Uncertainty Analysis of Oil Sands Reservoirs Using Features in Metric Space

Lim, Joong‐Soo; Jin, Jeongwoo; Lee, H.; Choe, Jonggeun

doi:10.1080/15567036.2011.606457

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…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]. PCA, which is a dimensional reduction technique, was applied to extract the main feature from permeability fields [24][25][26].…”

Section: Combined Distancementioning

confidence: 99%

“…Jung et al [26] used PCA to extract the general trends of eigenvalues for efficient analyses. Because PCA is helpful for representing the data according to their main properties, it can be employed to present the data in 2D views [22,23]. There are some rules of thumb to decide the number of reduced principal components (PCs) in PCA: (1) Keep cumulative proportion of PCs at least 0.8, (2) Keep only PCs with above-average variance, (3) Keep PCs before the elbow in scree plot.…”

Section: + ( − 3) =mentioning

confidence: 99%

“…Therefore, features related to shale barriers can be employed to measure the dissimilarity between two oil sands reservoirs. Connected hydrocarbon volume is applied in not only DBC but also ranking methods [22,23,71]. Figure 12a shows the concept of connected hydrocarbon volume (grey area).…”

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

“…An offshore turbidite reservoir in West Figure 12. Feature extraction using connected hydrocarbon volume (the grey area) concept in oil sands reservoirs (modified from [23]): (a) straight lines from the injection well (the red circle) until they meet shale facies (the white area); (b) the sight map is converted into a polar coordinate system, which has an x-axis and y-axis that is represented as the angle and the length, respectively; and (c) few parameters in the frequency domain by DFT are used for a feature.…”

Section: Unconventional Resources and Real Fieldsmentioning

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: + ( − 3) =mentioning

confidence: 99%

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

Section: Unconventional Resources and Real Fieldsmentioning

confidence: 99%

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

et al. 2019

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“…We propose a model selection scheme using principal component analysis (PCA) and clustering analysis. PCA is a useful mathematical tool to manage high-dimensional data by extracting primary parameters of the data (Lim et al 2015;Siena et al 2016;Jung et al 2018). Some researchers utilized PCA with other schemes to apply to multipoint geostatistics (Vo and Durlofsky 2014, 2016Chen et al 2016).…”

Section: Introductionmentioning

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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Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs

Lee

Lim²,

Ahn

et al. 2018

Journal of Petroleum Science and Engineering

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Uncertainty Analysis of Oil Sands Reservoirs Using Features in Metric Space

Cited by 3 publications

References 3 publications

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

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

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

Feature extraction using a deep learning algorithm for uncertainty quantification of channelized reservoirs

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