Numerical-Simulation Investigation of the Effect of Heavy-Oil Viscosity on the Performance of Hydrocarbon Additives in SAGD
Bibliographic record
Abstract
Summary 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. Steam-assisted gravity drainage (SAGD), which has shown considerable promise in all three of these major deposits, remains an expensive technique and requires large energy input. The energy intensity of SAGD and the environmental concerns make it imperative to find new oil-extraction technologies. Coinjecting hydrocarbon additives with steam offers the potential of lower energy and water consumption and reduced green-house-gas emission by improving the oil rates and recoveries. In a previous paper by the same authors (Hosseininejad Mohebati et al. 2010), we showed 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 main difference between these three reservoirs, could be a very important parameter in the performance of this hybrid process. Therefore, it is necessary to optimize the hydrocarbon additives to SAGD for these three oil-sand deposits separately. 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 with different heavy-oil viscosities (resembling those of Athabasca bitumen, Cold Lake heavy oil, and Lloydminster heavy oil). Varying mole percents of hexane, butane, and methane were coinjected with steam to each reservoir with different heavy-oil viscosity. The optimum amount of hydrocarbon injection was reported in each case. This culminated in a novel method for selecting the most advantageous hydrocarbon additive and its optimum concentration considering the heavy-oil viscosity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".