The Best Process for Cold Lake: CSS vs. SAGD
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Bibliographic record
Abstract
Abstract Hilda Lake is a Steam Assisted Gravity Drainage (SAGD) project. The Mahkeses project is an expansion of the Imperial Oil Resources Limited commercial Cold Lake project based on Cyclic Steam Stimulation (CSS). Both projects are being implemented in the same Clearwater reservoir and will be adjacent to each other at the lease boundary. The projected performance of CSS at Mahkeses as given in the Alberta Energy Board Application for Approval is compared with the predicted and actual performance of SAGD at Hilda Lake. Parameters to be compared include: production rates, steam requirements, electrical power requirements, projected recovery factors, and produced water quality. Introduction The CSS process as operated by Imperial Oil is described in the Application(1) to the Alberta Energy Utilities Board (EUB) for expansion of the Cold Lake Project which was submitted in February 1997. The SAGD process is described by Butler(2). In his 1997 paper Batycky(3) compared the actual CSS performance to numerical simulations of SAGD and concluded that the performance parameters for CSS were better in all respects. These parameters included cumulative steam oil ratio (CSOR) and ultimate recovery. Ali(4, 5) has repeated this assertion and in his more recent presentation has stated that SAGD is not economic. During 1997 two pilot projects were initiated to test the SAGD process in the Clearwater formation in the Cold Lake area. The first is the Burnt Lake Project operated by Suncor which consists of three horizontal well pairs. The second is the Hilda Lake Project operated by BlackRock which consists of one well pair. Baker(6) indicates that the Burnt Lake Project is meeting expectations. The design of the Hilda Lake Project is described by Donnelly(7) and the performance of the pilot is compared to numerical simulation predictions by Donnelly(8). This comparison given in Figure 1 for production rate and in Figure 2 for steam oil ratio indicates that the pilot is performing as predicted. In this paper parameters that can be extracted from the predictions for the Hilda Lake Project and from the actual operating data are compared to the projections for CSS provided in the Imperial Oil Application(1) and the supplemental information submitted to the EUB in January 1998(9). FIGURE 1: Hilda Lake, prediction compared to actual. (Available in full paper) FIGURE 2: Hilda Lake, steam oil ratio, prediction compared to actual. (Available in full paper) Reservoir Development Table 1 compares the typical reservoir properties of the Mahkeses Cold Lake Expansion with those of the Hilda Lake Pilot. The Mahkeses properties are based on Figure 2–7 of the of the expansion application(1) which gives the properties of the well TABLE 1: Average reservoir properties. (Available in full paper) FIGURE 3: Production forecast. (Available in full paper) 8–32–64–3 W4. The Hilda Lake properties are based on the 20 m above the horizontal well for the well 15-17-64-3 W4 which is located near the project because the other 18 m of reservoir are of very low quality. The reservoir quality based on permeability and initial oil saturation at Mahkeses is equivalent to that at Hilda Lake.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it