Joint Preconditioning for Time-Lapse Full Waveform Inversion
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Solution Overview
Problem
Current seismic imaging methods, particularly in time-lapse (4D) processing, face challenges in accurately capturing subsurface changes due to noise and unrepeatability in data acquisition geometries, which masks the small 4D signals and limits the resolution of subsurface models.
Innovation Solution
A joint preconditioning full waveform inversion (FWI) method that accounts for differences in acquisition geometry between baseline and monitor datasets by computing joint preconditioners, reflecting similarities and differences in geometrical features, to enhance the accuracy of subsurface model updates and reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional migration methods are used for seismic data processing, then the imaging process can be completed, but the images suffer from lack of resolution, bad event continuity and zones with dimmed amplitude due to band-limited sources and uneven illumination patterns
Solution Approach 1:
The patent applies parameter changes by transitioning from traditional migration methods to Full Waveform Inversion (FWI), which inverts the wave propagation equations to retrieve accurate velocity models. This inversion process fundamentally changes the parameters used for imaging, transforming the approach from direct migration to iterative model updating based on waveform matching, thereby resolving illumination artifacts and improving resolution
Solution Approach 2:
The patent replaces traditional mechanical migration algorithms with a physics-based FWI system that uses wave propagation theory. Instead of relying on conventional migration operators that suffer from band-limited effects, the system substitutes a physics-based inversion framework that models wave behavior more accurately, eliminating illumination-related imaging artifacts
2Reliability
If Least-Squares Migration (LSM) is used to overcome limitations, then robust inversion can be achieved, but the linear assumption between reflectivity and recorded seismic data fails on complex geological settings
Solution Approach 1:
The patent changes the fundamental assumption parameter from linear reflectivity-seismic data relationship to non-linear wave propagation physics. By using FWI, the system no longer relies on LSM's linear approximation but instead models the full non-linear wave equation, enabling reliable inversion in complex geological settings while maintaining robustness through iterative waveform matching
Solution Approach 2:
The patent applies inversion by reversing the conventional forward modeling approach. Instead of assuming linear relationships and predicting seismic data from reflectivity (as in LSM), the system inverts the wave propagation equations to retrieve velocity models from observed waveforms. This inversion approach handles non-linearity and complex geology effectively
3Measurement precision
If FWI is applied for time-lapse processing to monitor reservoir changes, then high-resolution models can be obtained, but the small 4D signals blend with noise and acquisition unrepeatability
Solution Approach 1:
The patent introduces an intermediary approach by computing separate velocity models for baseline and monitor datasets using FWI, then calculating the difference between these models to isolate the 4D signal. This intermediary modeling step separates the small 4D changes from the dominant static background and acquisition noise, enabling precise detection of temporal variations
Solution Approach 2:
The patent segments the imaging problem by processing baseline and monitor datasets separately through independent FWI inversions, then combining the results through model difference calculation. This segmentation allows the small 4D signals to be isolated from the large static components and acquisition noise, improving signal-to-noise ratio in the final 4D image
4Productivity
If separate inversion is performed for baseline and monitor datasets, then computational independence is maintained, but acquisition geometry differences introduce noise and reduce repeatability
Solution Approach 1:
The patent changes the inversion parameter approach by using separate velocity models for baseline and monitor datasets, then computing the difference between these models to isolate the 4D signal. This parameter separation allows independent processing of each dataset while maintaining reliability through the difference calculation that eliminates acquisition geometry differences
Solution Approach 2:
The patent extracts the 4D signal by computing the difference between separate baseline and monitor velocity models. This extraction process isolates the temporal variations from the static background and acquisition noise, maintaining computational independence while improving reliability through the differential approach
Data Source
AI summary
A joint timelapse full waveform inversion (FWI) method for estimating physical properties of a subsurface includes receiving seismic data related to the subsurface, wherein the seismic data includes a baseline dataset dB and a monitor dataset dM, defining an objective function of the FWI method, calculating a baseline gradient gB of the objective function for the baseline dataset dB, and a monitor gradient gM of the objective function for the monitor dataset dM, computing a baseline preconditioner P′B for the baseline dataset dB and a monitor preconditioner P′M for the monitor dataset dM so that each of the baseline preconditioner P′B and the monitor preconditioner P′M reflects similarities and/or differences of geometrical features of the baseline and monitor acquisition surveys, and determining physical properties of the subsurface based on a baseline physical properties update and a monitor physical properties update.