首页|Multi-surrogate framework with an adaptive selection mechanism for production optimization

Multi-surrogate framework with an adaptive selection mechanism for production optimization

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Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems.However,existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem.A surrogate model that has demonstrated success in one scenario may not perform as well in others.In the absence of prior knowledge,finding a promising surrogate model that performs well for an unknown reservoir is challenging.Moreover,the optimization process often relies on a single evolutionary algorithm,which can yield varying results across different cases.To address these limitations,this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism(MSFASM)to tackle production optimization problems.MSFASM consists of two stages.In the first stage,a reduced-dimensional broad learning system(BLS)is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period.In the second stage,the multi-objective algorithm,non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ),is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models.A novel optimal point criterion is utilized in this stage to select the Pareto solutions,thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator.The two stages are combined using sequential transfer learning.From the two most important perspectives of an evolutionary algorithm and a surrogate model,the proposed method im-proves adaptability to optimization problems of various reservoir types.To verify the effectiveness of the proposed method,four 100-dimensional benchmark functions and two reservoir models are tested,and the results are compared with those obtained by six other surrogate-model-based methods.The results demonstrate that our approach can obtain the maximum net present value(NPV)of the target pro-duction optimization problems.

Production optimizationMulti-surrogate modelsMulti-evolutionary algorithmsDimension reductionBroad learning system

Jia-Lin Wang、Li-Ming Zhang、Kai Zhang、Jian Wang、Jian-Ping Zhou、Wen-Feng Peng、Fa-Liang Yin、Chao Zhong、Xia Yan、Pi-Yang Liu、Hua-Qing Zhang、Yong-Fei Yang、Hai Sun

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School of Petroleum Engineering,China University of Petroleum(East China),Qingdao,266580,Shandong,China

Qingdao University of Technology,Qingdao,266580,Shandong,China

College of Science,China University of Petroleum,Qingdao,266580,Shandong,China

Zepu Oil and Gas Development Department,PetroChina Tarim Oilfield Company,Korla,841000,Xinjiang,China

Hainan Branch of CNOOC Ltd,Haikou,70311,Hainan,China

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国家自然科学基金国家自然科学基金国家自然科学基金Major Scientific and Technological Projects of CNPCMajor Scientific and Technological Projects of CNOOCScience and Technology Support Plan for Youth Innovation of University in Shandong Province高等学校学科创新引智计划(111计划)

522740575207434051874335ZD2019-183-008CCL2022RCPS0397RSN2019KJH002B08028

2024

10.1016/j.petsci.2023.08.028

石油科学(英文版)
中国石油大学（北京）

石油科学(英文版)

EI
影响因子：0.88
ISSN：1672-5107
年,卷(期)：2024.21(1)
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