Small aerial drones are used in a growing number of commercial applications. However, drones cannot fly in all weather, which impacts their reliability for time-sensitive operations. The magnitude and global variability of weather impact is poorly understood. We explore weather-limited drone flyability (the proportion of time drones can fly safely) by comparing historical wind speed, temperature, and precipitation data to manufacturer-reported thresholds of common commercial and weather-resistant drones with a computer simulation. We show that global flyability is highest in warm and dry continental regions and lowest over oceans and at high latitudes. Median global flyability for common drones is low: 5.7 h/day or 2.0 h/day if restricted to daylight hours. Weather-resistant drones have higher flyability (20.4 and 12.3 h/day, respectively). While these estimates do not consider all weather conditions, results suggest that improvements to weather resistance can increase flyability. An inverse analysis for major population centres shows the largest flyability gains for common drones can be achieved by increasing maximum wind speed and precipitation thresholds from 10 to 15 m/s and 0–1 mm/h, respectively.

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Alternative leak detection and repair (alt-LDAR) programs are being introduced by regulators in North America to provide flexibility in how oil and gas producers manage their fugitive methane emissions. However, emissions reduction equivalence must be established between a proposed program and a regulatory standard. We present LDAR-Sim, an open-source, agent-based numerical model for estimating equivalence among LDAR programs and exploring specific LDAR scenarios. Novel advancements include the ability to: (1) set facility-specific LDAR requirements and deployment constraints, (2) simultaneously deploy multiple technologies, each with multiple intelligent agents, (3) integrate screening and close-range methods in a collaborative work practice, (4) consider unique environmental limitations of different technologies, (5) evaluate the impact of limited equipment and labour, and (6) explore the impact of legally vented emissions on screening technologies. We examine several alt-LDAR scenarios using real assets and discuss model confidence and sensitivity to inputs. We show that equivalence determinations depend on explicit definition of reference standards, including weather and labour availability. Screening method performance is vulnerable to the confounding presence of vented emissions and to criteria that trigger follow-up surveys. Relative mitigation of programs is highly sensitive to leak production and null repair rates, two elusive parameters used in previous studies.

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New mobile platforms such as vehicles, drones, aircraft, and satellites have emerged to help identify and reduce fugitive methane emissions from the oil and gas sector. When deployed as part of leak detection and repair (LDAR) programs, most of these technologies use multi-visit LDAR (MVL), which consists of four steps: (a) rapidly screen all facilities, (b) triage by emission rate, (c) follow-up with close-range methods at the highest-emitting sites, and (d) conduct repairs. The proposed value of MVL is to identify large leaks soon after they arise. Whether MVL offers an improvement over traditional single-visit LDAR (SVL), which relies on undirected close-range surveys, remains poorly understood. We use the Leak Detection and Repair Simulator (LDAR-Sim) to examine the performance and cost-effectiveness of MVL relative to SVL. Results suggest that facility-scale MVL programs can achieve fugitive emission reductions equivalent to SVL, but that improved cost-effectiveness is not guaranteed. Under a best-case scenario, we find that screening must cost < USD 100 per site for MVL to achieve 30% cost reductions relative to SVL. In scenarios with non-target vented emissions and screening quantification uncertainty, triaging errors force excessive close-range follow-up to achieve emissions reduction equivalence. The viability of MVL as a cost-effective alternative to SVL for reducing fugitive methane emissions hinges on accurate triaging after the screening phase.