Cost Effective EOR in Heavy-Oil Containing Sands by Gas Injection: Improvement of the Efficiency of Foamy Flow and Pressurization
Bibliographic record
Abstract
Abstract Primary recovery of heavy-oil is remarkably low due to high viscosity and low energy by solution gas exsolution to drive the oil. Gas injection to improve foamy flow and also to dilute the oil in such reservoirs has been proposed as a secondary recovery method. However, because of the high costs of injected gases, efforts are needed to optimize the process by selection of proper gas type (or gas combinations) and suitable injection scheme. To achieve this goal, an experimental procedure was followed with rigorous analyses of the output. A 1.5 m long and 5 cm diameter sand-pack was first saturated with brine, which was replaced with dead oil. Then, gas solvents were injected to dead-oil containing core-holder until nearly reaching 500 psi followed by a two-day soaking period. Pressures all along the sand-pack were recorded with eight pressure transducers. Different combinations of various gas solvents (methane, CO2, and air) aiming to select the most competitive and economic formula were tested with a certain set of pressure depletion rates. The physics of the foamy oil flow for different solvent mixtures and depletion conditions were analyzed using pressure profiles acquired, recorded oil/gas data with time, and gas chromatography and SARA analyses of the produced gas and oil. Three huff-n-puff cycles were applied. Compared with other light hydrocarbon solvents and carbon dioxide, air has its high advantage in terms of accessibility and lowered cost. Hence, attention was given to air that was mainly used to pressurize the system and increase oil viscosity due to oxidation process with an expectation of better foam quality when injected with other gases such as CO2 and methane. Methane (CH4) yielded the quickest response in terms of gas drive but, in the long run, CO2 was observed to be more effective technically. Air was observed to be effective if mixed with CO2 or methane from an economics point of view. To sum up the results, air Huff-n-Puff (HnP) followed by 2-cycles of CH4 HnP yielded 36.21% recovery, while air HnP followed by 2-cycles of CO2 HnP delivered 30.36% oil. When the gases are co-injected, air 50%-CO2 50% and air 50%-CH4 50% recovered 29.85% and 23.74% of total oil-in-place, respectively.
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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".