Interpolated Starting Points for Geophysical Modeling
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Solution Overview
Problem
Geophysical survey data modeling, particularly inverse modeling, is computationally intensive, making it challenging to efficiently process and accurately predict hydrocarbon locations in subsurface formations.
Innovation Solution
The use of interpolated starting points and the integral equation method for forward and inverse modeling, combined with GMRES-based iterative solvers, reduces computational demands by interpolating solutions between base points to generate approximations for nearby points in the solution space, thereby decreasing processing time and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional direct solving methods are used for inverse modeling problems, then solution accuracy can be maintained, but computational time and power consumption increase significantly
Solution Approach 1:
The patent pre-computes and stores solutions at base points in the solution space before actual modeling operations. These pre-computed base point solutions serve as starting points for subsequent modeling tasks, eliminating the need to solve systems of equations from scratch and significantly reducing computational time while maintaining accuracy through interpolation.
Solution Approach 2:
The patent creates approximate solutions by copying and interpolating from pre-computed base point solutions. Instead of performing full computational solves for every modeling task, the system generates solution approximations by interpolating between stored base point solutions, then uses these copied approximations as starting points for iterative solvers, dramatically reducing computational effort.
2Reliability
If traditional direct solving methods are used for inverse modeling problems, then solution reliability can be maintained, but power consumption increases significantly
Solution Approach 1:
The system performs power-intensive computations in advance by pre-computing base point solutions and storing them. This preliminary action shifts the energy consumption to an earlier time when it can be managed more efficiently, allowing subsequent modeling operations to use far less power by leveraging these pre-computed results through interpolation and iterative refinement.
Solution Approach 2:
By copying and interpolating from pre-computed base point solutions rather than performing full computational solves, the system dramatically reduces power consumption for each modeling operation while maintaining solution reliability through the use of iterative solvers that refine the interpolated approximations.
3Measurement precision
If full iterative solving is performed for all points in solution space, then modeling accuracy is maintained, but the number of computations increases dramatically
Solution Approach 1:
The patent segments the solution space into discrete base points where full computations are performed, and intermediate points where interpolation is used. This segmentation allows the system to perform intensive computing only at strategically selected base points while using lighter interpolation operations for the remaining points, dramatically improving processing efficiency while maintaining accuracy through selective refinement.
Solution Approach 2:
The system changes the parameter of computational effort by using different solution methods for different points: full iterative solving at base points and interpolation-based approximate solving at intermediate points. This parameter change in computational approach allows the system to maintain accuracy where needed while dramatically improving overall productivity by reducing the number of full iterative solves required.
Data Source
AI summary
Techniques are disclosed relating to forward and inverse modeling of geophysical formations using interpolation. In one embodiment, a method includes generating a system of equations based on a background model that describes a geophysical formation. In this embodiment, the method also includes generating a first set of solutions for the system of equations using an initial set of base points from geophysical survey data obtained from the geophysical formation and interpolating, using the first set of solutions, to produce a set of solution approximations for a second set of data points from the geophysical survey data. In this embodiment, the method also includes generating a model of the geophysical formation, using the second set of data points and the set of solution approximations, where the model includes a second set of solutions for the system of equations.