A comparative study of MLP and LSTM neural networks for shale gas production prediction based on numerical simulation data.

ABSTRACT: Accurate prediction of shale gas production is essential for optimizing reservoir development and improving production efficiency. In this study, a numerical simulation model was first developed to systematically calculate daily shale gas production under various engineering parameter combinations, thereby establishing a comprehensive production prediction database. Two types of deep learning models-multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks-were then constructed to predict daily shale gas production. Comparisons with actual production data for three representative scenarios revealed that the MLP model achieved relative errors of 2.43%, 6.36%, and 4.16%, while the LSTM model achieved superior accuracy with relative errors of 0.42%, 1.1%, and 0.98%. The L

SUBMITTER: Fei X 

PROVIDER: S-EPMC12614617 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature