Fluid Factor Inversion With Prestack Seismic Data Based on Quadratic Reflectivity Approximation

The Gassmann fluid term, an important attribute for characterizing reservoir fluid variations, is widely used in seismic inversion for reservoir prediction and fluid identification. However, most existing inversion methods rely on first-order linear approximations of the reflection coefficient equat...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 16
Main Authors: Zhao, Lian, Cao, Danping, Xie, Yu
Format: Journal Article
Language:English
Published: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Alternating direction method of multiplier (ADMM) algorithm
Approximation
arctangent penalty function
Complexity
Convexity
Decomposition
Estimation
Fluids
Gassmann fluid term
Linear approximation
Linear programming
Mathematical models
Media
Nonlinear response
Objective function
Optimization
Penalty function
quadratic approximation reflection coefficient
Reflectance
Reflection
Reflection coefficient
Reservoirs
Rocks
Seismic data
Seismic surveys
Wave reflection
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:The Gassmann fluid term, an important attribute for characterizing reservoir fluid variations, is widely used in seismic inversion for reservoir prediction and fluid identification. However, most existing inversion methods rely on first-order linear approximations of the reflection coefficient equation, ignoring nonlinear responses in complex geological settings, thereby limiting the accuracy of inversion results. To address this limitation, this study derives a quadratic reflection coefficient approximation equation that explicitly incorporates the Gassmann fluid term by combining the Russell approximation with the quadratic PP-wave reflection coefficient equation. Based on this formulation, an inversion framework is developed using the quadratic approximation. The proposed method first uses the arctangent penalty function as a sparsity constraint, which enhances the overall convexity of the objective function. The Hadamard operator is then used to decompose the variables of the quadratic terms and reduce optimization complexity. Finally, the alternating direction method of multiplier (ADMM) algorithm is introduced to decompose the nonlinear optimization problem into multiple single-variable subproblems, which are solved through alternating iterations. Model tests and field data applications show that the proposed approach enhances the accuracy of reservoir fluid identification and validates the effectiveness of the quadratic approximation strategy.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3624912