arXiv Machine Learning June 29, 2026 By Romal Ramadhan, Seyyed A. Hosseini, Larry W. Lake

Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage

Read the original on arXiv Machine Learning →

arXiv:2606. 27515v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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