Seismic AVO statistical inversion incorporating poroelasticity

Kun Li,Zhao-Yun Zong,Hai-Kun Lin,Xing-Yao Yin

doi:10.1007/s12182-020-00483-5

Abstract

Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.

Highlights

Subsurface rock is composed of solid mineral matrix, dry pores/fractures and various fluid mixtures and set foundation for geophysical interpretation (Biot 1956; Han and Batzle 2004; Russell et al 2011; Yin et al 2015; Ding et al 2019a; Li et al 2020)
Based on Biot–Gassmann’s poroelasticity, the DE-MCMC model-based statistical amplitude variation with offset (AVO) inversion method is proposed and it can realize the simultaneous estimation of rock porosity, rock-matrix moduli, density and fluid modulus, which can be utilized in reservoir characterization and pore fluid discrimination
The main conclusions are as follows: 1. With Biot–Gassmann’s theory, the seismic AVO equation characterized by porosity, rock-matrix moduli, density and fluid bulk modulus is derived

Summary

Introduction

Subsurface rock is composed of solid mineral matrix, dry pores/fractures and various fluid mixtures and set foundation for geophysical interpretation (Biot 1956; Han and Batzle 2004; Russell et al 2011; Yin et al 2015; Ding et al 2019a; Li et al 2020). The statistical inversion based on the Markov chains Monte Carlo model (MCMC) is usually suggested to solve the seismic AVO inverse problem with strong nonlinearity and evaluate the uncertainty of the estimated model parameters simultaneously (Hansen et al 2006; Grana and Della Rossa 2010; Alemie and Sacchi 2011; Yuan et al 2015; Yin and Zhang 2014; Yin et al 2016; Li et al 2017a, b, 2019). AVO inversion and fluid identification methods are usually developed with linearized P-wave AVO approximate reflectivity including first-order partial derivatives or statistical rock physical models (Grana and Della Rossa 2010; Russell et al 2011; Yin and Zhang 2014; Zong et al 2015; Figueiredo et al 2018; Lang and Grana 2018). Models and field data example demonstrate that the statistical inversion of porosity, rock-matrix moduli and pore fluid modulus is helpful in reservoir characterization and fluid discrimination

AVO reflectivity equation with poroelasticity

Method of statistical AVO inversion

Model examples

Findings

Conclusions