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Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multitrace Bayesian amplitude-variation-with-offset petrophysics inversion in multiporosity reservoirs

Li Kun, Yin Xingyao, Zong Zhaoyun and Dario Grana
Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multitrace Bayesian amplitude-variation-with-offset petrophysics inversion in multiporosity reservoirs
Geophysics (November 2021) 87 (1): M25-M41

Abstract

The estimation of petrophysical and fluid-filling properties of subsurface reservoirs from seismic data is a crucial component of reservoir characterization. Seismic amplitude-variation-with-offset (AVO) inversion driven by rock physics is an effective approach to characterize reservoir properties. In general, PP-wave reflection coefficients, elastic moduli, and petrophysical parameters are nonlinearly coupled, especially in multiple-type pore-space reservoirs, which makes seismic AVO petrophysics inversion ill-posed. We have developed a new approach that combines Biot-Gassmann's poroelasticity theory with Russell's linear AVO approximation, to estimate the reservoir properties including elastic moduli and petrophysical parameters based on multitrace probabilistic AVO inversion algorithm. We first derive a novel PP-wave reflection coefficient formulation in terms of porosity, stiff-pore volume fraction, rock-matrix shear modulus, and fluid bulk modulus to incorporate the effect of pore structures on elastic moduli by considering the soft and stiff pores with different aspect ratios in sandstone reservoirs. Through the analysis of the four types of PP-wave reflection coefficients, the approximation accuracy and inversion feasibility of the derived formulation are verified. Our stochastic inversion method aims to predict the posterior probability density function in a Bayesian setting according to a prior Laplace distribution with vertical correlation and prior Gaussian distribution with lateral correlation of model parameters. A Metropolis-Hastings stochastic sampling algorithm with multiple Markov chains is developed to simulate the posterior models of porosity, stiff-pore volume fraction, rock-matrix shear modulus, and fluid bulk modulus from seismic AVO gathers. The applicability and validity of our inversion method is illustrated with synthetic examples and a real data application.


ISSN: 0016-8033
EISSN: 1942-2156
Coden: GPYSA7
Serial Title: Geophysics
Serial Volume: 87
Serial Issue: 1
Title: Estimation of porosity, fluid bulk modulus, and stiff-pore volume fraction using a multitrace Bayesian amplitude-variation-with-offset petrophysics inversion in multiporosity reservoirs
Affiliation: China University of Petroleum, School of Geosciences, Qingdao, China
Pages: M25-M41
Published: 20211118
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 52
Accession Number: 2022-004189
Categories: Applied geophysicsEconomic geology, geology of energy sources
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 3 tables
Secondary Affiliation: University of Wyoming, USA, United States
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2022, American Geosciences Institute.
Update Code: 202204
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