Segment Dynamic Image Warping for Seismic Velocity Model Refinement
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Solution Overview
Problem
Current methods for subsurface imaging and reservoir delineation face challenges in obtaining high-quality velocity models due to noise, migration artifacts, and slow convergence in data-domain reflection traveltime inversion, especially in complex geological settings with low velocity zones.
Innovation Solution
The implementation of a segment dynamic image warping function within the data-domain reflection traveltime inversion algorithm, which uses a point-wise segment-to-segment matching method to enhance signal alignment and estimate reliable time shifts between predicted and acquired seismic data, thereby improving the robustness of the inversion process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full waveform inversion is performed to build high resolution velocity model, then imaging resolution is improved, but computation cost increases exponentially
Solution Approach 1:
The patent segments the seismic data into multiple traces and processes them independently through parallel computation. Each trace is divided into time windows and processed separately, allowing the computation to be distributed across multiple processors or computing nodes, thereby reducing the computational burden of full waveform inversion while maintaining imaging resolution.
Solution Approach 2:
The patent performs preliminary velocity model building using reflection traveltime inversion before conducting full waveform inversion. This preliminary step provides a good initial velocity model that reduces the number of iterations required in the subsequent full waveform inversion, significantly lowering the overall computation cost while achieving high resolution imaging.
2Use of energy by moving object
If reflection traveltime inversion is used for velocity model building, then computation cost is reduced, but convergence speed becomes slow in complex geological settings
Solution Approach 1:
The patent applies local quality by using different processing strategies for different time windows and seismic traces. Each time window is processed with appropriate time shift estimates and wavelet deconvolution parameters tailored to local geological conditions, improving convergence speed in complex settings while keeping overall computation cost manageable through selective processing.
Solution Approach 2:
The patent implements dynamic time windowing and adaptive time shift estimation that adjusts processing parameters based on local seismic characteristics. The time windows are dynamically adjusted to capture relevant reflection events, and time shifts are updated iteratively based on residual analysis, enabling faster convergence in complex geological settings.
3Device complexity
If traditional image warping is used for time shift estimation, then processing is simple, but accuracy deteriorates in presence of noise and migration artifacts
Solution Approach 1:
The patent performs wavelet deconvolution and noise filtering as preliminary steps before time shift estimation. This preprocessing removes migration artifacts and enhances signal-to-noise ratio, allowing subsequent time shift estimation to achieve higher accuracy even in noisy conditions while maintaining relatively simple processing complexity.
Solution Approach 2:
The patent introduces an intermediate correlation-based time shift estimation step that bridges traditional image warping and full waveform inversion. This intermediary approach uses cross-correlation to estimate time shifts in a noise-robust manner before refining them through inversion, improving accuracy without significantly increasing processing complexity.
Data Source
AI summary
A computer-implemented method may include obtaining seismic data acquired in a time-domain for a subterranean region of interest. The method may further include obtaining a property model for the subterranean region of interest. The method may further include determining one or more time shifts using a segment dynamic image warping function based on the seismic data and the property model. The method may further include determining an adjoint source operator using the derived time shift and one-way wave equation. The method may further include updating the property model using a gradient solver in a data-domain reflection traveltime inversion. The method may further include outputting the updated property model for the subterranean region of interest. The method may further include generating a seismic image for the subterranean region of interest using the updated property model.