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Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, ECMOR XIV - 14th European Conference on the Mathematics of Oil Recovery, Sep 2014, Volume 2014, p.1 - 19
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 data assimilation of both flow data and deformation data for joint inversion of reservoir properties. Given the coupled nature of the process, 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 Kalman 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.
Research significance
- We perform joint inversion of flow and surface deformation data for parameter estimation in a real field with complex production-injection history based on the Bayesian inference model and coupled multiphase flow and geomechanics simulation.
- We develop a computationally efficient implementation of the Ensemble Kalman method for uncertainty reduction.
- We quantify the value of information from surface deformation data in uncertainty reduction in prior distributions.