Optimization of Hydrocarbon Additives With Steam in SAGD for Three Major Canadian Oil Sands Deposits
Bibliographic record
Abstract
Abstract Heavy oil and bitumen are expected to become increasingly important sources of fuel in the coming decades. There are extensive deposits in Alberta that could be a principal source of fuel in the coming century. The Athabasca Oil Sands, the largest petroleum accumulation in the world, the Cold Lake oil deposit, and the Lloydminster reservoir are all major Canadian oil sands deposits. SAGD, which has shown considerable promise in all three of these major deposits, remains an expensive technique and requires large energy input. Energy intensity of SAGD and the environmental concerns make it imperative to find new oil extraction technologies. Co-injecting hydrocarbon additives with steam offers the potential of lower energy and water consumption and reduced greenhouse gas emission by improving the oil rates and recoveries. In a previous paper by the same authors(Hosseininejad Mohebati, Maini et al. 2009), it was shown that the selection of a suitable hydrocarbon additive and the effectiveness of this hybrid process are strongly dependent on the operating conditions, reservoir fluid composition, the heavy oil viscosity, and the petrophysical properties of the reservoir. Among these factors, the heavy oil viscosity which is the most prominent difference between these three reservoirs could be a very important parameter in the performance of this hybrid process. Therefore, it is important to evaluate the effect of oil viscosity on solvent assisted SAGD. Extensive numerical studies in a 3D model by means of a fully implicit thermal simulator were conducted to evaluate the efficiency of each hydrocarbon additive in Athabasca, Cold Lake and Lloydminster reservoirs. Varying mole percents of hexane, butane and methane were co-injected with steam in with different values of heavy oil viscosity. The effect of oil viscosity on the performance of each solvent was compared in terms of oil production rate and cumulative steam oil ratio.
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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 | 0.000 | 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 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".