Detection and Impact of Carboxylic Acids at the Crude Oil–Water Interface
Bibliographic record
Abstract
The impact of surface active indigenous components on interfacial tension (IFT) of crude oil–water systems is an important parameter in many aspects of crude oil production such as emulsion stability, reservoir wettability, and capillary number calculations. These components may affect productivity across the reservoir due to variations in concentrations. In most cases simulation of IFT is not taking interfacial activity into account and is purely based on oil bulk properties. In this paper we examine two crude oils and their subfractions such as maltenes, deacidified crude, and natural acidic components. Films were prepared at the toluene–water interface with crude oil and its various fractions and studied for interfacial activity and chemical compositions. The chemical analysis of the interfacial active material indicated that carboxylic acids are preferentially adsorbed or concentrated at the oil/water interface. Infrared spectroscopic analysis of the interfacial films clearly demonstrates that carboxylic acids species (e.g., fatty acids, resins, or asphaltenes with a −COOH functionality) are concentrated at the interface. The GC-MS analysis of the interfacial film revealed the presence of a homologous series of linear chain carboxylic acids ranging from C 10 –C 25+ . 2D GC-MS analysis showed that heteroacids are also present. Acid free crude was prepared and back mixed with the original crude oil in different proportions confirming the role of the acids in decreasing IFT. The removal of these relatively small amounts of acids leads to a decrease in IFT between 1.3 and 2.2 mN/m. The results also indicate that acids can be preferentially removed by ion exchange resins without affecting the overall composition of the oil as is shown by back-mixing deacidified oil into the original 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".