Experimental Analysis of Heavy-Oil Recovery by Alternate Injection of Steam and Solvent (Hydrocarbon/CO2) in Unconsolidated Sand Reservoirs
Bibliographic record
Abstract
Summary This work examines the steam-over-solvent injection in fractured reservoirs (SOS-FR) method--which was originally suggested for heavy-oil/bitumen recovery from oil-wet fractured reservoirs—for unconsolidated sands containing heavy oil. One possible application of the method in such media is the post-cold-heavy-oil-production- with-sands (post-CHOPS) enhanced oil recovery (EOR) considering that the wormholes represent the high-permeability (“fractures”) medium while the unproduced original part of the reservoir is the matrix containing the essential portion of the oil. The methodology used in the experiments and the observations will also provide an insight into the applicability of this method in “homogeneous” oil sands producing under gravity drainage during the alternate injection of solvent and steam (or hot water). The SOS-FR method was initially proposed and tested using high-carbon-number (heptane and above) hydrocarbons for solvent at ambient pressure and temperatures lower than 90ºC for oil-wet consolidated sandstones and carbonates (Al-Bahlani and Babadagli 2008, 2009a; Naderi et al. 2013). Later, lower-carbon number (propane and butane) hydrocarbons were tested at higher pressures and temperatures for consolidated water-wet and oilwet sandstones and carbonates (Naderi and Babadagli 2012a). This study focuses on the unconsolidated sands (water-wet nature) and tests three different types of gases as solvent [propane, butane, and carbon dioxide (CO2)] at high-pressure and-temperature conditions (compatible with a typical heavy-oil-sand deposit in Canada). The use of CO2 as a solvent in this type of process was considered as a lower-cost alternative to more-expensive hydrocarbons and for its permanent-storage potential. Several issues are highly critical in this process. Like other hydrocarbon solvents used under nonisothermal conditions, the recovery process is highly sensitive to pressure and temperature because they determine the miscibility level. Also important is the capability of CO2 to extract matrix oil. Our earlier studies with light oil showed that heavier ends can be extracted if enough time is allowed for CO2 to interact with matrix oil (Trivedi and Babadagli 2009a, 2010). The same needs to be investigated for heavy oils. Another challenge is the inverse proportionality of CO2 solubility with temperature. Steam (or heating) is required to condition oil and decrease its viscosity before CO2 injection, but the temperature should be adjusted critically in order not to sacrifice the CO2 solubility of the oil. To clarify all these points and to determine the optimal application conditions (the duration of each cycle and the CO2 soaking time), we conducted a series of experiments by first soaking core samples saturated with heavy oil in steam, followed by CO2. In the third cycle, steam (or hot water) was injected again to produce upgraded oil in the matrix. The experiments were performed under static conditions (soaking sandpack samples in steam or CO2 chambers) at different temperatures and pressures to determine the optimal application conditions. A similar experimental protocol was followed using propane and butane, and the recovery potential of these hydrocarbon solvents was compared with that of CO2.
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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".