Experimental Investigation of Bitumen Recovery from Fractured Carbonates Using Hot-Solvents
Bibliographic record
Abstract
Abstract With the decrease in conventional oil and gas reserves throughout the world and an ever-increasing demand for fossil-fuel based energy and resulting high oil prices, focus has been shifting to unconventional and heavy oil and bitumen. Grosmont carbonates in Northern Alberta have been estimated to contain at least 300 billion barrels of heavy oil or bitumen. However, recovering this oil is extremely difficult because of the complexity associated with carbonate reservoirs in general, e.g., the Grosmont unit is known to possess a triple porosity system – matrix, fractures and vugs, based on core studies. The second problem is the fluid itself, which is highly viscous bitumen, immobile at reservoir conditions. To extract this bitumen from very heterogeneous carbonate rock, both heat and dilution using solvents may be needed. This paper reports the results and analysis of hot solvent experiments conducted on original Grosmont carbonate cores. Three experiments were conducted using propane and one using butane as solvent. After heating the entire system containing the core sample, solvent gas was injected. The rock was allowed to soak in the hot solvent for a long time. The experimental temperature and pressure were decided on the basis of the results of our earlier work that suggested to be just above the saturation line of the particular solvent.. An attempt was made to keep the conditions close to the saturation conditions of the solvent being used to maximize the dilution and hence, the recovery. The oil produced was analyzed for viscosity and asphaltene content. The results in terms of recovery, the degree of dilution and upgrading achieved suggested that butane was a better solvent for this bitumen. Finally, the optimum conditions for operation of the hot solvent process were verified for Grosmont carbonates.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".