Sharif Digital Repository

Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the... 

Accurate estimation of the reservoir parameters is crucial to predict the future reservoir behavior. Well testing is a dynamic method used to estimate the petro-physical reservoir parameters through imposing a rate disturbance at the wellhead and recording the pressure data in the wellbore. However, an accurate estimation of the reservoir parameters from well-test data is vulnerable to the noise at the recorded data, the non-uniqueness of the obtained match, and the accuracy of the optimization algorithm. Different stochastic optimization methods have been applied to this address problem in the literature. In this study, we apply the recently developed iterative ensemble Kalman filter in the... 

Ensemble Kalman filter, EnKF, as a Monte Carlo sequential data assimilation method has emerged promisingly for subsurface media characterization during past decade. Due to high computational cost of large ensemble size, EnKF is limited to small ensemble set in practice. This results in appearance of spurious correlation in covariance structure leading to incorrect or probable divergence of updated realizations. In this paper, a universal/adaptive thresholding method is presented to remove and/or mitigate spurious correlation problem in the forecast covariance matrix. This method is, then, extended to regularize Kalman gain directly. Four different thresholding functions have been considered... 

The Ensemble Kalman Filter (EnKF) is a Monte Carlo based method to assimilate the measurement data sequentially in time. Although, EnKF has some advantages over the other Kalman based methods to deal with non-linear and/or high dimensional reservoir models, it also suffers from deficiency in estimation of non-Gaussian parameters. In this work, we propose a re-parameterization method to handle non-Gaussian parameters via Ensemble Kalman Filter framework. For this purpose, concept of cumulative distribution function transformation has been used. In addition, the statistics of prior information have been aggregated in the state vector in order to capture the prior uncertainties of non-Gaussian... 

In this study, ensemble Kalman filter (EnKF) is first applied to estimate absolute and relative permeabilities jointly under three-phase flow condition in the porous media. By assimilating historical data, absolute permeability field is adjusted progressively towards its reference. However, assimilation process does not improve the estimation of all relative permeability parameters, and some of them are poorly estimated at the end of assimilation. To improve the estimation of the relative permeability curves, we propose a new hybrid approach in which the estimation process of the absolute and relative permeabilities is separated. In this approach, gridblock permeabilities are again estimated... 

As a non-invasive method, photographic imaging techniques offer some interesting potentials for characterization of soil moisture content in unsaturated porous media, enabling mapping at very fine resolutions in both space and time. Although less explored, the wealth of soil moisture data provided by photographic imaging is also appealing for the estimation of unsaturated soil hydraulic parameters through inverse modeling. However, imaging data have some unique characteristics, including high susceptibility to noise, which can negatively affect the parameter estimation process. In this study a sequential data assimilation approach is developed to simultaneously update soil moisture content... 

In this paper, a 3-dimensional mathematical model for one cell of an anode-supported SOFC (solid oxide fuel cells) is presented. The model is derived from the partial differential equations representing the conservation laws of ionic and electronic charges, mass, energy, and momentum. The model is implemented to fully characterize the steady state operation of the cell with countercurrent flow pattern of fuel and air. The model is also used for the comparison of countercurrent with concurrent flow patterns in terms of thermal stress (temperature distribution) and quality of operation (current density). Results reveal that the steady-state cell performance curve and output of simulations... 

In this study, the ensemble Kalman filter is used to characterize threephase flow in porous media through simultaneous estimation of three-phase relative permeabilities and capillary pressures from production data. Power-law models of relative permeability and capillary pressure curves are used and the associated unknown parameters are estimated by assimilating the measured historical data. The estimation procedure is demonstrated on a twin numerical setup with two different scenarios, in which a synthetic 2D reservoir under three-phase flow is considered. In the first scenario, all the endpoints are assumed to be known and only the shape factors are estimated during the assimilation...