Application of Silicate Scale Inhibitors for ASP Flooded Oilfields: A Novel Approach to Testing and Delivery
Bibliographic record
Abstract
Abstract Silicate scaling is often induced by alkaline surfactant polymer (ASP) flooding in sandstone reservoirs. Scaling of the production tubing, rod or progressive cavity pumps, and other surface equipment causes frequent workovers, resulting in increased costs to the operator and non-productive time. The formation of silicate scale is complicated by its dependence on multiple factors including pH, silica concentration, and magnesium concentration, which vary as the flood progresses (Gill 1998). These factors affect silicate scaling tendency, and consequently, severity of the problem. Silicate scale inhibitors have been developed to mitigate problems in oilfields afflicted with silicate scaling due to ASP flooding (Qing et al. 2002). A new test method using an optical scanning device was developed to better characterize inhibition and dispersant qualities of the developed products on silicate scale in brine under static conditions. The advantage of this method is more comprehensive data generated by multiple-point measurements. Scaling reactions are more easily modified and differentiated. Water chemistries from several wells in an ASP-flooded field in Southern Alberta with known silicate scaling issues defined the tested synthetic brine. By allowing the mixed synthetic brine to react before adding inhibitor, the effect of delayed chemical injection may be studied. Performance of the tested inhibitor was significantly reduced by slight injection delay and may be attributable to the discrepancy between laboratory performance data and failures observed in the field. It is proposed that the performance of silicate scale inhibitors may be improved when applied by squeeze, where the inhibitor can inhibit silica polymerization within the formation and may provide substantial improvement over conventional continuous down-hole injection, where inhibitor reaches the water after the perforations.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".