In Situ Combustion of Heavy Oil within a Vuggy Carbonate Reservoir: Part I—Feasibility Study
Bibliographic record
Abstract
Worldwide, the known recoverable heavy oil and bitumen reserves make up more than 64% of the total reserves, of which more than 60% are trapped in carbonates. Air injection has immense potential for hydrocarbon recovery from various reservoirs. While most of the successful air-based techniques are performed within carbonate reservoirs containing light oil, theoretically, in situ combustion (ISC) has also shown great potential for recovering heavy oil and bitumen. Carbonates are complex in terms of geology and are often associated with fractures and vugs that affect the fluid flow, pressure propagation and progression of the ISC reactions. This paper describes the first experiment in which the triple-porosity concept was applied to simulate heterogeneity through artificially induced vugs, core matrix and fractures. This approach was used to study the feasibility of the ISC recovery technique for heavy oil (14° API) within a dolomite reservoir using a combustion tube (CT) in an experiment performed at 1740 psig. The combustion front advanced through 78% of the core length prior to the termination of air injection, producing 80% of the initial oil. To differentiate between the various sources of the CO2 gas (a product of carbonate decomposition vs. the combustion reaction) the atomic ratios of (CO2 + CO)/CO = 6 and (CO2 + CO)/N2 = 0.21 were applied. Additionally, partial upgrading of the produced heavy oil was observed.
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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) | 0.000 | 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 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".