The Development and Application of a Novel Scale Inhibitor Deployment System
Bibliographic record
Abstract
Summary Water-in-oil emulsions have been developed that have been shown to be a credible means of deploying scale inhibitors in a nonaqueous medium. Furthermore, the use of slowly degrading emulsion systems provides a means of trapping the scale inhibitor within porous media and allowing the subsequent controlled release of the chemical. This has been demonstrated with sandpack and core-flood experiments in which the inhibitor's lifetime, as measured by pore volumes of brine eluted to a given inhibitor concentration, has been extended by up to (and possibly beyond) four times compared with the base-case, nonemulsified product. No permeability damage has been observed for the emulsion systems. The use of emulsion droplets to provide chemical reservoirs within the formation also considerably reduces the nonretained chemical waste observed with conventional squeeze treatments. The nonaqueous nature of the emulsion system allows the potential ingress of the scale inhibitor into parts of the reservoir normally denied access to an aqueous-based formulation, so it can improve contact between the scale inhibitor and the reservoir fluids on production. Furthermore, the whole treatment can be nonaqueous, allowing oil continuity to be maintained during the treatment and affording rapid well cleanup post-treatment. The efficacy of the emulsion system is illustrated with the results of a successful field trial performed on the Forties field in the U.K. sector of the North Sea.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".