裂缝型储层是一种含流体的裂缝-孔隙介质,其裂缝参数的定量表征对非常规油气藏的勘探与开发具有重要意义。然而,传统以振幅信息为主的储层预测方法存在局限性,难以全面揭示裂缝型储层的复杂特性。本文针对含饱和流体的正交裂缝型储层,...裂缝型储层是一种含流体的裂缝-孔隙介质,其裂缝参数的定量表征对非常规油气藏的勘探与开发具有重要意义。然而,传统以振幅信息为主的储层预测方法存在局限性,难以全面揭示裂缝型储层的复杂特性。本文针对含饱和流体的正交裂缝型储层,深入分析了含水平和垂直正交裂缝介质的速度频散与衰减特性,并采用各向异性反射率法模拟了单界面频散砂岩储层振幅随偏移距变化(amplitude variation with offset,AVO)的频变响应特征。在此基础上,构建了以水平和垂直正交裂缝模型响应为驱动的贝叶斯反演框架,实现了对裂缝型储层中孔隙度、裂缝密度及裂缝半径的多参数定量反演。研究结果表明,孔隙度、裂缝密度及裂缝半径对速度频散表现出高度敏感性,且在低频时PP波频变反射系数随频率和入射角发生显著变化,振幅随入射角的增大线性增加,揭示了裂缝参数对频变AVO响应有重要影响。反演结果表明,所提出的反演方法在不同裂缝参数条件下,后验概率分布都具有较高精度,尤其在小尺度裂缝型储层中,对裂缝半径预测表现出更好的适用性和可靠性。展开更多
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy wit...Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.展开更多
The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion i...The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.展开更多
文摘裂缝型储层是一种含流体的裂缝-孔隙介质,其裂缝参数的定量表征对非常规油气藏的勘探与开发具有重要意义。然而,传统以振幅信息为主的储层预测方法存在局限性,难以全面揭示裂缝型储层的复杂特性。本文针对含饱和流体的正交裂缝型储层,深入分析了含水平和垂直正交裂缝介质的速度频散与衰减特性,并采用各向异性反射率法模拟了单界面频散砂岩储层振幅随偏移距变化(amplitude variation with offset,AVO)的频变响应特征。在此基础上,构建了以水平和垂直正交裂缝模型响应为驱动的贝叶斯反演框架,实现了对裂缝型储层中孔隙度、裂缝密度及裂缝半径的多参数定量反演。研究结果表明,孔隙度、裂缝密度及裂缝半径对速度频散表现出高度敏感性,且在低频时PP波频变反射系数随频率和入射角发生显著变化,振幅随入射角的增大线性增加,揭示了裂缝参数对频变AVO响应有重要影响。反演结果表明,所提出的反演方法在不同裂缝参数条件下,后验概率分布都具有较高精度,尤其在小尺度裂缝型储层中,对裂缝半径预测表现出更好的适用性和可靠性。
基金the sponsorship of the Key Technology for Geophysical Prediction of Ultra-Deep Carbonate Reservoirs(P24240)the National Natural Science Foundation of China(U24B2020)the National Science and Technology Major Project of China for New Oil and Gas Exploration and Development(Grant No.2024ZD1400102)。
文摘Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development.However,the anisotropic reflection coefficient equation,based on the transverse isotropy with a vertical axis of symmetry(VTI)medium assumption,involves numerous parameters to be inverted.This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset(AVO)inversion results.In this study,a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten,which reduces the equation's dimensionality and increases its stability.Additionally,the traditional Markov Chain Monte Carlo(MCMC)inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution,limiting the algorithm's convergence and sample randomness.To address these limitations and evaluate the uncertainty of AVO inversion,the IADR-Gibbs algorithm is proposed,which incorporates the Independent Adaptive Delayed Rejection(IADR)algorithm with the Gibbs sampling algorithm.Grounded in Bayesian theory,the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection(DR)strategy.Rejected samples are then added to the support points to update the proposal distribution function adaptively.The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion.The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
基金supposed by the National Nature Science Foundation of China(Grant No.42304131)the Natural Science Foundation of Heilongjiang Province(Grant No.LH2023D012)+1 种基金the Heilongjiang Postdoctoral Fund(Grant No.LBH-Z22092)the Basic Research Fund for Universities in Xinjiang Uygur Autonomous Region(Grant No.XJEDU2023P166)。
文摘The brittleness index(BI)is crucial for predicting engineering sweet spots and designing fracturing operations in shale oil reservoir exploration and development.Seismic amplitude variation with offset(AVO)inversion is commonly used to obtain the BI.Traditionally,velocity,density,and other parameters are firstly inverted,and the BI is then calculated,which often leads to accumulated errors.Moreover,due to the limited of well-log data in field work areas,AVO inversion typically faces the challenge of limited information,resulting in not high accuracy of BI derived by existing AVO inversion methods.To address these issues,we first derive an AVO forward approximation equation that directly characterizes the BI in P-wave reflection coefficients.Based on this,an intelligent AVO inversion method,which combines the advantages of traditional and intelligent approaches,for directly obtaining the BI is proposed.A TransUnet model is constructed to establish the strong nonlinear mapping relationship between seismic data and the BI.By incorporating a combined objective function that is constrained by both low-frequency parameters and training samples,the challenge of limited samples is effectively addressed,and the direct inversion of the BI is stably achieved.Tests on model data and applications on field data demonstrate the feasibility,advancement,and practicality of the proposed method.
