Core Sample Image Analysis for Consistent Rock Characterization
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Solution Overview
Problem
Existing rock sample characterizations in resource fields rely heavily on subjective human observations, leading to inconsistent and variable results that can impact the efficiency and accuracy of field operations.
Innovation Solution
Implementing a system that utilizes machine learning models to analyze core sample imagery for rock characterization, providing objective and reproducible assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human observations are used to characterize rock samples, then the process is simple and quick, but the results are subjective and inconsistent
Solution Approach 1:
The patent replaces the manual human observation system with an automated machine learning-based image analysis system. The system captures images of rock samples and uses trained machine learning models to automatically characterize rock types, eliminating subjective human interpretation while maintaining simplicity through automated processing pipelines.
Solution Approach 2:
The patent creates digital copies (images) of physical rock samples and analyzes these copies using machine learning models. This allows multiple analyses of the same sample without physical manipulation, and the digital nature enables consistent, reproducible measurements across different users and time periods.
2Reliability
If machine learning models are implemented for rock characterization, then consistency and accuracy improve, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by training machine learning models in advance on large datasets of labeled rock sample images. Once trained, the models can rapidly characterize new samples without requiring time-consuming manual analysis. The preprocessing of images and training of models beforehand enables fast, consistent real-time or near-real-time characterization.
3Productivity
If manual rock sample assessment is used, then equipment and infrastructure requirements are minimal, but productivity and efficiency are limited
Solution Approach 1:
The patent replaces manual rock assessment with automated machine learning-based image analysis. The system captures images of rock samples and uses trained models to automatically identify rock types, significantly increasing productivity by processing multiple samples rapidly and consistently without human intervention, while the infrastructure requirements remain manageable through use of standard imaging equipment and computational platforms.
Data Source
AI summary
A method can include receiving rock sample imagery of rock; generating characterizations of the rock based at least in part on the imagery using one or more machine learning models; and outputting the characterizations of the rock.