Reservoir Porosity Prediction Based on BiLSTM-AM Optimized by Improved Pelican Optimization Algorithm-Reference-Cited by-同舟云学术

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

Published:2024-03-20 Issue:6 Volume:17 Page:1479
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Qiao Lei¹,He Nansi²,Cui You¹,Zhu Jichang³,Xiao Kun⁴

Affiliation:

1. Hebei Instrument & Meter Engineering Technology Research Center, Hebei Petroleum University of Technology, Chengde 067000, China

2. Department of Computer and Information Engineering, Hebei Petroleum University of Technology, Chengde 067000, China

3. Research Institute of Petroleum Exploration and Development, PetroChina, Beijing 100083, China

4. State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China

Abstract

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.

Funder

Natural Science Foundation of Jiangxi Province

Publisher

MDPI AG

Link

https://www.mdpi.com/1996-1073/17/6/1479/pdf

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