Automated Rock Cuttings Analysis via Image Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing rock cuttings in the oil and gas industry are human-dependent, time-consuming, and labor-intensive, limiting the efficiency and accuracy of subsurface characterization.
Innovation Solution
A system and method that utilize an analysis and control system to automatically analyze images of rock cuttings, extracting features and determining lithology classification through image similarity and geological information analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis methods are used for rock cuttings, then human expertise can be applied to interpret complex geological features, but the analysis process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated image processing system that uses computer vision algorithms to extract features from rock cutting images. The system automatically performs segmentation, feature extraction, and lithology classification, eliminating the need for manual human analysis while maintaining or improving accuracy through consistent application of computational algorithms.
Solution Approach 2:
The system creates a digital representation of rock cuttings through high-resolution imaging, then analyzes this digital copy using automated algorithms. This allows multiple analyses to be performed on the same sample without physical manipulation, and enables rapid comparison against reference databases of known lithologies.
2Productivity
If automated image analysis systems are implemented, then analysis speed and productivity are improved, but the complexity of the system increases
Solution Approach 1:
The analysis system is divided into distinct functional modules: image acquisition, segmentation module that separates rock cuttings from background, feature extraction module that identifies key characteristics, and classification module that determines lithology. This modular segmentation reduces overall system complexity by making each component independent and manageable.
Solution Approach 2:
The system employs a multi-functional analysis platform that can handle various types of rock cuttings images, different lithology classifications, and multiple feature extraction methods through a unified architecture. This universal approach consolidates what would otherwise require multiple separate systems into a single integrated solution.
3Measurement precision
If comprehensive feature extraction is performed on rock cutting images, then measurement precision and classification accuracy are improved, but the quantity of data to be processed increases
Solution Approach 1:
The system extracts only the most relevant features from rock cutting images, such as color characteristics, texture patterns, shape parameters, and mineralogical indicators. By selectively extracting only the critical features needed for lithology classification rather than processing all image data, the system maintains high accuracy while reducing data volume.
Solution Approach 2:
The system performs feature extraction at multiple levels of detail, starting with coarse-scale features for initial classification and progressively analyzing finer details only when needed. This partial action approach processes the minimum necessary data to achieve accurate classification, avoiding unnecessary computation on excessive data.
Data Source
AI summary
Systems and methods are provided to analyze rock cuttings and measure physical lithological features of the rock cuttings. An image analysis workflow is provided, which includes multiple computational modules to automatically estimate relevant geological information from rock cuttings. Reference data, manual descriptions, and well log values are associated and used to determine rock properties of the rock cuttings. A software is developed for the image analysis, and results are displayed in various views.