Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm

Autor: Lei Qiao, Nansi He, You Cui, Jichang Zhu, Kun Xiao
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Energies, Vol 17, Iss 6, p 1479 (2024)
Druh dokumentu: article
ISSN: 1996-1073
DOI: 10.3390/en17061479
Popis: To accurately predict reservoir porosity, a method based on bi-directional long short-term memory with attention mechanism (BiLSTM-AM) optimized by the improved pelican optimization algorithm (IPOA) is proposed. Firstly, the nonlinear inertia weight factor, Cauchy mutation, and sparrow warning mechanism are introduced to improve the pelican optimization algorithm (POA). Secondly, the superiority of IPOA is verified by using the CEC–2022 benchmark test functions. In addition, the Wilcoxon test is applied to evaluate the experimental results, which proves the superiority of IPOA against other popular algorithms. Finally, BiLSTM-AM is optimized by IPOA, and IPOA-BiLSTM-AM is used for porosity prediction in the Midlands basin. The results show that IPOA-BiLSTM-AM has the smallest prediction error for the verification set samples (RMSE and MAE were 0.5736 and 0.4313, respectively), which verifies its excellent performance.
Databáze: Directory of Open Access Journals
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