Recognition Of Sweet-spot In Tight Reservoir Based On Sparse Decomposition Inversion Method

With the increasing difficulty of acquiring conventional oil and gas resources,global oil and gas exploration has entered an era where both conventional and unconventional oil and gas arc developing at the same time.The unconventional dense shale oil/gas reservoirs and tight sandstone gas reservoirs are globally important replacement resources.Their exploration and development technologies have become hotspots and difficulties in the field of oil and gas exploration.Seismic data can provide data security for subsequent geological interpretation and judgment of reservoir conditions.The demand for seismic data quality and reservoir prediction accuracy in the process of petroleum exploration and development is increasing.Based on the convolution model and the Fourier scale transformation characteristics,this thesis proposes a seismic data resolution processing technique under the framework of an inverse problem.The method considers that the reflection coefficient sequence is an intrinsic property of the subsurface medium and remains unchanged.Due to the difference of the excitation wavelets,the observed seismic data have different characteristics.Therefore,the mapping relationship between raw wavelets and scale-transformed wavelets is established under the framework of an inverse problem,and then the original observation seismic data can be processed by using the operator.Seismic inversion is a process of selecting reasonable inversion strategies or optimization algorithms to estimate the subsurface media parameters which based on the relationship between observed data and model parameters,and considered the characteristics of actual data.The process of seismic inversion to obtain the reflection coefficient sequence can be understood as the process of sparse representation of seismic data under the wavelet dictionary,which agrees with the definition of sparse decomposition,Combining with the needs of geologic and geophysical problems in actual dense shale oil reservoirs,the new YPD equation was developed for predicting the brittleness of shale reservoirs,and a reasonable low-frequency operator regularization constraint was introduced to measure the accuracy of low-frequency components of inversion results.Sparsity,as an important issue in signal representation,gives new life to seismic signal processing.In this thesis,sparse representation and compression sampling theory are introduced into the seismic reflection coefficient inversion problem.A reasonable orthogonal basis fimction is chosen to construct the transformation dictionary,and the reflection coefficient sequence inversion problem is regarded as an sparse representation problem of the compression sampling result of the seismic data transform domain on the sparse dictionary,which provides a new idea for seismic inversion technology.It lot only oan get an high accuracy reflection coefficient inversion result,but also can save the data storage cost.The current inversiol methods are mostly based on the convolution model,and the inversion results have insufficient ability to distinguish the subsurface geological bodies.In current exploration situation,more detailed reservoir prediction techniques are needed to provied data support.Under such a background,this thesis proposes and preliminarily implements a target parameter simulation method under a non-convolution model.This method is based on the assumption that the similar seismic waveforms have equivalent reservoir structures similarity.It is believed that the lateral variation of seismic waveforms is essentially reflected the changes in reservoir spatial structure.Under the constraint of isochronal stratigraphic framework,the spatial variability of the reservoir is characterized by the mapping relationship between the seismic waveforms,and can be used to achieve the reservoir physical parameter simulation purpose.The method is driven by seismic data and well data,and the results have improved resolution ability and can meet the needs of sophisticated reservoir characterization.