Fault Population Uncertainty Analysis for Seismic Structural Modeling
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Solution Overview
Problem
Existing methods for constructing subsurface structural models with geological faults and layer boundaries from seismic data are plagued by uncertainty due to inherent data imperfections and model assumptions, leading to inaccuracies in fault representation.
Innovation Solution
A method and system for generating and analyzing multiple realizations of fault populations using seismic data, incorporating machine learning and image analysis to extract fault objects, and quantifying uncertainty through statistical and visual comparisons of fault populations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple fault volumes are generated from seismic data using machine learning or image analysis, then the representation of fault populations can be analyzed for uncertainty and sensitivity, but the complexity of the workflow and computational requirements increase
Solution Approach 1:
The workflow is segmented into distinct modules: seismic data reception, fault volume generation (with options for ML or image analysis), fault population extraction, quantitative value generation, and visual representation. This segmentation allows each component to be independently optimized and managed, reducing overall workflow complexity while maintaining uncertainty quantification capabilities.
Solution Approach 2:
The system dynamically selects between different fault volume generation methods (machine learning models or image analysis techniques) based on data characteristics and user requirements. This dynamic approach allows the workflow to adapt to different scenarios without requiring a completely separate process for each method.
2Reliability
If multiple fault volumes are generated and analyzed to determine uncertainty, then the reliability of fault population representation improves, but the time and computational resources required increase
Solution Approach 1:
The system generates a plurality of fault volumes (excessive action) to thoroughly characterize uncertainty, but then uses efficient quantitative comparison methods to analyze only the essential differences between them. This approach ensures comprehensive reliability assessment while minimizing unnecessary computational expenditure on redundant analyses.
Solution Approach 2:
Instead of re-analyzing entire fault volumes, the system extracts key quantitative values (such as fault connectivity metrics, compartmentalization parameters, and geometric characteristics) that capture the essential differences between multiple fault volume realizations. This copying of essential information significantly reduces analysis time while maintaining reliability.
3Loss of information
If quantitative values are generated from fault populations including geometric, topologic, and seismic measures, then the comprehensive understanding of fault characteristics improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The system merges multiple types of quantitative measurements (geometric measures such as fault surface area and volume, topologic measures such as connectivity and compartmentalization, and seismic amplitude characteristics) into a unified analysis framework. This integration provides comprehensive fault characterization while managing processing complexity through standardized data structures and comparison protocols.
Solution Approach 2:
The quantitative value generation module is designed to handle multiple types of measurements (geometric, topologic, seismic) through a universal processing framework. This multi-functional approach allows the same computational infrastructure to process diverse fault characteristics without requiring separate specialized systems for each measurement type.
4Ease of operation
If visual representations are generated to compare quantitative values of fault populations, then the interpretability of uncertainty analysis improves, but the complexity of visualization processing increases
Solution Approach 1:
The system extracts the most significant quantitative differences between fault populations and presents them in visual form (such as histograms, scatter plots, or heat maps showing distribution of fault properties). This extraction of essential visual information provides high interpretability while avoiding the complexity of visualizing all possible fault characteristics simultaneously.
Data Source
AI summary
A method for determining an uncertainty of a representation of a fault population includes receiving seismic data representing a subterranean domain. The subterranean domain includes a plurality of faults. The method also includes generating a plurality of fault volumes based upon the seismic data. The method also includes generating a plurality of fault populations based upon the fault volumes. The fault populations are generated by extracting one or more fault objects from one or more of the fault volumes. The method also includes generating quantitative values based upon the fault populations. The quantitative values represent on or more of the fault objects, one or more of the fault populations, or both. The method also includes comparing the quantitative values to determine the uncertainty of the representation of the fault populations. The method also includes generating or updating a visual representation based upon the comparison.