嵌入注意力机制的时空网络设计及孔隙度可靠性预测

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中图分类号：P631 文献标识码：A DOI：10. 13810/j. cnki. issn. 1000⁃7210. 20240201

Design of spatio⁃temporal network embedded with attention mechanism and prediction of porosity reliability

LI Yanhui1，2，TAO Yue2

（1. Bohai Rim Energy Research Institute，Northeast Petroleum University，Qinhuangdao，Hebei 066004，China； 2. School of Electrical & Information Engineering，Northeast Petroleum University，Daqing，Heilongjiang 163318，China）

Abstract：Porosity is an important indicator for evaluating reservoirs and calculating reserves. However，the traditional coring method is costly to obtain porosity，and the porosity prediction method based on regression analysis and a statistical model often has significant errors. Therefore，a reservoir porosity prediction model that combines convolutional neural network （ CNN ）， bidirectional long short ⁃ term memory network （ BiLSTM），and attention mechanism is constructed，and its performance is verified using actual well logging data. Firstly，the complex nonlinear spatio ⁃ temporal relationships of logging data are captured with CNN and BiLSTM. Then，the convolutional self⁃attention mechanism is embedded，which generates queries and keys by causal convolution and allows better integration of local information into the attention mechanism . Compared with traditional self⁃attention mechanisms，this approach avoids the influence of abnormal data on the prediction results. Finally，the Monte Carlo dropout approach is used to quantify the uncertainty of the model，providing confidence intervals for reservoir porosity prediction and further assessing prediction credibility. The compari⁃ son experiments among multiple models show that the proposed method has high accuracy in predicting reser⁃ voir porosity. The experiments on two wells with different characteristics show that the method has strong generalization ability.

Keywords：reservoir porosity prediction，convolutional neural network，bidirectional long short ⁃ term memory network，attention mechanism，uncertainty quantification

李艳辉，陶悦 . 嵌入注意力机制的时空网络设计及孔隙度可靠性预测［J］. 石油地球物理勘探，2025，60（3）：555‑563.LI Yanhui，TAO Yue. Design of spatio⁃temporal network embedded with attention mechanism and predictionof porosity reliability［J］. Oil Geophysical Prospecting，2025，60（3）：555‑563.

0 引言

孔隙度是评估储层油气储存能力的重要物理参数之一，其准确性对地质解释、油气勘探与开发工作至关重要[1]。（剩余12190字）