Solid-liquid-liquid Wettability and its role on Targeted Emulsified Solvent Injection (TESI)
Bibliographic record
Abstract
Canada possesses the third largest world reserves of crude oil, mostly in the forms of heavy oil or bitumen in the oil sands. However, 80% are too deep to be mined, requiring in-situ extraction methods. This work involved the design of a chemical enhanced oil recovery (EOR) approach, referred to as Targeted Emulsified Solvent Injection (TESI), that uses a surfactant-oil-water (SOW) system in the form of an emulsified solvent formulation at low surfactant concentration (~0.8%) to extract bitumen from oil sands. Different from previous approaches, TESI features an unstable emulsion delivered to the porous media where it is meant to break, dilute the bitumen, reducing its viscosity, and producing an ultralow interfacial tension (IFT). According to a known theory of capillary displacement, the ultralow IFT should facilitate bitumen removal via shear forces. A gap in this theory was addressed, concerning the effect of surfactants on wettability and its potential impact on bitumen recovery. To this end, existing Surface-Liquid-Air (SLA) wettability models were evaluated for their suitability to predict Solid-Liquid-Liquid (SLL) contact angles for a wide range of interfacial tensions (IFTs) of surfactant-free systems on various materials. The Neumann’s equation of state (EQS) was found to predict well wettability changes with IFT changes. This extended-EQS was then combined with the Hydrophilic-Lipophilic-Difference (HLD) + Net-Average Curvature (NAC) framework, used to predict IFT, to estimate changes in wettability around the phase inversion point (PIP) of SOW systems. It was found that at the PIP –where IFT is ultralow– the wettability is nearly neutral, and more importantly, is nearly independent of the solid’s hydrophilicity, explaining why wettability did not seem to affect TESI performance. The most influential factor on TESI performance was the delivery of solvent. Under optimal delivery conditions, bitumen recoveries of 60-80% can be achieved.
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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".