Fracture Network Mesh Adjustment for Memory Efficiency
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Solution Overview
Problem
Conventional fracture network simulations in the oil and gas industry require large amounts of memory, often exceeding RAM capacity and leading to performance issues or system failure, especially when dealing with small triangle meshes.
Innovation Solution
The system dynamically adjusts triangle mesh sizes based on the size of each fracture in the simulated network, using larger meshes for larger fractures and smaller meshes for smaller ones, thereby reducing memory requirements and improving simulation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If small triangle meshes are used in fracture network simulations, then the precision of fracture representation is improved, but the memory requirements increase significantly
Solution Approach 1:
The fracture network simulation domain is divided into multiple sub-domains or regions, each with its own triangle mesh. This segmentation allows different mesh densities to be applied to different regions, reducing the total number of elements while maintaining precision where needed.
Solution Approach 2:
Different triangle mesh densities are applied to different regions of the fracture network based on local requirements. Areas with complex fracture patterns use finer meshes, while simpler regions use coarser meshes, optimizing the balance between precision and memory usage.
2Quantity of substance
If large triangle meshes are used in fracture network simulations, then the memory requirements are reduced, but the precision of fracture representation deteriorates
Solution Approach 1:
The simulation domain is segmented into multiple regions with different mesh densities. This allows the use of larger meshes in some areas while maintaining small meshes in critical areas, thus reducing overall memory requirements without sacrificing precision where it matters most.
Solution Approach 2:
The mesh density is optimized locally for each region based on fracture complexity and simulation requirements. This ensures that memory is not wasted on unnecessarily fine meshes in simple regions while maintaining adequate precision in complex regions.
3Measurement precision
If uniform small triangle meshes are used throughout the fracture network, then the precision is maximized, but the computational efficiency and memory usage deteriorate
Solution Approach 1:
The fracture network is divided into multiple sub-regions that can be processed independently with different mesh densities. This segmentation enables parallel processing and reduces the computational burden on any single region, improving overall simulation performance.
Solution Approach 2:
Different mesh densities are assigned to different regions based on local fracture characteristics and simulation needs. This local optimization ensures that computational resources are focused where they are most needed, improving both precision and efficiency.
4Quantity of substance
If the triangle mesh size is dynamically adjusted based on fracture size, then the memory efficiency is improved, but the system complexity increases
Solution Approach 1:
The triangle mesh size is made dynamic and adaptive, automatically adjusting based on the size and characteristics of each fracture. This dynamic adjustment is achieved through automated algorithms that evaluate fracture properties and select appropriate mesh densities, managing system complexity through intelligent automation.
Solution Approach 2:
The mesh size parameter is changed dynamically based on fracture size parameters. This parameter adaptation is achieved through established algorithms that correlate fracture dimensions with optimal mesh densities, managing complexity through systematic parameter relationships.
Data Source
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AI summary
Fracture networks are simulated using a large triangle mesh size for large fractures and a smaller triangle mesh size for small fractures. Input data defining parameters of one or more fractures are input, the fractures being comprised of a triangle mesh. A first triangle mesh size for the fractures is determined based upon the input data. A second smaller triangle mesh size is then determined based upon the input data. The fracture network is then simulated using the large and small triangle mesh sizes.