Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density fu...Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.展开更多
In the Ken 71 development block, fluvial facies of the Neogene Guantao Formation and delta facies of the Paleogene Dongying Formation are the main pay beds. It is a multiple oil and water system which is complicated b...In the Ken 71 development block, fluvial facies of the Neogene Guantao Formation and delta facies of the Paleogene Dongying Formation are the main pay beds. It is a multiple oil and water system which is complicated by faults. Characteristics of the block include a dense well network, thin reservoirs, complicated horizontal relationships, and small velocity difference between reservoir and non-reservoir. Therefore, it is difficult to conduct detailed reservoir description for subsequent development project adjustment. We demonstrate a stochastic seismic inversion which aims at detailed reservoir description. It is a technology which utilizes multiple wells, seismic data, and geological calibration and integrates with 3D structural interpretation results to build a 3D multi-fault detailed and constrained geological model. On this basis, we adopted stochastic seismic inversion to conduct a multi-stratum parameters inversion such as impedance and lithology. As a result, thin interbedded strata in the block were well resolved and the results demonstrated the importance of detailed reservoir inversion for oilfield development.展开更多
基金supported by the National Major Science and Technology Project of China on Development of Big Oil-Gas Fields and Coalbed Methane (No. 2008ZX05010-002)
文摘Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.
文摘In the Ken 71 development block, fluvial facies of the Neogene Guantao Formation and delta facies of the Paleogene Dongying Formation are the main pay beds. It is a multiple oil and water system which is complicated by faults. Characteristics of the block include a dense well network, thin reservoirs, complicated horizontal relationships, and small velocity difference between reservoir and non-reservoir. Therefore, it is difficult to conduct detailed reservoir description for subsequent development project adjustment. We demonstrate a stochastic seismic inversion which aims at detailed reservoir description. It is a technology which utilizes multiple wells, seismic data, and geological calibration and integrates with 3D structural interpretation results to build a 3D multi-fault detailed and constrained geological model. On this basis, we adopted stochastic seismic inversion to conduct a multi-stratum parameters inversion such as impedance and lithology. As a result, thin interbedded strata in the block were well resolved and the results demonstrated the importance of detailed reservoir inversion for oilfield development.
