Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Lee, Kyungbook; Kim, Sung-Il; Choe, Jonggeun; Min, Baehyun; Lee, Hyun Suk

doi:10.1007/s12182-018-0254-x

Cited by 11 publications

(12 citation statements)

References 38 publications

(43 reference statements)

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“…These methods are used to measure the dissimilarity of data in an extensive range of research areas, which manage complex and numerous datasets, including reservoir engineering. The Euclidean distance is one of the most popular formulas because it is simple and straightforward [6,15,20,26,37,41,[48][49][50][51]. It is a straight-line length between two data in Euclidean space.…”

Section: Distance Matrixmentioning

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: Distance Matrixmentioning

confidence: 99%

Section: + ( − 3) =mentioning

confidence: 99%

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

et al. 2019

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“…MPS is used in different disciplines ranging from soil science (Meerschman et al 2014), prediction of the occurrence of rainfall (Oriani et al 2014), water resources modeling, i.e., remote sensing, (Mariethoz et al 2012), hydrogeology (Huysmans and Dassargues 2012), medical imaging (Pham 2012) to the fluid flow through underground formations (Okabe and Blunt 2004;Huysmans and Dassargues 2009;Xu et al 2012;Tamayo-Mas et al 2016;Lee et al 2019;Mosser et al 2018).…”

Section: Mps Applicationsmentioning

confidence: 99%

“…MPS methods could also be implemented for inversion problems (Lee et al 2019). Caers and Hoffman introduced the probability perturbation method (PPM) in this regard (Caers and Hoffman 2006).…”

Section: Mps Applicationsmentioning

confidence: 99%

Enduring effect of permeability texture for enhancing accuracy and reducing uncertainty of reservoir fluid flow through porous media

et al. 2019

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Modeling reservoir permeability is one of the crucial tasks in reservoir simulation studies. Traditionally, it is done by kriging-based methods. More rigorous modeling of the permeability results in more reliable outputs of the reservoir models. Recently, a new category of geostatistical methods has been used for this purpose, namely multiple point statistics (MPS). By this new category of permeability modeling methods, one is able to predict the heterogeneity of the reservoir permeability as a continuous variable. These methods consider the direction of property variation in addition to the distances of known locations of the property. In this study, the reservoir performance of a modified version of the SPE 10 solution project as a pioneer case is used for investigating the efficiency of these methods and paralleling them with the kriging-based one. In this way, the permeability texture concept is introduced by applying some MPS methods. This study is accomplished in the conditions of real reservoir dimensions and velocities for the whole reservoir life. A continuous training image is used as the input of calculation for the permeability modeling. The results show that the detailed permeability of the reservoir as a continuous variable makes the reservoir simulation show the same fluid front movement and flooding behavior of the reservoir similar to the reference case with the same permeability heterogeneity. Some MPS methods enable the reservoir simulation to reproduce the fluid flow complexities such as bypassing and oil trapping during water flooding similar to the reference case. Accordingly, total oil production is predicted with higher accuracy and lower uncertainty. All studied cases are identical except for the permeability texture. Even histograms and variograms of permeabilities for the studied reservoir are quite similar, but the performance of the reservoir shows that kriging-based method results have slightly less accuracy than some MPS methods. Meanwhile, it results in lower uncertainty in outputs for this water flooding case performance.

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“…In recent years, deep learning has provided new ideas and methods for oilfield development. Many scholars use longand short-term memory (LSTM) to predict production dynamics, pressure, and other time series data [23][24][25][26][27][28][29]. Based on the existing problems of the existing well control optimization methods and the inspiration of deep learning, this article establishes a new method of production dynamic prediction based on deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

Tang

et al. 2021

Geofluids

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A well control optimization method is a key technology to adjust the flow direction of waterflooding and improve the effect of oilfield development. The existing well control optimization method is mainly based on optimization algorithms and numerical simulators. In the face of larger models, longer optimization periods, or reservoir models with a large number of optimized wells, there are many optimization variables, which will cause algorithm convergence difficulties and optimization costs. The application effect is not good because of the problems of time length, few comparison schemes, and only fixed control frequency. This paper proposes a new method of a well control optimization method based on a multi-input deep neural network. This method takes the production history data of the reservoir as the main input and the saturation field as the auxiliary input and establishes a multi-input deep neural network for learning, forming a production dynamic prediction model instead of conventional numerical simulators. Based on the production dynamic prediction model, a series of model generation, production prediction, comparison, and optimization are carried out to find the best production plan of the reservoir. The calculation results of the examples show that (1) compared with the single-input production dynamic prediction model, the production dynamic prediction model based on multiple inputs has better prediction accuracy, and the results are close to the calculation results of the conventional numerical simulator; (2) the well control optimization method based on the multiple-input deep neural network has a fast optimization speed, with many comparison schemes and good optimization effect.

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Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Cited by 11 publications

References 38 publications

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

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

Enduring effect of permeability texture for enhancing accuracy and reducing uncertainty of reservoir fluid flow through porous media

Well Control Optimization of Waterflooding Oilfield Based on Deep Neural Network

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