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Validation of an Optimized qPCR Workflow for MIC Risk Identification and Oilfield Microbial Monitoring

2023· article· en· 0 citations· W4409486179 sur OpenAlex· 10.5006/mecc2023-20130

Pourquoi ce travail est-il dans la base ?

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

Affiliation canadienneUne personne signataire a déclaré un établissement canadien. C'est la seule voie dont dispose la base habituelle.

Le tri à trois modèles

les 1 000 travaux triés →

Les trois modèles l'ont jugé hors champ.

strate : aff_core · poids de sondage : 5595.24 (l'échantillon est stratifié ; tout taux calculé sans le poids est faux)
Claude Opus 4.8OUT
genre : empirical
porte sur le Canada: non
confiance: high

Validation of a qPCR assay workflow for oilfield microbial monitoring; assay validation in the laboratory sense (polysemy), not research-methods research.

GPT-5.6 (high)OUT
genre : empirical
porte sur le Canada: non
confiance: high

Here validation refers to an oilfield qPCR workflow, not research reproducibility or methodology.

Grok 4.5OUT
genre : empirical
porte sur le Canada: non
confiance: high

Industrial qPCR workflow validation for oilfield MIC monitoring; assay validation polysemy, not research integrity.

Résumé

In this work, a scalable workflow for field sample preservation, DNA extraction, and quantitative polymerase chain reaction (qPCR) was developed and validated for accurate and rapid oilfield microbial monitoring and microbiologically influenced corrosion (MIC) risk identification. Validation experiments were performed on a variety of challenging oilfield sample types including produced water and pigging sludge to assess the complete optimized qPCR workflow and eight MIC-related qPCR targets including sulfate reducing prokaryotes (SRP) and corrosive methanogens (micH). The predicted in silico taxonomic coverage of these eight MIC-related qPCR targets were compared to a complete microbial community analysis of the samples using 16S rRNA gene sequencing and were found to capture >95% of the taxa present, indicating method reliability for identifying MIC-related microorganisms. The simplified qPCR workflow validated in this work brings qPCR closer to the field to replace or supplement current microbial monitoring practices for higher information yield, ultimately allowing for optimized mitigation strategies and identification of MIC-risk.

Conservé avec la notice de tri, où il sert de preuve aux étiquettes ci-dessus.

La notice

Revue
Thématique
Drilling and Well Engineering
Domaine
Engineering
Établissements canadiens
LuminUltra Technologies (Canada)
Organismes subventionnaires
Mots-clés
WorkflowIdentification (biology)Computer scienceComputational biologyBiologyDatabase
Résumé présent dans OpenAlex
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