Bibliographic record
Abstract
This article, written by Technology Editor Dennis Denney, contains highlights of paper SPE 110738, "Enhanced Heavy-Oil Recovery by Alkali/Surfactant Flooding," by J. Bryan, SPE, and A. Kantzas, SPE, University of Calgary and TIPM Laboratory, prepared for the 2007 SPE Annual Technical Conference and Exhibition, Anaheim, California, 11–14 November. The paper has not been peer reviewed. This study presents the results of laboratory core studies investigating the recovery mechanisms of alkali/surfactant (A/S) flooding in heavy-oil reservoirs. Specifically, mixtures of water and A/S systems have been injected into cores containing heavy oil. Salinity is varied to generate oil-in-water (O/W) vs. water-in-oil (W/O) emulsion systems, and the effects of generating different emulsions were compared. It was demonstrated that in heavy-oil systems, emulsion formation was necessary to produce the heavy oil. Introduction Several countries, including Canada and Venezuela, contain massive resources of unconventional heavy oil and bitumen. These oil sands are characterized as unconsolidated, high-porosity, and high-permeability reservoirs. The single biggest obstacle to successful recovery from the oil sands is the high oil viscosity. Heavy-oil reservoirs are a special subset of the oil sands, whereby the oil viscosity at reservoir temperature and pressure varies on the order of 50 to 50 000 mPa·s. While this oil is very viscous, it does have limited mobility at reservoir conditions. As much as 20% of the oil may be recovered by solution-gas drive, but in many cases, the recovery is much lower. To recover additional heavy oil, a fluid usually must be injected to displace oil to the production wells. However, mobility-ratio concerns dominate displacement of viscous oil, and most enhanced-oil-recovery processes focus on reducing oil viscosity or improving the mobility ratio. Unfortunately, many of the heavy-oil reservoirs in Canada are relatively small and thin, making them poor candidates for expensive thermal processes. This work investigates the potential of A/S flooding for enhanced heavy-oil recovery. Surfactants are a special class of molecule that is both hydrophobic and hydrophilic; thus, the most stable configuration for these molecules is at the oil/water interface. In surfactant flooding, these molecules generally are injected along with water to reduce the oil/water interfacial tension (IFT), which reduces capillary forces that may trap oil in rock pores. Alkali flooding is a special subset of chemical flooding whereby the surfactant is generated in situ through the reaction between the injected alkali and the acidic crude oil. It is likely that in actual reservoir applications, several processes are working together to improve oil recovery. This work considers the possible mechanisms that could be responsible for enhanced recovery of high-viscosity heavy oil.
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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".