Lundisim: Model Meshes for Flow Simulation and Scientific Data Compression Benchmarks

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Опубликовано в::Geoscience Data Journal vol. 12, no. 4 (Oct 1, 2025)
Главный автор: Duval, Laurent
Другие авторы: Payan, Frédéric, Preux, Christophe, Bouard, Lauriane
Опубликовано:
John Wiley & Sons, Inc.
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100 1 |a Duval, Laurent  |u IFP Energies Nouvelles, Rueil‐Malmaison, France 
245 1 |a Lundisim: Model Meshes for Flow Simulation and Scientific Data Compression Benchmarks 
260 |b John Wiley & Sons, Inc.  |c Oct 1, 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Oceanography 
653 |a Reservoir engineering 
653 |a Geology 
653 |a Datasets 
653 |a Astronomy 
653 |a Availability 
653 |a Machine learning 
653 |a Performance evaluation 
653 |a High performance computing 
653 |a Computer simulation 
653 |a Efficiency 
653 |a Porous media 
653 |a Simulation 
653 |a Flow simulation 
653 |a Earth sciences 
653 |a Data compression 
653 |a Multimedia 
653 |a Permeability 
653 |a Performance assessment 
653 |a Algorithms 
653 |a Compression 
653 |a Benchmarks 
653 |a Porosity 
653 |a Performance testing 
653 |a Climate studies 
653 |a Meteorology 
653 |a Artificial intelligence 
653 |a Reservoirs 
653 |a Mathematical models 
653 |a Environmental 
700 1 |a Payan, Frédéric  |u Université Côte d'Azur, CNRS, I3S, Sophia Antipolis, France 
700 1 |a Preux, Christophe  |u IFP Energies Nouvelles, Rueil‐Malmaison, France 
700 1 |a Bouard, Lauriane  |u IFP Energies Nouvelles, Rueil‐Malmaison, France 
773 0 |t Geoscience Data Journal  |g vol. 12, no. 4 (Oct 1, 2025) 
786 0 |d ProQuest  |t Publicly Available Content Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3265997033/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3265997033/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3265997033/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch