A comparative study of MLP and LSTM neural networks for shale gas production prediction based on numerical simulation data
Xiaoou Fei, Man Ye, Zhou Du, Haibin Miao
TL;DR
This study compares MLP and LSTM neural networks for predicting shale gas production, finding that LSTM provides higher accuracy due to its ability to capture long-term dependencies.
Contribution
The novel contribution is demonstrating LSTM's superior performance in shale gas production prediction compared to MLP using numerical simulation data.
Findings
LSTM models achieved lower relative errors (0.42%-0.98%) compared to MLP (2.43%-6.36%) in predicting shale gas production.
LSTM's gating mechanisms effectively capture long-term dependencies in production data.
Deep learning techniques, especially LSTM, show excellent generalization and engineering applicability for shale gas forecasting.
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 LSTM network’s gating mechanisms effectively captured the long-term dependencies in shale gas…
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Taxonomy
TopicsHydraulic Fracturing and Reservoir Analysis · Hydrocarbon exploration and reservoir analysis · Phase Equilibria and Thermodynamics