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Record W3035873346 · doi:10.2118/200706-ms

Iron Sulfide Scale Inhibition: Limitations at Sour Conditions

2020· article· en· W3035873346 on OpenAlexaboutno aff
Yaser K. Al-Duailej, K. S. Sorbie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsDispersantSulfideAnoxic watersSolubilityChemistryParticle sizeParticle (ecology)Hydrogen sulfideChemical engineeringEnvironmental chemistrySulfurOrganic chemistryDispersion (optics)GeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Sour oil and gas production is commonly associated with sulfide scaling challenges originating from the produced aqueous phase. Iron sulfide (FeS) is one of the most common sulfide scales, and recent studies have shown promising dispersant chemicals are available to mitigate its deposition. In addition, successful applications have been reported in the literature, particularly from the North Sea. However, some of the limitations of these FeS chemical dispersants become evident under more severe (high H2S) sour conditions, such as those found in the Middle East, Russia and Canada. The dispersant efficiency depends on the scale particle size, and larger particle sizes usually require higher dispersant dosages. Other factors that may influence the inhibitor dosage include reactant concentrations (cations and anions), pH, salinity and inhibition time. These factors were investigated using a newly developed anaerobic experimental setup that allows the careful addition and withdrawal of fluids from a closed anoxic system. Anaerobic vessels, such as vials and tubes, are deployed equipped with septa (thin membranes). Syringes were used to infiltrate the septum with minimal interference from sulfide retention while maintaining isolation from atmospheric oxygen. Testing was performed over a sulfide concentration range from 100 to 1,000 mg/L. Higher levels of sulfide required higher loadings of scale inhibitor, essentially as a result of particle size increase. In addition, varying the salinity also had a significantly influence on the required dispersant concentration to maintain FeS suspension in solution. At lower pH condition, smaller FeS particles were produced and often inhibition was somewhat obscured by solubility effects. Also, suspending the FeS for longer periods of time required higher dispersant concentrations. More severe sour conditions exceeding 1,000 mg/L of aqueous sulfide, have a detrimental effect on the both the efficiency and economics of the FeS inhibition treatments. In addition, the current high- performance dispersants cannot be squeezed into tight formations or shales, as their high molecular weight may cause severe formation damage. For such applications, alternative inhibition methodologies are required, and non-chemical inhibition may be considered.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.043
GPT teacher head0.247
Teacher spread0.204 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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