Carbonate,tight sandstone,and shale reservoirs have many pore types,and the relationship between the porosity and elastic parameters is extremely discrete due to the complex pore shape.This paper presents a method for...Carbonate,tight sandstone,and shale reservoirs have many pore types,and the relationship between the porosity and elastic parameters is extremely discrete due to the complex pore shape.This paper presents a method for predicting reservoir pore types based on pore shape substitution.The pore shape substitution allows for accurately characterizing the changes in the elastic properties of the rock with the changes in pore shape,assuming there are no changes in terms of minerals,porosity,or fl uids.By employing a multiple-porosity variable critical porosity model,the eff ective pore aspect ratio could be inverted from the velocities of the rock.To perform pore shape substitution,we could replace the eff ective pore aspect ratio with another pore aspect ratio or increase/decrease the volume content of diff erent pore shapes.The reservoir pore types could be evaluated by comparing the differences in the reservoir velocities before and after the substitution of the pore shape.The test results pertaining to the theoretical model and the well logging data indicated that the pore shape substitution method could be applied to characterize pore types in terms of separating the eff ects of the pore shapes from the eff ects of the minerals,porosity,or fl uids on the velocities.展开更多
Wavelet estimation is an important part of high-resolution seismic data processing.However,itis difficult to preserve the lateral continuity of geological structures and eff ectively recover weak geologicalbodies usin...Wavelet estimation is an important part of high-resolution seismic data processing.However,itis difficult to preserve the lateral continuity of geological structures and eff ectively recover weak geologicalbodies using conventional deterministic wavelet inversion methods,which are based on the joint inversionof wells with seismic data.In this study,starting from a single well,on the basis of the theory of single-welland multi-trace convolution,we propose a steady-state seismic wavelet extraction method for synchronizedinversion using spatial multi-well and multi-well-side seismic data.The proposed method uses a spatiallyvariable weighting function and wavelet invariant constraint conditions with particle swarm optimization toextract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve thespatial adaptability of the extracted wavelet and inversion stability.The simulated data demonstrate that thewavelet extracted using the proposed method is very stable and accurate.Even at a low signal-to-noise ratio,the proposed method can extract satisfactory seismic wavelets that refl ect lateral changes in structures andweak eff ective geological bodies.The processing results for the field data show that the deconvolution resultsimprove the vertical resolution and distinguish between weak oil and water thin layers and that the horizontaldistribution characteristics are consistent with the log response characteristics.展开更多
基金the National Natural Science Foundation of China(Nos.42074136,41674130)National Key S&T Special Project of China(No.2016ZX05027-004-001)the Fundamental Research Funds for the Central University(No.18CX02061A).
文摘Carbonate,tight sandstone,and shale reservoirs have many pore types,and the relationship between the porosity and elastic parameters is extremely discrete due to the complex pore shape.This paper presents a method for predicting reservoir pore types based on pore shape substitution.The pore shape substitution allows for accurately characterizing the changes in the elastic properties of the rock with the changes in pore shape,assuming there are no changes in terms of minerals,porosity,or fl uids.By employing a multiple-porosity variable critical porosity model,the eff ective pore aspect ratio could be inverted from the velocities of the rock.To perform pore shape substitution,we could replace the eff ective pore aspect ratio with another pore aspect ratio or increase/decrease the volume content of diff erent pore shapes.The reservoir pore types could be evaluated by comparing the differences in the reservoir velocities before and after the substitution of the pore shape.The test results pertaining to the theoretical model and the well logging data indicated that the pore shape substitution method could be applied to characterize pore types in terms of separating the eff ects of the pore shapes from the eff ects of the minerals,porosity,or fl uids on the velocities.
文摘Wavelet estimation is an important part of high-resolution seismic data processing.However,itis difficult to preserve the lateral continuity of geological structures and eff ectively recover weak geologicalbodies using conventional deterministic wavelet inversion methods,which are based on the joint inversionof wells with seismic data.In this study,starting from a single well,on the basis of the theory of single-welland multi-trace convolution,we propose a steady-state seismic wavelet extraction method for synchronizedinversion using spatial multi-well and multi-well-side seismic data.The proposed method uses a spatiallyvariable weighting function and wavelet invariant constraint conditions with particle swarm optimization toextract the optimal spatial seismic wavelet from multi-well and multi-well-side seismic data to improve thespatial adaptability of the extracted wavelet and inversion stability.The simulated data demonstrate that thewavelet extracted using the proposed method is very stable and accurate.Even at a low signal-to-noise ratio,the proposed method can extract satisfactory seismic wavelets that refl ect lateral changes in structures andweak eff ective geological bodies.The processing results for the field data show that the deconvolution resultsimprove the vertical resolution and distinguish between weak oil and water thin layers and that the horizontaldistribution characteristics are consistent with the log response characteristics.
