Use of Carbon Dioxide and Hydrocarbon Solvents During the Method of Steam-Over-Solvent Injection in Fractured Reservoirs for Heavy-Oil Recovery From Sandstones and Carbonates
Bibliographic record
Abstract
Summary Because of low efficiencies and the high cost of the individual injection of steam and solvent for heavy-oil recovery, their hybrid applications have gained significant attention recently. Although numerous laboratory studies exist and there are a considerable number of field projects for sandstone environments, fractured carbonates lack technologies to drain matrix oil efficiently. An alternative-method injection of solvent and steam was proposed and tested earlier (Babadagli and Al-Bahlani 2008). This process applies steam initially to condition the matrix oil for succeeding solvent injection and steam reinjection to retrieve solvent in the matrix and to recover additional upgraded oil. The present study uses carbon dioxide (CO2) as a solvent and compares it with hydrocarbon solvents in this type of application. To clarify the physics of the process and to test the applicability of the method for different reservoir and injection conditions, we conducted a series of experiments by first injecting steam, followed by CO2 injection. In the third cycle, steam was injected again to produce upgraded oil in the matrix. The experiments were performed under static conditions (soaking sand and carbonate samples in steam or CO2 chambers) at different temperatures and pressures. CO2 is shown to be a reasonable alternative for hydrocarbon solvents in such a process in terms of cost and benefits by reducing the solvent expenses, keeping the oil-production levels, and disposing of a greenhouse gas.
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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 | 0.001 |
| 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".