Examples

Hello world

The examples/hello_world folder contains configuration files with low grid resolution and shorter injetion times (for initial testing of the framework). For example, by executing:

pyopmspe11 -i spe11b.txt -o spe11b -m all -g all -t 5 -r 50,1,15 -w 1

The following is one of the figures generated related to the CO2 mass in the domain over time (i.e., the simulations results from the corner-point grid mapped to the equidistance reporting grid of 50 x 15 as defined by the -r flag):

Let us now change the grid type from corner-point to tensor in line 7 of the configuration file. Then, we run the simulations and we save the results in a different output folder:

pyopmspe11 -i spe11b.txt -o tensor -m deck_flow_data -g performance_sparse -t 5 -r 50,1,15 -w 1

Here we have just set the framework to generate the deck, run the simulations, and generate the performance and sparse data. Then, to visualize the comparison between both runs, this can be achived by executing:

pyopmspe11 -c spe11b

The following are some of the figures generated in the compare folder:

This example uses a very coarser grid to run fast. See the following section for finer grids.

Before the 13.06.2024 workshop

The results below are the ones presented in the second CSP workshop. There have been two bug fixes that change some of the results (see this and this PR). See the results in benchmark for the final results.

SPE11A

In a 1 mm Cartesian grid for the spe11a (spe11a1mm.txt) and in a 1 cmish corner-point grid (spe11a_cp_1cmish.txt):

pyopmspe11 -i spe11a1mm.txt -o spe11a1mm -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -i spe11a_cp_1cmish.txt -o spe11a_cp_1cmish -m all -g all -t 1 -r 280,1,120 -w 0.16666666666666666
pyopmspe11 -c spe11a

_images/spe11a_masses.png — Final CO2 mass in the reporting grid of 1 cm size for (left) spe11a1mm and (right) spe11a_cp_1cmish.

_images/spe11a_performance.png — Performance data.

_images/spe11a_sparse_data.png — Sparse data.

SPE11B

The following are simulation results in a 1 m Cartesian grid (spe11b1m.txt), as well as for a 10 m Cartesian grid (spe11b10m.txt) which is the grid resolution for the reporting of dense data in the benchmark:

pyopmspe11 -i spe11b1m.txt -o spe11b1m -m all -g all -r 840,1,120 -t 5 -w 0.1
pyopmspe11 -i spe11b10m.txt -o spe11b10m -m all -g all -r 840,1,120 -t 5 -w 0.1
pyopmspe11 -c spe11b

_images/spe11b_sparse_data_1m-10m.png — Sparse data.

_images/spe11b_performance_1m-10m.png — Performance data.

SPE11C

The following are simulation results in a corner-point grid (spe11c_cp.txt) with 21729920 active cells and in a Cartesian grid with the same resolution as in the benchmark reporting data (spe11c_168_100_120.txt):

pyopmspe11 -i spe11c_cp.txt -o spe11c_cp -m all -g all -r 168,100,120 -t 0,5,10,15,20,25,30,35,40,45,50,75,100,150,200,250,300,350,400,450,500,600,700,800,900,1000 -w 0.1
pyopmspe11 -i spe11c_168_100_120.txt -o spe11c_168_100_120 -m all -g all -r 168,100,120 -t 0,5,10,15,20,25,30,35,40,45,50,75,100,150,200,250,300,350,400,450,500,600,700,800,900,1000 -w 0.1
pyopmspe11 -c spe11c

_images/spe11c_sparse_data.png — Sparse data.

_images/spe11c_performance.png — Performance data.

After the 13.06.2024 workshop

For these results, the maximum time step for the simulator during the injection period and after the injeciton period has been restricted after looking to the debug files to reduce the number of time step chops and mass conservation issues. We welcome PRs with configuration files with better tunning. “CP” are the results in a corner-point grid, while “CART” in a Cartesian grid.

SPE11A

SPE11B

SPE11C