Using the EnKF for Assisted History Matching of a North Sea Reservoir Model

Evensen, Geir; Hove, Joakim; Meisingset, Hilde; Reiso, Edel; Seim, Knut Sponheim; Espelid, Øystein

doi:10.2118/106184-ms

Cited by 137 publications

(40 citation statements)

References 26 publications

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“…If C D n is also represented by a sample of size N e , then loss-of-rank issues can become significant (Kepert, 2004;Evensen et al, 2007). If C D n is the true full-rank covariance matrix for measurement errors, then the N d ×N d matrix C D n is nonsingular and there is no loss of rank when updating the covariance matrix, Kepert (2004).…”

Section: Assimilation Of Seismic Datamentioning

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%

“…Since its introduction into the petroleum engineering literature (Naevdal et al, 2002) for sequential or real-time automatic history matching, it has been applied to a number of synthetic problems for estimation or stochastic simulation of reservoir model parameters and reservoir simulator primary variables , Gu and Oliver (2005), Gao and Reynolds (2006) Lorentzen et al (2005), Skjervheim et al (2006) Wen and Chen (2006) Wen and Chen (2007), Reynolds et al (2006), ?, Thulin et al (2007)) as well as to field cases (Haugen et al, 2006;Evensen et al, 2007;Bianco et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Assimilation Of Seismic Datamentioning

confidence: 99%

Section: Assimilation Of Seismic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Additionally, researchers have focused more on honoring the geostatistical constraint of the model when we update the reservoir parameters by using parameterisation methods like pilot points with Kriging (De Marseily et al 1984), the gradual deformation method (Hu 2001). probability perturbation (Caers 2003) and the Ensemble Kalman Filter (Haugen et al 2006;Evensen et al 2007). For some, the aim has been to use assisted tools to find important parts of the reservoir which need updating by using the streamline concepts (Baker 2001;Agarwal and Blunt 2004;Maschio and Schiozer 2005) or other measures of sensitivity such as the adjoint approach (Chen et al 1974;Chavent et al 1975).…”

Section: Introductionmentioning

confidence: 99%

Optimal Parameter Updating in Assisted History Matching of the Nelson Field using Streamlines as a Guide

Kazemi¹,

Stephen²

2010

SPE EUROPEC/EAGE Annual Conference and Exhibition

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It is becoming more and more common to use assisted history matching methods to find different combinations of reservoir simulation models that agree with production data. Models with a large number of cells contain millions of unknown parameters and selecting the correct values can be difficult. In practice not all are important but finding which parts of the reservoir require updating can be difficult. In this work we investigate methods of history matching by focusing on subvolumes of the parameter space and we use streamlines to help us choose where the model requires change. We identify localities in the reservoir that affect particular wells and we update reservoir properties (net:gross and permeability) within. We control changes using the pilot point method combined with a Neighbourhood Algorithm.We apply these approaches to the Nelson field where uncertainty of the shale distribution controls predictions. The field is divided into localities based on the performance of the worst well predictions. We history match to improve production rates.The localities that require change are sufficiently separate that we can modify them one at a time. We also compare our result with a more ad hoc approach where the whole area around the well is modified. We find that, for the wells of interest, the streamline guided approach gives a 70% improvement in the history match from our starting model and around 40% reduction of misfit in prediction. This improvement is twice that of the total area approach.Reservoir simulation and history matching are crucial for reservoir management especially when developing field management plans. By application of a good updating workflow in the right area of the model through an automatic history matching process we have gained a greater insight into reservoir behaviour and have been able to better predict flow from simulation models.

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“…The ensemble Kalman filter (EnKF), with some modifications to account for non-linearity and non-Gaussianity, has been shown to work quite well on problems of history matching applied to facies (Agbalaka and Oliver, 2008;Liu and Oliver, 2005). The EnKF technique introduced by Evensen (1994) is a Monte Carlo approach to history matching and has since its introduction been applied to different problems in Petroleum engineering (Chen et al, 2008;Evensen et al, 2007;Gu and Oliver, 2005;Naevdal et al, 2005). One key strength of this approach is the ability to sequentially adjust, in a straightforward manner, the reservoir model parameters as more production data become available while honoring the history of previous adjustments.…”

Section: Introductionmentioning

confidence: 99%

Automatic History Matching of Production and Facies Data with Non-stationary Proportions using EnKF

Agbalaka

Oliver

2009

SPE Reservoir Simulation Symposium

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Most geostatistical methods for generating plausible facies models require prior knowledge of the facies proportions distribution to account for heterogeneity within the reservoir. This parameter affects the flow behavior, predictive performance and fluid volume distribution of the model. In practice, it is hardly known with certainty and it is important to account for this uncertainty in the modeling and history matching phases.The ensemble Kalman filter (EnKF), when modified appropriately, to account for non-linearity and non-Gaussianity, is quite robust for automatic history matching problems. However, because the problem of history matching applied to facies is complex, most attempts at solving this problem typically assume constant petrophysical properties and stationary facies proportions. However, realistic representation of facies distribution requires incorporating the variation in petrophysical properties in the geologic model, in addition to any associated uncertainty. This is critical to correctly defining the flow connectivity within the model and the variations in storativity within facies.In this paper, we investigate a number of practical issues in history matching applied to facies with heterogeneous petrophysical properties and non-stationary proportions: (1) Can we account for heterogeneity in facies petrophysical properties during history matching and subsequently reduce any associated uncertainty in them by sequential calibration to data? To the best of our knowledge, this is the first time that heterogeneity in facies petrophysical properties has ever been applied to problems of this kind; (2) What is the EnKF performance when the facies proportions are stationary but implicitly uncertain? (3) Can EnKF efficiently adjust the facies boundaries and distributions when the prior proportions are assumed known but non-stationary?To answer these questions we designed three different test problems. We show that when the facies proportion is non-stationary the EnKF is a robust in estimating the facies properties and distribution.

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Using the EnKF for Assisted History Matching of a North Sea Reservoir Model

Cited by 137 publications

References 26 publications

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

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

Optimal Parameter Updating in Assisted History Matching of the Nelson Field using Streamlines as a Guide

Automatic History Matching of Production and Facies Data with Non-stationary Proportions using EnKF

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