Numerical Thermal Simulation and Optimization of Hybrid CSS/SAGD Process in Long Lake with Lean Zones
Bibliographic record
Abstract
Abstract Cyclic steam stimulation (CSS) and steam-assisted gravity drainage (SAGD) are successful commercial methods for enhanced oil recovery (EOR) used in Canada. Although SAGD exhibits higher oil recovery than CSS, it is more sensitive to reservoir heterogeneities such as top water and lean zones. In most cases, these baffles cause channeling in steam injection operations, thus leading to a high steam-oil ratio (SOR) and low injectivity. Therefore, recovering bitumen by using SAGD in a pilot well pad with such baffles in Long Lake has been a considerable challenge. To overcome the aforementioned issues, a hybrid process that combines CSS and SAGD (hybrid CSS/SAGD) is investigated in this study. Hybrid CSS/SAGD takes advantage of the benefits of both methods. During this process, all SAGD wells operate as cyclic injection/production wells. When the wells are shut in for a soak period, the injectivity of SAGD is highly improved. Simultaneously, the producer can also function as a heat source. After good conductivity is achieved between the injector and the producer, oil recovery is improved significantly. This study aims to construct a hybrid CSS/SAGD simulation model for the Long Lake pilot well pad. The simulation is based on a well-defined 3D geostatistical model. By strictly controlling detailed lithological facies, lean zones and top water zones are accurately described in the geological model. These baffles are mainly located in the upper-middle portion of the McMurray Formation. By adjusting relative permeability curves, a SAGD model with a ten-year production history (beginning in April 2003) is history matched well. Based on this model, we then run hybrid CSS/SAGD simulations by changing well constraints from April 2003 with a submodel of well pair 1. Both wells in the well pair 1 operate as producers and injectors. The simulation results show that CSS-SAGD exhibits higher oil recovery and lower SOR than SAGD alone. Moreover, sensitivity analysis on preheating pressure is performed. Based on the net present value, we optimize the steam injection pressure and steam injection rate to establish criteria for each parameter for the pilot well pad.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".