An Ensemble Smoother for assisted History Matching

Skjervheim, Jan-Arild; Evensen, Geir

doi:10.2118/141929-ms

Cited by 148 publications

(92 citation statements)

References 13 publications

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“…The ES was recently applied for reservoir history matching by Skjervheim et al [41]. In the ES, all data are assimilated at once, which means that only a single approximate GN iteration is done to history match all data.…”

Section: Discussionmentioning

confidence: 99%

“…Among the elastic properties typically used for seismic data history matching, the most common choices are pressure-wave impedance (P-impedance or acoustic impedance) and Poisson's ratio; see, e.g., [15,21,22,40,47]. However, other seismic attributes such as amplitudes [23] and time shifts [27,41] have also been used. Fahimuddin et al [16] investigated different kinds of seismic data for history matching with EnKF.…”

Section: Time-lapse Seismic Datamentioning

confidence: 99%

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History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

2012

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The ensemble Kalman filter (EnKF) has become a popular method for history matching production and seismic data in petroleum reservoir models. However, it is known that EnKF may fail to give acceptable data matches especially for highly nonlinear problems. In this paper, we introduce a procedure to improve EnKF data matches based on assimilating the same data multiple times with the covariance matrix of the measurement errors multiplied by the number of data assimilations. We prove the equivalence between single and multiple data assimilations for the linearGaussian case and present computational evidence that multiple data assimilations can improve EnKF estimates for the nonlinear case. The proposed procedure was tested by assimilating time-lapse seismic data in two synthetic reservoir problems, and the results show significant improvements compared to the standard EnKF. In addition, we review the inversion schemes used in the EnKF analysis and present a rescaling procedure to avoid loss of information during the truncation of small singular values.

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Section: Discussionmentioning

confidence: 99%

Section: Time-lapse Seismic Datamentioning

confidence: 99%

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

2012

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“…ES is a useful method to estimate the state parameters when these parameters do not change with time during the assimilation period. Recently, ES has been proposed as an efficient history-matching method for oil and gas reservoirs because of the diffusive nature of the solution [40]. We use the ES [40,41] for estimation and uncertainty reduction of model parameters in the beta field.…”

Section: Ensemble Smoothermentioning

confidence: 99%

“…Recently, ES has been proposed as an efficient history-matching method for oil and gas reservoirs because of the diffusive nature of the solution [40]. We use the ES [40,41] for estimation and uncertainty reduction of model parameters in the beta field. We choose the model parameters in our study based on the following criteria: importance of a parameter in determining the surface displacement due to UGS operations, effect of assimilating two types of data (well pressure and InSAR displacements) in reducing uncertainty in a parameter, and prior uncertainty in a parameter in the beta field.…”

Section: Ensemble Smoothermentioning

confidence: 99%

Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data

Jha¹,

Bottazzi²,

Wójcik³

et al. 2014

Proceedings

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SUMMARYCharacterization 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.

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“…In reservoir history-matching applications, DA has been used to update the dependent variables of multiphase flow models, such as pressure and saturations, and as an inverse modelling tool to "condition" model parameters, such as porosity and permeability, based on the observed data (e.g. Lorentzen et al 2003, Gu & Oliver 2005, Skjervheim et al 2011, Emerick & Reynolds 2013.…”

Section: Introductionmentioning

confidence: 99%

On the importance of the heterogeneity assumption in the characterization of reservoir geomechanical properties

et al. 2016

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SUMMARYThe geomechanical analysis of a highly compartmentalized reservoir is performed to simulate the seafloor subsidence due to gas production. The available observations over the hydrocarbon reservoir consist of bathymetric surveys carried out before and at the end of a ten-year production life. The main goal is the calibration of the reservoir compressibility c M , i.e., the main geomechanical parameter controlling the surface response. Two conceptual models are considered: in one (a) c M varies only with the depth and the vertical effective stress (heterogeneity due to lithostratigrafic variability); in another (b) c M varies also in the horizontal plane, that is, it is spatially distributed within the reservoir stratigraphic units. The latter hypothesis accounts for a possible partitioning of the reservoir due to the presence of sealing faults and thrusts that suggests the idea of a block heterogeneous system with the number of reservoir blocks equal to the number of uncertain parameters. The method applied here relies on an ensemble-based data assimilation (DA) algorithm (i.e., the Ensemble Smoother, ES), which incorporates the information from the bathymetric measurements into the geomechanical model response to infer and reduce the 2 C. Zoccarato, D. Baù, F. Bottazzi, M. Ferronato, G. Gambolati, S. Mantica, & P. Teatini uncertainty of the parameter c M . The outcome from conceptual model (a) indicates that DA is effective in reducing the c M uncertainty. However, the maximum settlement still remains underestimated, while the areal extent of the subsidence bowl is overestimated.We demonstrate that the selection of the heterogeneous conceptual model (b) allows to reproduce much better the observations thus removing a clear bias of the model structure.DA allows significantly reducing the c M uncertainty in the five blocks (out of the seven) characterized by large volume and large pressure decline. Conversely, land subsidence can constrain only partially the partitions that marginally contributes to the cumulative displacements of the seafloor.

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An Ensemble Smoother for assisted History Matching

Cited by 148 publications

References 13 publications

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

Reservoir Characterization in an Underground Gas Storage Field Using Joint Inversion of Flow and Geodetic Data

On the importance of the heterogeneity assumption in the characterization of reservoir geomechanical properties

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