Importance of Distributed Temperature Sensor (DTS) Placement for SAGD Reservoir Characterization and History Matching Within Ensemble Kalman Filter (EnKF) Framework
- Amit Panwar (U. of Petr & Energy Studies - India) | Japan J. Trivedi (U. of Alberta) | Siavash Nejadi
- Document ID
- Society of Petroleum Engineers
- SPE Latin America and Caribbean Petroleum Engineering Conference, 16-18 April, Mexico City, Mexico
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.5.8 History Matching, 5.1.5 Geologic Modeling, 5.4.6 Thermal Methods, 5.6.9 Production Forecasting, 5.3.2 Multiphase Flow, 5.6.11 Reservoir monitoring with permanent sensors, 5.5 Reservoir Simulation, 2.4.3 Sand/Solids Control, 5.1 Reservoir Characterisation, 5.3.9 Steam Assisted Gravity Drainage, 5.8.5 Oil Sand, Oil Shale, Bitumen
- 1 in the last 30 days
- 265 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Distributed Temperature Sensing (DTS), an optical fiber downhole monitoring technique, provides a continuous and permanent well temperature profile. In 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 underestimate 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.
The Steam Assisted Gravity Drainage (SAGD) provides significantly greater production rates, high recoveries, and lower Stem-Oil ratio (SOR), as compared to conventional surface mining extraction techniques and other thermal recovery methods. It is due to these advantages that the SAGD technique is considered the most promising process for recovering Athabasca oil sands' deposits, which contains 140 billion cubic meters or one trillion barrels of bitumen-in-place; this amount accounts for 20% of Canada's total oil reserve and two-third of Alberta's (Redford et al. 1999).
|File Size||2 MB||Number of Pages||18|