Improvement of Ensemble Smoother with Clustered Covariance for Channelized Reservoirs

Lee, Kyungbook; Jeong, Hoonyoung; Jung, Sun-Young; Choe, Jonggeun

doi:10.1260/0144-5987.31.5.713

Cited by 32 publications

(24 citation statements)

References 25 publications

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“…Some researchers applied domain transformation techniques, such as discrete Fourier transform (DFT), principal component analysis (PCA), and discrete cosine transform (DCT). Main information on channelized facies models was successfully extracted by DFT [21]. After a facies image is converted into the frequency domain by using DFT, the boundary of the image can be detected in low-frequency areas (refer to [21]).…”

Section: Combined Distancementioning

confidence: 99%

“…Main information on channelized facies models was successfully extracted by DFT [21]. After a facies image is converted into the frequency domain by using DFT, the boundary of the image can be detected in low-frequency areas (refer to [21]). In the case of oil sands, a transformed connected hydrocarbon volume image was converted to the frequency domain by DFT to measure the dissimilarity of shale barriers [22,23].…”

Section: Combined Distancementioning

confidence: 99%

“…If more than three datasets exist, an exact solution in 2D space does not exist but we numerically approximate the position. Since MDS is a simple but very powerful algorithm to manage the data, many studies used MDS to reduce the data dimension [2][3][4][6][7][8][9]14,15,18,20,21,26,[36][37][38][39][40][43][44][45]51,53]. MDS can maintain the distance information while reducing the dimension in a lower space.…”

Section: + ( − 3) =mentioning

confidence: 99%

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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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Section: Combined Distancementioning

confidence: 99%

Section: Combined Distancementioning

confidence: 99%

Section: + ( − 3) =mentioning

confidence: 99%

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

et al. 2019

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“…The other variables, which are not significant regarding the objective functions, were fixed according to their mean values, as follows: the matrix permeability is 0.00002 md, the horizontal enhanced ratio is 0.3, and the vertical enhanced ratio is 1.2. History matching is implemented for a total duration of 540 days (1.5 years) according to the objective functions defined by Equations (15)- (18). The prediction accuracy from 540 days to 1800 days is compared with the true data, as depicted in Figure 9.…”

Section: Multi-objective History Matchingmentioning

confidence: 99%

Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir

et al. 2017

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This paper presents a fast, reliable multi-objective history-matching method based on proxy modeling to forecast the production performances of shale gas reservoirs for which all available post-hydraulic-fracturing production data, i.e., the daily gas rate and cumulative-production volume until the given date, are honored. The developed workflow consists of distance-based generalized sensitivity analysis (DGSA) to determine the spatiotemporal-parameter significance, fast marching method (FMM) as a proxy model, and a multi-objective evolutionary algorithm to integrate the dynamic data. The model validation confirms that the FMM is a sound surrogate model working within an error of approximately 2% for the estimated ultimate recovery (EUR), and it is 11 times faster than a full-reservoir simulation. The predictive accuracy on future production after matching 1.5-year production histories is assessed to examine the applicability of the proposed method. The DGSA determines the effective parameters with respect to the gas rate and the cumulative volume, including fracture permeability, fracture half-length, enhanced permeability in the stimulated reservoir volume, and average post-fracturing porosity. A comparison of the prediction accuracy for single-objective optimization shows that the proposed method accurately estimates the recoverable volume as well as the production profiles to within an error of 0.5%, while the single-objective consideration reveals the scale-dependency problem with lesser accuracy. The results of this study are useful to overcome the time-consuming effort of using a multi-objective evolutionary algorithm and full-scale reservoir simulation as well as to conduct a more-realistic prediction of the shale gas reserves and the corresponding production performances.

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“…However, most of proposed methods still need much simulation time and the restart option of a forward simulator due to recursive update of EnKF. After Van Leeuwen and Evensen (1996) (Gervais et al, 2012;Lee et al, 2013). Reynolds et al (2006) proposed iterative forms of EnKF.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification of Channelized Reservoir Using Ensemble Smoother with Selective Measurement Data

Lee

Jung²,

Shin

et al. 2014

Energy Exploration & Exploitation

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Ensemble smoother (ES) assimilates all available dynamic data without iterations as global update. Therefore, ES is much faster than ensemble Kalman filter (EnKF), which uses recursive updates. Iterative concepts are introduced for ES to increase accuracy of history matching. However, they lose advantages of simulation time and cost over EnKF.We propose ES with selective use of observation data in assimilation to improve history matching results and to keep simulation time short. Three methods, EnKF with all data, ES with all data, and the proposed, are applied to 2D synthetic channelized reservoirs with a nine spot waterflooding. Ensemblebased methods interpret the reason of reduced oil production rate after water breakthrough as low permeability. In this research, we suggest selective measurement data to manage misinterpretable data for the ensemble-based methods. As a logical choice, oil production rate before water breakthrough and water cut after water breakthrough are used for assimilation.EnKF with all data cannot predict true performances of oil and water productions on each well. ES with all data shows severe overshooting and filter divergence problems, which are two typical problems in the ensemble-based methods. However, the proposed method overcomes the two problems and shows good history matching results. It provides reliable uncertainty quantification of reservoir performances for both each well production and field total productions. Simulation cost of the proposed method is about 2.2% of that of EnKF, which uses 45 times update. It has clear advantage over EnKF or iterative ES methods.

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Improvement of Ensemble Smoother with Clustered Covariance for Channelized Reservoirs

Cited by 32 publications

References 25 publications

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

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

Multi-Objective History Matching with a Proxy Model for the Characterization of Production Performances at the Shale Gas Reservoir

Uncertainty Quantification of Channelized Reservoir Using Ensemble Smoother with Selective Measurement Data

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