Research Papers: Petroleum Engineering

Efficient Prediction of SAGD Productions Using Static Factor Clustering

[+] Author and Article Information
Haeseon Lee

Department of Energy System Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: HLee7@slb.com

Jeongwoo Jin

Department of Energy System Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: jin8146@snu.ac.kr

Hyundon Shin

Department of Energy Resources Engineering,
Inha University,
Incheon 402-751, Korea
e-mail: hyundon.shin@inha.ac.kr

Jonggeun Choe

Department of Energy Resources Engineering,
Seoul National University,
Seoul 151-744, Korea
e-mail: johnchoe@snu.ac.kr

1Present address: Schlumberger Information Solution, Seoul, 100-768, Korea

2Corresponding author.

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received January 13, 2013; final manuscript received January 10, 2015; published online February 9, 2015. Assoc. Editor: Arash Dahi Taleghani.

J. Energy Resour. Technol 137(3), 032907 (May 01, 2015) (6 pages) Paper No: JERT-13-1018; doi: 10.1115/1.4029669 History: Received January 13, 2013; Revised January 10, 2015; Online February 09, 2015

Oil sands have great amount of reserves in the world with increasing commercial productions. Prediction of reservoir performances of oil sands is challenging mainly due to long simulation time for modeling heat and fluids flows in steam assisted gravity drainage (SAGD) operations. Because of accurate modeling difficulties and limited geophysical data, it requires many simulation cases of geostatistically generated fields to cover uncertainty 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 be used for efficient prediction of oil sands production. The features vector proposed can reflect shale barrier effects in terms of 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 for full simulation run and they will cover cumulative probability distribution function (CDF) of all the fields examined. Accuracy of the prediction is high when cluster number is more than 10 based on cases of cluster number 5, 10, and 15. This technique is applied to fields with 3%, 5%, 10%, and 15% shale fraction and all the cases allow efficient and economical predictions of oil sands productions.

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Fig. 1

Examples of shale distribution with correlation lengths (5% shale). (a) Correlation length = 1, (b) correlation length = 5, (c) correlation length = 10, (d) correlation length = 15, and (e) correlation length = 20.

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Fig. 2

Analysis of shale barrier effects on shale correlation length and shale amount. (a) Shale amount and (b) correlation length.

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Fig. 3

The flow of this study

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Fig. 4

The process for extracting the features vector proposed in this study. (a) Shale distribution in an oil sands field (black represents shale), (b) shale barrier effects processed, and (c) steam chamber expanding area.

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Fig. 5

Example of extracting the features vector components. (a) Example of features vector target and (b) ith component of the features vector.

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Fig. 6

Validation of the features vector proposed. (a) Example of the ranking factor by Fenik et al. [10] and (b) comparison of cumulative oil production prediction with the ranking factor by Fenik et al. [10] and the features vector in this study.

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Fig. 7

Prediction of cumulative oil productions with different cluster numbers (3% shale). The solid line represents the whole 100 models. (a) 5 clusters, (b) 10 clusters, and (c) 15 clusters.

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Fig. 8

Prediction of cumulative oil productions with different cluster numbers (10% shale). The solid line represents the whole 100 models. (a) 5 clusters, (b) 10 clusters, and (c) 15 clusters.



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