SPE Annual Technical Conference and Exhibition 2020
DOI: 10.2118/201325-ms
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Data-Driven Rate Optimization Under Geologic Uncertainty

Abstract: We propose a novel approach for rate optimization during a waterflood under geologic uncertainty in reservoir properties such as permeability and porosity. The traditional approach typically involves several runs of the forward simulator. This may not scale well when the optimization is to be performed at the full field-level and over multiple geologic realizations. A machine-learning (ML) based approach which is quick and scalable for rate optimization over multiple geologic realizations is proposed instead. … Show more

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Cited by 10 publications
(1 citation statement)
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“…It comprises of several algorithms with specific purposes such as regression, classification and clustering problems. The ability of this algorithm to self-learn from available data makes it a choice of preference for waterflooding optimization [152]. A typical framework for machine implementation is presented in Fig 4 . Machine learning is classified based on the nature of learning.…”
Section: Data-driven Optimization Approachmentioning
confidence: 99%
“…It comprises of several algorithms with specific purposes such as regression, classification and clustering problems. The ability of this algorithm to self-learn from available data makes it a choice of preference for waterflooding optimization [152]. A typical framework for machine implementation is presented in Fig 4 . Machine learning is classified based on the nature of learning.…”
Section: Data-driven Optimization Approachmentioning
confidence: 99%