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Production optimization under waterflooding with long short-term memory and metaheuristic algorithm

Petroleum 2023年第1期9卷 53-60页

作者： Cuthbert Shang Wui Ng Ashkan Jahanbani Ghahfarokhi Menad Nait Amar Department of Geoscience and Petroleum Norwegian University of Science and TechnologyTrondheimNorway Département Etudes Thermodynamiques Division LaboratoiresSonatrachBoumerdesAlgeria

In petroleum domain,optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of the petroleum companies,but also fulfills the increasing global demand of ***,applying numerical reservoir simulation(NRS)to optimize production can induce high computational *** models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate *** this paper,we demonstrated how a machine learning technique,namely long short-term memory(LSTM),was applied to develop proxies of a 3D reservoir *** techniques were employed to create numerous simulation cases which served as the training database to establish the *** blind validating the trained proxies,we coupled these proxies with particle swarm optimization to conduct production *** training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination,R2 of *** also compared the optimization results produced by NRS and the *** comparison recorded a good level of accuracy that was within 3%*** proxies were also computationally 3 times faster than ***,the proxies have served their practical purposes in this study.

关键词： Production optimization Numerical reservoir simulation Machine learning Long short-term memory(LSTM) dynamic proxies Particle swarm optimization(PSO)

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