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.