Investigation of the microorganisms living in the fluids of oil production facilities is fundamental for an early determination of the presence of microbes that can potentially produce localised attack to the infrastructure; phenomenon acknowledges as microbiologically influenced corrosion (MIC). In the last decades, the microbiological characterisation has been mostly carried out by the implementation of traditional growth-based techniques. However, with advances in biotechnology, molecular microbiological methods (MMMs), which are culture-independent techniques, have begun to replace the conventional methods. Microbial diversity profiling based on DNA sequencing of the 16S rRNA gene is one of the methods being used by MIC researchers and oil operators to help identify and characterise the microorganisms present in the systems. DNA-based analysis has been found useful in the classification of the corrosive microorganisms, however, it is unable to differentiate among dormant, active or dead cells. Considering that only metabolically active microorganisms can cause corrosion processes, other methodologically approaches are required for a better understanding of the implications of the microorganisms in the deterioration of metals. To assess the diversity and composition of the total and active microorganisms present in the produced water of a Western Australia oilfield, six samples were collected in different locations of the production facility. DNA and RNA were co-extracted from each sampling site, and 16S rRNA gene was sequenced by Illumina MiSeq platform. Bioinformatics analysis of the sequencing data revealed that the microbial community structure is similar along the facility. Comparison of the DNA- and RNA-based diversity profiling showed that the majority of the microorganisms present in the system were in an active state. Significant differences were found in the relative abundance of the microbial species associated with the Euryarchaeota and Thermotogae phyla, which were possibly linked to the changing environmental conditions in the facility. Our results highlight that the RNA-based 16S rRNA diversity profiling can significantly complement the information currently generated with the DNA-based methods for the study of MIC.