Seismic Attribute‐Oriented Post‐Stack Seismic Inversion

ABSTRACT Seismic inversion can effectively establish the connection between seismic data and underground reservoir parameters. Aiming at the current problem of low accuracy of deterministic inversion, a seismic attribute‐oriented deterministic inversion method is proposed. The method is similar to most inversion algorithms and consists of two parts: modelling and inversion. The innovation of the model lies in extracting seismic attributes and learning the mapping between the seismic attributes of the well‐side and the parameters to be inverted based on the support vector regression (SVR) algorithm. Then, the mapping relationship is used to realize the modelling of the parameters to be inverted in the well‐free area. Under the constraints of this model, seismic inversion is implemented through the Markov Chain Monte Carlo (MCMC) approach, yielding inversion results that exhibit strong consistency with the corresponding seismic responses. As the multi‐trace structural attributes contain more high‐frequency information, the resolution of the parameters to be inverted based on this model is also higher. We continue to conduct inversion tests using post‐stack seismic data. The results show that seismic attribute‐oriented inversion has a significant advantage in inversion resolution over partially deterministic inversion algorithms (model‐based inversion, sparse spike inversion, etc.).