Advanced Technologies For Produced Water Treatment And Reuse
Bibliographic record
Abstract
Abstract Produced Water (PW) is the highest volume liquid waste stream generated by the petroleum industry. Historically, the treatment of PW has been limited to free oil and suspended solids removal, using physical separation technologies, and injection in disposal wells. However, because of new regulations combined with geological restrictions and local water scarcity, the drive to have a greater fraction of the PW more extensively treated and ultimately reused is increasing. Moreover, the growth in the application of water intensive processes to extract unconventional oil&gas resources, in particular in shale plays and oil sands, has increased the need for cost-effective treatment and reuse of PW to reduce fresh water uptakes. Therefore, the petroleum industry is investigating new PW treatment technologies given that the physical separation technologies traditionally used in the past are, in most cases, not capable of producing water of suitable quality to replace fresh water uptakes. This paper presents the results of a laboratory investigation carried out by the ConocoPhillips Global Water Sustainability Center (GWSC), where various treatment processes (membrane processes, membrane-bioreactors (MBRs), membrane distillation (MD) and ozonation) were evaluated as treatment methods for PW from different oil&gas fields. The key conclusions of this paper are:Membrane Processes and Thermal Evaporators are currently operating within the petroleum industry in full scale PW treatment and reuse applications.The preliminary results of investigations performed by GWSC confirmed the potential of Membrane Filtration, MBRs and Ozonation to treat PW and produce an effluent suitable for reuse. Membrane Distillation may have potential in the longer term. Further investigation is ongoing.If successfully implemented, the above technologies will contribute to provide the petroleum industry with a broad range of technologies to cost-effectively treat and reuse PW.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".