摘要
岩石孔隙度是表征储层的重要参数之一,对孔隙度进行准确预测有利于更精细地刻画高孔高渗储层位置。然而地震弹性参数与孔隙度之间的关系较为复杂,给储层孔隙度的准确预测带来一定困难。深度学习为地震准确预测孔隙度提供了新思路。提出了一种基于双向门控循环单元神经网络(GRU)和注意力机制(BiGRU-Attention)的叠前地震孔隙度预测方法,该方法利用双向GRU实现信息的双向传播并加入Attention机制放大关键信息,将叠前同时反演得到的纵波速度和密度信息作为输入,以测井孔隙度值作为标签来训练和测试BiGRU-Attention网络,建立起地震弹性参数与孔隙度之间的复杂映射关系,进而实现孔隙度的准确预测。实际数据测试结果表明,相比于常规多元线性回归方法(MLR)、密集神经网络(DNN)和门控循环单元神经网络(GRU)等预测方法,BiGRU-Attention网络预测方法在盲井测试中预测精度更高。将该方法应用于某实际三维工区地震数据预测的孔隙度值与测井孔隙度值匹配良好,说明该方法具有较好的实用价值。
Rock porosity is one of the critical parameters to characterize the reservoir.High-precision prediction of porosity is conducive to more detailed description of the location of highly porous and permeable reservoirs.Due to the strong heterogeneity and complex pore structure of the reservoir,there are many factors affecting the reservoir porosity,which brings difficulties to the accurate prediction of reservoir porosity.In recent years,the development of deep learning has provided a new idea for high-precision seismic porosity prediction.Accordingly,this paper presents a prestack seismic porosity prediction method based on the bidirectional gated recurrent unit neural network and attention mechanism(BiGRU-Attention).In this method,BiGRU is used to realize the bidirectional propagation of information and the attention mechanism is added to amplify the key information.The P-wave velocity and density information obtained from the prestack simultaneous inversion is used as the input and the logging porosity value is used as the label to train and test the BiGRU-Attention network,so as to obtain an optimal model.This method establishes a complex mapping relationship between seismic elastic parameters(P-wave velocity and density)and porosity to achieve high-precision prediction of porosity.The actual data test results showed that compared with the conventional multiple linear regression(MLR),dense neural network(DNN)and gated recurrent unit neural network(GRU),the proposed method based on BiGRU-Attention had higher prediction accuracy in blind well testing.The root mean square error(RMSE)of prediction results and logging data was less than 0.0022,and the average absolute error(MAE)was less than 0.0014.Application of this method to the seismic data of an actual 3D work area demonstrated that the predicted porosity profile matched well with the logging porosity value,indicating that the method has good practical value.
作者
杨菲
刘洋
常锁亮
陈桂
YANG Fei;LIU Yang;CHANG Suoliang;CHEN Gui(College of Artificial Intelligence,China University of Petroleum(Beijing),Beijing 102249,China;College of Geophysics,China University of Petroleum(Beijing),Beijing 102249,China;College of Petroleum,China University of Petroleum(Beijing)at Karamay,Karamay 834000,China;College of Mining Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《石油物探》
CSCD
北大核心
2024年第3期598-609,共12页
Geophysical Prospecting For Petroleum
基金
山西省揭榜招标项目(20201101004)资助。
关键词
深度学习
注意力机制
双向门控循环单元神经网络
孔隙度预测
储层参数反演
deep learning
attention mechanism
bidirectional gated recurrent unit neural network(BiGRU)
porosity prediction
reservoir parameter inversion
