The Use of Modeling and Monitoring to Control Scale in Alberta ASP Floods
Bibliographic record
Abstract
Abstract Two commercial Alkali Surfactant Polymer (ASP) floods became operational in the Taber area of Alberta, Canada in 2006 and 2008. Both of these floods used NaOH as the Alkali. Throughout the course of both projects extreme scale deposition was observed in downhole production equipment, gathering systems, and in the production facility. Early in the life of the floods the scale composition was predominantly calcium carbonate, however over time the scale changed to consist of greater amounts of amorphous silicate. Scale inhibition and remediation strategies have been developed which include a comprehensive monitoring program, chemical scale inhibition, and mechanical scale prevention techniques. As a result of the large amount of data gathered, models were created to predict scale severity, content, and develop specific mitigation plans. Although clear field wide scaling trends can be identified over the life of these projects, scale mitigation strategies still need to be customized for each well. Although scale remains an operational challenge in these fields, with proper mitigation procedures it can be managed. This report documents the success in reducing the impact of the scale problem and slowing the depositional rate. When designing ASP floods it is important to plan for scale deposition and be proactive on scale mitigation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".