石油科学通报2025，Vol.10Issue(5)：983-996,14.DOI:10.3969/j.issn.2096-1693.2025.03.019
基于TPE优化的时空图神经网络油藏产量动态预测
Reservoir production dynamics prediction using TPE-optimized spa-tio-temporal graph neural networks
摘要
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.关键词
产量预测/多井预测/时空图神经网络/时空图建模/TPE优化策略/概率预测Key words
production prediction/multi-well Prediction/spatio-temporal graph neural networks/spatio-temporal graph modeling/TPE optimization strategy/probabilistic forecasting分类
能源科技引用本文复制引用
张博维,刘月田,黄晋江,薛亮,宋来明..基于TPE优化的时空图神经网络油藏产量动态预测[J].石油科学通报,2025,10(5):983-996,14.基金项目
中国海洋石油有限公司联合研究院科技项目"基于流场适配性的砂岩油藏开发动态智能分析与评价方法"(CL2O22RCPS2018XNN)资助 （CL2O22RCPS2018XNN）
