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

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 energy. However, applying numerical reservoir simulation (NRS) to optimize production can induce high computational footprint. Proxy models are suggested to alleviate this challenge because they are computationally less demanding and able to yield reasonably accurate results. In this paper, we demonstrated how a machine learning technique, namely long short-term memory (LSTM), was applied to develop proxies of a 3D reservoir model. Sampling techniques were employed to create numerous simulation cases which served as the training database to establish the proxies. Upon blind validating the trained proxies, we coupled these proxies with particle swarm optimization to conduct production optimization. Both training and blind validation results illustrated that the proxies had been excellently developed with coefficient of determination, R2 of 0.99. We also compared the optimization results produced by NRS and the proxies. The comparison recorded a good level of accuracy that was within 3% error. The proxies were also computationally 3 times faster than NRS. Hence, the proxies have served their practical purposes in this study. (c) 2022 Southwest Petroleum University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Automated summary

In the petroleum domain, optimizing hydrocarbon production is crucial for economic prospects and meeting global energy demand. This paper demonstrates the development of proxies using a machine learning technique (LSTM) for a 3D reservoir model, and their successful application in production optimization. The proxies show high accuracy and computational efficiency compared to numerical reservoir simulation.