ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Atmosphere and Climate
This article is part of the Research TopicAssessing Greenhouse Gas Emissions at City and Regional Levels: Challenges and MethodsView all articles
Application of CH4 monitoring technology based on UAV platform in Shengli Oilfield
Provisionally accepted
- 1Technical Test Centre of Sinopec, Shengli OilField, Dongying City, China
- 2Engineering Technology Management Center, Shengli OilField, Dongying, Shandong Province, China
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Accurate identification and quantification of methane (CH4) emissions in oilfield environments remain challenging due to spatially dispersed sources and complex near-surface atmospheric conditions. In this study, an unmanned aerial vehicle–AirCore system (UAV–AirCore) was deployed over the Shengli Oilfield in Dongying, China, to acquire high-resolution three-dimensional observations of CH4 concentrations. A two-stage flight strategy integrating horizontal screening and top-down vertical spiral profiling was implemented to rapidly identify anomalous concentration regions and constrain plume spatial structures. Based on these observations, an integrated Emission-Partition inversion framework was applied, in which a multi-source Gaussian dispersion model is coupled with a hybrid Particle Swarm Optimization–Interior Point Penalty Function (PSO–IPPF) scheme to retrieve CH4 emission rates and effective release heights. The results demonstrate that the proposed approach effectively reconstructs the observed concentration fields (R2 ≈0.85). For the two investigated areas, the retrieved emission intensities are 3.22±0.12 g s−1 and 3.68±0.04 g s−1, corresponding to relative uncertainties of approximately 3.7% and 1.1%, respectively, while the effective emission heights are estimated with relative uncertainties of approximately 9%. Further comparison with several established quantification methods, including the mass balance approach, Other Test Method 33A (OTM 33A), nonlinear least-squares fitting (NLSF), and facility-based emission inventory calculations, shows good consistency in emission magnitudes, with the proposed method exhibiting lower uncertainty and improved robustness. These results indicate that the integration of the UAV–AirCore system with the Emission-Partition framework provides a reliable and practical solution for methane emission monitoring and quantification in complex oilfield environments.
Keywords: Emission-Partition model, Gaussianplume dispersion, Methane emission quantification, Oilfield pollution monitoring, UAV–AirCore sampling
Received: 15 Nov 2025; Accepted: 27 Jan 2026.
Copyright: © 2026 He, Zhang, Gu, Li, Ma and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hu He
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