SPE11A#

SPE11A is the laboratory-scale benchmark case. These configurations compare Cartesian and corner-point grids, two capillary-pressure limits, and solver tolerances.

Configurations#

r1_Cart_1cm: Uniform Cartesian grid with a cell size of 1 cm.
r2_Cart_1cm_capmax2500Pa: Uniform 1 cm Cartesian grid with a maximum capillary pressure of 2500 Pa instead of 95000 Pa.
r3_cp_1cmish_capmax2500Pa: Corner-point grid with an approximate cell size of 1 cm and a maximum capillary pressure of 2500 Pa.
r4_Cart_1mm_capmax2500Pa: Uniform 1 mm Cartesian grid with a maximum capillary pressure of 2500 Pa.
r5_Cart_1mm_capmax2500Pa_strictol: Uniform 1 mm Cartesian grid with stricter solver tolerances and a maximum capillary pressure of 2500 Pa.

Run the cases#

Run these commands from the benchmark/spe11a directory:

pyopmspe11 -i r1_Cart_1cm.toml -o r1_Cart_1cm -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -i r2_Cart_1cm_capmax2500Pa.toml -o r2_Cart_1cm_capmax2500Pa -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -i r3_cp_1cmish_capmax2500Pa.toml -o r3_cp_1cmish_capmax2500Pa -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -i r4_Cart_1mm_capmax2500Pa.toml -o r4_Cart_1mm_capmax2500Pa -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -i r5_Cart_1mm_capmax2500Pa_strictol.toml -o r5_Cart_1mm_capmax2500Pa_strictol -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666

These commands generate decks, run OPM Flow, write all benchmark data, and create the standard figures. See pyopmspe11 -m, pyopmspe11 -g, pyopmspe11 -t, pyopmspe11 -r, and pyopmspe11 -w.

Key observations#

Reducing the maximum capillary pressure from 95000 Pa to 2500 Pa has little effect on the reported results when comparing r1 and r2, while reducing simulation time.
The approximate 1 cm corner-point grid in r3 compares well with the fine 1 mm Cartesian case in r4.
The stricter tolerances in r5 improve mass-balance behavior compared with r4, but increase runtime.
This illustrates the trade-off between simulation cost and accuracy, particularly for optimization studies requiring many runs.

See the SPE11 CSP description for the benchmark context.

Results#

Performance data#

Sparse data#

Spatial maps#

SPE11A liquid-phase CO2 mass-fraction maps

Continue#

See Configuration reference for configuration-variable definitions.
Follow the Tutorial for a guided introductory workflow.
Use Visualizing benchmark results with plopm to reproduce the simulation-grid maps.
See Convergence for the SPE11B grid-refinement study.

Back to the benchmark gallery