Digital Rock Analysis for Porosity-Permeability Trend Prediction
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Solution Overview
Problem
Current digital rock analysis methods face challenges in accurately predicting porosity-permeability trends due to limitations in model size, which can result in either underrepresentation or excessive computational resource consumption, and existing methods often rely on random subvolume positioning leading to inaccurate permeability measurements.
Innovation Solution
The proposed method involves determining a representative elementary volume (REV) size and using conditionally-selected subvolumes to analyze porosity and permeability, ensuring subvolumes meet specific criteria such as standard deviation and connectivity thresholds, allowing for a porosity-permeability relationship to be extracted over an extended range even with reduced model sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a larger digital rock model is used to improve representativeness of the physical material, then measurement precision is improved, but computational resources and time are excessively consumed
Solution Approach 1:
The digital rock model is divided into multiple subvolumes, each satisfying the REV criteria. Instead of processing one large model, the system segments it into smaller representative units that can be processed independently and efficiently, reducing computational time while maintaining measurement precision through statistical aggregation of subvolume properties.
Solution Approach 2:
The patent extracts only the essential representative subvolumes that meet specific porosity and connectivity criteria, rather than processing the entire large model. This extraction of key representative elements allows accurate porosity-permeability trend prediction with reduced computational resources.
2Productivity
If a smaller digital rock model is used to reduce computational resource consumption, then productivity is improved, but measurement precision deteriorates due to underrepresentation of pore structure
Solution Approach 1:
The patent applies local quality by identifying and selecting subvolumes with specific local characteristics (porosity ranges, connectivity thresholds) that are representative of the overall pore structure. Each subvolume is evaluated against local criteria to ensure it captures essential pore space geometry and connectivity, enabling accurate predictions with smaller model sizes.
Solution Approach 2:
The system changes parameters by varying subvolume size, position, and selection criteria to optimize the balance between model size and representativeness. By adjusting these parameters and selecting subvolumes that satisfy REV criteria, the method achieves accurate porosity-permeability trends with computationally efficient smaller models.
3Ease of operation
If random subvolume positioning is used to simplify the analysis process, then ease of operation is improved, but measurement precision deteriorates due to inaccurate permeability measurements
Solution Approach 1:
The patent applies preliminary action by pre-evaluating subvolumes against connectivity thresholds and porosity criteria before selecting them for permeability analysis. This preliminary filtering ensures that only subvolumes meeting representative quality standards are chosen, improving measurement precision while maintaining ease of operation through automated criteria-based selection.
Solution Approach 2:
The system incorporates feedback by evaluating subvolume properties (porosity, connectivity) and using this information to guide selection of representative subvolumes. The feedback loop ensures that selected subvolumes accurately represent the pore structure, improving permeability measurement precision while maintaining operational simplicity through automated quality assessment.
Data Source
AI summary
The pore structure of rocks and other materials can be determined through microscopy and subjected to digital simulation to determine the properties of fluid flows through the material. To determine a porosity-permeability over an extended range even when working from a small model, some disclosed method embodiments obtain a three-dimensional pore/matrix model of a sample; measure a distribution of porosity-related parameter variation as a function of subvolume size; measure a connectivity-related parameter as a function of subvolume size; derive a reachable porosity range as a function of subvolume size based at least in part on the distribution of porosity-related parameter variation and the connectivity-related parameter; select a subvolume size offering a maximum reachable porosity range; find permeability values associated with the maximum reachable porosity range; and display said permeability values as a function of porosity.