Investigation into the Processes Responsible for Heavy Oil Recovery by Alkali-Surfactant Flooding
Bibliographic record
Abstract
Abstract This paper describes a suite of alkali-surfactant (AS) floods that were performed in systems containing viscous heavy oil (11,500 mPas). The study investigates how AS injection can be used to generate oil and water emulsions, which can in turn lead to improved sweep efficiencies and oil recovery. Data is obtained from core flooding, with in-situ saturation measurements made using low field NMR. This work is applicable to the many heavy oil reservoirs in countries like Canada and Venezuela that contain viscous oil that still has some limited mobility under reservoir conditions. In previous studies, improved oil recovery compared to waterflooding was observed. This work provides additional information that can be used to better understand how chemical injection can lead to oil recovery. The core floods in this study indicate that emulsification is most efficient when used to block pre-formed water channels and improve the sweep efficiency of the flood. Both O/W and W/O emulsions may form in the same system, even under controlled salinity conditions. The re-distribution of water from the flooded channels into emulsified droplets in the oil is at least partially responsible for the pressure increase seen in these systems. W/O emulsification is accompanied by wettability alteration, as evidenced by the NMR spectra obtained. After the chemical flood is completed, it may be possible to restore the original water wet condition of the rock, which can provide potential for future non-thermal improved oil recovery.
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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".