Lundisim: Model Meshes for Flow Simulation and Scientific Data Compression Benchmarks
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| Publicado en: | Geoscience Data Journal vol. 12, no. 4 (Oct 1, 2025) |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
John Wiley & Sons, Inc.
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| Resumen: | ABSTRACT The volume of scientific data produced for and by numerical simulation workflows is increasing at an incredible rate. This raises concerns either in computability, interpretability, and sustainability. This is especially noticeable in earth science (geology, meteorology, oceanography, and astronomy), notably with climate studies. We highlight five main evaluation issues: efficiency, discrepancy, diversity, interpretability, availability. Among remedies, lossless and lossy compression techniques are becoming popular to better manage dataset volumes. Performance assessment—with comparative benchmarks—requires open datasets shared under FAIR principles (Findable, Accessible, Interoperable, Reusable), provided in a MWE (Minimal Working Example) with ancillary data for reuse. We share Lundisim, an exemplary faulted geological mesh. It is inspired by the SPE10 comparative Challenge. It is not meant to be compared to the latter for reservoir simulation. It is instead tailored—with power‐of‐two dimensions and additional faults—to both more challenging fluid displacement and upscaling methods, and allowing versatile compression benchmarks. Enhanced by porosity/permeability datasets, this dataset proposes four distinct subsurface environments. They were primarily designed for flow simulation in porous media. Several consistent resolutions (with HexaShrink multiscale representations) are proposed for each model. We also provide a set of reservoir features for reproducing typical two‐phase flow simulations on all Lundisim models in a reservoir engineering context. This dataset is chiefly meant for benchmarking and evaluating data size reduction (upscaling) or genuine composite mesh compression algorithms. It is also suitable for other advanced mesh processing workflows in geology and reservoir engineering, from visualisation to machine learning. Lundisim meshes are available at 10.5281/zenodo.14641958. |
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| ISSN: | 2049-6060 |
| DOI: | 10.1002/gdj3.70030 |
| Fuente: | Publicly Available Content Database |