Advanced Electrostatic Technologies for Dehydration of Heavy Oils
Bibliographic record
Abstract
Abstract Effective oil/water separation continues to be a major challenge in heavy oil (HO) production operations and often involves high capital costs (large, heated vessels) and high operation costs (heat, fouling, upsets, chemicals). Application of new electrostatic dehydration technologies has the potential to have a major impact in reducing these costs. A systematic evaluation of four electrostatic dehydration technologies was performed using lab, bench scale, and pilot scale (40 gallons) testing. Four heavy oils ranging from 8 to 21 API were used. Performance criteria measured were effective emulsion separation rate (vessel throughput), separated oil and brine quality, water droplet size distribution for inlet and outlet emulsions, and comparison with field data (as available) for older electrostatic technologies. Traditional bottle tests were performed for reference. A 2 to 4 fold increase in emulsion treating rate was observed for some of the heavy oils using the newer electrostatic technologies relative to the traditional Alternating Current (AC) method with the same output quality of crude and brine. Relative cost data per barrel of emulsion processed were developed from system cost estimates and throughput data developed in the pilot tests. Treatment with acid to bring the separated brine into a 6 to 6.5 pH range had a very beneficial effect on the oil/water separation for some of the heavy oils with high TAN.
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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 | 0.001 |
| 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".