Multiscale Reservoir Modeling via Overlapping Subdomains
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Solution Overview
Problem
Reservoir simulations face long runtimes due to the vast amount of data and increasing computations as resolution becomes finer, despite the use of high-performance computing resources, necessitating methods to reduce calculations while maintaining sufficient resolution.
Innovation Solution
The implementation of a multiscale modeling method that defines coarse and fine grid cells and uses basis functions to reconstruct pressure fields, allowing for efficient fluid flow modeling by interpolating properties across overlapping fine grid cells, thereby reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fine-scale resolution is used to model the subterranean domain, then measurement precision and manufacturing precision are improved, but the number of computations increases leading to longer simulation runtimes
Solution Approach 1:
The subterranean domain is divided into multiple subdomains, each modeled at different scales. Coarse-scale models capture broad geological features while fine-scale models provide detailed local information only where needed, reducing overall computational complexity while maintaining necessary resolution in critical areas.
Solution Approach 2:
Different regions of the subterranean domain are modeled with different levels of detail. Areas of interest or complex geological features receive fine-scale modeling, while less critical areas use coarse-scale models, optimizing the balance between resolution and computational efficiency.
2Productivity
If upscaling is used to reduce the number of calculations, then productivity is improved, but measurement precision and manufacturing precision deteriorate due to loss of resolution
Solution Approach 1:
The modeling approach dynamically adapts the scale of representation based on local geological complexity and data availability. Rather than applying a uniform upscale factor, the system adjusts the level of detail in each subdomain to maintain necessary resolution while improving overall computational efficiency.
Solution Approach 2:
Fine-scale models are nested within coarse-scale models, with the fine-scale details embedded in the appropriate locations of the coarse-scale framework. This allows the system to maintain high resolution where needed while using lower resolution elsewhere, avoiding the need to upscale entire domains.
3Productivity
If high-performance computing resources are used to perform simulations, then productivity is improved by calculating at a higher rate, but the complexity of the device increases and costs increase
Solution Approach 1:
Rather than applying fine-scale modeling to the entire subterranean domain (excessive action), the method applies fine-scale modeling only to specific subdomains where it is most beneficial (partial action). This reduces the total number of computations required while maintaining accuracy in critical areas.
Data Source
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Figure 3A~3B
AI summary
Methods, computing systems, and computer-readable media for multi-scale modeling. The method includes determining a first matrix for a plurality of fine cells of a model based at least in part on a physical property value represented by respective fine cells, identifying one or more overlapped cells of the plurality of fine cells that are part of at least two of the plurality of subdomains, and determining a second matrix. Determining the second matrix includes determining an intermediate product by multiplying the first matrix by a prolongation matrix, which includes predicting a row of zeros in the intermediate product for the plurality of fine cells that are not the one or more overlapped cells and are not part of the at least two of the plurality of subdomains that include the one or more overlapped cells. Determining the second matrix also includes multiplying the intermediate product by a restriction matrix.