Managing Produced Water through Total Oil Recovery: An Integrated Innovative Technology
Bibliographic record
Abstract
Abstract Produced water can become a major operational, environmental and economic concern when oil fields continue to mature. Managing and handling large quantities of produced water efficiently and effectively requires both understanding of the fundamental physical and mechanical characteristics of the water as well as the changes of those characteristics with both time and additional mechanisms. TORR Canada Inc. (the company) have developed an innovative technology called TORR™ – Total Oil Remediation and Recovery (the technology) that integrates several oil and water separation and recovery principles is presented. The basis of operation of the technology to handle produced water oil separation and recovery characteristics is explained. The critical produced water fluid characteristics such as oil droplet size distribution, dispersed oil stability and nature of the dispersed oil are presented and discussed. Performance is demonstrated through field data. Oil concentrations in the range of 100 to 300 mg/L have been reduced to well below 20 mg/L with the technology. The performance of the technology represents a wide operational range of applications and associated economic and environmental benefits. Operators on offshore platforms who need to comply with stringent regulations may achieve the discharge targets with one technology and one stage. Additionally the technology deserves serious consideration to for the application of de-oiling produced water. Operational complexity of more traditional water treatment process trains can be greatly reduced.
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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".