Gas operators are increasingly being challenged to reduce methane emissions, and to do so in a continuous, verifiable way. This challenge at its most extreme has some cities recently banning natural gas in new construction, with affected utilities facing an existential threat to the extent that this potentially reduces growth in their revenue base. Further, as the infrastructure ages, gas operators face challenges in managing budgets and unplanned work due to an accelerating – not constant – rate of growth in the number of leaks emerging from leak-prone pipe. These budget pressures along with increasing renewables cutting into electric revenues in dual utilities are driving the need to manage gas assets as cost-effectively as possible.

These headwinds are driving a revolutionary change in how gas utilities approach asset management – they are actively seeking out technologies and new business practices that enable them to more rapidly reduce risk and emissions in the infrastructure while simultaneously improving capital efficiency and financial outcomes – Risk Spend Efficiency (RSE) is now a term of art in the industry.

The advances in mobile methane detection technology and analytics pioneered by Picarro allow natural gas emissions data to be collected at a speed and scale not previously possible. Concurrent advances in “Big Data” Analytics allow better-informed conclusions to be drawn from that data. The resulting data-driven decisions in asset management are consistently being shown to yield better outcomes in such applications as pipe replacement, risk-based leak survey and emissions reduction as compared to what has been possible with traditional processes that are slow, labor-intensive and have a 66% false negative detection rate on average. As utilities see their peers successfully adopting and benefiting from a digital, data-driven approach to asset management, more and more are embracing such technologies.

The commonly referenced “digital transformation” in the gas industry benefits utilities in the “decision support” aspect of “going digital” (which is reliant on big data and machine learning) perhaps even more than the “process automation” aspect of the move to digital. The benefits of improved decision support are maximized when they are combined with – rather than entirely replacing – inputs from subject matter experts (SMEs). Although utilities are making progress constructing “digital twins” of their networks (such as pipeline integrity and risk models), in doing so, they face challenges in proper geolocation of assets. They are also frequently plagued by a loss of historical information on what these buried assets actually are or how they are interconnected. Such gaps in information reduce the fidelity of the digital twin so that outputs from analysis performed using the twin as a foundation in simulations or models may therefore be erroneous, driving incorrect decisions. Recent incidents have been caused by an incorrect digital twin leading to the wrong decisions. But all is not lost – it is possible to augment and “true up” these imperfect digital representations of gas networks with actual, current data collected on the network. The most relevant data that can be taken to assess the health of a gas network is geospatial methane emissions data.

With the heightened attention to methane emissions as a backdrop, utilities are discovering that the most significant impact they can make in reducing emissions (and, in fact, also in reducing costs) is by making the best decisions they can on how to prioritize their ongoing pipe replacement programs and how to find and repair their highest-emitting leaks. Using methane data collected on their networks to better inform capital replacement decisions leads to significant cost savings (often O&M) through avoided leak repair. This is accomplished by prioritizing replacements of pipelines with high leak densities before the leaks are found by compliance survey or odor calls and incur repair cost. When this methane data is combined with traditional risk models and SME inputs, the leak repair cost avoided through pipe replacement is maximized. Utilities don't replace more pipe annually as a result of this information, they just slightly change which pipes to replace in a given year. Additional benefits of optimized capital project prioritization include accelerated risk reduction, emissions reduction and reduction in odor calls.

The collected methane data also allows the highest-emitting leaks to be identified and prioritized for repair to take advantage of the significant reduction in overall network emissions that these leaks represent. Multiple studies have shown that an extremely small fraction (1% to 10%) of “super emitter” leaks on gas networks account for 50% or more of the total network emissions. Repairing this small number of leaks is not particularly costly, especially in light of the emissions reduction benefit and reduction in lost and unaccounted for gas (LUFG) their repair provides. In fact, utilities in which cost recovery of LUFG is capped at, say, 2% of throughput can actually realize cost savings in reduction of LUFG through the repair of their highest-emitting leaks. Further, these super emitting leaks often turn out to be hazardous leaks, and utilities that are scanning their network annually for super emitters are able to find and fix these leaks much faster than if they are found by leak survey on a three or five year cadence. These emissions reduction programs therefore have a concomitant safety benefit.

Only in the past few years has the gas industry so noticeably pivoted in response to their concerns about methane emissions. The technologies the industry can leverage to meet these challenges have matured just in time and are becoming a key part of the digital transformation and asset management strategies at many utilities.