Digital Core Model Construction via Segmentation Analysis
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Solution Overview
Problem
Current methods for analyzing geological structures in oilfield operations lack accuracy in modeling and simulating the properties of core samples, leading to inefficiencies in wellbore operations and fluid flow predictions.
Innovation Solution
A method and system for generating a digital core model by segmenting a digital core image using multiple approaches, statistically analyzing the results to select the most suitable approach, and performing simulation tests to validate the model, which can be updated based on matching sample and model test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple segmentation approaches are used to improve model accuracy, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex model construction process into multiple independent segmentation approaches (e.g., threshold-based, region-growing, edge-detection methods). Each approach processes the digital core image separately to generate segmented images, which are then statistically analyzed to select the most suitable segmentation result. This segmentation of the modeling process enables comparison and selection of optimal segmentation outcomes, thereby improving model accuracy while managing complexity through modular processing.
Solution Approach 2:
The patent employs parameter changes by systematically varying segmentation parameters and approaches to optimize model accuracy. Different segmentation methods use distinct parameter sets (e.g., threshold values, region criteria, edge detection sensitivity), and statistical analysis evaluates which parameter configuration produces the most accurate representation of the core sample. This parameter optimization process improves manufacturing precision by selecting the best parameter combination while the automated statistical framework manages the complexity of evaluating multiple configurations.
2Reliability
If simulation tests are performed to validate the model, then reliability is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by performing simulation tests on the digital core model before actual field operations or experiments. The simulation tests validate the model's predictive accuracy for fluid flow and permeability under various conditions, allowing identification and correction of model deficiencies beforehand. This preliminary validation improves reliability by ensuring the model performs accurately in advance, while the automated simulation framework reduces overall time loss by preventing costly rework during actual operations.
Solution Approach 2:
The patent implements feedback mechanisms where simulation test results are systematically compared against known experimental data or expected outcomes. The statistical analysis of simulation results provides feedback on model accuracy, identifying which segmentation approaches and parameter configurations produce the most reliable predictions. This feedback loop improves reliability by continuously optimizing the model based on validation results, while the automated feedback process manages time loss through efficient iterative refinement rather than extensive manual testing.
3Measurement precision
If multiple segmentation approaches are statistically analyzed to select the best approach, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies parameter changes by systematically varying segmentation parameters across multiple approaches and using statistical analysis to identify the optimal parameter configuration. Different segmentation methods employ distinct parameter sets, and statistical metrics (e.g., accuracy measures, error distributions) evaluate which parameters produce the most precise segmentation results. This parameter optimization improves measurement precision by selecting the best parameter combination while the automated statistical framework manages analysis time through efficient computational evaluation.
Data Source
AI summary
A method and system for analysis of a digital core image obtained from a sample are disclosed. The method includes performing segmentations on the digital core image using multiple approaches to obtain multiple segmented images which are statistically analyzed to select the most suitable approach of the multiple approaches. Thereafter, a digital core model is generated using the segmented image corresponding to the most suitable approach. A simulation test may be performed on the digital core model to obtain a model test result and an oilfield operation may be performed based on the model test result. The system includes measurement and testing equipment to obtain the digital core image and a computing system including a data repository for storing a digital core image and a digital core model, and a digital core modeling tool. The digital core modeling tool performs the segmentations, statistical analysis, and generates the digital core model.