Ergodic Geophysical Sampling Pattern Optimization
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Solution Overview
Problem
Current geophysical data acquisition methods are costly due to the large number of spatial sampling locations required, which hinders their effective application in fields like oil and gas, mineral exploration, and carbon capture, as existing techniques fail to determine an optimal irregular sampling pattern.
Innovation Solution
Ergodic sampling is employed, which uses attributes like interval distribution, angle distribution, sample density function, and spectral resolution function to optimize the sampling pattern, reducing the number of locations needed while maintaining data accuracy, by selecting a subset of samples that can represent the entire system effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Nyquist sampling is used to determine spatial sampling locations, then sufficient data coverage is achieved, but the number of sampling locations increases significantly
Solution Approach 1:
The patent changes the sampling pattern from regular grid to irregular pattern optimized for ergodic properties. By modifying the spatial distribution parameters of sampling locations rather than using fixed Nyquist spacing, the system achieves equivalent or superior data coverage with fewer locations. The optimization process adjusts parameters like inter-sample distances and angular distributions to maximize information content while minimizing sample count.
2Loss of energy
If the number of sensors is reduced to save resources, then cost decreases, but data acquisition quality may deteriorate
Solution Approach 1:
The patent applies local quality by creating non-uniform sampling distributions where certain regions have higher sample density than others. Rather than uniform spacing, the optimized irregular pattern concentrates samples in areas of high information content while reducing them in redundant areas. This local optimization ensures that each sensor contributes maximally to data quality, maintaining measurement precision with fewer total sensors.
Solution Approach 2:
The patent performs preliminary optimization of sampling patterns before actual data acquisition. By pre-calculating ergodic-based optimal locations that maximize information content, the system ensures that even with reduced sensor numbers, the acquired data maintains high quality. This preliminary design phase identifies the minimal set of locations needed to achieve target data quality thresholds.
3Ease of manufacture
If regular sampling patterns are used, then implementation is simple, but information efficiency is suboptimal
Solution Approach 1:
The patent transitions from static regular patterns to dynamic irregular patterns that adapt to the specific characteristics of the survey area and target parameters. The sampling locations are optimized based on local geology, target depth, and information content requirements. This dynamic approach allows the pattern to maximize information efficiency for each specific application while still being implementable through automated optimization algorithms.
Data Source
AI summary
Determining locations to gather information to reduce the number of locations without reducing the information gathered is of key importance when such observations require drilling or other resource-intensive activities. By utilizing ergodic sampling, the same information (volume and/or resolution) may be obtained when compared to an exhaustive grid approach but with significantly fewer observations.