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
Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of dat...Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.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supported by Mission No. 9 "Geological Environment and Hazards" (2019QZKK0900) of "The Second Tibetan Plateau Scientific Expedition and Research" projectNational Natural Science Foundation of China (No.42101087)
文摘Spatial seismic vulnerability assessments are primally conducted at the community and grid level,using heuristic and empirical approaches.Building-based spatial statistical vulnerability models are rare because of data limitations.Generating open-access spatial inventories that document seismic damage and building attributes and test their effectiveness in assessing damage would promote the advancement of spatial vulnerability assessment.The 2022 Mw 6.7 Luding earthquake in the western Sichuan Province of China provides an opportunity to validate this approach.The local government urgently dispatched experts to survey building damage,marking all buildings with damage class stickers.In this work,we sampled 2889 buildings as GPS points and documented the damage classes and building attributes,including structure type,number of floors,and age.A polygon-based digital inventory was generated by digitizing the rooftops of the sampled buildings and importing the attributes.Statistical regressions were created by plotting damage against shaking intensity and PGA,and Random Forest modeling was carried out considering not only buildings and seismic parameters but also environmental factors.The result indicates that statistical regressions have notable uncertainties,and the Random Forest model shows a≥79%accuracy.Topographical factors showed notable importance in the Random Forest modeling.This work provides an open-access seismic building damage inventory and demonstrates its potential for damage prediction and vulnerability assessment.
