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

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 reservoirmore » simulation model.« less

Authors:
; ORCiD logo
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1532773
Grant/Contract Number:  
FOA0000023-03.
Resource Type:
Published Article
Journal Name:
Fluids
Additional Journal Information:
Journal Name: Fluids Journal Volume: 4 Journal Issue: 3; Journal ID: ISSN 2311-5521
Publisher:
MDPI AG
Country of Publication:
Country unknown/Code not available
Language:
English

Citation Formats

Amini, Shohreh, and Mohaghegh, Shahab. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Country unknown/Code not available: N. p., 2019. Web. doi:10.3390/fluids4030126.
Amini, Shohreh, & Mohaghegh, Shahab. Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media. Country unknown/Code not available. doi:10.3390/fluids4030126.
Amini, Shohreh, and Mohaghegh, Shahab. Tue . "Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media". Country unknown/Code not available. doi:10.3390/fluids4030126.
@article{osti_1532773,
title = {Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media},
author = {Amini, Shohreh and Mohaghegh, Shahab},
abstractNote = {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.},
doi = {10.3390/fluids4030126},
journal = {Fluids},
number = 3,
volume = 4,
place = {Country unknown/Code not available},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3390/fluids4030126

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