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Title: Smart Proxy Modeling of SACROC CO2-EOR

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 machinemore » 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.« less

Authors:
; ORCiD logo;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1511061
Grant/Contract Number:  
-
Resource Type:
Published Article
Journal Name:
Fluids
Additional Journal Information:
Journal Name: Fluids Journal Volume: 4 Journal Issue: 2; Journal ID: ISSN 2311-5521
Publisher:
MDPI AG
Country of Publication:
Country unknown/Code not available
Language:
English

Citation Formats

Vida, Gholami, Shahab, Mohaghegh D., and Mohammad, Maysami. Smart Proxy Modeling of SACROC CO2-EOR. Country unknown/Code not available: N. p., 2019. Web. doi:10.3390/fluids4020085.
Vida, Gholami, Shahab, Mohaghegh D., & Mohammad, Maysami. Smart Proxy Modeling of SACROC CO2-EOR. Country unknown/Code not available. doi:10.3390/fluids4020085.
Vida, Gholami, Shahab, Mohaghegh D., and Mohammad, Maysami. Fri . "Smart Proxy Modeling of SACROC CO2-EOR". Country unknown/Code not available. doi:10.3390/fluids4020085.
@article{osti_1511061,
title = {Smart Proxy Modeling of SACROC CO2-EOR},
author = {Vida, Gholami and Shahab, Mohaghegh D. and Mohammad, Maysami},
abstractNote = {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.},
doi = {10.3390/fluids4020085},
journal = {Fluids},
number = 2,
volume = 4,
place = {Country unknown/Code not available},
year = {2019},
month = {5}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.3390/fluids4020085

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