of Surrogate Reservoir Model (SRM) to anOnshore Green Field in Saudi Arabia: Case Study. Paper SPE 151994presented at the North Africa Technical Conference and Exhibition, Cairo,Egypt, February 20-22. http://dx.doi.org/10.2118/151994-MS.
Mohaghegh, S. D. 2011. Reservoir Simulation and Modeling Based On ArtificialIntelligence and Data Mining (AI&DM). J. Nat. Gas Sci. Eng. 3 (6): 697-705. http://dx.doi.org/10.1016/j.jngse.2011.08.003.
Mohaghegh, S, Al-Fattah, S. and Popa, A. eds. 2011. ArtificialIntelligence and Data Mining Applications in the E&P Industry.Richardson, Texas, SPE.
Moridis, G. J., Reagan, M. T., Santos, R. et al. 2011. SeTES: ASelf-Teaching Expert System for the Analysis, Design, and Prediction of GasProduction from Unconventional Gas Resources. Paper SPE 149485 presented at theCanadian Unconventional Resources Conference, Calgary, Alberta, Canada, 15-17November. http://dx.doi.org/10.2118/149485-MS.
Ølmheim, J., Landre, E. and Quale, E. Improving Production by Use ofAutonomous Systems. Paper SPE 112078 presented at the 2008 SPE IntelligentEnergy Conference and Exhibition, Amsterdam, The Netherlands, 25-27February. http://dx.doi.org/10.2118/112078-MS.
PABADIS Promise. 2005. PABADIS based Product Oriented Manufacturing Systemsfor Re-Configurable Enterprises, http://www.uni-magdeburg.de/iaf/cvs/pabadispromise/.
Sankaran, S., Lugo, J., Awasthi, A., et al. 2009. The Promise and Challengesof Digital Oil Field Solutions: Lessons Learned for Global Implementation andFuture Directions. Paper SPE 122855 presented at the SPE Digital EnergyConference and Exhibition, Houston, Texas, 7-8 April. http://dx.doi.org/10.2118/122855-MS.
Saputelli, L., Nikolaou, M. and Economides, M. J. 2003. Self-LearningReservoir Management. Paper SPE 84064 presented at the SPE Annual TechnicalConference and Exhibition, Denver, Colorado, 5-8 October. http://dx.doi.org/10.2118/84064-MS.
Soma, R., Bakshi, A., Prassanna, V., et al. 2008. Semantic Web Technologiesfor Smart Oilfield Applications. Paper SPE 112267 presented at the IntelligentEnergy Conference and Exhibition, Amsterdam, the Netherlands, 25-27 February.http://dx.doi.org/10.2118/112267-MS.
Szatny, M. 2007. Enabling Automated Workflows for Production. Paper SPE109859 presented at the SPE Annual Technical Conference and Exhibition,Anaheim, California, 11-14 November. http://dx.doi.org/10.2118/109859-MS.
Winston, P. H. 1992. Artificial Intelligence, third edition.Addison-Wesley.
Zangl, G., Al-Kinani, A. and Stundner, M. 2011. Holistic Workflow forAutonomous History Matching Using Intelligent Agents: A Conceptual Approach.Paper SPE 143842 presented at the SPE Digital Energy Conference and Exhibition,The Woodlands, Texas, 19-21 April. http://dx.doi.org/10.2118/143842-MS.
Zangl, G., Graf, T. and Al-Kinani, A. 2006. Proxy Modeling in ProductionOptimization. Paper SPE 100131 presented at SPE Europe/EAGE Annual Conferenceand Exhibition, Vienna, Austria, 12-15 June. http://dx.doi.org/10.2118/100131-MS.