Assessment of Barium Sulfate Scaling in Conventional Gas Well Production using Real-Time Monitoring
Bibliographic record
Abstract
Abstract Scaling has been a major concern in a conventional gas condensate well production facility in northwest Louisiana. Severe barium sulfate deposits have been noted throughout the facility causing operational issues resulting in increased lifting costs. The production is primarily from two formations, the Cotton Valley and Hosston. The Hosston has relatively high sulfate while the Cotton Valley has relatively high barium. Combining these waters creates a barium sulfate scaling environment similar to worst case North Sea or Gulf of Mexico examples. The applications of a bench-top scale deposition monitor as well as a real-time monitor are described. The use of both techniques is illustrated by presenting results of field testing performed at location to get a better understanding of the severity of the scaling problem as well as the treatment rate of scale inhibitor necessary to control scaling. The rate of scaling was determined for the Hosston and Cotton Valley waters, independent of each other and also as mixtures of the two, using the bench-top monitoring technique. The rate of scaling of the combination of the waters was an order of magnitude greater than either of the waters by themselves. The inhibitor dosage required to inhibit scale was also determined and reported for each water condition. Real-time monitoring was conducted in the facility at a point just prior to water disposal. The results obtained to date agreed with the bench-top monitor results. Inhibitor treatment is currently being adjusted based on real-time monitoring to optimize field treatment and further validate the bench-top method.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".