Advances in the Prediction and Management of Elemental Sulfur Deposition Associated with Sour Gas Production from Fractured Carbonate Reservoirs
Bibliographic record
Abstract
Abstract Shell Canada has experienced significant deposition of solid sulfur during the production of dry sour gas from several of its deep carbonate pools located in Southern Alberta. In several cases, wells have become completely plugged with sulfur in the reservoir within several months. Accurate prediction and effective management of the sulfur deposition are crucial to the economic viability of these fields. A new analytical model has been developed for predicting sulfur deposition associated with sour gas production in naturally fractured reservoirs. Key features of the model include incorporation of reservoir temperature profiles and the concept of critical velocity, which accounts for dynamic effects, resulting in a zone of reduced deposition close to the wellbore. The model has been used to successfully match and predict sulfur deposition in several sour gas producers. The modeling results have been used as a design basis for downhole sulfur treatments and clean-out operations, the optimization of well completions and off-take rates to minimize the impact of sulfur deposition, and the development of new well designs and operating strategies for sulfur producers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".