Challenges and Innovation in Testing and Producing Offshore Sour Gas Fields Expected to Contain Problematic Elemental Sulfur Deposition
Bibliographic record
Abstract
Abstract Sour gas reservoirs with very little C2+ have the potential to produce native elemental sulfur which can lead to elemental sulfur deposition, which in turn causes flow assurance and corrosion issues. For an economically successful mitigation of sulfur deposition, a producer needs to have an accurate determination of the elemental sulfur loading, a robust phase behavior model and a comprehensive solvent deployment plan. With knowledge of the sulfur loading in the reservoir and the phase behavior model, the optimum solvent type and injection rate to mitigate the sulfur can be selected. Traditionally, sulfur dissolved in these gases is measured using downhole sampling tools during flowback testing. Where continuous-flow solvents are injected downhole, the returned solvent can be analysed for elemental sulfur to get more accurate sulfur contents and subsequently more accurate predictions of sulfur deposition profiles after comparing to accurate thermodynamic models. This paper describes (a) preliminary solvent selection, (b) solvent analysis during flowback testing and (c) production management to optimize or reduce future solvent needs. Utilization of an injection string upon flow testing of an offshore test well has allowed ADNOC to flow sulfur solvent and capture downhole samples during the well test period. While several solvents were considered, three solvents were proposed for use based on (i) sulfur solubility, (ii) future availability to the producing company (ADNOC), (iii) favourable physical properties, (iv) chemical compatibility and (v) low volatility. While not ideal, a diesel solvent was used during well testing due to immediate availability. Realtime analysis of the solvent during the flow test demonstrated the ability to optimize the early solvent injection rate to gas flow rate ratio and keep the flow string clean. While solvents can be collected and returned to a laboratory for future determination of sulfur saturation, testing on-site ensures that the solvent properties have not changed through slow chemical reactions and allows the producer to optimize the solvent flow rate during production testing, versus during field production only. This requires specialized pumping and solvent capacity during a flow test.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".