Geobody Decomposition Using Eigenvector Connectivity Analysis
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Solution Overview
Problem
Current methods for decomposing complex geobodies in seismic data interpretation often result in either simple, isolated bodies or complex, amorphous geobodies that are difficult to interpret, due to limitations in connectivity criteria and threshold settings, leading to inaccuracies in geobody definition and subsequent reservoir modeling.
Innovation Solution
A method involving the transformation of geobodies into a vector space based on connectivity, where eigenvectors are computed and lineal subspaces are identified to decompose complex geobodies into simpler components, allowing for interactive or automated decomposition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional connectivity criteria and threshold settings are used for geobody decomposition, then the processing is simpler and faster, but the decomposition accuracy deteriorates resulting in either simple isolated bodies or complex amorphous geobodies
Solution Approach 1:
The patent segments the geobody decomposition process into multiple stages: initial segmentation using thresholding, followed by iterative refinement using connectivity analysis and morphology operations. This multi-stage segmentation approach resolves the contradiction by breaking down the complex decomposition task into manageable steps that progressively improve accuracy without overwhelming computational complexity.
Solution Approach 2:
The patent employs dynamic connectivity criteria that adapt during the decomposition process. The connectivity threshold and morphological operations are adjusted iteratively based on the evolving geobody structure, allowing the method to maintain high decomposition accuracy while managing computational complexity through adaptive parameter tuning.
2Productivity
If bulk processing is used to reduce interpretation time, then productivity improves, but the ability to capture complex connectivity patterns deteriorates
Solution Approach 1:
The patent performs preliminary bulk processing to quickly identify candidate geobodies and establish initial connectivity relationships. This preliminary action captures the majority of connectivity patterns efficiently, while subsequent targeted refinement operations focus computational resources on complex regions, thereby maintaining both high productivity and accurate connectivity capture.
Solution Approach 2:
The patent applies partial refinement operations selectively to regions where connectivity patterns are most complex or uncertain. Rather than applying exhaustive processing uniformly across the entire seismic volume, the method focuses computational effort where needed, maintaining productivity while ensuring accurate capture of critical connectivity patterns in key areas.
Data Source
AI summary
Method for decomposing a complexly shaped object in a data set, such as a geobody (31) in a seismic data volume, into component objects more representative of the true connectivity state of the system represented by the data set. The geobody is decomposed using a basis set of eigenvectors (33) of a connectivity matrix (32) describing the state of connectivity between voxels in the geobody. Lineal subspaces of the geobody in eigenvector space are associated with likely component objects (34), either by a human interpreter (342) cross plotting (341) two or more eigenvectors, or in an automated manner in which a computer algorithm (344) detects the lineal sub-spaces and the clusters within them.