Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China.

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.