Laboratory Two-Dimensional Experimental Simulation of Catalytic in Situ Upgrading
Bibliographic record
Abstract
Since 2002, the Steam Assisted Gravity Drainage (SAGD) production technique has had a skyrocketing growth in the province of Alberta, increasing production from 31,000 barrels per day (bpd) in 2002 up to 577,000 bpd by 2013. SAGD is a highly energy intensive method that consumes large quantities of natural gas and water for the production of steam. Once heavy oils are extracted – via SAGD – they must meet pipeline specifications in order to be commercialized, thus dilution with a higher value hydrocarbon or invest in a long-term upgrading project. One way of optimizing and integrating the extraction and upgrading of heavy oils is proposed with the development of the Dense Hot Fluid Injection (DHFI) process, a catalytic in situ upgrading technology. The process targets the substitution – at least partially – of steam by a high heat capacity fluid carrying to the reservoir heat, dispersed nanocatalyst, and hydrogen in order to generate a more competitive oil sand product. It targets the conversion of the vacuum residue (VR) fraction and generates an upgraded synthetic crude oil (SCO) with no vacuum residue and with pipeline transportable viscosity. In this work a two-dimensional bench scale plant is used for the experimental simulation of production and upgrading of an Athabasca type reservoir via DHFI processing. The arrangement is designed to study the heat distribution and oil production from a system with different permeabilities. VR is injected at different residence times to study its conversion levels, and a postmortem product mapping is performed to the residual oil left in the sand packed media. Results confirmed that important upgrading occurs at 500 psi, 350 °C, and hydrogen injection ratios of 300 sccm H2/cc VR. Under the most severe studied case, products reached API gravities of 16°API from a feedstock of 2.4°API. Extremely light hydrocarbons were found within high permeable areas of the rig, while the thermal distribution of the process confirmed the differences between steam injection, presenting “V” type chambers, and dense fluid injection with elliptical shape distributions.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".