Vector-Based Geophysical Modeling for Subsurface Dip Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for modeling subsurface geophysical properties are limited in accurately estimating dips and azimuths, especially in areas with poor data quality and sharp discontinuities, and are not resistant to noise.
Innovation Solution
A method involving the transformation of geophysical data into a stratigraphic model using a vector volume, where each sample is assigned a weight based on its data value, and a modified polynomial fit is calculated to generate a subsurface model, allowing for improved dip estimation resistant to noise and data quality issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If correlation-based methods are used to estimate local dips, then local geometry can be determined, but the method is limited to pairs of spatially adjacent traces and cannot cover larger spatial correlations
Solution Approach 1:
The patent divides the spatial correlation problem into multiple overlapping local windows across the seismic volume. Each window performs correlation-based dip estimation independently, and the results are integrated to provide both local precision and extended spatial coverage beyond single trace pairs.
2Measurement precision
If global optimization calculation is used to obtain local dips, then prediction errors are reduced, but areas of poor data quality skew predictions and affect total quality of dip estimates
Solution Approach 1:
The patent applies different processing qualities and weighting schemes to different local regions based on data quality assessment. High-quality regions contribute more to the global optimization, while poor-quality regions are downweighted or excluded, preventing them from skewing overall dip estimates.
3Stability of the object's composition
If structure tensor method is used to provide smooth approximation to dips and azimuths, then smooth data areas are well handled, but inadequate results are produced in areas of sharp discontinuity
Solution Approach 1:
The patent dynamically adjusts the smoothing parameter of the structure tensor method based on local data characteristics. In smooth regions, stronger smoothing is applied for stability, while in regions with sharp discontinuities, smoothing is reduced or disabled to preserve accurate dip estimates at boundaries.
4Productivity
If conventional dip estimation methods are used, then processing can be completed, but the results are not resistant to noise and data quality issues
Solution Approach 1:
The patent implements an iterative feedback loop where dip estimates are continuously refined by comparing predictions with actual seismic data. Noise-resistant weighting schemes are applied in each iteration, and the process converges to robust dip estimates that maintain both processing efficiency and noise resistance.
Data Source
AI summary
Method and system are described for modeling one or more geophysical properties of a subsurface volume. The method includes computing vector volumes from geophysical data to enhance subsurface features, where the vectors may be estimated by steps, including the following. Samples are extracted from a neighborhood around a selected data location (121). Coordinates are assigned to each sample (122). A weight is assigned to each sample as a function of the sample value (123). The sample coordinates and weights are used to fit a polynomial (124). A new value is then determined for the data location based on the polynomial fit, e.g. from the slope (125).