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Data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

Petroleum Science 2023年第5期20卷 2951-2966页

作者： Shu-Yi Du Xiang-Guo Zhao Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song School of Civil and Resource Engineering University of Science and Technology BeijingBeijing100083China National&Local Joint Engineering Lab for Big Data Analysis and Computer Technology Beijing100190China CNPC International Hong Kong Limited Abu DhabiUnited Arab Emirates Computer Network Information Center Chinese Academy of SciencesBeijing065007China China National Petroleum Corporation Langfang065007HebeiChina

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir *** physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production *** establish an ensemble proxy-model-assisted optimization framework combining the bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production *** the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front *** experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for *** addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate *** proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

关键词： Production optimization Random forest the bayesian algorithm Ensemble learning Particle swarm optimization

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