Modeling, Monitoring Aid Fight Against Scale in Alkali/Surfactant/Polymer Floods
Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 165285, ’The Use of Modeling and Monitoring To Control Scale in Alberta ASP Floods,’ by Kate Hunter, SPE, Lee McInnis, SPE, and Tyler Ellis-Toddington, SPE, Husky Energy, and Scot Kerr, Baker Hughes, prepared for the 2013 SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 2-4 July. The paper has not been peer reviewed. Two alkali/surfactant/polymer (ASP) floods became operational in the Taber area of Alberta, Canada, in 2006 and 2008. Throughout the course of both projects, extreme scale deposition was observed. Scale-inhibition and -remediation strategies were developed that included a comprehensive monitoring program, chemical scale inhibition, and mechanical scale-prevention techniques. From the large amount of data gathered, models were created to predict scale severity and content and to develop specific mitigation plans. Introduction In southern Alberta, the Mannville B Taber South (Warner) pool was discovered in 1963 and has been under waterflood since 1967 and the Glauconite K Taber (Crowsnest) pool was discovered in 1943 and has been under waterflood since 1971. The Warner ASP flood was the first fieldwide ASP flood in Canada and began injection in May 2006. The Crowsnest ASP flood began injection in January 2008. Both floods used a 0.75-wt% concentration of NaOH as the alkali and continue to use recycled produced water for injection. Scale and its associated problems and mitigation strategies have evolved over the course of these projects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".