Prestack seismic facies-controlled joint inversion of reservoir elastic and petrophysical parameters for sweet spot prediction. Issue 6 (November 2017)

• Record Type: Journal Article
• Authors: Zhang, Sheng; Huang, Handong; Li, Huijie; Wang, Gaofei; Dong, Yinping; Luo, Yaneng
• Is Part Of: Energy exploration & exploitation. Volume 35:Issue 6(2017)
• Page Start: 767
• Page End: 791
• Publication Date: 2017-11
• Subjects: Conjugate gradient inversion -- elastic parameters -- petrophysical parameters -- rock physics equations -- seismic facies controlled; Power resources -- Periodicals; 333.79
• DOI: 10.1177/0144598717716286
• Languages: English
• ISSNs: 0144-5987

Abstract

The elastic and petrophysical parameters of a reservoir can be directly applied to lithology prediction and fluid identification. Existing seismic joint inversion methods for estimating the elastic and petrophysical parameters of a reservoir are primarily based on either the Gassmann equation, with which these parameters are inverted from prestack seismic data through stochastic optimization methods, or Wyllie's modified equation, with which these parameters are inverted from poststack seismic data using deterministic optimization methods. Regardless of the stochastic or deterministic inversion of reservoir parameters, only the microscopic connection between parameters is considered, without considering the constraints of the macroscopic geological background. The purpose of this work is to develop a strategy for estimating the elastic and petrophysical parameters of a reservoir based on the Gassmann equation and seismic facies using deterministic seismic prestack inversion. We employ the Gassmann equation to construct the relationship between the prestack seismic data and petrophysical parameters and use a low-pass filter matrix to obtain the combination of seismic facies and seismic inversion and improve the robustness of the inversion results. We treat the joint posterior probability of elastic and petrophysical parameters as the objective function under a Bayesian framework by expanding the objective function with the Taylor formula; the joint equations composed of elastic and petrophysical parameters are obtained through differentiation. The conjugate gradient method is subsequently used to find the optimal solutions for the P-wave velocity, S-wave velocity, density, porosity, water saturation, and clay content. Theoretical calculations and actual data inversion results prove the feasibility and applicability of the method.