Reservoir characterization in an underground gas storage field using joint inversion of flow and geodetic data
Name
Hager EAPS Jha 2015.pdf
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Author(s)
Bottazzi, F.
Mantica, S.
Jha, Birendra
Wojcik, Rafal
Coccia, Martina
Bechor Ben Dov, Noah
McLaughlin, Dennis
Juanes, Ruben
Herring, Thomas A
Hager, Bradford H
Date Issued
October 2015
Journal
International Journal for Numerical and Analytical Methods in Geomechanics
Publisher
Wiley Blackwell
Citation
Jha, B., F. Bottazzi, R. Wojcik, M. Coccia, N. Bechor, D. McLaughlin, T. Herring, B. H. Hager, S. Mantica, and R. Juanes. “Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data.” International Journal for Numerical and Analytical Methods in Geomechanics 39, no. 14 (August 26, 2015): 1619–1638.
Version
Author's final manuscript
Abstract
Characterization of reservoir properties like porosity and permeability in reservoir models typically relies on history matching of production data, well pressure data, and possibly other fluid-dynamical data. Calibrated (history-matched) reservoir models are then used for forecasting production and designing effective strategies for improved oil and gas recovery. Here, we perform assimilation of both flow and deformation data for joint inversion of reservoir properties. Given the coupled nature of subsurface flow and deformation processes, joint inversion requires efficient simulation tools of coupled reservoir flow and mechanical deformation. We apply our coupled simulation tool to a real underground gas storage field in Italy. We simulate the initial gas production period and several decades of seasonal natural gas storage and production. We perform a probabilistic estimation of rock properties by joint inversion of ground deformation data from geodetic measurements and fluid flow data from wells. Using an efficient implementation of the ensemble smoother as the estimator and our coupled multiphase flow and geomechanics simulator as the forward model, we show that incorporating deformation data leads to a significant reduction of uncertainty in the prior distributions of rock properties such as porosity, permeability, and pore compressibility.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
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DOI of Published Version
https://doi.org/10.1002/nag.2427
