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With increasing accessibility of methane emission monitoring technology and advancements in emission modeling, numerous approaches have been developed to create measurement-based emission inventories. Yet inventories often leave emissions unaccounted for due to limited detection sensitivity, limited temporal sampling, or unscalable spatial deployment. We present a framework for building measurement-based methane inventories using Bridger Photonics Gas Mapping LiDAR (GML), a high-resolution, source-resolved aerial technology with a 90% probability of detection at 1 kg h⁻¹. We apply this framework in 2024 across major U.S. oil and gas basins including the Permian, Bakken, Appalachia, Haynesville, and Denver-Julesburg. The framework produces basin-scale methane inventories, attributed to the facility- and equipment-levels, along with methane intensity benchmarks derived from basin-representative sampling designs. Multiple survey deployments are used to characterize temporal variability and sub-basin results provide operator and supply-chain relevant benchmarks that support prioritization of LDAR campaigns and emissions reporting. Inventory methods integrate 1) the GML quantification error model that accurately accounts for uncertainty and bias of emissions estimates, and 2) the GML probability of detection model that estimates missed emissions in the partial detection region of the sensor (0.4-3 kg/h). We describe how representative sampling plans are constructed using U.S. energy infrastructure databases, and how emissions are extrapolated across heterogeneous facility populations with varying equipment and operational characteristics. In regions where public energy infrastructure data are sparse, operators can provide facility and equipment datasets to support sampling and inventory development, which often yield the highest quality results. Applying a single workflow across basins provides comparable, policy-relevant benchmarks for U.S. methane emissions and intensities. Ongoing work addresses remaining limitations, including diurnal variability and quantification uncertainty driven by regional wind models. The framework is transferable to international basins where comparable infrastructure data are available, enabling transparent, scalable, and globally comparable measurement-based methane inventories.