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Smart Proxy Modeling of SACROC CO2-EOR

1
Southern California Gas Company, Los Angeles, CA 90013, USA
2
Dept. of Petroleum & Natural Gas Engineering, West Virginia University, Morgantown, WV 26506, USA
3
Nielsen Company, Boston, MA 02494, USA
*
Author to whom correspondence should be addressed.
Fluids 2019, 4(2), 85; https://doi.org/10.3390/fluids4020085
Received: 26 February 2019 / Revised: 17 April 2019 / Accepted: 26 April 2019 / Published: 3 May 2019

Abstract

Large CO2-enhanced oil recovery (EOR) projects usually contain an abundance of geological and good performance data. While this volume of data leads to robust models, it often results in difficult to manage, slow-running numerical flow models. To dramatically reduce the numerical run-times associated with the traditional simulation techniques, this work investigated the feasibility of using artificial intelligence and machine learning technologies to develop a smart proxy model of the Scurry Area Canyon Reef Operators Committee (SACROC) oilfield, located in the Permian Basin, TX, USA. Smart proxy models can be used to facilitate injection-production optimization for CO2-EOR projects. The use of a coupled grid-based, and well-based surrogate reservoir model (SRM) (also known as smart proxy modeling) was investigated as the base of the optimization. A fit-for-purpose coupled SRM, which executes in seconds, was built based on high-resolution numerical reservoir simulation models of the northern platform of the SACROC oilfield. This study is unique as it is the first application of coupled SRM at a large oilfield. The developed SRM was able to identify the dynamic reservoir properties (pressure, saturations, and component mole-fraction) at every grid-block, along with the production characteristics (pressure and rate) at each well. Recent attempts to use machine learning and pattern recognition to build proxy models have been simplistic, with limited predictive capabilities. The geological model used in this study is comprised of more than nine million grid blocks. The high correlation between the actual component and SRM, which can be visualized by mapping the properties, along with the fast footprint of the developed model demonstrate the successful application of this methodology. View Full-Text
Keywords: surrogate reservoir model; proxy modeling; artificial intelligence; reservoir simulation; miscible CO2 flood; enhanced oil recovery surrogate reservoir model; proxy modeling; artificial intelligence; reservoir simulation; miscible CO2 flood; enhanced oil recovery
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Vida, G.; Shahab, M.D.; Mohammad, M. Smart Proxy Modeling of SACROC CO2-EOR. Fluids 2019, 4, 85.

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