Applications of drag reducers for the pipeline transportation of heavy crude oils: A critical review and future research directions
Bibliographic record
Abstract
Abstract The long‐distance pipeline transportation of heavy crudes demands optimal pumping procedures to abate overhead expenditures in the midstream phase. This can be facilitated by reducing both the oil viscosity and the frictional losses induced by the turbulent flow zone. The dualistic approach of reducing both viscosity and drag requires the optimization of several parameters such as concentrations and types of diluents and additives. This manuscript critically reviews various technologies being undertaken to facilitate the pipeline transportation of heavy crude oils by highlighting the technique of dilution coupled with the addition of drag reducing agents (DRA). DRA such as surfactants, nanoparticles, bio‐additives, polymers, and fibres are blended with the diluted crudes to suppress the proliferation of turbulent eddies which in turn assist in pumping the oil at a higher flow rate under constant pressure conditions. Several investigations have reported that drag reduction is significantly enhanced by varying the molecular structure of the DRA. In addition, an optimum flow rate coupled with a specific surfactant concentration at an ideal temperature can significantly reduce head losses and pumping expenditures. Further research is imperative to study the effect of the polymer structure with respect to the induced shear degradation and dissolution behavior of the crude.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".