Bayesian inversion of multilayer CO2 migration from seismic plume observations using a graph-based finite-rate invasion-percolation model
Abstract
Vertical migration of CO2 in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical CO2 migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of CO2 across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.
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BibTeX
@misc{svee2026bayesian,
title = {{Bayesian inversion of multilayer $\mathrmCO_2$ migration from seismic plume observations using a graph-based finite-rate invasion-percolation model}},
author = {Elling Svee and Jo Eidsvik and Kasper Hunnestad and Philip Ringrose},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04937},
eprint = {2609.04937},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04937},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bayesian inversion of multilayer CO_2 migration from seismic plume observations using a graph-based finite-rate invasion-percolation model
AU - Elling Svee
AU - Jo Eidsvik
AU - Kasper Hunnestad
AU - Philip Ringrose
PY - 2026
DA - 2026/09/04
PB - arXiv
DO - 10.48550/arxiv.2609.04937
UR - https://arxiv.org/abs/2609.04937
AB - Vertical migration of CO_2 in layered sandstone reservoirs is controlled by thin shale barriers whose properties are often poorly known. We develop a Bayesian framework that uses time-lapse seismic plume observations to estimate effective parameters governing lateral and vertical CO_2 migration. The forward model is a fast graph-based invasion-percolation model that extends conventional IP by representing both capillary-controlled filling of structural traps beneath shale barriers and finite-rate transfer through them. Parameters are inferred with approximate Bayesian computation and sequential Monte Carlo sampling, and the posterior samples are propagated into forecasts. Applied to real data from Sleipner, posterior simulations reproduce the broad distribution of CO_2 across nine sand units and its redistribution between 2010 and 2023. In contrast, the quasi-static model fails to reproduce this temporal evolution. Complementary synthetic experiments assess parameter recovery, forecasting, monitoring duration, and model misspecification. These experiments show that the information gained from monitoring depends on the migration events captured, with breakthrough and post-breach redistribution providing particularly strong constraints. This combination of fast simulation, probabilistic updating, and interpretable effective parameters makes the framework well suited to repeated forecast revision during active injection, especially when full-physics inference is too computationally demanding.
ER -