Joint Bayesian inversion method and system for lithologic and physical parameters of reservoir

Abstract

The invention provides a joint Bayesian inversion method and system for lithologic and physical parameters of a reservoir. The method comprises the following steps: establishing a rock physical model and a statistical prior model according to core or logging data; constructing an optimization problem of joint inversion based on the Bayesian theory; and solving the problem by adopting a continuous-discrete variable genetic algorithm. According to the method, thoughts of lithology classification and physical property parameter estimation are combined, a new method of joint Bayesian inversion of reservoir lithology and physical property parameters is developed, quantitative prediction of underground reservoir properties is achieved, and then reliable support is provided for fine oil and gas exploration and development.

Description

technical field [0001] The invention belongs to the field of reservoir parameter inversion calculation, in particular to a combined Bayesian inversion method and system for reservoir lithology and physical property parameters. Background technique [0002] Because the posterior distribution of the Bayesian inversion method can flexibly introduce multi-type data and petrophysical models, it has been widely used in reservoir parameter inversion in recent years. 1992, pointed out that methods based on Bayesian inversion can improve seismic inversion results. In 2002, Houck showed that Bayesian methods can be used to quantify uncertainty in geological predictions. In 2006, Escobar et al proposed that Bayesian methods can add spatial constraints in seismic inversion. In 2018, Grana used the Gaussian mixture model to develop a set of lithology classification and quantitative prediction methods for physical properties. In 2020, he further developed the Bayesian inversion metho...

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Problems solved by technology

[0003] The petrophysical model is the basis for inversion of reservoir physical parameters. If the adopted petrophysical model fails to accurately describe the mechanism of wave propagation in the reservoir, good results cannot be obtained even with the most efficient inversion algorithm.

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