The methodology of a post-stack stochastic seismic inversion with the co-constraint of deterministic inversion
ZHANG Fengqi1,2,3, LIU Junzhou1,2,3, LIU Lanfeng1,2,3, SHI Lei1,2,3, HAN Lei1,2,3
1. State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing 100083, China; 2. Sinopec Key Laboratory of Seismic Elastic Wave Technology, Beijing 100083, China; 3. Sinopec Petroleum Exploration and Production Research Institute, Beijing 102206, China
Abstract：Based on the previous work, a post-stack stochastic seismic inversion with the co-constraint of deterministic inversion was proposed. This new algorithm centered on sequential Gibbs sampling and an extended Metropolises-Hastings (M-H) algorithm. Owing to the introduction of sequence stratigraphy grids, geostatistics, structures, and sedimentary modes were integrated into the stochastic seismic inversion adaptively, thereby saving the effort of calculating the local dip of the strata or implementing complicated coordinate transformations. The high-frequency components (HFCs) in the stochastic seismic inversion result still featured large uncertainty, which resulted in big differences in reservoir characteristics among different implementations of stochastic seismic inversion. Given this problem, this paper combined sequential Gibbs sampling with collocated cokriging and introduced the co-constraint of deterministic inversion to restrict the candidate solution space and ultimately to reduce the uncertainty of the HFCs in the stochastic seismic inversion result. The following conclusions were obtained: ① Compared with deterministic inversion, stochastic seismic inversion can produce high-resolution results. The vertical resolution of the results is affected by the vertical variation whereas the lateral continuity of the results is affected by the lateral variation. ② Compared with uniform seismic grids, sequence strati-graphy grids are more suitable for stochastic inversion with a lateral variation constraint because of their integration with structures and sedimentary modes. With the help of a resampling matrix, sample simulation is carried out on sequence stratigraphy grids and convolutional forward modeling is carried out on uniform seismic grids. The whole inversion process not only meets the constraints of the structures and sedimentary modes but also conforms to geophysical principles. ③ Due to the introduction of the co-constraint of deterministic inversion, the candidate solution space is further restricted and the correlation between the results of stochastic inversion and deterministic inversion is enhanced. Furthermore, the uncertainty of the HFCs in the stochastic inversion results is reduced. Trial calculations with real data reveal that this new algorithm is verified by a comparison among four implementations of stochastic inversion.
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ZHANG Fengqi, LIU Junzhou, LIU Lanfeng, SHI Lei, HAN Lei. The methodology of a post-stack stochastic seismic inversion with the co-constraint of deterministic inversion. Oil Geophysical Prospecting, 2021, 56(5): 1137-1149.
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