Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding

Delijani, Ebrahim Biniaz; Pishvaie, Mahmoud Reza; Boozarjomehry, Ramin Bozorgmehry

doi:10.1016/j.advwatres.2014.04.011

Cited by 8 publications

(6 citation statements)

References 43 publications

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“…It is applicable to a variety of nonlinear problems (Evensen, 2003;Weerts and El Serafy, 2006) and has been widely applied to hydrological models (Abaza et al, 2014;DeChant and Moradkhani, 2014;Delijani et al, 2014;Samuel et al, 2014;Tamura et al, 2014;Xue and Zhang, Deng et al, 2015). Furthermore, the EnKF has been successfully used in time-invariant parameter estimations for hydrological models (Moradkhani et al, 2005;Wang et al, 2009;Zhang, 2010, 2013).…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Identification of hydrological model parameter variation using ensemble Kalman filter

Deng

Liu

Guo

et al. 2016

Hydrol. Earth Syst. Sci.

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“…Also, evolution of the Rel-Perm parameters versus assimilation time is displayed. RMSE illustrates the difference between the updated and the true values, as (Delijani et al, 2014):…”

Section: Permeabilitiesmentioning

confidence: 99%

“…The ensemble mean is updated in the same way as in the EnKF, while anomalies are updated via an explicitly calculated transformation matrix T: (Delijani et al, 2014). Kalman gain is calculated based on the minimization of the analysis error covariance matrix:…”

Section: -2 Etkfmentioning

confidence: 99%

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Joint estimation of absolute and relative permeabilities using ensemble-based Kalman filter

Jahanbakhshi

Pishvaie

Boozarjomehry

2015

Journal of Natural Gas Science and Engineering

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“…Total production history is 720 days and measurements are available every 60 days; hence, we have 12 assimilation cycles. To mimic actual noisy measurements [44], a white Gaussian noise is added to the synthetic historical data. Standard deviation of this noise is 10 psi for bottom-hole pressure, 5 STB/D for oil and water production rates, and 8 MSCF/D for gas production rate.…”

Section: Half-iteration Ensemble-based Assimilation Proceduresmentioning

confidence: 99%

Characterization of three-phase flow in porous media using the ensemble Kalman filter

2017

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Abstract. In this study, the ensemble Kalman lter is used to characterize threephase ow 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 di erent scenarios, in which a synthetic 2D reservoir under three-phase ow is considered. In the rst scenario, all the endpoints are assumed to be known and only the shape factors are estimated during the assimilation process. In the second, all the endpoints and shape factors are estimated by assimilating observed data. Accurate estimation of the unknown model parameters is achieved by assimilating oil, water, and gas production rates of the producers and bottom-hole pressure of the injector. Moreover, sensitivity analysis of the observations with respect to the parameters de ning the relative permeabilities and capillary pressures shows that for the most sensitive parameters, better estimation and lower uncertainty are obtained at the end of the assimilation process.

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Subsurface characterization with localized ensemble Kalman filter employing adaptive thresholding

Cited by 8 publications

References 43 publications

Identification of hydrological model parameter variation using ensemble Kalman filter

Identification of hydrological model parameter variation using ensemble Kalman filter

Joint estimation of absolute and relative permeabilities using ensemble-based Kalman filter

Characterization of three-phase flow in porous media using the ensemble Kalman filter

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