Wang Zisong, Cheng Zhiliang, Wang Wenxiang, Ding Xiujian, Xia Lu
School of Civil Engineering and Transportation, Weifang University, Weifang, 261061, Shandong, China.
China University of Petroleum (East China) Geological College, Qingdao, 266000, Shandong, China.
Sci Rep. 2025 Jul 17;15(1):25950. doi: 10.1038/s41598-025-11077-9.
Well-log analysis contributes significantly to effective oil and gas extraction, but inconsistent logs may render subsequent geological analyses useless. This study tackles this problem by devising a deep Long Short-Term Memory (LSTM) model that uses the new Parallel and Distributed Chimp Optimization Algorithm (PDCOA). PDCOA's primary goal is to speed up the process of hyperparameter tuning for LSTMs by letting them work in parallel and across multiple computers, with separate groups of computers communicating with each other regularly to ensure the system is diverse and reliable. It is designed for reconstructing missing well-log data, showing that the proposed method is more scalable, efficient, and accurate as a predictor. This feature makes it a valuable tool for geological interpretation and estimating hydrocarbon resources.
测井分析对有效的油气开采有重大贡献，但不一致的测井数据可能会使后续的地质分析变得无用。本研究通过设计一种深度长短期记忆（LSTM）模型来解决这个问题，该模型使用了新的并行分布式黑猩猩优化算法（PDCOA）。PDCOA的主要目标是通过让LSTM在多台计算机上并行工作来加速其超参数调整过程，不同组的计算机定期相互通信以确保系统的多样性和可靠性。它旨在重建缺失的测井数据，表明所提出的方法作为一种预测器更具可扩展性、高效性和准确性。这一特性使其成为地质解释和估算油气资源的宝贵工具。
