Alpha Shape SRV Estimation for Microseismic Data
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Solution Overview
Problem
Current methods for estimating stimulated reservoir volume (SRV) using microseismic event data often result in overestimation due to the convexity constraint of minimum convex polygons, which enclose large unstimulated voids.
Innovation Solution
The use of locally adaptive alpha shapes and a density-optimized alpha-shape (DOAS) algorithm for improved data-driven estimation of SRV, which generates an optimized surface that encloses the data points more accurately by relaxing the convexity constraint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a minimum convex polygon is used to enclose microseismic data, then the SRV estimation is simple to compute, but the estimation accuracy deteriorates due to overestimation from enclosing unstimulated voids
Solution Approach 1:
The patent changes the geometric parameter from convex hull to alpha shape by introducing an alpha parameter that controls the level of concavity. This allows the boundary to adapt its shape based on data density, creating a more accurate representation of the stimulated reservoir volume while maintaining computational feasibility through parameter-driven geometry generation.
Solution Approach 2:
The patent applies different geometric properties to different regions of the SRV boundary. In areas with high microseismic event density, the boundary follows a more convex path, while in low-density regions, it allows for concavities to exclude unstimulated voids. This local adaptation of boundary quality improves overall estimation accuracy without requiring uniform complexity throughout.
2Productivity
If a minimum convex polygon is used to enclose microseismic data, then the computation is fast, but the SRV estimation includes large unstimulated voids leading to overestimation
Solution Approach 1:
By introducing the alpha parameter, the patent enables control over the balance between computation speed and accuracy. Larger alpha values produce simpler, faster-to-compute shapes, while smaller alpha values create more accurate but computationally intensive alpha shapes. This parameterized approach allows optimization based on specific application requirements.
Solution Approach 2:
The patent makes the SRV boundary dynamic by allowing it to adapt its concavity based on the alpha parameter and local data distribution. Rather than a fixed convex hull, the boundary can dynamically adjust its shape to match the actual stimulated volume, excluding voids while maintaining computational efficiency through algorithmic optimization.
3Measurement precision
If locally adaptive alpha shapes are used to improve SRV estimation accuracy, then the inclusion of unstimulated voids is reduced, but the device complexity increases
Solution Approach 1:
The patent manages algorithmic complexity by introducing a single controlling parameter (alpha) that simplifies the overall structure. Rather than requiring complex adaptive algorithms throughout, the entire SRV boundary generation is controlled by adjusting this one parameter, making the complexity manageable and tunable based on application needs.
Solution Approach 2:
The patent applies computational complexity locally only where needed. The alpha shape algorithm processes different regions of the microseismic data with appropriate detail, focusing computational effort on areas with complex fracture patterns while using simpler representations in homogeneous regions. This localized approach reduces overall algorithmic complexity while maintaining accuracy where it matters most.
Data Source
AI summary
A method for improved data-driven estimation of a stimulated reservoir volume may generate an optimized surface that encloses a set of data points including microseismic event data corresponding to a treatment of a subterranean formation. A Delaunay triangulation may be performed on the set of data points to generate a set of polytopes. A Voronoi polygon may be generated about each data point and used to obtain a local density measure that is locally and adaptively determined for each data point. Based on the local density measure, polytopes in the set of polytopes may be discriminated for inclusion in the optimized surface.