Suitability of Local Demulsifier as an Emulsion Treating Agent in Oil and Gas Production
Bibliographic record
Abstract
Abstract There is an increasing need to limit the use of chemical treating agents during oil and gas production and to search for safer and cost effective ones mainly due to environmental constraints. Therefore the use and performance of demulsifiers have to be improved from the application and cost as well as from the environmental issues. This means that new formulations must be less toxic and efficient compared to the general classical chemical families of demulsifiers which contain toxic molecules like phenol groups. This paper is on the performance and the comparison of four chemical demulsifiers (local and foreign) on their demulsification of four crude oil emulsions of different asphaltene contents from different oil wells in the Niger Delta. The chemical families of these demulsifiers were screened with effective separation ability of different surfactants using classical "Bottle test". The Bottle test helped to determine the type of demulsifier that will most effectively break the emulsion of the crude samples. The basic aim of this screening was to compare and rank the efficiency of the various demulsifiers both local and foreign (V4404 of Nigeria, 92LTM174 of USA, EN/82/2 of France and DS 964 of Canada) in terms of percentage (%) volume of water that will be separated out of the samples. The results showed that the viscosity of the emulsions increased as the water content increased with an assumption that only oil and water were present. The nature of the emulsions were subject to changes, therefore no treatment method was conclusively generalized as best for every emulsion problem. Finally from the preliminary screening, the result also revealed that V4404 a local demulsifier exhibited very interesting performance and was also environmentally friendly compared to the imported ones.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".