Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuatio...Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological conditions.Conventional multiple attenuation methods rely on prior geological information and involve extensive computations.Using deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple suppression.This study proposes an improved U-net-based method for multiple attenuation.The conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic data.Emphasis is placed on distinguishing between seismic multiples and primaries.The improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as output.The eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity model.Transfer learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.展开更多
Pore structure characteristics are important to oil and gas exploration in complex low-permeability reservoirs. Using multifractal theory and nuclear magnetic resonance （NMR）, we studied the pore structure of low-pe...Pore structure characteristics are important to oil and gas exploration in complex low-permeability reservoirs. Using multifractal theory and nuclear magnetic resonance （NMR）, we studied the pore structure of low-permeability sandstone rocks from the 4th Member （Es4） of the Shahejie Formation in the south slope of the Dongying Sag. We used the existing pore structure data from petrophysics, core slices, and mercury injection tests to classify the pore structure into three categories and five subcategories. Then, the T2 spectra of samples with different pore structures were interpolated, and the one- and three-dimensional fractal dimensions and the multifractal spectrum were obtained. Parameters a （intensity of singularity） andf（a） （density of distribution） were extracted from the multifractal spectra. The differences in the three fractal dimensions suggest that the pore structure types correlate with a andf（a）. The results calculated based on the multifractal spectrum is consistent with that of the core slices and mercury injection. Finally, the proposed method was applied to an actual logging profile to evaluate the pore structure of low-permeability sandstone reservoirs.展开更多
Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to inter...Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to internal multiples.The Marchenko imaging method is a target-oriented technique;thus,it can image a user specified area.In the traditional Marchenko method,an accurate velocity model is critical for estimating direct waves from imaging points to the surface.An error in the velocity model results in the inaccurate estimation of direct waves.In turn,this leads to errors in computation of one-way Green’s functions,which then affects the final Marchenko images.To solve this problem,in this paper,we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method.The proposed method calculates the time shift of direct waves caused by the error in the velocity model,and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions.The proposed method improves the results of imaging using an inaccurate velocity model.By comparing the results from traditional Marchenko and the new method using synthetic data experiments,we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions.展开更多
基金supported by the Open Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(PLN2022-51,PLN2021-21)the Open Fund of the Science and Technology Bureau of Nanchong City,Sichuan Province(23XNSYSX0089,SXQHJH046).
文摘Eff ective attenuation of seismic multiples is a crucial step in the seismic data processing workfl ow.Despite the existence of various methods for multiple attenuation,challenges persist,such as incomplete attenuation and high computational requirements,particularly in complex geological conditions.Conventional multiple attenuation methods rely on prior geological information and involve extensive computations.Using deep neural networks for multiple attenuation can effectively reduce manual labor costs while improving the efficiency of multiple suppression.This study proposes an improved U-net-based method for multiple attenuation.The conventional U-net serves as the primary network,incorporating an attentional local contrast module to effectively process detailed information in seismic data.Emphasis is placed on distinguishing between seismic multiples and primaries.The improved network is trained using seismic data containing both multiples and primaries as input and seismic data containing only primaries as output.The eff ectiveness and stability of the proposed method in multiple attenuation are validated using two horizontal layered velocity models and the Sigsbee2B velocity model.Transfer learning is employed to endow the trained model with the capability to suppress multiples across seismic exploration areas,eff ectively improving multiple attenuation efficiency.
基金supported by the National Natural Science Foundation of China(Grant No.41202110)Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University)(Grant No.PLN201612)+1 种基金the Applied Basic Research Projects in Sichuan Province(Grant No.2015JY0200)Open Fund Project from Sichuan Key Laboratory of Natural Gas Geology(Grant No.2015trqdz07)
文摘Pore structure characteristics are important to oil and gas exploration in complex low-permeability reservoirs. Using multifractal theory and nuclear magnetic resonance （NMR）, we studied the pore structure of low-permeability sandstone rocks from the 4th Member （Es4） of the Shahejie Formation in the south slope of the Dongying Sag. We used the existing pore structure data from petrophysics, core slices, and mercury injection tests to classify the pore structure into three categories and five subcategories. Then, the T2 spectra of samples with different pore structures were interpolated, and the one- and three-dimensional fractal dimensions and the multifractal spectrum were obtained. Parameters a （intensity of singularity） andf（a） （density of distribution） were extracted from the multifractal spectra. The differences in the three fractal dimensions suggest that the pore structure types correlate with a andf（a）. The results calculated based on the multifractal spectrum is consistent with that of the core slices and mercury injection. Finally, the proposed method was applied to an actual logging profile to evaluate the pore structure of low-permeability sandstone reservoirs.
基金supported by the National Natural Science Foundation of China(No.41874167)the National Science and Technology Major Project of China(No.2017YFB0202904)the National Natural Science Foundation of China(No.41904130)。
文摘Marchenko imaging obtains the subsurface reflectors using one-way Green’s functions,which are retrieved by solving the Marchenko equation.This method generates an image that is free of spurious artifacts due to internal multiples.The Marchenko imaging method is a target-oriented technique;thus,it can image a user specified area.In the traditional Marchenko method,an accurate velocity model is critical for estimating direct waves from imaging points to the surface.An error in the velocity model results in the inaccurate estimation of direct waves.In turn,this leads to errors in computation of one-way Green’s functions,which then affects the final Marchenko images.To solve this problem,in this paper,we propose a self-adaptive traveltime updating technique based on the principle of equal traveltime to improve the Marchenko imaging method.The proposed method calculates the time shift of direct waves caused by the error in the velocity model,and corrects the wrong direct wave according to the time shift and reconstructs the correct Green’s functions.The proposed method improves the results of imaging using an inaccurate velocity model.By comparing the results from traditional Marchenko and the new method using synthetic data experiments,we demonstrated that the adaptive traveltime updating Marchenko imaging method could restore the image of geological structures to their true positions.
