Distributed temperature sensing (DTS), an optical fiber down-hole monitoring technique, provides a continuous and permanent well temperature profile. In steam assisted gravity drainage (SAGD) reservoirs, the DTS plays an important role to provide depth-and-time continuous temperature measurement for steam management and production optimization. These temperature observations provide useful information for reservoir characterization and shale detection in SAGD reservoirs. However, use of these massive data for automated SAGD reservoir characterization has not been investigated. The ensemble Kalman filter (EnKF), a parameter estimation approach using these real-time temperature observations, provides a highly attractive algorithm for automatic history matching and quantitative reservoir characterization. Due to its complex geological nature, the shale barrier exhibits as a different facies in sandstone reservoirs. In such reservoirs, due to non-Gaussian distributions, the traditional EnKF underestimates the uncertainty and fails to obtain a good production data match. We implemented discrete cosine transform (DCT) to parameterize the facies labels with EnKF. Furthermore, to capture geologically meaningful and realistic facies distribution in conjunction with matching observed data, we included fiber-optic sensor temperature data. Several case studies with different facies distribution and well configurations were conducted. In order to investigate the effect of temperature observations on SAGD reservoir characterization, the number of DTS observations and their locations were varied for each study. The qualities of the history-matched models were assessed by comparing the facies maps, facies distribution, and the root mean square error (RMSE) of the predicted data mismatch. Use of temperature data in conjunction with production data demonstrated significant improvement in facies detection and reduced uncertainty for SAGD reservoirs. The RMSE of the predicted data is also improved. The results indicate that the assimilation of DTS data from nearby steam chamber location has a significant potential in significant reduction of uncertainty in steam chamber propagation and production forecast.
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July 2015
Research-Article
Importance of Distributed Temperature Sensor Data for Steam Assisted Gravity Drainage Reservoir Characterization and History Matching Within Ensemble Kalman Filter Framework
Amit Panwar,
Amit Panwar
1
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural Resources
Engineering Facility,
e-mail: apanwar@ualberta.ca
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural Resources
Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: apanwar@ualberta.ca
1Now with Alberta Energy Regulator, Calgary, AB T2P 0R4, Canada.
Search for other works by this author on:
Japan J. Trivedi,
Japan J. Trivedi
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural
Resources Engineering Facility,
e-mail: jtrivedi@ualberta
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural
Resources Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: jtrivedi@ualberta
Search for other works by this author on:
Siavash Nejadi
Siavash Nejadi
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural
Resources Engineering Facility,
e-mail: nejadi@ualberta.ca
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural
Resources Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: nejadi@ualberta.ca
Search for other works by this author on:
Amit Panwar
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural Resources
Engineering Facility,
e-mail: apanwar@ualberta.ca
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural Resources
Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: apanwar@ualberta.ca
Japan J. Trivedi
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural
Resources Engineering Facility,
e-mail: jtrivedi@ualberta
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural
Resources Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: jtrivedi@ualberta
Siavash Nejadi
Department of Civil and
Environmental Engineering,
School of Mining and Petroleum Engineering,
3-122 Markin/CNRL Natural
Resources Engineering Facility,
e-mail: nejadi@ualberta.ca
Environmental Engineering,
School of Mining and Petroleum Engineering,
University of Alberta
,3-122 Markin/CNRL Natural
Resources Engineering Facility,
Edmonton, AB T6G 2R3
, Canada
e-mail: nejadi@ualberta.ca
1Now with Alberta Energy Regulator, Calgary, AB T2P 0R4, Canada.
Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received June 10, 2013; final manuscript received May 26, 2014; published online April 6, 2015. Assoc. Editor: G. Robello Samuel.
J. Energy Resour. Technol. Jul 2015, 137(4): 042902 (12 pages)
Published Online: July 1, 2015
Article history
Received:
June 10, 2013
Revision Received:
May 26, 2014
Online:
April 6, 2015
Citation
Panwar, A., Trivedi, J. J., and Nejadi, S. (July 1, 2015). "Importance of Distributed Temperature Sensor Data for Steam Assisted Gravity Drainage Reservoir Characterization and History Matching Within Ensemble Kalman Filter Framework." ASME. J. Energy Resour. Technol. July 2015; 137(4): 042902. https://doi.org/10.1115/1.4027763
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