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

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

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.

The three-model screen

all 1,000 screened works →

All three models called this out of scope.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: 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
about Canada: no
confidence: high

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

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

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

Abstract

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.

Stored with the screening record, where it is evidence for the labels above.

The record

Venue
Topic
Drilling and Well Engineering
Field
Engineering
Canadian institutions
LuminUltra Technologies (Canada)
Funders
Keywords
WorkflowIdentification (biology)Computer scienceComputational biologyBiologyDatabase
Has abstract in OpenAlex
yes