The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy ...The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy and effect can not meet the precise requirement of the inversion. Two typical models of the geological bodies were designed, and forward calculation was carried out using finite element method. The forward-modeled profiles were obtained. 1% Gaussian random error was added in the forward models and then 2D and 3D inversions using a high-density resistivity method were undertaken to realistically simulate field data and analyze the sensitivity of the 2D and 3D inversion algorithms to noise. Contrast between the 2D and 3D inversion results of least squares inversion shows that two inversion results of high-density resistivity method all can basically reflect the spatial position of an anomalous body. However, the 3D inversion can more effectively eliminate the influence of interference from Gaussian random error and better reflect the distribution of resistivity in the anomalous bodies. Overall, the 3D inversion was better than 2D inversion in terms of embodying anomalous body positions, morphology and resistivity properties.展开更多
Traditionally, fluid substitutions are often conducted on log data for calculating reservoir elastic properties with different pore fluids. Their corresponding seismic responses are computed by seismic forward modelin...Traditionally, fluid substitutions are often conducted on log data for calculating reservoir elastic properties with different pore fluids. Their corresponding seismic responses are computed by seismic forward modeling for direct gas reservoir identification. The workflow provides us with the information about reservoir and seismic but just at the well. For real reservoirs, the reservoir parameters such as porosity, clay content, and thickness vary with location. So the information from traditional fluid substitution just at the well is limited. By assuming a rock physics model linking the elastic properties to porosity and mineralogy, we conducted seismic forward modeling and AVO attributes computation on a three-layer earth model with varying porosity, clay content, and formation thickness. Then we analyzed the relations between AVO attributes at wet reservoirs and those at the same but gas reservoirs. We arrived at their linear relations within the assumption framework used in the forward modeling. Their linear relations make it possible to directly conduct fluid substitution on seismic AVO attributes. Finally, we applied these linear relations for fluid substitution on seismic data and identified gas reservoirs by the cross-plot between the AVO attributes from seismic data and those from seismic data after direct fluid substitution.展开更多
基金Projects(41074085,41374118)supported by the National Natural Science Foundation of ChinaProject(20120162110015)supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-12-0551)supported by Program for New Century Excellent Talents in University,China
文摘The 2D data processing adopted by the high-density resistivity method regards the geological structures as two degrees, which makes the results of the 2D data inversion only an approximate interpretation;the accuracy and effect can not meet the precise requirement of the inversion. Two typical models of the geological bodies were designed, and forward calculation was carried out using finite element method. The forward-modeled profiles were obtained. 1% Gaussian random error was added in the forward models and then 2D and 3D inversions using a high-density resistivity method were undertaken to realistically simulate field data and analyze the sensitivity of the 2D and 3D inversion algorithms to noise. Contrast between the 2D and 3D inversion results of least squares inversion shows that two inversion results of high-density resistivity method all can basically reflect the spatial position of an anomalous body. However, the 3D inversion can more effectively eliminate the influence of interference from Gaussian random error and better reflect the distribution of resistivity in the anomalous bodies. Overall, the 3D inversion was better than 2D inversion in terms of embodying anomalous body positions, morphology and resistivity properties.
基金sponsored by the National Natural Science Foundation of China (No.41074098)
文摘Traditionally, fluid substitutions are often conducted on log data for calculating reservoir elastic properties with different pore fluids. Their corresponding seismic responses are computed by seismic forward modeling for direct gas reservoir identification. The workflow provides us with the information about reservoir and seismic but just at the well. For real reservoirs, the reservoir parameters such as porosity, clay content, and thickness vary with location. So the information from traditional fluid substitution just at the well is limited. By assuming a rock physics model linking the elastic properties to porosity and mineralogy, we conducted seismic forward modeling and AVO attributes computation on a three-layer earth model with varying porosity, clay content, and formation thickness. Then we analyzed the relations between AVO attributes at wet reservoirs and those at the same but gas reservoirs. We arrived at their linear relations within the assumption framework used in the forward modeling. Their linear relations make it possible to directly conduct fluid substitution on seismic AVO attributes. Finally, we applied these linear relations for fluid substitution on seismic data and identified gas reservoirs by the cross-plot between the AVO attributes from seismic data and those from seismic data after direct fluid substitution.
