Sustainability

Data-Science-Informed Operation, Maintenance Practices Reduce Emissions

This paper examines how data-science-driven work practices can result in substantial reductions in methane emissions compared with other leak-detection and -repair methods.

Fig. 1—Improved workflow. AVO = audio, visual, and olfactory.
Fig. 1—Improved workflow. AVO = audio, visual, and olfactory.
Source: URTeC 3866061

Diverse methods are used by oil and gas operators for methane leak detection and repair (LDAR). Deploying continuous monitoring (CM) point-sensor technologies to an oil and gas facility allows operators to implement novel operation and maintenance work practices to respond efficiently to methane emissions. The authors examine how data-science-driven work practices can result in substantial reduction in methane emissions compared with other LDAR methods.

CM Technology

CM point sensors track changes in parts per million of methane in the air. When several fixed sensors are deployed at a site, a large area can be surveilled. Using proprietary algorithms based on a plume-dispersion model, the volume and site rate of the methane emission are calculated.

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