ABSTRACT
Identifying the right tools for monitoring microbial risk in a production system is key to managing and controlling the risks of MIC-induced failures. Frequently, inadequate monitoring tools are used to perform biomonitoring programs in the field, and failure to connect the obtained data to the producers output dramatically limits the usefulness of these tools. The purpose of this chapter is to discuss recent developments in microbial monitoring, best practices for analyzing results in the context of operational data, and future developments to improve MIC monitoring practices.
The last 10 years have seen a major change in the accepted practices for microbial monitoring. Traditional culture-based analyses are beginning to be replaced by culture-independent methods that overcome some of the major challenges historically encountered, such as the inability to detect organisms that do not grow in culture bottles. Despite a steady increase in the use of new culture-independent techniques in the field, the industry has not yet come to full adoption of these methods as standards. Culture-independent methods include analysis of molecules involved in energy metabolism, such as adenosine mono and triphosphate or specific metabolic enzymes within a microbial cell as well 278as DNA-based methods, such as quantitative polymerase chain reaction (qPCR), fluorescence in situ hybridization (FISH), and DNA sequencing, among others.
Recent publications have highlighted that microbial numbers alone are insufficient to identify the risk microbes pose in a given system (Geesey 1994; Wei et al. 2007; De Paula et al. 2012; Keasler et al. 2013). Instead, it is critical to compare collected microbial monitoring data to operational outputs from the asset, such as corrosion rates, hydrogen sulfide generation, iron sulfide deposition, fluid injectivity, and oil and water quality. When these correlations are available, specific key performance indicators (KPIs) and intelligent decisions can be made about the immediate and/or potential risk that a microbial population presents in a given system. The holistic data sets also provide insight into specific locations in the system where risks exist and where remediation strategies should be implemented.
Despite significant progress, there is still significant room for development in the area of microbial monitoring and MIC control. As the industry moves to greater levels of automation, the ability to identify and control microbial threats must move from sample collection and analysis to tools that can provide real-time monitoring and response to increased microbial risks. This next generation of development will continue to advance the industry to new levels of microbial risk prediction and control.
