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Record W2522184164 · doi:10.2118/181181-ms

Evaluation of an Advanced Metal Bonded Coating Technology for Improved SAGD Performance

2016· article· en· W2522184164 on OpenAlexaff
Da Jiang Zhu, Michael Leitch, Jingyi Wang, Xingwei Shi, Lu Gong, Ermia Aghaie, Jing‐Li Luo, Hongbo Zeng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectrodeposition and Electroless Coatings
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoatingFoulingWettingCorrosionMaterials scienceAsphaltMetallurgyContact anglePetroleum engineeringComposite materialEngineeringChemistry

Abstract

fetched live from OpenAlex

Abstract Due to the high temperature, high pressure and sophisticated chemical environment in heavy oil wells, Steam-Assisted Gravity Drainage (SAGD) is considered one of the most challenging service conditions for carbon steel equipment. This paper introduces an advanced metal-metal bonded coating technology-electroless nickel (EN) coating and its applications in SAGD operations to protect downhole equipment from fouling due to corrosion, adhesion of inorganic and organic materials as well as the impacts of wettability alteration. In the first chapter, we will describe the complex and hostile thermal-chemical environment, their impacts on the SAGD production system and the fouling causes. We will compare the performance of EN coating to that of regular carbon steel pipe in the following aspects: a) corrosion resistance in the presence of sour gases using potentiostatic and potentiodynamic polarization methods; b) adhesion of silica, bitumen coated silica using atomic force microscopy, and c) surface energy and wettability alteration using contact angle measurements. The set-up of the facility, procedures of the tests and governing science and engineering principles will be reviewed. In the end, we will introduce the results of laboratory and SAGD field experiments designed to investigate the EN coating technology in protecting the substrate materials under SAGD 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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.287
Threshold uncertainty score0.268

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.244
Teacher spread0.235 · 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 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

Citations8
Published2016
Admission routes1
Has abstractyes

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