Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

Methane (CH4) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH4 emissions at different spatial scales are ~1.5–2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH4 emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH4 mitigation policy priorities.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Limiting emissions of climate-warming methane from oil and gas (O&G) is a major opportunity for short-term climate benefits. We deploy a basin-wide airborne survey of O&G extraction and transportation activities in the New Mexico Permian Basin, spanning 35 923 km2, 26 292 active wells, and over 15 000 km of natural gas pipelines using an independently validated hyperspectral methane point source detection and quantification system. The airborne survey repeatedly visited over 90% of the active wells in the survey region throughout October 2018 to January 2020, totaling approximately 98 000 well site visits. We estimate total O&G methane emissions in this area at 194 (+72/–68, 95% CI) metric tonnes per hour (t/h), or 9.4% (+3.5%/–3.3%) of gross gas production. 50% of observed emissions come from large emission sources with persistence-averaged emission rates over 308 kg/h. The fact that a large sample size is required to characterize the heavy tail of the distribution emphasizes the importance of capturing low-probability, high-consequence events through basin-wide surveys when estimating regional O&G methane emissions.

Methane leakage from point sources in the oil and gas industry is a major contributor to global greenhouse gas emissions. The majority of such emissions come from a small fraction of “super-emitting” sources. We evaluate the emission detection and quantification capabilities of Kairos Aerospace’s airplane-based hyperspectral imaging methane emission detection system for methane fluxes of 18–1,025 kg per hour of methane (kgh(CH4)). In blinded controlled releases of methane conducted over 4 days in San Joaquin County, CA, Kairos detected 182 of 200 valid nonzero releases, including all 173 over 15 kgh(CH4) per meter per second (mps) of wind and none of the 12 nonzero releases below 8.3 kgh(CH4)/mps. Nine of the 26 releases in the partial detection range of 5–15 kgh(CH4)/mps were detected. There were no false positives: Kairos did not detect methane during any of the 21 negative controls. Plume quantification accuracy depends on the wind measurement technique, with a parity slope of 1.15 (σ = 0.037, R2 = 0.84, N = 185) using a cup-based wind meter and 1.45 (σ = 0.059, R2 = 0.80, N = 157) using an ultrasonic anemometer. Performance is comparable even with only modeled wind data. For emissions above 15 kgh/mps, quantification error scales as roughly 30%–40% of emission size, even when using wind reanalysis data instead of ground-based measurements. This reflects both uncertainty in wind measurements and in Kairos’ estimates. These findings suggest that at 2 mps winds under favorable environmental conditions in the United States, Kairos could detect and quantify over 50% of total emissions by identifying super-emitting sources.