@article{ZHANG2025, 
author = {Bowei ZHANG and Yuetian LIU and Jinjiang HUANG and Liang XUE and Laiming SONG},
title = {Reservoir production dynamics prediction using TPE-optimized spatio-temporal graph neural networks},
year = {2025},
journal = {Petroleum Science Bulletin},
volume = {10},
number = {5},
pages = {983-996},
keywords = {production prediction, spatio-temporal graph neural networks, probabilistic forecasting, multi-well Prediction, spatio-temporal graph modeling, TPE optimization strategy},
url = {https://www.sciopen.com/article/10.3969/j.issn.2096-1693.2025.03.019},
doi = {10.3969/j.issn.2096-1693.2025.03.019},
abstract = {In the process of oilfield development, accurate production performance prediction can provide crucial support for adjusting production measures and optimizing development strategies. The complex spatial structure of underground well networks and the dynamic stochastic time-varying characteristics hinder the effective learning of spatiotemporal relationships between injection and production wells in existing prediction methods. Furthermore, current approaches fail to account for the cross-time-step spatiotemporal response relationships among multiple parameters, resulting in limitations in extracting temporal features and conducting correlation analysis of multi-well production performance sequences. These constraints ultimately restrict the improvement of production prediction accuracy. This study proposes a spatiotemporal graph neural network-based multi-well production forecasting method, incorporating a Tree-structured Parzen Estimator (TPE)-driven model parameter optimization strategy. The approach effectively aggregates multivariate information from neighboring nodes, enhancing reservoir production prediction accuracy and robustness. The model is validated using production data from an offshore waterflood reservoir. Results demonstrate that the optimized model achieves high accuracy, with improved production trend and confidence interval predictions. Comparative experiments confirm the model’s effectiveness in leveraging multi-dynamic information, significantly improving prediction accuracy. Specifically, the mean squared error is reduced by 23.67%~56.96%, and the quantile loss function decreases by 18.31%~59.58% compared to existing methods. The proposed framework provides reliable support for waterflood reservoir production forecasting and decision-making.}
}