AI Borehole Image Analysis for Accurate Stress Orientations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for borehole image interpretation in geostress and geostrain analysis fail to accurately determine stress orientations due to variable tectonic events, reactivation of pre-existing planes, and the complexity of separating tectonic phases, leading to inaccurate fault-slip analysis and stress tensor calculations.
Innovation Solution
A computer-implemented system using inversion algorithms and graphical components to visualize data, perform geometrical classification, and calculate principal stress orientations, integrating borehole image interpretation with geological features and stress analysis to optimize stress tensor determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault-slip analysis methods are used to determine stress orientations, then the analysis can be performed with basic geological data, but the accuracy of stress tensor calculations is reduced due to variable tectonic events and reactivation of pre-existing planes
Solution Approach 1:
The system segments the stress analysis process into distinct modules: borehole image acquisition, fault-slip data extraction, tectonic phase separation, and stress tensor calculation. Each module handles specific aspects of the analysis, allowing complex geological problems to be broken down into manageable components that can be processed systematically
Solution Approach 2:
The system introduces graphical components and visualization tools as intermediaries between raw geological data and stress analysis results. These visualizations help interpret complex fault-slip patterns and identify tectonic phases, acting as a bridge that enhances the accuracy of stress orientation determination without requiring direct complex mathematical operations on raw data
2Measurement precision
If manual separation of tectonic phases is performed to improve fault-slip analysis accuracy, then the precision of stress tensor calculations increases, but the time required for analysis increases significantly
Solution Approach 1:
The system implements automated algorithms that perform tectonic phase separation and fault-slip data interpretation without requiring manual geological analysis. The software automatically identifies different tectonic phases, separates fault-slip data by phase, and calculates stress tensors, enabling the system to serve itself in performing complex analytical tasks that would otherwise require significant manual intervention
Solution Approach 2:
The system replaces manual mechanical analysis methods with computerized algorithms and automated processing. Instead of manual separation of tectonic phases and hand-calculated stress tensors, the system uses software-based inversion algorithms and automated fault-slip analysis to rapidly compute stress orientations with high precision
3Measurement precision
If comprehensive borehole image interpretation is performed to enhance geostress analysis, then the accuracy of stress orientation determination improves, but the complexity of data processing increases
Solution Approach 1:
The system creates a multi-functional integrated platform that combines borehole image acquisition, interpretation, fault-slip data extraction, tectonic phase separation, and stress tensor calculation into a single unified system. This universal platform handles multiple types of geological data and analysis tasks simultaneously, reducing the complexity that would arise from using separate tools for each function
Data Source
AI summary
Computer-implemented system and method for conducting borehole image interpretation, by analyzing data relative to geological features planes orientations and the associated striae data, automatically checking the data consistency, visualizing the data in specify graphical components, performing geometrical data classification, performing stress and strain analysis to assess principal axes orientations and stress ratios, and visualizing the results in specific graphical components, including the application of inversion algorithms to calculate principal stress orientations by using specific graphical components to visualize input data and results.


