Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of...Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.展开更多
裂缝预测是非常规油气储层勘探开发的重要环节.地震振幅随入射角和方位变化(Seismic Amplitude Variation with the Angle And Azimuth,即AVOAZ)的反演方法是地下裂缝参数预测的重要手段.然而裂缝型储层的模型参数化,正演方程的构建及...裂缝预测是非常规油气储层勘探开发的重要环节.地震振幅随入射角和方位变化(Seismic Amplitude Variation with the Angle And Azimuth,即AVOAZ)的反演方法是地下裂缝参数预测的重要手段.然而裂缝型储层的模型参数化,正演方程的构建及反问题的求解依旧存在较大的挑战.所以本文将以均匀各向同性岩石中发育一组垂直定向裂缝(可等效为具有水平对称轴的横向各向同性介质,Horizontal Transversely Isotropic medium,即HTI介质)为研究目标,利用线性滑动模型,推导基于裂缝弱度参数表征的HTI介质刚度矩阵,然后采用Born逆散射理论和稳相法,在界面弱差异假设下,推导HTI介质PP波反射系数方程.为了提高裂缝弱度参数的预测精度,接着提出了方位差异弹性阻抗方程.在此基础上,发展了一套模型低频信息正则化约束的Bayesian反演方法.将该方法应用到裂缝型工区,应用结果表明本文所提出的方法是合理的.展开更多
文摘Buiding data-driven models using machine learning methods has gradually become a common approach for studying reservoir parameters.Among these methods,deep learning methods are highly effective.From the perspective of multi-task learning,this paper uses six types of logging data—acoustic logging(AC),gamma ray(GR),compensated neutron porosity(CNL),density(DEN),deep and shallow lateral resistivity(LLD)and shallow lateral resistivity(LLS)—that are inputs and three reservoir parameters that are outputs to build a porosity saturation permeability network(PSP-Net)that can predict porosity,saturation,and permeability values simultaneously.These logging data are obtained from 108 training wells in a medium₋low permeability oilfield block in the western district of China.PSP-Net method adopts a serial structure to realize transfer learning of reservoir-parameter characteristics.Compared with other existing methods at the stage of academic exploration to simulating industrial applications,the proposed method overcomes the disadvantages inherent in single-task learning reservoir-parameter prediction models,including easily overfitting and heavy model-training workload.Additionally,the proposed method demonstrates good anti-overfitting and generalization capabilities,integrating professional knowledge and experience.In 37 test wells,compared with the existing method,the proposed method exhibited an average error reduction of 10.44%,27.79%,and 28.83%from porosity,saturation,permeability calculation.The prediction and actual permeabilities are within one order of magnitude.The training on PSP-Net are simpler and more convenient than other single-task learning methods discussed in this paper.Furthermore,the findings of this paper can help in the re-examination of old oilfield wells and the completion of logging data.
文摘裂缝预测是非常规油气储层勘探开发的重要环节.地震振幅随入射角和方位变化(Seismic Amplitude Variation with the Angle And Azimuth,即AVOAZ)的反演方法是地下裂缝参数预测的重要手段.然而裂缝型储层的模型参数化,正演方程的构建及反问题的求解依旧存在较大的挑战.所以本文将以均匀各向同性岩石中发育一组垂直定向裂缝(可等效为具有水平对称轴的横向各向同性介质,Horizontal Transversely Isotropic medium,即HTI介质)为研究目标,利用线性滑动模型,推导基于裂缝弱度参数表征的HTI介质刚度矩阵,然后采用Born逆散射理论和稳相法,在界面弱差异假设下,推导HTI介质PP波反射系数方程.为了提高裂缝弱度参数的预测精度,接着提出了方位差异弹性阻抗方程.在此基础上,发展了一套模型低频信息正则化约束的Bayesian反演方法.将该方法应用到裂缝型工区,应用结果表明本文所提出的方法是合理的.
