Effect of sulphur deposition on well performance in a sour gas reservoir
Bibliographic record
Abstract
Abstract Sulphur deposition has been the centre of attention in sour gas reservoir development. While Roberts's solubility model is widely used in sour reservoir damage to describe elemental sulphur precipitation and deposition in a near‐wellbore area, this model cannot accurately predict a change in sulphur solubility. A productivity prediction model, with consideration of sulphur deposition, is also not complete, particularly for modelling sulphur deposition and fractured horizontal wells. In this study, based on the unsteady percolation mechanics theory and superposition principle, we develop a new numerical model to quantify fractured horizontal well production by combining various sulphur solubility models. We examine the effects of sulphur deposition on permeability, porosity, and production performance. The results show that sulphur deposition damages reservoirs and thus decreases well production in later stages. These results also show that the permeability and porosity reduction using Roberts's sulphur solubility model is greater than that using Hu et al.'s sulphur solubility model. The damage degree is relatively smaller when using Hu et al.'s sulphur solubility model, and the effect of sulphur deposition on a production performance curve is negligible. Moreover, Hu et al.'s model is closer to actual production data. The results can guide reservoir engineers to optimize development plans in sour gas reservoirs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".