Bibliographic record
Abstract
Abstract Fast-SAGD, a modification of the SAGD process, makes use of additional single horizontal wells alongside the SAGD well pair to expand the steam chamber laterally. This method uses fewer wells and could reduce costs compared to a SAGD operation requiring paired parallel wells one above the other. In this study, the Fast-SAGD process has been optimized through numerical reservoir simulations for the three typical oil sands areas in Alberta: Athabasca, Cold Lake, and Peace River. Two key reservoir parameters, reservoir thickness and vertical permeability, were screened under various operating conditions to characterize the optimal reservoir and operating conditions for the Fast-SAGD process. Economic analysis was then used for optimizing the Fast-SAGD operating conditions. In most cases, the simulation results indicated improved energy efficiency and productivity for the Fast-SAGD process. In those cases, the project economics were enhanced compared to the SAGD process. Both Cold Lake- and Peace River-type reservoirs are good candidates for Fast-SAGD. In shallow Athabasca- type reservoirs, which are thick with high permeability, Fast-SAGD was shown to be almost as good as SAGD. This new process demonstrates improved efficiency and lower costs for extracting heavy oil from these important reservoirs. Introduction The steam assisted gravity drainage (SAGD) process was first implemented in Alberta and is now well established for the commercial production of bitumen from oil sands. Research studies(1–3) have found that the SAGD process is feasible for reservoirs thicker than 20 m with permeability in excess of 2 Darcies. In the Fast-SAGD process, wells offset to the SAGD well pair, on either or both sides, are operated with cyclic steam stimulation (CSS) in order to accelerate the growth of the steam chamber Sideways(4). Moreover and consequent to such CSS, geomechanical stresses are larger than provided by industry's preferred relatively low-pressure SAGD to the extent that vertical drainage is enhanced significantly(5). Thus, Fast-SAGD uses fewer wells and achieves greater conformance and reduced costs compared to a SAGD operation. Previous numerical studies(1, 5) of a typical Cold Lake-type reservoir have shown that the Fast-SAGD process enhances thermal efficiency, resulting in better production performance as compared to the conventional SAGD process. In our study, Fast-SAGD operating conditions were optimized through numerical reservoir simulation for the three typical oil sands areas in Alberta: the shallow Athabasca (AB), the Cold Lake (CL), and the Peace River (PR). Two key reservoir parameters, reservoir thickness and permeability, were screened under various operating conditions to characterize the optimal reservoir for the Fast-SAGD process in each deposit. A simple thermal efficiency parameter (STEP) was developed on the basis of three production performance parameters: cumulative steam-oil ratio (CSOR), calendar day oil rate (CDOR), and recovery factor (RF). It was validated as an economic indicator for optimizing SAGD performance(6, 7). This same economic indicator will also be used in this study to optimize the Fast-SAGD operating conditions. Optimizing the Fast-SAGD Process The Fast-SAGD process introduced by Polikar et al.(4) combines the SAGD and CSS processes. CSS helps the steam chamber formed by SAGD propagate sideways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".