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Non-parametric simulations-based conditional stochastic predictions of geologic heterogeneities and leakage potentials for hypothetical CO₂ sequestration sites

Author:
Han, Weon Shik, Kim, Kue-Young, Choung, Sungwook, Jeong, Jina, Jung, Na-Hyun, Park, Eungyu
Source:
Environmental earth sciences 2014 v.71 no.6 pp. 2739-2752
ISSN:
1866-6280
Subject:
Markov chain, carbon dioxide, prediction, simulation models
Abstract:
The present study focuses on understanding the leakage potentials of the stored supercritical CO₂ plume through caprocks generated in geostatistically created heterogeneous media. For this purpose, two hypothetical cases with different geostatistical features were developed, and two conditional geostatistical simulation models (i.e., sequential indicator simulation or SISIM and generalized coupled Markov chain or GCMC) were applied for the stochastic characterizations of the heterogeneities. Then, predictive CO₂ plume migration simulations based on stochastic realizations were performed and summarized. In the geostatistical simulations, the results from the GCMC model showed better performance than those of the SISIM model for the strongly non-stationary case, while SISIM models showed reasonable performance for the weakly non-stationary case in terms of low-permeability lenses characterization. In the subsequent predictive simulations of CO₂ plume migration, the observations in the geostatistical simulations were confirmed and the GCMC-based predictions showed underestimations in CO₂ leakage in the stationary case, while the SISIM-based predictions showed considerable overestimations in the non-stationary case. The overall results suggest that: (1) proper characterization of low-permeability layering is significantly important in the prediction of CO₂ plume behavior, especially for the leakage potential of CO₂ and (2) appropriate geostatistical techniques must be selectively employed considering the degree of stationarity of the targeting fields to minimize the uncertainties in the predictions.
Agid:
375633