Multi-parameter Inversion in Elastic FWI via Offset Segmentation
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Solution Overview
Problem
Current elastic full wavefield inversion (FWI) methods are expensive and computationally intensive due to the need for dense grids and numerous iterations to converge, making them impractical for rapid reservoir characterization and velocity model building, especially when aiming to robustly invert for acoustic impedance and velocity ratios.
Innovation Solution
A method that decomposes seismic data into offset or angle groups and performs elastic FWI sequentially, focusing on small angle reflections for acoustic impedance, middle and far offset reflections for velocity ratios, and density, reducing crosstalk between parameters and speeding up convergence by a factor of approximately 10.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If elastic FWI is performed with dense computational grids to accurately model shear wave propagation, then measurement precision of elastic parameters (VP/VS) is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent segments the inversion process into two distinct stages: first inverting for acoustic impedance using only near-offset data, then inverting for velocity ratio using middle and far-offset data with fixed acoustic impedance. This segmentation allows each stage to focus on specific parameters, reducing computational complexity while maintaining accuracy
Solution Approach 2:
The patent applies partial action by using only near-offset data for acoustic impedance inversion and only middle/far-offset data for velocity ratio inversion, rather than using all offset data for all parameters simultaneously. This reduces the computational burden while achieving convergence in fewer iterations
2Measurement precision
If multi-parameter inversion is performed simultaneously for acoustic impedance and velocity ratio, then comprehensive reservoir characterization is achieved, but parameter crosstalk increases and convergence requires many more iterations
Solution Approach 1:
The patent divides the multi-parameter inversion into sequential steps: Step 1 inverts only for acoustic impedance using near-offset data, Step 2 inverts only for velocity ratio using middle and far-offset data with fixed acoustic impedance. This eliminates parameter crosstalk and accelerates convergence
Solution Approach 2:
The patent performs preliminary inversion of acoustic impedance using near-offset data before proceeding to velocity ratio inversion. This preliminary action establishes a fixed baseline that stabilizes subsequent inversion and reduces iterative complexity
3Loss of information
If all offset data is used simultaneously in elastic FWI, then complete utilization of seismic information is achieved, but computational expense increases due to denser grids and more iterations
Solution Approach 1:
The patent segments offset data into near-offset, middle-offset, and far-offset groups, assigning each to specific inversion targets. This segmentation enables efficient use of computational resources by processing different data subsets in sequence rather than simultaneously
Solution Approach 2:
The patent applies local quality by matching specific offset ranges to specific inversion objectives: near-offset data (sensitive to acoustic impedance) is used for impedance inversion, while middle and far-offset data (sensitive to velocity ratio) are used for anisotropy inversion, optimizing information extraction
Data Source
AI summary
Method for multi-parameter inversion using elastic inversion. This method decomposes data into offset/angle groups and performs inversion on them in sequential order. This method can significantly speed up convergence of the iterative inversion process, and is therefore most advantageous when used for full waveform inversion (FWI). The present inventive approach draws upon relationships between reflection energy and reflection angle, or equivalently, offset dependence in elastic FWI. The invention uses recognition that the amplitudes of small angle (near offset) reflections are largely determined by acoustic impedance alone (1), independent for the most part of Vp/Vs. Large angle (middle and far offset) reflections are affected by Ip, Vp/Vs (2) and other earth parameters such as density (3) and anisotropy. Therefore, the present inventive method decomposes data into angle or offset groups in performing multi-parameter FWI to reduce crosstalk between the different model parameters being determined in the inversion.