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Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

1
Big Data Center of Excellence, Halliburton, Houston, TX 77032, USA
2
Petroleum & Natural Gas Engineering Department, West Virginia University, Morgantown, WV 26506, USA
*
Author to whom correspondence should be addressed.
Fluids 2019, 4(3), 126; https://doi.org/10.3390/fluids4030126
Received: 1 May 2019 / Revised: 1 July 2019 / Accepted: 3 July 2019 / Published: 9 July 2019
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Abstract

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model. View Full-Text
Keywords: fluid flow model; proxy modeling; machine learning; artificial intelligence (AI); data driven modeling fluid flow model; proxy modeling; machine learning; artificial intelligence (AI); data driven modeling
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Amini, S.; Mohaghegh, S. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Fluids 2019, 4, 126.

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