Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble 
Kalman Filter

Skjervheim, Jan-Arild; Evensen, Geir; Aanonsen, Sigurd Ivar; Ruud, Bent Ole; Johansen, Tor Arne

doi:10.2523/95789-ms

Cited by 21 publications

(22 citation statements)

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“…In DA with EnKF, seismic data has also been used by Skjervheim et al . [71] and synthetic electrical resistivity data were used to update groundwater states and parameters by Camporese et al . [72].…”

Section: Data Assimilation Theorymentioning

confidence: 99%

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Montzka

Pauwels

Franssen

et al. 2012

Sensors

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More and more terrestrial observational networks are being established to monitor climatic, hydrological and land-use changes in different regions of the World. In these networks, time series of states and fluxes are recorded in an automated manner, often with a high temporal resolution. These data are important for the understanding of water, energy, and/or matter fluxes, as well as their biological and physical drivers and interactions with and within the terrestrial system. Similarly, the number and accuracy of variables, which can be observed by spaceborne sensors, are increasing. Data assimilation (DA) methods utilize these observations in terrestrial models in order to increase process knowledge as well as to improve forecasts for the system being studied. The widely implemented automation in observing environmental states and fluxes makes an operational computation more and more feasible, and it opens the perspective of short-time forecasts of the state of terrestrial systems. In this paper, we review the state of the art with respect to DA focusing on the joint assimilation of observational data precedents from different spatial scales and different data types. An introduction is given to different DA methods, such as the Ensemble Kalman Filter (EnKF), Particle Filter (PF) and variational methods (3/4D-VAR). In this review, we distinguish between four major DA approaches: (1) univariate single-scale DA (UVSS), which is the approach used in the majority of published DA applications, (2) univariate multiscale DA (UVMS) referring to a methodology which acknowledges that at least some of the assimilated data are measured at a different scale than the computational grid scale, (3) multivariate single-scale DA (MVSS) dealing with the assimilation of at least two different data types, and (4) combined multivariate multiscale DA (MVMS). Finally, we conclude with a discussion on the advantages and disadvantages of the assimilation of multiple data types in a simulation model. Existing approaches can be used to simultaneously update several model states and model parameters if applicable. In other words, the basic principles for multivariate data assimilation are already available. We argue that a better understanding of the measurement errors for different observation types, improved estimates of observation bias and improved multiscale assimilation methods for data which scale nonlinearly is important to properly weight them in multiscale multivariate data assimilation. In this context, improved cross-validation of different data types, and increased ground truth verification of remote sensing products are required.

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Section: Data Assimilation Theorymentioning

confidence: 99%

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Montzka

Pauwels

Franssen

et al. 2012

Sensors

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“…Note that the two data sets are assimilated at the time they are "measured," i.e., we assimilate 3D data, not time-lapse seismic data. It is worthwhile to mention that for a small 2D synthetic problem, (Skjervheim et al, 2007)) found that better estimates of the permeability field model were obtained by assimilating inverted seismic data at the time they were measured instead of using 4D data. Although the authors give no explanation of why difference data give worse results than assimilating seismic data directly, we believe there may be two reasons for this.…”

Section: Case 2 Results Global Analysis and Local Analysis Of Seismimentioning

confidence: 99%

“…One can then use a subspace projection method to avoid loss of rank. The basic idea (Evensen, 2004;Evensen et al, 2007;Skjervheim et al, 2007) follows. First, we compute the singular value decomposition of ∆D p n as…”

Section: Assimilation Of Seismic Datamentioning

confidence: 99%

Generating Facies Maps by Assimilating Production Data and Seismic Data with the Ensemble Kalman Filter

