Combining Machine Learning with Traditional Reservoir Physics for Predictive Modeling and Optimization of a Large Mature Waterflood Project in the Gulf of San Jorge Basin in Argentina
- Carlos Calad (Tachyus Corp) | Fernando Gutierrez (Tachyus Corp) | Paola Pastor (Tachyus Corp) | Pallav Sarma (Tachyus Corp)
- Document ID
- Society of Petroleum Engineers
- SPE Latin American and Caribbean Petroleum Engineering Conference, 27-31 July, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 7 Management and Information, 7.6.6 Artificial Intelligence, 7.6 Information Management and Systems, 5.5 Reservoir Simulation, 7.6.4 Data Mining, 5.4 Improved and Enhanced Recovery, 5 Reservoir Desciption & Dynamics, 5.4.1 Waterflooding
- Oil Field Test, Machine Learning, Data Physics, Reservoir Optimization, Water Redistribution
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A novel technology that combines the benefits of speed of data sciences with the predictivity capabilities of traditional simulation is being applied to model two blocks of a large waterflood project in the Gulf of San Jorge basin in southern Argentina. The tool is being used to provide a prescription of injection water redistribution that optimizes production and reserves development and reduces injection cost.
The technology used is called DataPhysics* and combines the robustness of reservoir physics with the speed of data sciences techniques. The process solves a limited number of unknowns in a continuous scale making it several orders of magnitude faster than traditional numeric simulation. The reservoir model is created from raw (uninterpreted) data and is updated continuously allowing for close loop reservoir optimization in real time. Long term predictivity is enabled by the fact that the tool honors the reservoir physics.
At the time of writing this paper the recommendations of the predictive model have been implemented in the pilot sector of the field and early positive results have been observed.
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Holyoak, S. 2018 paper SPE190419, "Increasing Value Through Digital Transformation: A Case Study From the A Field EOR Asset, Sultanate of Oman," by S. Holyoak, SPE, A. Alwazeer, S. Choudhury, M. Sawafi, A. Belghache, T. Aulaqi, SPE, S. Bahri, R. Yazidi, A. Yahyai, and K. D’Amours, Petroleum Development Oman, prepared for the 2018 SPE EOR Conference at Oil and Gas West Asia, Muscat, Oman, 26-28 March.
Sarma, P, Kyriacou, S., Henning, M., Orland, P., Thakur, G., Sloss, D. 2017. Redistribution of Steam Injection in Heavy Oil Reservoir Management to Improve EOR Economics, Powered by a Unique Integration of Reservoir Physics and Machine Learning. Paper SPE 185507-MS presented at the SPE Latin America and Caribbean Petroleum Engineering Conference in Buenos Aires.