Production optimization under waterflooding with long short-term memory and metaheuristic algorithm
نویسندگان
چکیده
In petroleum domain, optimizing hydrocarbon production is essential because it does not only ensure the economic prospects of companies, but also fulfills increasing global demand energy. However, applying numerical reservoir simulation (NRS) to optimize can induce high computational footprint. Proxy models are suggested alleviate this challenge they computationally less demanding and able yield reasonably accurate results. paper, we demonstrated how a machine learning technique, namely Long Short-Term Memory, was applied develop proxies 3D model. Sampling techniques were employed create numerous cases which served as training database establish proxies. Upon blind validating trained proxies, coupled these with particle swarm optimization conduct optimization. Both validation results illustrated that had been excellently developed coefficient determination, R 2 0.99. We compared produced by NRS The comparison recorded good level accuracy within 3% error. 3 times faster than NRS. Hence, have their practical purposes in study.
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ژورنال
عنوان ژورنال: Petroleum
سال: 2023
ISSN: ['2405-6561', '2405-5816']
DOI: https://doi.org/10.1016/j.petlm.2021.12.008