Experimental and Numerical Modelling of Hybrid Steam In-Situ Upgrading Process for Immobile Oil
Bibliographic record
Abstract
The Canadian oil sands constitute the third largest accumulation of oil in the world. Various in situ recovery technologies are applied to extract the bitumen, most of them rely on steam injection which requires a large amount of energy while releasing a considerable amount of carbon dioxide. Because the bitumen produced from the Oil Sands is not pipeline transportable at surface conditions, it is necessary to improve its characteristics by lowering the viscosity and increasing the API gravity. An integrated concept for recovering and upgrading is presented by the In-Situ Upgrading Technology (ISUT), which partially replaces the injection of steam with a hot catalytic mixture that includes the heaviest fraction of the bitumen and hydrogen. ISUT is an alternative option for enhancing the recovery and upgrading the oil in the reservoir to produce a synthetic crude oil that meets the pipeline requirements reducing costs, environmental emissions, while eliminating diluent. In this work, a version of ISUT, which involves steam and hot nano-fluid injection, was evaluated by performing experiments in a vacuum insulated core-holder with a well arrangement similar to SAGD using the typical Athabasca reservoir properties and operating conditions of 450psig, 350⁰C and 8 hours of reaction-residence time. In-situ upgrading was assessed by performing comprehensive analyses such as viscosity, API gravity, simulated distillation, micro carbon residue, sulphur, and stability (P-value) of the products plus detailed characterization of the porous media after running the experiments. The main results indicated that the injection of the hot catalytic mixture enhanced the bitumen recovery and upgrading of the Athabasca vacuum residue. The occurrence of hydrogenation reactions allowed the production of upgraded products while coke formation was avoided.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".