L1–2 minimization for P- and S-impedance inversion

Author:

Nie Wenliang1ORCID,Wen Xiaotao2,Yang Jixin3,He Jian3,Lin Kai4ORCID,Yang Longcheng3

Affiliation:

1. Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China, Chongqing Three Gorges University, School of Electronic and Information Engineering, Wanzhou 404000, Chongqing, China, and Chengdu University of Technology, School of Geophysics, Chengdu 610059, China..

2. Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China.(corresponding author).

3. Chengdu University of Technology, School of Geophysics, Chengdu 610059, China..

4. Chengdu University of Technology, State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu 610059, China and Chengdu University of Technology, School of Geophysics, Chengdu 610059, China..

Abstract

Amplitude variation with offset (AVO) inversion has been widely used in reservoir characterization to predict lithology and fluids. However, some existing AVO inversion methods that use [Formula: see text] norm regularization may not obtain the block boundary of subsurface layers because the AVO inversion is a severely ill-posed problem. To obtain sparse and accurate solutions, we have introduced the [Formula: see text] minimization method as an alternative to [Formula: see text] norm regularization. We used [Formula: see text] minimization for simultaneous P- and S-impedance inversion from prestack seismic data. We first derived the forward problem with multiangles and set up the inversion objective function with constraints of a priori low-frequency information obtained from well-log data. Then, we introduced minimization of the difference of [Formula: see text] and [Formula: see text] norms, denoted as [Formula: see text] minimization, to solve this objective function. The nonconvex penalty function of the [Formula: see text] minimization method is decomposed into two convex subproblems via the difference of convex algorithm, and each subproblem is solved by the alternating direction method of multipliers. Compared to [Formula: see text] norm regularization, the results indicate that [Formula: see text] minimization has superior performance over [Formula: see text] norm regularization in promoting blocky/sparse solutions. Tests on synthetic and field data indicate that our method can provide sparser and more accurate P- and S-impedance inversion results. The overall results confirm that our method has great potential in the detection and identification of fluids.

Funder

National Natural Foundation of China

Major Program of National Natural Science Foundation of China

Project of Science Technology Research program of Chongqing Education Commission of China

Publisher

Society of Exploration Geophysicists

Subject

Geology,Geophysics

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