Experimental and Numerical Modeling of Sulfur Plugging in a Carbonate Oil Reservoir
Bibliographic record
Abstract
Many oil and gas reservoirs in the United Arab Emirates produce large amounts of sour gas, mainly in the form of hydrogen sulfide. In addition to creating problems in the production line, wellbore damage is often reported due to the precipitation of elemental sulfur in the vicinity of the wellbore. While there have been several studies performed on the role of solid deposition in a gas reservoir, the role of sulfur deposition in oil reservoirs has not been investigated. This article presents experimental results along with a comprehensive wellbore model that predicts sulfur precipitation as well as plugging. The experiments were conducted in a core (linear) system. Both analytical and numerical modelings were performed in a linear coordinate system. Data for the numerical model was obtained from both test tube and coreflood experiments. By using a phenomenological model, the wellbore plugging was modeled with an excellent match (with experimental results). The crude oil was de-asphalted prior to conducting the experiment in order to isolate the effect of asphaltene plugging. A series of coreflood tests was carried out to observe sulfur precipitation and plugging in a carbonate rock. Significant plugging was observed and was found to be dependent on flow rate and initial sulfur concentration. This information was used in the phenomenological model and can be incorporated in the wellbore numerical model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".