Reference dataset and MRST simulation script for "Entropy-Satisfying Physics-Informed Neural Networks for the Two-Dimensional Buckley–Leverett Equation"

This deposit contains the reference dataset and the MRST simulation script used to generate it for the paper Entropy-Satisfying Physics-Informed Neural Networks for the Two-Dimensional Buckley–Leverett Equation. Simulation setup. Quarter five-spot waterflood on a 1000 m × 1000 m homogeneous, isotropic reservoir, solved with the implicit-pressure explicit-saturation (IMPES) operator-splitting scheme: incompTPFA for the pressure step, explicitTransport for the saturation step. The injector is rate-controlled (500 m³/day, water composition) at one corner of the domain; the producer is bottom-hole-pressure-controlled (100 bar) at the opposite corner. Quadratic Corey relative-permeability curves with no connate water or residual oil. Mobility ratio M = 1. Grid: 100 x 100 cells. Total simulated time: 500 days = 1.25 pore volumes injected, stored in 500 timesteps of 1 day each (plus the t = 0 state). Full parameter list in Table 1 of the paper. Contents. bl2d.m - the MATLAB / MRST script that runs the simulation and writes the three CSV files. dataset.csv - per-cell water saturation and pressure for each of 501 stored timesteps (5 010 000 data rows; columns: x, y, t, P, Sw). well_cells.csv - cell indices and coordinates of the injector and producer. well_report.csv - per-timestep bottom-hole pressures, volumetric rates, and cumulative volumes for both wells. README.md - full documentation: file schemas, units, run instructions, and physical parameters. Requirements to reproduce. MATLAB R2019a or later (tested with R2023b) and MRST (MATLAB Reservoir Simulation Toolbox, https://www.sintef.no/projectweb/mrst/, tested with MRST 2024a, incomp module only). Licences. Code (bl2d.m): MIT. Data (dataset.csv, well_cells.csv, well_report.csv): CC-BY 4.0. Reuse, including for commercial purposes, is permitted under these licences, provided the accompanying paper is cited. Citation. Imankulov, T., Kenzhebek, Y., Bekele, S. D., Stolyarov, D., Akhmed-Zaki, D., & Panfilova, I. Entropy-Satisfying Physics-Informed Neural Networks for the Two-Dimensional Buckley–Leverett Equation (manuscript submitted for publication).