Attention-Based Bi-Directional Gated Recurrent Unit (Bi-GRU) for Sequence Shale Gas Production Forecasting

Abstract

Abstract This paper presents an innovative application of an Attention-Based Bi-directional Gated Recurrent Unit (Bi-GRU) network for predicting shale gas production. Traditional machine learning models applied to gas production prediction often struggle to capture the complexity of the production process and accurately model temporal dependencies in the data. The proposed model addresses these limitations by integrating an attention mechanism into a Bi-GRU framework. The attention mechanism assigns relative importance to each time step in the input sequence, focusing on the most influential factors that drive shale gas production over time. Consequently, our model effectively learns long-term dependencies and identifies critical features in the historical data, thereby enhancing prediction accuracy. Furthermore, the bidirectional nature of the Bi-GRU enables the proposed model to consider both past and future time step information in the prediction process, leading to a comprehensive understanding of the sequence data. The results demonstrated the performance of the proposed model on a significant shale gas production dataset, showcasing substantial improvements in prediction accuracy over conventional machine learning and deep learning hybrid-based models. The findings of this study underscore the potential of the Attention-Based Bi-GRU model as a powerful tool for predictive modeling in the domain of energy production.