GPU-Accelerated NMR Simulation Resolving 3D Pore Surface Roughness
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional NMR simulations struggle to accurately resolve the surface roughness effect in pore-scale simulations due to the lack of accurate 3D pore surface roughness measurement methods, leading to underestimation of pore size distribution and incorrect predictions of permeability and fluid types in porous media.
Innovation Solution
A GPU-accelerated NMR simulation method that identifies a segmented micro-CT image as a computational domain, assigns random walkers to pore voxels, initiates a random walk simulation, moves walkers to new positions, and updates NMR relaxation rates to account for surface roughness, using boundary conditions and relaxation correction factors to control surface relaxation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NMR simulation techniques are used, then computational simplicity is maintained, but accuracy in resolving surface roughness effect is insufficient
Solution Approach 1:
The patent creates a digital rock model that copies the actual pore structure from micro-CT images, allowing virtual simulation of NMR processes without physical experiments. This digital copy enables accurate resolution of surface roughness effects while maintaining computational efficiency through parallel processing.
Solution Approach 2:
The patent replaces conventional CPU-based sequential computation with GPU-based parallel computation architecture. This substitution of computational mechanics enables handling of large-scale 3D pore models with millions of voxels, resolving surface roughness effects that were previously computationally intractable.
2Measurement precision
If 3D pore surface roughness measurement methods are developed, then measurement precision improves, but device complexity and measurement difficulty increase
Solution Approach 1:
The patent uses micro-CT imaging as an intermediary to capture 3D pore structure information non-invasively. This intermediary technique provides accurate surface roughness measurements without requiring direct physical access to pore surfaces, avoiding the complexity of traditional measurement methods.
Solution Approach 2:
The patent creates a digital copy of the pore structure from micro-CT data, allowing virtual measurement and analysis of surface roughness without physical measurement challenges. This digital replica enables precise 3D roughness characterization through computational methods.
3Productivity
If GPU-accelerated simulation is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent replaces traditional CPU-based computation with GPU-based parallel computation, achieving speedups of 10-100x for NMR simulations. This architectural substitution leverages the massively parallel structure of GPUs to handle the computational intensity of 3D pore-scale simulations with millions of voxels.
Solution Approach 2:
The patent segments the computational domain into a 3D voxel grid representing pore space, with each voxel independently processable. This segmentation enables parallel computation across GPU cores, achieving high productivity while managing device complexity through systematic data organization and parallel algorithm design.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively resolves the surface roughness effect, improving the accuracy of pore size distribution analysis and enhancing the interpretation of NMR data without compromising GPU utilization, offering faster and more accurate simulations compared to commercial software.
Implementation Method 1
initiating a random walk numerical simulation with the plurality of random walkers; moving, with the GPU, each random walker of the plurality of random walkers from a previous position to a new position
Implementation Method 2
updating, with the GPU, an NMR relaxation rate to resolve a surface roughness effect based on the new position of each random walker
Data Source
AI summary
Techniques for resolving a surface roughness effect with a GPU-accelerated NMR simulation include identifying, with a graphics processing unit (GPU), a segmented micro-CT image input as a computational domain; assigning, with the GPU, a plurality of random walkers into pore voxels of the segmented micro-CT image; initiating, with the GPU, a random walk numerical simulation with the plurality of random walkers; moving, with the GPU, each random walker of the plurality of random walkers from a previous position to a new position; determining, with the GPU, the new position of each random walker; and updating, with the GPU, an NMR relaxation rate to resolve a surface roughness effect based on the new position of each random walker.