TCCS-13#
Publication#
Here we describe the steps to reproduce the results in:
Landa-Marbán, D., Sandve, T.H., and Gasda, S.E., 2026. A coarsening approach to the troll aquifer model. In Nils Anders Røkke, Philip Stefan Ringrose, & Stefan Marcell Götz (Eds.), TCCS-13. CO2 Capture, Transport and Storage Trondheim, Norway | 16–19 June 2025 Short Papers from the 13th Trondheim CCS Conference (pp. 13–20). SINTEF akademisk forlag. https://hdl.handle.net/11250/5559902
Troll aquifer model access and preparation#
To this end, the deck ORIGINAL.DATA has the same dimensions and number of total cells (12,450,809) as the Troll aquifer model, and the rest of the properties are set to common homogeneous values from literature, while the well locations match the ones described in the TCCS-13 paper. Then, you could contact the Norwegian Offshore Directorate to get the actual Troll aquifer model, and adapt those files to the ones in the tccs-13 folder.
Warning
You should not run the ORIGINAL.DATA file before adapting it with the actual Troll aquifer model, since it has a lot of active cells.
Prerequisites#
For the first figures, plopm and pycopm are used, which can be installed by:
pip install git+https://github.com/cssr-tools/plopm.git
pip install git+https://github.com/cssr-tools/pycopm.git
To run the optimization, Everest is needed, which is installed via ert when pycopm is installed with the command above.
Methodology: Figures 1 and 2#
The following commands generate Figures 1 and 2 (a few features such as the transmissibilities, wells, and sensor are added using PowerPoint) using FIG1.DATA:
pycopm -i FIG1.DATA -z 1:4 -m all -a max -w COARSENED_TRANS -l TRANS -t 2
pycopm -i FIG1.DATA -c 1,1,4 -m all -a max -w COARSENED_PERMS -l PERMS
flow FIG1.DATA
flow COARSENED_TRANS.DATA
flow COARSENED_PERMS.DATA
plopm -i FIG1 -v poro -c '#bfebf2' -asp 0 -ge 'black,1e0' -y '[5,-1]' -ynt 5 -xnt 7 -r 0 -hide 0,0,1,1 -fs 24,16 -fz 60 -fn fig1
plopm -i 'COARSENED_PERMS FIG1 COARSENED_TRANS' -v 'pressure - 0pressure' -sg 3,1 -r 1 -asp 0 -rdl 1 -y '[5,-1]' -cbf .0f -ge 'black,1e0' -cbp 0.1,0.95,0.8,0.02 -st 0 -cbl 'Pressure increase [bar]' -cbn 5 -t 'Coarsened (permeabilities) Before coarsening Coarsened (transmissibilities)' -fs 24,48 -fz 80 -fn fig2
Original and coarsened models#
For Figs. 3b, 5, 6, and 8, 9, and 10 using ORIGINAL.DATA in the expreccs/tccs-13/original folder:
flow ORIGINAL.DATA --enable-opm-rst-file=true --linear-solver=cpr_trueimpes --time-step-control=newtoniterationcount --newton-min-iterations=1 --tolerance-cnv-relaxed=1e-2 --relaxed-max-pv-fraction=0
pycopm -i ORIGINAL.DATA -t 2 -a max -z 1:30,31:56,57:111,112:116,117:217 -w COARSENED -l C
flow COARSENED.DATA --enable-opm-rst-file=true --linear-solver=cpr_trueimpes --time-step-control=newtoniterationcount --newton-min-iterations=1 --tolerance-cnv-relaxed=1e-2 --relaxed-max-pv-fraction=0
plopm -i ORIGINAL -v 'depth * 0.001' -s ,,1:217 -agg min -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -cbl 'Depth [km]' -cbn 5 -cbf .2f -c managua_r -fn fig3b
plopm -i 'ORIGINAL ORIGINAL ORIGINAL' -v 'porv' -s ',,1:30 ,,57:111 ,,117:217' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,3 -fs 24,12 -cbp 0.1,0.95,0.8,0.02 -cbf .1f -fz 24 -cbn 5 -st 0 -cbl 'Total pore volume [m$^3$]' -clog 1 -c brg -t 'Cook Johansen Statfjord' -rdl 1 -fn fig5
plopm -i 'ORIGINAL' -v 'faults' -s ',,1:217' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -fz 8 -gr 1 -t 'Total no. faults = 65' -fn fig6a
