Mitigating Silicate Scale in Production Wells in an Oilfield in Alberta
Bibliographic record
Abstract
Abstract Alkaline surfactant polymer (ASP) floods in sandstone reservoirs are associated with silicate scaling of production wells. Many wells require numerous workovers and some must be abandoned. Mineral scale inhibitors are generally ineffective at treating silicate scale in these wells. An oilfield in southern Alberta was placed under an ASP flood for enhanced oil recovery and subsequently experienced severe silicate scaling of production wells. The wells were under artificial lift with submersible progressing cavity pumps (PCPs). The silicate scaling resulted in numerous well workovers to replace the pumps and associated rods. Two new silicate scale inhibitors were developed and applied down hole to production wells via continuous injection. The dose of chemical applied was approximately 250-500 ppm per well. The inhibitors decreased silicate scaling as evidenced by significantly increased run life of most wells. One well that required consecutive workovers after three and four months, respectively, was treated with inhibitor at 500 ppm and has subsequently produced without problems for more than 12 months. Similar results were seen with other wells. PCP torque has generally not increased, and, in some cases, has actually decreased with the scale treatment. In addition, coupons of treated wells have generally been clean, indicating that silicate scale is not depositing on metal surfaces. Even though the inhibitors must be applied at several hundred ppm to mitigate silicate scaling, significant cost savings have been realized because of the reductions in well workovers and associated lost production. This paper will discuss the issues encountered at this oilfield and the resultant solutions and successful outcome.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".