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Record W2042638737 · doi:10.2523/iptc-13281-ms

Evaluating Corrosion Inhibitors For Sour Gas Subsea Pipelines

2009· article· en· W2042638737 on OpenAlexaff
Hejian Sun, David Blumer, Mike Swidzinski, Josh Davis

Bibliographic record

VenueInternational Petroleum Technology Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSubseaCitationPipeline transportSour gasLibrary scienceComputer scienceEngineeringNatural gasMechanical engineeringMarine engineeringWaste management

Abstract

fetched live from OpenAlex

Abstract Using subsea carbon steel pipelines to transport wet sour gas possesses huge challenges to the operators to maintain the high level of the Assets and Operating Integrity. In many cases, carbon steel is still the primary choice for the subsea pipeline material. This choice significantly reduces the capital expense, and the industry has gathered a lot of successful experience in onshore facilities to mitigate the risk of using carbon steel. In order to produce and transport wet sour gas safely, corrosion of a subsea carbon steel pipelines must be controlled to prevent leaks. The common and effective way to mitigate corrosion risk of a subsea carbon steel pipeline is to apply corrosion inhibitor continuously, and in some cases in combination of batch corrosion inhibition. Selecting right corrosion inhibitor, deterring a right dosage, applying in a right way and at right time (4R) become extremely critical to the successes of pipeline integrity management. Since simulated flow dynamics degraded certain inhibitor's performance, it is critical to include the flow parameter in the experimental evaluation of corrosion inhibitors for this application. The high surface area solids will compete against the steel surface for effective inhibitor molecules, raise the cost and reduce the effectiveness of the inhibition program. The paper describes the selection of corrosion inhibitors for protecting pipelines transporting wet gas containing relatively high concentrations of H2S and CO2, which requires special techniques and equipment. Solids generated from corrosion play a large role in performance of corrosion inhibition, particularly inhibitor adsorption, under-deposit corrosion and passivation. Localized corrosion is the primary concern rather than general wall loss in pipelines transporting wet sour gas. The corrosion inhibitor selection process simulates flow dynamics of the pipeline system in order to find the best corrosion inhibitor. The Rotating Cylinder Autoclave (RCA) and Impinging Jet (IJ) effectively demonstrate the flow dynamic performance of different corrosion inhibitors under field conditions. Corrosion product solids were examined by variety of techniques, including SEM, EDAX and particle size analysis. The results show that some corrosion inhibitors failed under flow dynamic conditions, while other do much better. The temperature effect was observable, but did not cause inhibitors to lose significant performance within operating range of the pipeline. Solids generated in these tests were characterized. They were iron sulfides with different morphologies. The high surface area framboids were found both suspended in the solution and loose adhered to the metal surface. A tight layer of iron sulfide was also observed. The layer is about 10 µm thick, impermeable and dense. It provides passivation except there are cracks and holidays, which could provide initiation spots for localized corrosion. The high surface area framboid solids will remove substantial amount of corrosion inhibitor due to adsorption. We found corrosion inhibitors that demonstrate low corrosion rates under flow dynamic conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2009
Admission routes1
Has abstractyes

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