スナップショットを超えて：アパラチア盆地における中流石油・ガスサイト向けの測定情報に基づくインベントリ構築

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:測定情報型インベントリの方法論と、ボトムアップ推計の裾野補完における航空観測の役割を理解できる。

🏢実務担当者:自社のGHG報告に測定データを統合する際の手法と、未報告排出の規模感を把握し、開示リスク管理に活用できる。

🏛政策担当者:規制インベントリの精度向上に向け、航空サーベイと確率モデルを組み合わせた測定ベースの検証枠組みの設計に参考となる。

This paper presents the application of a novel methodology to build a comprehensive Measurement-Informed Inventory (MII) by integrating unreported emissions into government inventories. These emissions are often excluded from current regulatory requirements or associated with upset conditions, and are identified through aerial surveys and confirmed by oil and gas (O&G) operators. This methodology utilizes the Mechanistic Air Emissions Simulator (MAES) tool to generate spatially and temporally resolved emission estimates for O&G sites, using annual inventory data submitted to the Greenhouse Gas Reporting Program (GHGRP) and quarterly aerial surveys conducted in 2023 by Bridger Photonics at partner-operated sites in the Appalachian Basin. The analysis is part of the Appalachian Methane Initiative (AMI) coalition efforts to improve methane emissions characterization and mitigation efforts in the Appalachian Basin. On average, results show that emissions from the MII models are 58.9% higher than the emissions reported to the GHGRP for reporting year 2022 (submitted in 2023) for these facilities, which was the most recent inventory available at the time of the 2023 surveys, when the reported total is restricted to normal operation. Measured against the total reported inventory, which includes 479.2 mt/year of operator-reported fugitive emissions, the increase is 40.0%. Site-level methane emission rates exceeding 15 kg/h are estimated to account for 94.9% of total emissions across all midstream sites, while rates above the 95th percentile of the site-level distribution (105 kg/h) contribute 33.4%, highlighting the disproportionate influence of large emitters. These results are based on 19 partner-operated facilities from two operators and are not a representative sample of the Appalachian midstream sector. The simulated average loss rate for the participating companies under analysis was 6.45 × 10−4, lower than the loss rate values reported in the literature for this sector, which span different supply chain scopes. The principal contribution of this work is methodological: it shows how aerial observations, operator-provided information, and a stochastic facility model can be combined to account for the tail end of the emissions distribution that is often absent from conventional bottom-up (BU) estimates, and it indicates that aerial campaigns optimized to detect events from upset conditions can support more accurate MIIs.

• semanticscholar https://doi.org/10.3390/gases6030044first seen 2026-09-15 04:47:05 · last seen 2026-09-22 04:54:07