Nonlinear Amplitude Transforms for Complex Seismic Velocity Modeling
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Solution Overview
Problem
Existing seismic survey methods struggle to accurately determine seismic velocity models, particularly in complex subterranean regions, leading to incomplete or inaccurate images of hydrocarbon reservoirs due to variations in seismic wave propagation and noise interference.
Innovation Solution
A method involving nonlinear amplitude transforms is applied to observed and simulated seismic datasets to form an objective function, allowing for the determination of an updated seismic velocity model by iteratively matching simulated and observed datasets, thereby improving the accuracy of seismic imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic survey methods are used to determine seismic velocity models, then the processing can be completed with standard techniques, but the accuracy of velocity modeling deteriorates in complex subterranean regions
Solution Approach 1:
The patent applies nonlinear amplitude transforms to the seismic data, changing the mathematical parameters of the data representation. This transformation allows for more accurate velocity modeling by nonlinearly scaling the amplitude values to better represent the physical properties of subsurface rocks, directly improving measurement precision without requiring additional hardware
Solution Approach 2:
The patent replaces conventional linear seismic processing methods with nonlinear transformation techniques. This substitution of the processing methodology (from linear to nonlinear mathematics) enables accurate velocity modeling in complex regions while avoiding the need for additional physical measurement devices or hardware modifications
2Manufacturing precision
If nonlinear amplitude transforms are applied to improve velocity model accuracy, then the precision of seismic imaging improves, but the computational complexity increases
Solution Approach 1:
The nonlinear amplitude transform changes the parameter space of the seismic data, transforming amplitudes according to a nonlinear function. This parameter transformation improves imaging precision by better preserving the relationships between seismic amplitudes and rock properties, while the computational complexity is managed through efficient algorithmic implementation
Solution Approach 2:
The patent introduces a transformed amplitude dimension through nonlinear transformation, adding a new computational dimension to the processing. This allows the system to operate in an expanded parameter space that captures more information about subsurface properties, improving image accuracy while the computational burden is distributed across the transformed dimensional space
3Measurement precision
If iterative matching of simulated and observed datasets is performed, then the velocity model determination accuracy improves, but the processing time increases
Solution Approach 1:
The patent implements an iterative feedback loop where simulated seismic data is compared with observed data, and the velocity model is adjusted based on the misfit. This feedback mechanism progressively refines the velocity model accuracy with each iteration, converging toward the true subsurface velocity structure while managing processing time through efficient convergence criteria
Solution Approach 2:
The patent applies preliminary nonlinear amplitude transforms to both the observed and simulated datasets before performing the iterative matching. This preliminary action pre-processes the data to enhance the effectiveness of subsequent iterative optimization, reducing the number of iterations required to achieve convergence and thereby reducing total processing time while maintaining accuracy
Data Source
AI summary
Methods and systems are disclosed for forming an image of a subterranean region of interest. The method includes receiving an observed seismic dataset and a seismic velocity model for the subterranean region of interest, and generating a simulated seismic dataset based on the seismic velocity model and the geometry of the observed seismic dataset. The method further includes determining a transformed observed seismic dataset by applying a nonlinear amplitude transform to the observed seismic dataset and determining a transformed simulated seismic dataset by applying the same transform to the simulated seismic dataset. The method still further includes forming an objective function based on the transformed observed seismic dataset and the transformed simulated seismic dataset, and determining an updated seismic velocity model based upon finding an extremum of the objective function.