SBH: A Thermal Hybrid Process Tailored for Challenging Reservoirs
Bibliographic record
Abstract
Abstract Hybrid processes combine the benefits of both thermal and solvent processes. The results generated to date in this area demonstrate potential to develop improved processes that will significantly reduce energy, water and greenhouse gas (GHG) intensity, and therefore operating costs, while maintaining economic oil recovery rates. The Steam Butane Hybrid (SBH) process has emerged as a promising hybrid process that could be considered as an alternative to SAGD, where the performance of SAGD is expected to be less than ideal. This paper summarizes the results of a study designed to evaluate the performance of the SBH process in comparison with the SAGD process for three different reservoir models (Geomodel I, II and III) using numerical simulation. Geomodel III is the cleanest (highest average permeability) and most homogeneous of the three reservoir models. Geomodel II has the lowest average permeability, and the highest shale content, while Geomodel I is midway between the other two, in terms of average permeability and shale content. Five well pairs were used in all of the simulations. Three different injection pressures were investigated: 2000, 1000 & 800 kPa*. It was found that geology plays a very important role in determining the competitive advantage of SBH vs. SAGD. It also plays an important role in establishing optimal operating conditions for the process in the field. The predicted performance of SBH, in terms of cumulative oil production, was better than that of SAGD at low injection pressures in the two geomodels (I & II) with lower average permeabilities. In the geomodel with the highest average permeability (III), the predicted performance of the SBH process was slightly poorer than that of SAGD at a relatively low injection pressure (800 kPa), but slightly better than that of SAGD at a relatively high injection pressure (2000 kPa). These numerical simulation results indicate that the relative performance of SBH as compared with SAGD is likely to be reservoir specific, meaning that the optimal injection pressures and butane concentrations should be tailored to specific reservoir characteristics, on a case by case basis, rather than generalizing the process to a range of reservoir conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".