Effect of Steam-Assisted Gravity Drainage Produced Water Properties on Oil/Water Transient Interfacial Tension
Bibliographic record
Abstract
Steam-assisted gravity drainage (SAGD) produced water (PW) consists of oil, solids, clays, petroleum-derived compounds, and other dissolved organic matters (DOMs), which make the SAGD PW highly stable and, therefore, very hard to treat. Developing a correlation between SAGD PW properties and dynamics of interfacial tension (IFT) between dispersed and continuous phases is important to understand the coalescence of dispersed phase droplets, which, in turn, leads to demulsifications of these difficult emulsions produced during SAGD operations. This work sheds light on the interfacial activity of SAGD PW endogenous surfactants, humic acids (HAs), as well as the interaction dynamics of these compounds with naphtha-diluted Alberta oil sand bitumen (AOSB) present in a model SAGD PW. We quantify the dynamics of the IFT of a naphtha-diluted AOSB oil drop in pure water as well as SAGD synthetic brine. Our results pinpoint the distinctive influence of the percentage weight composition of the naphtha-diluted AOSB and the surrounding model SAGD PW pH on the dynamics of this oil–water IFT. We anticipate that the results of this study will bring about a better understanding of interfacial film properties, leading to a predictable coalescence mechanism in SAGD PW emulsions, facilitating the design of next-generation SAGD deoiling unit operations.
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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".