[2606.27515] Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage
[Submitted on 25 Jun 2026]
Title:Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage
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Abstract:Accurate prediction of bottom-hole pressure (BHP) and CO2 plume migration is essential for safe geological carbon storage, yet practical simulations often rely on truncated domains where artificial boundaries distort pressure diffusion and CO2 saturation footprints. In this study, we evaluate how boundary-condition fidelity affects BHP and CO2 plume prediction by comparing ten reduced-domain boundary treatments against full-domain reference simulations in homogeneous and heterogeneous reservoirs. We test uniform pore-volume multipliers, transmissibility modifiers, corner-adjusted pore-volume corrections, layered corrections, and gradual modifiers using BHP RMSE, NRMSE, peak pressure deviation, and plume Intersection over Union (IoU) as performance metrics. Our results show that conserving corner pore volume is the most important requirement for truncated-domain modeling. We find that uniform treatments which neglect corner storage generate large pressure errors, with BHP RMSE of 362 to 382 psi in the homogeneous model and 250 to 304 psi in the heterogeneous model, and yield plume IoU values near 0.80 to 0.84, indicating roughly 16 to 20% of the combined plume area is misrepresented. Corner-adjusted scenarios substantially reduce pressure errors and raise plume IoU above 0.94, but we observe that transmissibility correction is not universally beneficial. In homogeneous reservoirs, uniform transmissibility adjustment improves pressure fidelity; in heterogeneous reservoirs, it can over-restrict flow across variable-permeability boundary faces, increasing BHP error and contracting the predicted plume. We find the gradual modifier with transmissibility correction provides the most consistent performance, achieving BHP NRMSE below 3.7% and plume IoU above 0.97 in both reservoir types.
Subjects:
Machine Learning (cs.LG); Geophysics (physics.geo-ph)
Cite as: arXiv:2606.27515 [cs.LG]
(or arXiv:2606.27515v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.27515
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Romal Ramadhan [view email] [v1] Thu, 25 Jun 2026 19:59:59 UTC (4,186 KB)
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