Ajo-Franklin, J.B.

Carrigan, C.R.

Daley, T.M.

Doetsch, J.

Doughty, C.

Finsterle, S.

Hovorka, Susan D.

Kowalsky, M.B.

Yang, X.

International Journal of Greenhouse Gas Control

Texas ScholarWorks

Keywords: monitoring, permeability, reservoirCited as:Doetsch, J., M.B. Kowalsky, C. Doughty, S. Finsterle, J.B. Ajo-Franklin, C.R. Carrigan, X. Yang, S.D. Hovorka, T.M. Daley, 2013, Constraining CO2 simulations by coupled mod-eling and inversion of electrical resistance and gas composi-tion data, GCCC Publication Series #2013-07, originally published in International Journal of Greenhouse Gas Con-trolConstraining CO2 simulations by coupled modeling and inversion of electrical resistance and gas com-position dataGCCC Publication Series #2013-07        J. Doetsch    M.B. Kowalsky    C. Doughty    S. Finsterle    J.B. Ajo-Franklin    C.R. Carrigan    X. Yang    S.D. Hovorka    T.M. Daley1 AbstractThis study investigates how model predictions of subsurface CO2 migration can be constrained and improved with time-lapse electrical resistance tomog-raphy (ERT) data for a pilot experiment located at Cranfield, Mississippi. To this end, we first invert the time-lapse ERT dataset using structurally con-strained and unconstrained inversions. With the ERT time-lapse inversions, we image the increasing supercritical CO2 saturation in the reservoir and find that including the reservoir boundaries as structural constraints significantly improves the images. We then use ERT-derived changes in subsurface elec-trical resistivity along with gas composition data to constrain and calibrate hydrological models. We use the inversion framework iTOUGH2 and test several simplified conceptual models for the reservoir. Our analysis shows that the reservoir response cannot be adequately reproduced with a radial model; rather, the system exhibits 1D behavior. A model with three 1D lay-ers, whose permeability values and width were estimated by inversion, is able to explain the ERT and gas composition data. Derived permeabilities agree with those from core measurements and a well test. Despite high noise levels, the ERT data provided crucial information in the inversion thanks to its high sensitivity at the inter-well scale, its stabilizing effect on the inver-sion, and the direct link it provides between electrical resistivity and CO2 sat-uration.

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Constraining CO2 simulations by coupled modeling and inversion of electrical resistance and gas composition data

Constraining CO2 simulations by coupled modeling and inversion of electrical resistance and gas composition data

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