Three-dimensional unstructured gridding for complex wells and geological features in subsurface reservoirs, with CVD-MPFA discretization performance

Manzoor, Shahid; Edwards, Michael G.; Doğru, Ali H.

doi:10.1016/j.cma.2020.113389

Cited by 5 publications

(2 citation statements)

References 72 publications

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“…The simulation domain has structured grids without adapted meshing to changing slopes. Unstructured grids may better present geological features following their boundaries and are beneficial for mechanistic modeling in complex domains (Käser et al, 2014;Su et al, 2020;Manzoor et al, 2021). But structured grids have been commonly chosen for simulating large domains given their sufficiency and computational affordability.…”

Section: Coupled Surface-subsurface Flow Modelmentioning

confidence: 99%

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Ren

Song

Fang

et al. 2021

Front. Artif. Intell.

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Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

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Section: Coupled Surface-subsurface Flow Modelmentioning

confidence: 99%

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Ren

Song

Fang

et al. 2021

Front. Artif. Intell.

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“…It is well known that an unstructured mesh is one where its inner points do not have identical neighboring units, i.e., it differs from the number of meshes that connect different inner points within the mesh division area. Unstructured mesh is often used for the numerical simulation of coastal oceans, because it has no regular topological relation and well fits the complex coastal boundaries [5][6][7][8][9][10]. Therefore, the automatic generation of high-quality unstructured meshes is of critical importance in ocean scientific calculations.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Machine Learning for Improved Delaunay Triangulation

Song

Wang

et al. 2021

JMSE

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Physical oceanography models rely heavily on grid discretization. It is known that unstructured grids perform well in dealing with boundary fitting problems in complex nearshore regions. However, it is time-consuming to find a set of unstructured grids in specific ocean areas, particularly in the case of land areas that are frequently changed by human construction. In this work, an attempt was made to use machine learning for the optimization of the unstructured triangular meshes formed with Delaunay triangulation in the global ocean field, so that the triangles in the triangular mesh were closer to equilateral triangles, the long, narrow triangles in the triangular mesh were reduced, and the mesh quality was improved. Specifically, we used Delaunay triangulation to generate the unstructured grid, and then developed a K-means clustering-based algorithm to optimize the unstructured grid. With the proposed method, unstructured meshes were generated and optimized for global oceans, small sea areas, and the South China Sea estuary to carry out data experiments. The results suggested that the proportion of triangles with a triangle shape factor greater than 0.7 amounted to 77.80%, 79.78%, and 79.78%, respectively, in the unstructured mesh. Meanwhile, the proportion of long, narrow triangles in the unstructured mesh was decreased to 8.99%, 3.46%, and 4.12%, respectively.

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A Local Grid-Refined Numerical Groundwater Model Based on the Vertex-centred Finite-Volume Method

Qian

Zhu

Zhang

et al. 2023

Advances in Water Resources

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Three-dimensional unstructured gridding for complex wells and geological features in subsurface reservoirs, with CVD-MPFA discretization performance

Cited by 5 publications

References 72 publications

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Unsupervised Machine Learning for Improved Delaunay Triangulation

A Local Grid-Refined Numerical Groundwater Model Based on the Vertex-centred Finite-Volume Method

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