Anisotropy Parameter Estimation Using Isotropic Depth-Migrated Image Gathers
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Solution Overview
Problem
Conventional seismic data processing methods fail to accurately estimate anisotropy parameters in depth domains without prior constraints, leading to incoherent depth images due to the lack of Thomsen anisotropy parameters ε and δ, especially in the absence of well data.
Innovation Solution
A semblance-based method for estimating the anellipticity parameter η using isotropic depth-migrated common image gathers, which analyzes residual move-out functions to derive effective values and convert them into an intrinsic anisotropy model, allowing for coherent depth imaging without prior knowledge of vertical velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional seismic data processing methods are used without prior constraints, then the processing can be performed without well data, but the anisotropy parameter estimation becomes non-unique and produces incoherent depth images
Solution Approach 1:
The patent changes the parameter being estimated from the traditional Thomsen parameters (ε and δ) to the anellipticity parameter η. This parameter transformation allows for unique and deterministic estimation without requiring well data or prior constraints, as η can be directly determined from the residual moveout characteristics in depth-migrated common image gathers.
Solution Approach 2:
The patent extracts and utilizes the residual moveout (RMO) information from depth-migrated common image gathers obtained through conventional isotropic pre-stack depth migration. By focusing on the RMO characteristics rather than requiring full anisotropic modeling, the method extracts the necessary information for anisotropy parameter estimation without needing well data or iterative anisotropic migration.
2Measurement precision
If iterative pre-stack depth migration with special treatment of near- and far-offset data is performed, then anisotropy parameter estimation can be attempted, but the process becomes highly complex and requires multiple iterations
Solution Approach 1:
The patent employs a semblance-based automatic picking method that allows the system to self-determine the anellipticity parameter η without requiring complex iterative processing or manual intervention. The semblance function automatically identifies the correct parameter value by maximizing coherence, eliminating the need for multiple iterative PSDM passes and special treatment procedures.
Solution Approach 2:
The patent replaces the mechanical iterative processing system with a direct calculation approach using semblance analysis. Instead of performing multiple iterations of complex PSDM with special near- and far-offset treatments, the method uses a single pass of isotropic migration followed by automatic parameter picking through semblance optimization, significantly simplifying the workflow.
3Ease of operation
If Thomsen parameters ε and δ are not known in advance, then processing can proceed without prior constraints, but the depth images become incoherent and inaccurate
Solution Approach 1:
The patent performs preliminary isotropic pre-stack depth migration to generate depth-migrated common image gathers before estimating the anisotropy parameter. This preliminary action creates the necessary data structure with residual moveout that contains the information needed for subsequent anellipticity parameter estimation, enabling the process to proceed without prior knowledge of anisotropy parameters while ensuring reliable results.
Solution Approach 2:
The patent uses semblance analysis as a feedback mechanism to automatically determine the correct anellipticity parameter η. The semblance function provides feedback by measuring coherence across offsets for different parameter values, allowing the system to iteratively refine and select the parameter that maximizes image coherence, thereby ensuring reliable depth imaging without prior constraints.
Data Source
AI summary
Methods and systems are presented in this disclosure for semblance-based anisotropy parameter estimation using isotropic depth-migrated common image gathers. Far-offset image gathers can be generated from seismic data associated with a subterranean formation migrated based on an isotropic depth migration that uses an isotropic velocity model. Based on the far-offset image gathers, a plurality of semblance values can be calculated as a function of an anisotropy parameter of the subterranean formation for the different depths and the surface locations. Effective values of the anisotropy parameter of the subterranean formation can be then chosen that result in maxima of the plurality of semblance values for the different depths and the surface locations. Anisotropy model of the subterranean formation can be obtained based on the effective values of the anisotropy parameter.