plopm -i 'ORIGINAL' -v 'faults' -s ',,30:117' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -fz 8 -agg max -t 'Cook-Johansen-Statfjord' -hide 1,0,0,0 -fn fig6b
plopm -i 'ORIGINAL ORIGINAL ORIGINAL' -v 'pressure - 0pressure' -s ',,1:30 ,,57:111 ,,117:217' -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,3 -fs 24,12 -cbp 0.1,0.95,0.8,0.02 -fz 24 -cbn 5 -st 0 -cbl 'Pore-volume-weighted-average pressure increase [bar]' -rdl 1 -r 25 -cl '[0,80]' -t 'Cook, t=25 years Johansen, t=25 years Statfjord, t=25 years' -cl '[0,80]' -fn fig8upper
plopm -i 'ORIGINAL ORIGINAL ORIGINAL' -v 'pressure - 0pressure' -s ',,1:30 ,,57:111 ,,117:217' -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,3 -fs 24,12 -cbp 0.1,0.95,0.8,0.02 -fz 24 -cbn 5 -st 0 -cbl 'Pore-volume-weighted-average pressure increase [bar]' -rdl 1 -r 30 -cl '[0,80]' -t 'Cook, t=525 years Johansen, t=525 years Statfjord, t=525 years' -cl '[0,80]' -fn fig8lower
plopm -i 'ORIGINAL ORIGINAL ORIGINAL' -v 'limipres' -s ',,1:30 ,,57:111 ,,117:217' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,3 -fs 24,12 -cbp 0.1,0.95,0.8,0.02 -cbf .1f -fz 24 -cbn 5 -st 0 -cbl 'Maximum allowable pressure increase limit [bar]' -c gnuplot2 -t 'Cook Johansen Statfjord' -rdl 1 -fn fig9
plopm -i 'ORIGINAL' -v 'imbnum' -s ',,1:217' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -hide 0,0,1,1 -c '203;203;203' -fn fig10a
plopm -i 'ORIGINAL COARSENED' -v 'overpres' -s ',,1:217 ,,1:5' -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,2 -fs 16,12 -cbp 0.1,0.95,0.8,0.02 -fz 24 -cbn 5 -st 0 -cbl 'Overpressure (p - p$_{lim}$) [bar], t=25 years' -rdl 1 -hide 1,0,0,0 -t 'Troll aquifer model Coarsened version' -cl '[-126.7,-1.1]' -c cet_diverging_rainbow_bgymr_45_85_c67 -fn fig10bc
ResInsight figures#
Figs. 4 and 7 are generated using ResInsight.
Everest optimization#
Using the coarsened files from pycopm, you could adapt the existing files in the coarsened folder to run the optimization:
everest run coarsened.yml --skip-prompt
Note
You might need to adapt the number of cores and mpirun depending on your resources, i.e., the current file is set to use mpirun -np 5 flow ... and 14 cores in parallel.
Figure 11: optimization results#
After the study, to generate Fig. 11, execute the postprocessing.py file:
python3 postprocessing.py
Figure 12: improved well locations#
Finally, you can look at the improved well locations in the generated figures/optimal_solution folder, and take those locations into the ORIGINAL.DATA deck and save it as IMPROVED.DATA, similar to the coarsened version to COARSENED_IMPROVED.DATA, to generate Fig. 12:
flow IMPROVED.DATA --enable-opm-rst-file=true --linear-solver=cpr_trueimpes --time-step-control=newtoniterationcount --newton-min-iterations=1 --tolerance-cnv-relaxed=1e-2 --relaxed-max-pv-fraction=0
flow COARSENED_IMPROVED.DATA --enable-opm-rst-file=true --linear-solver=cpr_trueimpes --time-step-control=newtoniterationcount --newton-min-iterations=1 --tolerance-cnv-relaxed=1e-2 --relaxed-max-pv-fraction=0
plopm -i 'IMPROVED' -v 'imbnum' -s ',,1:217' -r 0 -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -hide 0,0,1,1 -c '203;203;203' -fn fig12a
plopm -i 'IMPROVED COARSENED_IMPROVED' -v 'overpres' -s ',,1:217 ,,1:5' -tr '[-495000,-6.605e6]' -x '[0,95000]' -xu km -xnt 2 -y '[0,160000]' -yu km -yf .0f -ynt 2 -xf .0f -sg 1,2 -fs 16,12 -cbp 0.1,0.95,0.8,0.02 -fz 24 -cbn 5 -st 0 -cbl 'Overpressure (p - p$_{lim}$) [bar], t=25 years' -rdl 1 -hide 1,0,0,0 -t 'Troll aquifer model Coarsened version' -cl '[-126.7,-1.1]' -c cet_diverging_rainbow_bgymr_45_85_c67 -fn fig10bc