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Research Papers: Petroleum Engineering

Importance of Distributed Temperature Sensor Data for Steam Assisted Gravity Drainage Reservoir Characterization and History Matching Within Ensemble Kalman Filter Framework

[+] Author and Article Information
Amit Panwar

Department of Civil and
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,
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,
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 137(4), 042902 (Jul 01, 2015) (12 pages) Paper No: JERT-13-1177; doi: 10.1115/1.4027763 History: Received June 10, 2013; Revised May 26, 2014; Online April 06, 2015

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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Figures

Grahic Jump Location
Fig. 1

Permeability map of the single facies model. (a) Average permeability of the initial ensemble. (b) Permeability distribution of the reference model.

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Fig. 2

Permeability map of the two facies model. Average permeability of the initial ensemble (left). Permeability distribution of the reference model (right).

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Fig. 3

(a) Oil production rate, (b) steam oil ratio, and (c) block temperature responses of the initial ensemble, before history matching

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Fig. 4

Single facies model: comparison of the permeability update for the three different cases with 0, 4, and 8 temperature observations. The average permeability map at different update steps compared to the true case is shown for each case.

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Fig. 5

Oil production rate of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 6

Cumulative steam–oil ratio of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 7

Block temperature of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 8

Comparison of root mean square error of the updated ensemble for the three cases with 0, 4, and 8 temperature observations for the single facies model

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Fig. 9

Two facies model: (a) Oil production rate, (b) steam oil ratio, and (c) block temperature responses of the initial ensemble, before history matching

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Fig. 10

Two facies model: comparison of the permeability update for the three different cases with 0, 4, and 8 temperature observations. The average permeability map at different update steps compared to the true case is shown for each case.

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Fig. 11

Two facies model; Oil production rate of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 12

Two facies model: Cumulative steam-oil ratio of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 13

Two facies model: Block temperature of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 14

Two facies model: Comparison of root mean square error of the updated ensemble for the three cases with 0, 4, and 8 temperature observations

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Fig. 15

Two facies model: Sensitivity analysis of the locations of temperature observations

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Fig. 16

Two facies model: comparison of the permeability update for three cases with different locations of four temperature observations. The average permeability map at different update steps compared to the true case is shown for each case.

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Fig. 17

Two facies model: Comparison of root mean square error of the updated ensemble for the three cases with different locations of the four temperature observations

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Fig. 18

Temperature profile of the SAGD model. (a) Reference model. (b) Initial ensemble members (Ensemble average).

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Fig. 19

Temperature profile of the updated ensemble for the SAGD model. (a) No temperature observation. (b) Four temperature observations. (c) Eight temperature observations.

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Fig. 20

Permeability map of the 3D-SAGD model (layer number 5). (a) Permeability distribution of the reference model. (b) Average permeability of the initial ensemble. (c) Average permeability of the updated ensemble.

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Fig. 21

Permeability map of the 3D-SAGD model (layer number 1). (a) Permeability distribution of the reference model. (b) Average permeability of the initial ensemble. (c) Average permeability of the updated ensemble.

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Fig. 22

Oil Production rate (SM3/DAY) for the 3D-SAGD model. (a) Before and (b) after history match.

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Fig. 23

Cumulative steam–oil ratio for the 3D-SAGD model. (a) Before and (b) after history match.

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Fig. 24

Block temperature for the 3D-SAGD model. (a) Before and (b) after history match.

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