Iterative Reflection-Based FWI for Deep Subsurface Velocity Modeling
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Solution Overview
Problem
Conventional full-waveform inversion (FWI) techniques are limited in generating high-resolution velocity models from deep water marine seismic surveys due to the limitations in recording head and diving waves, leading to inaccurate seismic images and reduced reliability in identifying hydrocarbon deposits.
Innovation Solution
The implementation of iterative reflection-based FWI, which uses an impedance sensitivity kernel and velocity sensitivity kernel to generate low-wavenumber component updates, allowing for large-scale deep FWI velocity model updates without the need for longer streamers, and optimizes dynamic weights to suppress high-wavenumber components, enabling the generation of a high-resolution velocity model with both low- and high-wavenumber components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional full-waveform inversion techniques are used, then the processing can be completed with standard streamer lengths, but the velocity model resolution is insufficient and head/diving waves cannot be properly recorded
Solution Approach 1:
The patent changes the fundamental parameter of wave type utilization from conventional reflected waves to reflection-based head and diving waves. By inverting for these specific wave types using iterative FWI with appropriate sensitivity kernels, the method achieves high-resolution velocity models without requiring extended streamer configurations, thus resolving the contradiction between measurement precision and device complexity
Solution Approach 2:
The patent introduces an intermediary approach by using reflection-based head and diving waves as mediators between the seismic source and receivers. These waves serve as intermediate carriers that provide the necessary low-wavenumber information for accurate velocity model building, eliminating the need for direct head wave recording configurations
2Loss of information
If longer streamers are used to record head and diving waves, then low-wavenumber information can be obtained, but the device complexity and survey cost increase
Solution Approach 1:
Instead of extending streamers to capture head and diving waves directly, the patent inverts the approach by using iterative FWI to synthesize and invert for reflection-based head and diving waves from standard streamer data. This inversion methodology recovers low-wavenumber information without the need for longer streamers, resolving the contradiction between information recovery and device complexity
Solution Approach 2:
The patent changes the parameter of wave propagation path by utilizing reflection-based head and diving waves that bounce off subsurface interfaces before reaching receivers. This parameter change allows low-wavenumber information to be obtained through standard streamer configurations rather than requiring extended streamer lengths
3Measurement precision
If iterative reflection-based FWI is applied to generate low-wavenumber updates, then large-scale deep velocity model accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the inversion process into iterative steps, where each iteration focuses on updating specific components of the velocity model using reflection-based head and diving waves. This segmentation allows the computational task to be divided into manageable iterations, improving deep velocity model accuracy while controlling computational complexity through progressive refinement
Solution Approach 2:
The patent uses sensitivity kernels as intermediaries to efficiently compute the relationship between reflection-based head and diving waves and velocity model parameters. These kernels serve as computational mediators that reduce the overall computational burden while maintaining accuracy in deep velocity model building
4Stability of the object's composition
If high-wavenumber components are suppressed using dynamic weights, then the velocity model stability improves, but the detail resolution may be reduced
Solution Approach 1:
The patent employs dynamic weights that adapt during the iterative inversion process, dynamically adjusting the suppression of high-wavenumber components based on the current state of the velocity model. This dynamic approach maintains model stability in early iterations while preserving detail resolution in later iterations, resolving the contradiction between stability and precision
Solution Approach 2:
The patent applies periodic adjustments to the dynamic weights throughout the iterative process, cycling between stronger and weaker high-wavenumber suppression as needed. This periodic action allows the model to stabilize when necessary while maintaining the ability to resolve details when the inversion converges, balancing stability and precision
Data Source
AI summary
This disclosure describes processes and systems for generating a high-resolution velocity model of a subterranean formation from recorded seismic data gathers obtained in a marine seismic survey of the subterranean formation. A velocity model is computed by iterative FWI using reflections, resolving the velocity field of deep subterranean targets without requiring ultralong offsets. The processes and systems use of an impedance sensitivity kernel to characterize reflections in a modeled wavefield, and then use the reflections to compute a velocity sensitivity kernel that is used to produce low-wavenumber updates to the velocity model. The iterative process is applied in a cascade such that position of reflectors and background velocity are simultaneously updated. Once the low-wavenumber components of the velocity model are updated, the velocity model is used as an input of conventional FWI to introduce missing velocity components (i.e., high-wavenumber) to increase the resolution of the velocity model.