0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Hoffman, B. T., and Shoaib, S., 2014, “CO2 Flooding to Increase Recovery for Unconventional Liquids-Rich Reservoir,” ASME J. Energy Resour. Technol., 136(2), p. 022801. [CrossRef]
Butler, R., 1998, Thermal Recovery of Oil and Bitumen, Prentice-Hall ECS Professional, Toronto, ON, Canada.
Chen, Q., Gerritsen, M. G., and Kovscek, A. R., 2008, “Effects of Reservoir Heterogeneities on the Steam-Assisted Gravity-Drainage Process,” SPE Reservoir Eval. Eng., 11(5), pp. 921–932. [CrossRef]
Mohebbifar, M., Ghazanfari, M. H., and Vossoughi, M., 2015, “Experimental Investigation of Nano-Biomaterial Applications for Heavy Oil Recovery in Shaly Porous Models: A Pore-Level Study,” ASME J. Energy Resour. Technol., 137(1), p. 014501. [CrossRef]
Shin, H., and Choe, J., 2009, “Shale Barrier Effects on the SAGD Performance,” SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, UAE, Oct. 19–21, SPE Paper No. 125211. [CrossRef]
Panwar, A., Trivedi, J. J., and Nejadi, S., “Importance of Distributed Temperature Sensor (DTS) Data for SAGD Reservoir Characterization and History Matching Within Ensemble Kalman Filter (EnKF) Framework,” ASME J. Energy Resour. Technol. (in press). [CrossRef]
Cheng, Y., Lee, W. J., and McVay, D. A., 2008, “Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs,” ASME J. Energy Resour. Technol., 130(4), p. 043201. [CrossRef]
Vicente, R., Sarica, C., and Ertekin, T., 2004, “A Numerical Model Coupling Reservoir and Horizontal Well Flow Dynamics—Applications in Well Completions, and Production Logging,” ASME J. Energy Resour. Technol., 126(3), pp. 169–176. [CrossRef]
McLennan, J. A., and Deutsch, C. V., 2005, “Ranking Geostatistical Realizations by Measures of Connectivity,” SPE/PS-CIM/CHOA International Thermal Operations and Heavy Oil Symposium, Calgary, AB, Canada, Nov. 1–3, SPE/PS-CIM/CHOA Paper No. 98168.
Fenik, D. R., Nouri, A., and Deutsch, C. V., 2009, “Criteria for Ranking Realizations in the Investigation of SAGD Reservoir Performance,” Canadian International Petroleum Conference, Calgary, AB, Canada, June 16–18, Paper No. 2009-191.
Jin, J., Lim, J., Lee, H., and Choe, J., 2011, “Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation,” Geosyst. Eng., 14(3), pp. 109–113. [CrossRef]
Lim, J., Jin, J., and Choe, J., 2014, “Features Modeling of Oil Sands Reservoirs in Metric Space,” Energy Sour. Part A, 36(24), pp. 2725–2735. [CrossRef]
McLennan, J. A., Ren, W., Leuangthong, O., and Deutsch, C. V., 2006, “Optimization of SAGD Well Elevation,” Nat. Resour. Res., 15(2), pp. 119–127. [CrossRef]
Card, C. C., Kumar, A., Close, J. C., Kjosavik, A., Agustsson, H., and Picone, M. M., 2014, “A New and Practical Workflow for Large Multipad SAGD Simulation—An Oil-Sands Case Study,” J. Can. Pet. Technol., 53(1), pp. 14–31. [CrossRef]

Figures

Grahic Jump Location
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.

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

The flow of this study

Grahic Jump Location
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.

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
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.

Grahic Jump Location
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.

Grahic Jump Location
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.

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In