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Record W2527496087 · doi:10.2118/0915-0156-jpt

Corrosion and Scale Formation in High-Temperature Sour-Gas Wells

2015· article· en· W2527496087 on OpenAlexaboutno aff
Adam Wilson

Bibliographic record

VenueJournal of Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSour gasCorrosionIron sulfideHydrogen sulfideFerrousSulfideChemistrySolubilityCarbon capture and storage (timeline)MetallurgyFossil fuelNatural gasGeologyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 173713, “Corrosion and Scale Formation in High-Temperature Sour- Gas Wells: Chemistry and Field Practice,” by Sunder Ramachandran, SPE, Aramco Service Company, and Ghaithan Al-Muntasheri, SPE, Jairo Leal, SPE, and Qiwei Wang, SPE, Saudi Aramco, prepared for the 2015 SPE International Symposium on Oilfield Chemistry, The Woodlands, Texas, USA, 13–15 April. The paper has not been peer reviewed. Sour gas is being produced from a number of carbon-steel-completed wells in the US, Canada, France, and Saudi Arabia. The gas stream contains various levels of hydrogen sulfide and carbon dioxide (CO2) 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. FeS Scales FeS is found naturally in different forms. The gas-production systems studied in this paper have large concentrations of hydrogen sulfide, so iron is a limiting reactant in these systems. FeS formation is favored thermodynamically. In anoxic conditions, the solubility of the ferrous ion is aided by the formation of aqueous iron sulfide complexes. As FeS scales sulfidize, they become increasingly difficult to dissolve with acid. Source of Iron. Iron can come from reservoir rock, drilling fluids, and corrosion during acidization and production. Many reservoir rocks contain small amounts of iron. Contamination and corrosion during the drilling process also could lead to high iron content in drilling fluids. Acidization has been considered to be a primary source of reprecipitated FeS. FeS scale has been found in well tubulars following acid treatments of deep sour-gas wells. Sour Corrosion The corrosion of iron tubulars forms one source for iron scale. General corrosion rates of mild steel in sour systems are less when compared with sweet corrosion. FeS scales are less dense than iron, so sour corrosion is associated often with FeS deposits three to five times thicker than the corroded iron. Corrosion monitoring is important in operating a sourgas production facility. Corrosion inhibition has been used by different producers to prevent sour corrosion and the associated buildup of FeS scale. Corrosion Monitoring. It is essential to monitor corrosion and scale formation. This is often assisted by measuring various operational parameters that can give insight into the scaling condition of a gas well. Corrosion coupons are weighed samples of metal representative of the metallurgy of the well or pipe that are introduced into the process and later removed, cleaned of all corrosion products, and weighed. Electrical-resistance probes measure the electrical resistance of a wire made of material similar to the metallurgy of the well or pipe that is placed in a well or pipe. As the wire corrodes, its electrical resistance increases, allowing one to measure the general corrosion of the wire, which should be similar to that of the pipe or well.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.193
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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