Rock Sample Image Analysis for Porosity Calculation
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Solution Overview
Problem
Conventional methods for characterizing rock samples are qualitative and lack the accuracy and reliability needed for precise numerical model building in hydrocarbon-bearing porous media, particularly in determining porosity and geometrical properties essential for predicting reservoir productivity.
Innovation Solution
A digital imaging process that binarizes rock sample images to distinguish between empty space and solid matrix, applying algorithms like watershedding and Hoshen-Kopelman multi-cluster labeling to identify and analyze geometrical properties, generating digital models that enhance the accuracy of porosity calculations and reservoir characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional thin section analysis is used to characterize rock samples, then the process is simple and quick, but the characterization is qualitative and lacks accuracy for numerical model building
Solution Approach 1:
The patent replaces conventional mechanical thin section analysis with digital image processing and automated particle analysis algorithms. Digital images of rock samples are processed through binarization, noise removal, and automated particle identification to extract geometrical properties quantitatively, eliminating the need for manual microscopic examination while significantly improving measurement precision
Solution Approach 2:
The patent transforms the characterization approach from qualitative description to quantitative measurement by extracting specific geometrical parameters (area, perimeter, circularity, aspect ratio) from digital images. This parameter transformation enables direct use of data in numerical models while maintaining operational feasibility through automated image processing
2Measurement precision
If digital image processing with particle analysis is applied, then quantitative characterization and measurement precision are improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the image processing task into distinct sequential steps: binarization to separate pore space from solid matrix, noise removal to eliminate artifacts, particle identification to locate individual pores and grains, and geometrical property extraction to calculate metrics. This segmentation reduces overall complexity by making each step manageable and automated
Solution Approach 2:
The patent implements self-service through automated algorithms that perform binarization, noise filtering, and particle identification without manual intervention. The system automatically adjusts parameters and generates results, reducing the need for complex manual processing while maintaining high measurement precision for porosity and geometrical properties
Data Source
AI summary
A workflow for rock sample image processing is established to determine geometrical parameters of rock texture. Individual pores and grains are identified using Hoshen-Kopelman multi-cluster labeling algorithm and watershedding technique. Separated elements are fitted with ellipses and pore/grain size distributions, aspect ratios and orientations of fitting ellipses are obtained. Such information is especially valuable in interpretation and forward modeling of dielectric responses using textural models. Rock sample images by high-resolution confocal microscopy are selected to test the workflow and results are analyzed.