Joint elastic and petrophysical inversion using prestack seismic and well log data. Issue 4 (1st December 2016)

• Record Type: Journal Article
• Authors: Li, Zhiyong; Song, Beibei; Zhang, Jiashu; Hu, Guangmin
• Is Part Of: Exploration geophysics. Volume 47:Issue 4(2017)
• Page Start: 331
• Page End: 340
• Publication Date: 2016-12-01
• Subjects: deterministic inversion, joint inversion, rock physics, stochastic inversion.; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1071/EG14074
• Languages: English
• ISSNs: 0812-3985

Abstract

Abstract : Seismic inverse problems aim to infer the properties of subsurface geology, such as elastic and petrophysical properties. Existing seismic inversion methods for the joint estimation of these properties are mainly based either on Gassmann theory for prestack seismic data processed with stochastic optimisation techniques or on the Wyllie formula for poststack seismic data processed by deterministic optimisation techniques. The purpose of this study is to develop a strategy for the joint estimation of elastic and petrophysical properties from prestack seismic data based on Gassmann equations with deterministic optimisation techniques. Given poor-quality prestack seismic data, two regularisation parameters are introduced to control the trade-off between fidelity to the data and the smoothness of the solution. An appropriate linearised system of equations for the joint model update is derived from Newton's method, which fits seismic data, the description of the rock physics medium and prior information, simultaneously. We show the preliminary results obtained with the proposed framework for synthetic and real data examples. Abstract : We present a strategy for the joint estimation of elastic and petrophysical properties from prestack seismic data based on Gassmann equations with deterministic optimisation techniques. Given poor-quality prestack seismic data, two regularisation parameters are introduced to control the trade-off between fidelity to the data and the smoothness of the solution.