A Modern Framework for Emissions Management

This playbook introduces Picarro’s Network Intelligence framework—a transformative approach that empowers gas utilities to move from reactive leak response to proactive, data-driven operations.

By combining advanced mobile leak detection (AMLD), geospatial data integration, machine learning analytics, and seamless software connectivity, Network Intelligence offers a unified, GIS-centric view of your entire gas network.

Operational efficiency, data quality, and consistency are crucial for implementing an Advanced Mobile Leak Detection (AMLD) program that achieves comprehensive coverage of gas distribution assets. To optimize these aspects, Picarro recommends data collection at night.

Methane emissions are at the center of the current climate crisis due to methane’s outsized contribution to global warming. Methane is one of the most potent greenhouse gases, being approximately 86 times more negatively impactful on climate change than carbon dioxide within the first 20 years in the atmosphere. It represents as much as 25% of the historic warming that can be attributed to human activities and is considered the greenhouse gas most responsible for driving warming in the near-term.

Picarro’s solution enables emissions data to be taken rapidly at scale. Emissions reductions are easily quantified for individual pipe replacement projects, individual leak repairs as well as across a network. Large emitters, which frequently contribute disproportionately to emissions reduction goals, can be easily identified so they are remediated first as part of an informed and cost-effective emission reduction strategy.

Utilities worldwide are using Picarro’s Asset Management Solution to further improve the choices they make regarding capital pipe replacement projects informed by traditional risk models. The Picarro solution combines data analytics with a vehicle-based methane emissions data collection platform to assist with capital replacement decisions. This leads to significant O&M cost savings through avoided leak repair by prioritizing replacements of pipelines with high leak densities. When this data is combined with traditional risk (DIMP) models, such benefits are maximized.