Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation

Jin, Jeongwoo; Lim, Joong‐Soo; Lee, Haeseon; Choe, Jonggeun

doi:10.1080/12269328.2011.10541338

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Many studies have employed proxy or tracer simulations to reduce the simulation cost of forward simulation. The results of streamline simulation, such as generalized travel time, streamline density map, and oil production rates, are fast and reliable to replace conventional reservoir simulation [6,[40][41][42][43][44]. A proxy model, which is built to predict specific reservoir performances, has been used to predict pseudo dynamic data, such as the water breakthrough time and monthly steam injection rate [45,46].…”

Section: Combined Distancementioning

confidence: 99%

“…Their objective is to determine the reduced number of ensembles, which has the minimum difference with the original ensemble. Jin et al [40] measured the distance by counting the number of grid blocks that are passed by streamlines to understand the flow patterns. An efficient distance matrix calculation is a very important process in DBC, because hundreds of reservoir models and hundreds of thousands of grid blocks may be employed for the analysis.…”

Section: Datamentioning

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: Datamentioning

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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Efficient Prediction of SAGD Productions Using Static Factor Clustering

Lee

Jin

Shin

et al. 2015

Journal of Energy Resources Technology

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OH sands have great amount o f reserves in the world with increasing commercial produc tions. Prediction of reservoir performances o f oil sands is challenging mainly due to long simulation time fo r modeling heat and fluids flows in steam assisted gravity drainage (SAGD) operations. Because o f accurate modeling difficulties and limited geophysical data, it requires many simulation cases o f geostatistically generated fields to cover uncer tainty in reservoir modeling. Therefore, it is imperative to develop a new technique to analyze production performances efficiently and economically. This paper presents a new ranking method using a static factor that can he used fo r efficient prediction o f oil sands production. The features vector proposed can reflect shale barrier effects in terms o f shale length and relative distance from the injection well. It preprocesses area that steam chamber bypasses, and then counts steam chamber expanding an area cumulatively. K-means clustering selects a few fields fo r fu ll simulation run and they will cover cumula tive probability distribution function (CDF) o f all the fields examined. Accuracy o f the prediction is high when cluster number is more than 10 based on cases o f cluster number 5, 10, and 15. This technique is applied to fields with 3%, 5%. 10%, and 15% shale fra c tion and all the cases allow efficient and economical predictions o f oil sands productions

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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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Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation

Cited by 4 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

Efficient Prediction of SAGD Productions Using Static Factor Clustering

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

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