Validation of an Optimized qPCR Workflow for MIC Risk Identification and Oilfield Microbial Monitoring
Bibliographic record
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.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Validation of a qPCR assay workflow for oilfield microbial monitoring; assay validation in the laboratory sense (polysemy), not research-methods research.
Here validation refers to an oilfield qPCR workflow, not research reproducibility or methodology.
Industrial qPCR workflow validation for oilfield MIC monitoring; assay validation polysemy, not research integrity.
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".