Artificial Intelligence–Driven Asset Optimizer
- Supriya Gupta (Schlumberger) | Abhishek Sharma (Schlumberger) | Aria Abubakar (Schlumberger)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 24-26 September, Dallas, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 3 Production and Well Operations, 7.6.6 Artificial Intelligence, 7.2 Risk Management and Decision-Making, 7 Management and Information, 7.6 Information Management and Systems, 7.2.1 Risk, Uncertainty and Risk Assessment, 3.2 Well Operations and Optimization, 3.2.7 Lifecycle Management and Planning
- Asset Management, Autonomous Operations, Statistical Learning, Artifical Intelligence, Production Optimization
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Currently, as oil and gas companies continue to face risk of volatility in oil prices, production optimization and maintenance play a critical role in driving operational excellence for the industry while maintaining good profit margins. E&P companies must maintain a focus on reducing unit cost/barrel. This can be achieved by reducing operating costs, increasing production, and reducing downtime. We propose a recommendation engine driven by artificial intelligence (AI) that seamlessly integrates subsurface information and production characteristics for knowledge extraction needed to optimize production operations across conventional and unconventional assets. We used a three-phase approach to designing and building an advisory system that ingests data, learns patterns, and feeds these learnings from the data into different functional workflows necessary for improving the efficiency and effectiveness of production operations. The system uses these mechanisms of knowledge extraction, statistical learning, and contextual adaptation as it evolves into an autonomous asset optimization system that can proactively recommend actions for effective decision making to lower the unit cost/barrel.
|File Size||793 KB||Number of Pages||7|
Boot, B. and Gordon, G. J. 2011. Two-Manifold Problems. http://arxiv.org/abs/1112.6399 (accessed 21 June 2018).
Denney, D. 2010. Exception-Based Surveillance. Journal of Petroleum Technology 62 (10): 66-67. SPE-1010-oo66-JPT. https://doi.org/10.2118/1010-0066-JPT
John, M.-P. U., Ibukun, S., Pius, J.. 2011. A Stochastic Approach to Well Spacing Optimization of Oil Reservoirs. Presented at the Nigeria Annual International Conference and Exhibition, Abuja, Nigeria, 30 July-3 August. SPE-105736-MS. https://doi.org/10.2118/150736-MS
Launchbury, J. n.d. D.I. DARPA Perspective on AI. https://www.darpa.mil/about-us/darpa-perspective-on-ai (accessed 21 June 2018).
Pankaj P., Shukla P., Sharma A., Menasria S., Judd T., Schlumberger; Geetan S. and MacDonald R., EP Energy Corp. SPE 189790. Need for speed: data analytics coupled to reservoir characterization fast tracks well completion optimization, presented at SPE Canada Unconventional Resources Conference, Calgary, Alberta, Canada, 13-14 March 2018 .
Vincent, M. C. 2010. Restimulation of Unconventional Reservoirs: When are Refracs Beneficial? Presented at the Canadian Unconventional Resources and International Petroleum Conference, Calgary, Alberta, Canada, 19-21 October. SPE-136757-MS. https://doi.org/10.2118/136757-MS