Recursive update of channel information for reliable history matching of channel reservoirs using EnKF with DCT

Jung, Hun Soon; Jo, Honggeun; Kim, Sung-Il; Lee, Kyungbook; Choe, Jonggeun

doi:10.1016/j.petrol.2017.04.016

Cited by 30 publications

(21 citation statements)

References 31 publications

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“…In EnKF, all reservoir models, called ensemble, are a group of state vectors y and they are constructed by reservoir variables and predicted data. We update permeability values in this paper, but other properties such as porosity, facies ratio, and aquifer strength can be put into the state vectors (Zhao et al 2008;Jung et al 2017a;Kim et al 2017). In the assimilation step, ensemble Y is updated by Eq.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

et al. 2019

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sEnsemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemble size, and then, EnKF is utilized to predict production performances stochastically. In the model selection scheme, representative models are chosen by using principal component analysis (PCA) and clustering analysis. The dimension of initial models is reduced using PCA, and the reduced models are grouped by clustering. Then, we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data. One representative model with the minimum error is considered as the best model, and we use the ensemble members near the best model in the cluster plane for applying EnKF. We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.

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Section: Ensemble Kalman Filtermentioning

confidence: 99%

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

et al. 2019

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“…DCT is also used to extract the main information of a static model in a history matching area [27][28][29][30][31][32][33][34][35]. After original data are transformed to coefficients of discrete cosine functions, the coefficients are arranged in descending order of frequencies of cosine functions.…”

Section: Combined Distancementioning

confidence: 99%

“…The selection of a subset of DCT coefficients depends on the objective of the research. For example, to characterize channel reservoirs, several papers applied DCT to permeability data to extract only the main channel trends in the ensemble models with low frequencies' coefficients [27][28][29][30][31][32]34,35]. The channel trends in reservoirs can be described with only a small number of coefficients.…”

Section: + ( − 3) =mentioning

confidence: 99%

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

et al. 2019

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This paper presents a review of 71 research papers related to a distance-based clustering (DBC) technique for efficiently assessing reservoir uncertainty. The key to DBC is to select a few models that can represent hundreds of possible reservoir models. DBC is defined as a combination of four technical processes: distance definition, distance matrix construction, dimensional reduction, and clustering. In this paper, we review the algorithms employed in each step. For distance calculation, Minkowski distance is recommended with even order due to sign problem. In the case of clustering, K-means algorithm has been commonly used. DBC has been applied to various reservoir types from channel to unconventional reservoirs. DBC is effective for unconventional resources and enhanced oil recovery projects that have a significant advantage of reducing the number of reservoir simulations. Recently, DBC studies have been performed with deep learning algorithms for feature extraction to define a distance and for effective clustering.

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“…These methods typically involve the following steps: (1) estimating a prior ensemble of facies distribution using indicator geostatistical models, (2) translating the ensemble facies distribution to continuous shape parameters that describe the facies boundaries, (3) performing EDA on the shape parameters to improve the match between model predictions and observations, and (4) updating the facies distribution using the updated shape parameters. All of the aforementioned parameterization methods impose no spatial structure constraints on facies (e.g., facies volume proportions, correlation lengths, or juxtapositional tendencies) in the process of data assimilation, which may lead to an unrealistic discontinuity and/or overfitting in resulted facies distribution (Jung et al, 2017;Ma & Jafarpour, 2018;Nejadi et al, 2015;Vo & Durlofsky, 2016;Zhao et al, 2017). Chang and Zhang (2014) proposed to alleviate the facies discontinuity issue by updating facies indicators on a coarse representation of the computational grid and then using interpolation to generate the rest of the facies field.…”

Section: Introductionmentioning

confidence: 99%

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

Song

Dai

et al. 2019

Water Resources Research

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The facies‐based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble‐based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. We introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability‐based geostatistics. A level‐set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies‐based permeability field. The first example is a two‐dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three‐dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.

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Recursive update of channel information for reliable history matching of channel reservoirs using EnKF with DCT

Cited by 30 publications

References 31 publications

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Efficient Assessment of Reservoir Uncertainty Using Distance-Based Clustering: A Review

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

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