The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution....The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the Ll-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the Ll-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.展开更多
Cooperative inversion for petroleum reservoir characterization produces an Earth model that fits all available geological, geophysical and reservoir production data to within acceptable error criteria. The mathematica...Cooperative inversion for petroleum reservoir characterization produces an Earth model that fits all available geological, geophysical and reservoir production data to within acceptable error criteria. The mathematical formulation for the inversion requires an appropriate modeling description of both seismic wave propagation and reservoir fluid flow. The inversion requires the minimization of an objective function which is the weighted sum of model misfits for both geophysical and production data. While the complete automation of cooperative inversion may be unrealistic or intractable, geophysical data can provide useful information for enhancing heavy oil production. A methodology is given to demonstrate possible cooperative inversion application in heavy oil reservoirs.展开更多
基金Projects(U1562215,41674130,41404088)supported by the National Natural Science Foundation of ChinaProjects(2013CB228604,2014CB239201)supported by the National Basic Research Program of China+1 种基金Projects(2016ZX05027004-001,2016ZX05002006-009)supported by the National Oil and Gas Major Projects of ChinaProject(15CX08002A)supported by the Fundamental Research Funds for the Central Universities,China
文摘The classical elastic impedance （EI） inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the Ll-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the Ll-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.
文摘Cooperative inversion for petroleum reservoir characterization produces an Earth model that fits all available geological, geophysical and reservoir production data to within acceptable error criteria. The mathematical formulation for the inversion requires an appropriate modeling description of both seismic wave propagation and reservoir fluid flow. The inversion requires the minimization of an objective function which is the weighted sum of model misfits for both geophysical and production data. While the complete automation of cooperative inversion may be unrealistic or intractable, geophysical data can provide useful information for enhancing heavy oil production. A methodology is given to demonstrate possible cooperative inversion application in heavy oil reservoirs.
