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Record W4323657470 · doi:10.2118/212718-ms

A Multifaceted Laboratory Approach to Screen Paraffin Inhibitors for Canadian Unconventional Resources

2023· article· en· W4323657470 on OpenAlexaboutno aff
Kenny Tsui, Ali Habibi, Shu Jun Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmulsionProtocol (science)Plastic bottleEnvironmental scienceMaterials sciencePetroleum engineeringChemistryGeologyBottleComposite materialOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Abstract Paraffin deposition during oil and gas production is a common challenge and may partially or completely plug the wellbore, production tubing and flowlines. This results in significant reduction in well production and frequent paraffin remediation jobs. Chemical treatment is used widely and is one of the most practical ways to mitigate paraffin deposition. In previous studies, conventional test methods such as cold finger testing have been implemented to screen paraffin inhibitors for field applications. However, poor correlations between laboratory results and field observations challenge the reliability of the method. Developing a comprehensive laboratory protocol is imperative for screening effective paraffin inhibitors. In this study, we introduce a systematic laboratory procedure to assess the performance of paraffin inhibitors on oil samples produced from formations located in the Western Canadian Sedimentary Basin (WCSB). These formations include Duvernay, Montney, and Cardium. The laboratory protocol is composed of three test procedures. First, we measure the viscosity of the oil samples mixed with paraffin inhibitors over a wide range of temperature values. Second, we perform cold finger tests using oil samples mixed with the various paraffin inhibitors. Lastly, we quantify the fouling tendency of oil samples with and without paraffin inhibitors using a para-window instrument by dynamically measuring near-infrared light transmittance on a temperature controlled reflective surface. Several polymeric chemical families including ethylene vinyl acetate (PI-1), maleic ester (PI-2), maleic amide (PI-3), and alkylphenol (PI-4) are evaluated using this laboratory protocol. The measured performance of the paraffin inhibitors varies depending on the technique used and the temperature at which the evaluation is performed. In the case of experiments performed on the Montney oil sample, it is found that inhibitor containing maleic ester (PI-2) demonstrates 31% of reduction in viscosity testing, 75% of inhibition from cold finger testing, but only 8% of fouling reduction in the para-window testing. As this protocol is implemented over a wide range of temperature values, it provides valuable insights about the effectiveness and versatility of paraffin inhibitors at different operational conditions. In the case of PI-2, it shows higher inhibition at temperature near 0°C, rather than near the Wax Appearance Temperature (WAT) of 30°C, indicating that it might not be a suitable candidate for inhibiting the more problematic high molecular weight paraffins generated at 30°C. The laboratory protocol developed in this study helps narrow the gap between laboratory results and field observations. It highlights the importance of matching representative field temperature conditions within the laboratory; and provides new insights about the performance of paraffin inhibitors for oil field applications.

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 score0.730
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.018
GPT teacher head0.245
Teacher spread0.228 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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