Corrosion and Scale Formation in High Temperature Sour Gas Wells: Chemistry and Field Practice
Bibliographic record
Abstract
Abstract Sour gas is being produced from a number of carbon-steel-completed wells in the US (Mississippi, Alabama), Canada, France and Saudi Arabia. The gas stream contains various levels of hydrogen sulfide, carbon dioxide and is produced from high temperature reservoirs (with temperatures ranging from 160 to 410°F). The combination of hydrogen sulfide with high temperatures introduces challenges related to corrosion and iron sulfide (FeS) scale formation. The thermodynamics and kinetics of iron sulfide formation will be reviewed. There is a large literature on the thermodynamics and kinetics of iron sulfide scales in the context of corrosion of mild steel. High temperatures and high concentration of hydrogen sulfide favor the formation of pyrrhotite and trolite which are an order of magnitude less soluble than cubic FeS or mackinawite. Saudi Aramco has been producing sour gas from deep carbonate reservoirs since 1984. The mole percentage of hydrogen sulfide (H2S) in the gas of these wells range from 1 to 23, while the mole percentage of carbon dioxide in the gas range from 3.7 to 8. Bottomhole temperatures vary from 265 to 320°F. Corrosion inhibition treatment has been sporadic. Chemical and mechanical methods of scale removal have been used in different wells. In a specific instance, the amount of scale has been measured from a given well and the composition has been noted as a function of depth. Assessment about the amount of scale and the potential sources of iron will be provided. Thermodynamic studies of iron sulfide scales will also be reported to explain some of the field observations. The paper provides a summary of the current fundamental understanding in iron sulfide scale (FeS) and corrosion kinetics. It reveals that many oilfield operators do have special stimulation procedures for these wells. These include: special acid stimulation packages, well pickling and processes for continuous injection of corrosion inhibitors to mitigate the iron sulfide scale problems. Results and analysis concerning corrosion and scale problems for specific wells in deep hot gas wells will be presented. The paper will provide a reference point for iron sulfide scale problems in high temperature sour gas wells and for future development in the area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".