Journal Article10.1016/J.PETROL.2019.106633
Surrogate-assisted evolutionary algorithm with dimensionality reduction method for water flooding production optimization
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TL;DR: A new framework is proposed based on surrogate-assisted evolutionary algorithm and dimensionality reduction to maximize the expected NPV in life-cycle production optimization to take advantage of surrogate model in low dimension.
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About: This article is published in Journal of Petroleum Science and Engineering. The article was published on 01 Feb 2020. The article focuses on the topics: Surrogate model & Evolutionary algorithm.
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Citations
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A surrogate-assisted multi-objective evolutionary algorithm with dimension-reduction for production optimization
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Adaptive dropout for high-dimensional expensive multiobjective optimization
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TL;DR: An SAEA with an adaptive dropout mechanism that takes advantage of the statistical differences between different solution sets in the decision space to guide the selection of some crucial decision variables in high-dimensional expensive multiobjective optimization problems.
Multi-surrogate assisted multi-objective evolutionary algorithms for feature selection in regression and classification problems with time series data
TL;DR: In this article , a multi-surrogate assisted multi-objective evolutionary algorithm for feature selection was proposed to improve generalization error of wrapper-type feature selection, and the proposed method has been compared with conventional feature selection wrapper methods that use random forest, support vector machine and long short-term memory learning algorithms.
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References
Water flooding optimization with adjoint model under control constraints
TL;DR: In this paper, a fully implicit reservoir simulator is developed for the optimization of the injection-production rate of water-oil wells in a complex oil-gas filtration system.
Optimization of Well Placement in a Gulf of Mexico Waterflooding Project
TL;DR: A hybrid optimization technique based on the genetic algorithm, polytope algorithm, kriging algorithm and neural networks is proposed, which was investigated on a set of exhaustive simulations for the single well placement problem and it was observed that the number of simulations required was reduced significantly.