Ensemble smoother with clustered covariance for 3D channelized reservoirs with geological uncertainty

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

doi:10.1016/j.petrol.2016.05.029

Cited by 32 publications

(25 citation statements)

References 30 publications

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“…It is highly correlated with future production because it is an important parameter that affects flow rate in Darcy's law. Facies for each grid also have been applied for the criterion in many previous researches [8][9][10][11]. Facies can substantially affect the reservoir responses because parameters for a reservoir simulation, such as the relative permeability function and saturation function, are assigned according to the facies.…”

Section: Combined Distancementioning

confidence: 99%

“…For this reason, some researchers applied other distance calculation methods for effective pattern recognition. One of these methods is the Hausdorff distance, which measures the distance between two datasets and enables many-to-many correspondence [3,[8][9][10][11][12]. Given the two point sets A and B, the Hausdorff distance calculates the distance H(A, B) using Equations (1) and (2).…”

Section: 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%

“…Since reservoir models usually have high dimensionality, some researchers have attempted to perform the algorithm on the featured plane using feature extraction methods, such as PCA and singular value decomposition [5,24,25]. MDS can also be utilized for dimensionality reduction of reservoir models on a 2D plane but distances between reservoir models should be predefined [3,4,[6][7][8][9]21,38,53].…”

Section: K-means Clusteringmentioning

confidence: 99%

“…During K-means clustering, we have to know two types of distance: distance between two points and the distance between a point and a centroid. These distances are obtained by Equations (9) and (10). Equation 11shows a popular kernel function-Gaussian radial basis function-and the band width σ can be set to 10-20% of the distance between points [63].…”

Section: K-means Clusteringmentioning

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%