This work presents a mechanistic modeling approach for simulating methane emissions from tri-ethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (< 0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.

This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.

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.