Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Chinese Journal of Geophysics》 2016-09
Add to Favorite Get Latest Update

Seismic sparse inversion method implemented on image data for detecting discontinuous and inhomogeneous geological features

ZHAO Jing-Tao;YU Cai-Xia;PENG Su-Ping;MA De-Bo;LI Ming;ZHANG Yan;State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology(Beijing);Institute of Geology and Geophysics,Chinese Academy of Sciences,Key Laboratory of Petroleum Resources Research, Chinese Academy of Sciences;Research Institute of Petroleum Exploration & Development,Petro China;  
The small-scale discontinuous and inhomogeneous geological features,such as tiny faults,cavities and fractures,play an important role in reservoir analysis.However,to effectively identify them from seismic data is a challenging problem because of their weaker energy than reflections.On the other hand,the seismic responses of these small-scale bodies are generally contaminated with noises,which can make their analysis difficult to perform if there is no proper strategy adopted for removing noises.By combining a non-linear filter and sparsity-constraint model,a seismic extraction method implemented on seismic image data is developed for inverting these discontinuous and inhomogeneous geological bodies.The core of extracting discontinuous and inhomogeneous information lies in removing strongreflections and noises.The plane-wave destruction method uses the local plane-wave model to represent seismic structures and thus is appropriate for estimating reflections.Through subtracting the predicted reflections from seismic image data,the residual containing discontinuous and inhomogeneous information and noises are obtained.Considering the sparsity property of these small-scale geological features,an L2-L1 norm model is built using a nonlinear filter for promoting the inversion signalto-noise ratio of discontinuous and inhomogeneous information.In order to guarantee the computation efficiency in solving this sparsity model,an L1 norm approximation scheme and a quasi-Newton algorithm are introduced.A numerical experiment is designed to demonstrate the effectiveness of the proposed method in extracting small-scale discontinuous and inhomogeneous geologies.The used cavity-fracture model is composed of fractures,faults and cavities.The obvious geological targets are four series of cavities in the shallow part and three series of cavities in the deep part.Using the proposed sparse inversion method,aprofile with reflections eliminated and noises destroyed is obtained and the edges,faults,fractures and cavities are completely resolved.In field application,a carbonate reservoir analysis is performed.The three-dimensional pre-stack time migration profile can clearly display large-scale layers but exposes a failure in describing discontinuous and inhomogeneous geologic features.Although coherency techniques can reveal discontinuous information,the small-scale tiny faults,fractures and cavities are beyond its detection.The proposed method succeeds in clarifying and locating these small-scale geologies.The attribute analysis based on seismic spares inversion data also provides a reference about the planer distribution of the smallscale discontinuous and inhomogeneous geologies.Resorting to the sparsity-constraint model,a seismic inversion method performed on image data is proposed for extracting small-scale discontinuous and inhomogeneous geologies.The method can achieve high-resolution information by removing the interference of reflections and the elimination of noises.In method application,seismic discontinuous and inhomogeneous structures are required to be completely imaged.Otherwise,a further velocity analysis is needed.As an end,we suggest future research on individually extracting discontinuity and inhomogeneity,especially for carbonate reservoirs research.
【Fund】: 国家重点研发计划课题(2016YFC0501102);; 国家科技重大专项课题(2016ZX05066-001);; 国家自然基金煤炭联合项目(U1261203);; 山西自然基金项目(2013012001)联合资助
【CateGory Index】: P631.4
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved