Bibliographic record
Abstract
Emulsions and foams occur or are created throughout the full range of processes in the petroleum producing industry, including drilling and completion, fracturing and stimulation, reservoir recovery, surface treating, transportation, oil spill and tailings treating, refining and upgrading, and fire fighting. This chapter provides an overview of these examples of surfactants in action. Introduction In a petroleum industry context, emulsions comprise a mixture of oil and water in which one of the phases, the dispersed phase, occurs as droplets dispersed within the other, the continuous phase. The droplet diameters are typically of the order of 0.1 to 100 μm, but may be as small as a few nanometres or as large as many hundreds of micrometres. Two types of emulsion are readily distinguished, oil-dispersed-in-water (O/W) and water-dispersed-in-oil (W/O). However, emulsion characterization is not always so simple and it is not unusual to encounter multiple emulsions, O/W/O, W/O/W, and even more complex types. Figure 1 shows an example of a crude oil W/O/W/O emulsion. Petroleum industry foams comprise a mixture of gas with either oil or water, where the gas phase occurs in the form of bubbles dispersed within the liquid. The bubble diameters are typically on the order of 10 to 1000 μm, but may be as large as several centimetres. Although both aqueous and oleic foams may be encountered, the former are by far the most common.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".