Zhao

Reynolds

2008

SPE Symposium on Improved Oil Recovery

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Truncated pluri-Gaussian models have proven to be useful for generating realistic geological models of facies distributions. In this approach, truncation maps are applied to the underlying Gaussian random fields to generate the facies distribution on a reservoir simulator gridblock basis. Here, we investigate methods to condition such a facies map to production and seismic data by the sequential assimilation of data using the ensemble Kalman filter (EnKF). Although EnKF is a highly efficient data assimilation scheme, our experience is that it can be difficult to match watercut data with EnKF, and for highly nonlinear/non-Gaussian problems, EnKF may provide only a very approximate quantification of uncertainty. Moreover, EnKF is designed to generate an approximation to the conditional mean, but for a pluri-Gaussian model, the mean does not represent a plausible geological model, so it is important that the ensemble gives a reasonable sampling of the conditional probability density function. For two and three-dimensional pluri-Gaussian models, we present a new procedure to ensure that facies observations at wells are honored at each data assimilation step. The difficulty in matching water cut data is more clearly seen for the channel model considered where the EnKF update of water saturation is inconsistent with the updated location of the channel system. The result is that in the updated state vector, much of the injected water is moved into gridblocks occupied by the low permeability facies.As the erroneous saturation distribution obtained with EnKF can only result from nonlinearity or the failure of the assumption that the ensemble of predictions is approximately Gaussian, we investigate the application of a global and local normal score transform to transform water saturation to a Gaussian variable before applying the EnKF analysis step. We also consider matching breakthrough time directly before matching watercut data as well and also apply an iterative EnKF scheme to obtain more plausible saturations distributions.For the first time, we also show results on applying EnKF to generate realizations of both the distribution of facies and the permeability and porosity within each facies.The integration of seismic data poses problems because of the large number of data that are assimilated. With a global assimilation procedure based on subspace projection, filter divergence becomes severe. On the other hand, our implementation of a local updating method to reduce filter divergence results in an unrealistic rough facies map. We introduce a projection method to obtain a more realistic map of the distribution of facies, which retain the inherent smoothness of the underlying geological model.After assimilating data with EnKF, it is common to rerun the ensemble of models from time zero when predicting future reservoir performance and assessing its uncertainty. Our results indicate that more accurate predictions of future reservoir performance will be obtained by simply making predictions forward from the last data assim...

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“…Several field examples have shown that EnKF is able to efficiently integrate various types of production data and seismic data, and to history match many types of model variables (Skjervheim et al, 2007;Haugen et al, 2008;Seiler et al, 2009a;Zhang and Oliver, 2010;Cominelli et al, 2009). The recent trend in EnKF applications is an increase in the number and types of parameters estimated.…”

Section: Introductionmentioning

confidence: 99%

Parameterization Techniques to Improve Mass Conservation and Data Assimilation for Ensemble Kalman Filter

Chen

Oliver

2010

SPE Western Regional Meeting

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The ensemble Kalman filter (EnKF) is an attractive method for assisted or automatic history matching. In particular, it has been shown to be possible to efficiently match many types of data by adjusting many types of reservoir model variables, including gridblock permeabilities and porosities, facies locations, relative permeability curves, fault transmissibilities, and initial fluid contacts. At each analysis step of EnKF, model and state variables are updated to honor the production history and prior knowledge of the reservoir description using a correction that is constructed from a linear combination of the forward models. Because the update in the ensemble Kalman filter is linear and variables may be adjusted beyond their plausible range in which case the updated values are sometimes truncated, mass conservation may be violated in the updated models.Proper selection of the model and state variables to be updated in EnKF is critically important for achieving good data match, retaining geological realism of the updated ensemble and satisfying nonlinear constraints that may apply. In this paper, we discuss parameterization techniques for these three different purposes. We revisit the updating formulation of EnKF and investigate the constraint on the weighting coefficients at the analysis step. We show that by reparameterization of the state vector, nonlinear constraints such as conservation of mass or volume, can be satisfied without complicating the updating process. The ensemble approximation of the covariance tends to be strongly corrupted by noise when the true correlation is small. Localization can regularize the ensemblebased estimate and reduce the detrimental effects of the spurious correlation, but also eliminate the effect of true correlations whose magnitude in individual gridblocks is small, but whose cumulative effect may be large. Appropriate parameterization in addition to localization could better constrain the solution of the inverse problem. Finally, the choice of model variables may also depend on observations that are available. Including model variables that are highly sensitive to certain types of data provides improved data match and more realistic updates.A large scale reservoir model is used to illustrate these parameterization techniques. By using these techniques multiple realistic history matched models were obtained that provide reliable basis for accessing uncertainty and for design of reservoir management strategy.

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Incorporating 4D Seismic Data in Reservoir Simulation Models Using Ensemble Kalman Filter

Cited by 21 publications

References 0 publications

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review

Generating Facies Maps by Assimilating Production Data and Seismic Data with the Ensemble Kalman Filter

Parameterization Techniques to Improve Mass Conservation and Data Assimilation for Ensemble Kalman Filter

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