A Source-Level Estimation and Uncertainty Analysis of Methane Emission in China’s Oil and Natural Gas Sector

Increasing impacts of China's oil and gas demands on global CH4 emissions

The energy sector stands out as a main contributor to increasing global methane (CH4) emissions. Given China's heavy dependence on energy imports, a closer examination of its oil and gas-related CH4 emissions becomes imperative. This study conducts an in-depth analysis of China's contribution to global CH4 emissions stemming from its consumption of crude oil and natural gas since 2000. The results indicate that CH4 emissions from crude oil and natural gas imports rose from 614 Gg in 2000 to 7692 Gg in 2019. When considering domestic production, the demand-induced CH4 emissions in 2019 increased to approximately 10754 Gg (equivalent to 320 Mt CO2-eq and 887 Mt CO2-eq based on global warming potential (GWP) values at the 100-year and 20-year time period), of which 72 % were related to crude oil and natural gas imports. The primary contributor to this increase in CH4 emissions was the expansion of the trade scale. The growth trend of crude oil imports-induced CH4 emissions was also positively influenced by emission intensity and trade structure, but these two drivers had a negative impact on the growth of natural gas imports-induced CH4 emissions. The virtual transfer of CH4 emissions via international oil and gas trade requires urgent policy attention. In collaboration with its trading partners, China should take aggressive actions to achieve meaningful mitigation in CH4 emissions associated with the oil and gas trade.

Vertical Distribution Mapping for Methane Fugitive Emissions Using Laser Path-Integral Sensing in Non-Cooperative Open Paths

Observing the vertical diffusion distribution of methane fugitive emissions from oil/gas facilities is significant for predicting the pollutant's spatiotemporal transport and quantifying the random emission sources. A method is proposed for methane's vertical distribution mapping by combining the laser path-integral sensing in non-non-cooperative open paths and the computer-assisted tomography (CAT) techniques. It uses a vertical-plume-mapping optical path configuration and adapts the developed dynamic relaxation and simultaneous algebraic reconstruction technique (DR-SART) into methane-emission-distribution reconstruction. A self-made miniaturized TDLAS telemetry sensor provides a reliable path to integral concentration information in non-non-cooperative open paths, with Allan variance analysis yielding a 3.59 ppm·m sensitivity. We employed a six-indexes system for the reconstruction performance analysis of four potential optical path-projection configurations and conducted the corresponding validation experiment. The results have shown that that of multiple fan-beams combined with parallel-beam modes (MFPM) is better than the other optical path-projection configurations, and its reconstruction similarity coefficient (ε) is at least 22.4% higher. For the different methane gas bag-layout schemes, the reconstruction errors of maximum concentration (γm) are consistently around 0.05, with the positional errors of maximum concentration (δ) falling within the range of 0.01 to 0.025. Moreover, considering the trade-off between scanning duration and reconstruction accuracy, it is recommended to appropriately extend the sensor measurement time on a single optical path to mitigate the impact of mechanical vibrations induced by scanning motion.

Methane emission and influencing factors of China's oil and natural gas sector in 2020-2060: A source level analysis

The Chinese oil and gas industry requires targeted policies to reduce methane emissions. To achieve this goal, it is necessary to predict future methane emission trends and analyze the factors that influence them. However, changing economic development patterns, insufficient analysis of various factors influencing emissions, and inadequate resolution of methane emission inventories have made these goals difficult to achieve. Accordingly, this study aims to expand the methane emission estimation method to compile source-level emission inventories for future emissions, analyze the factors influencing them, and form a mechanistic understanding of the methane emissions from the local oil and gas industry. The research results indicate that methane emissions deriving from this industry will increase rapidly before 2030, after which they will decline slowly in all scenarios. The production and utilization processes in the natural gas supply chain, i.e., compressors and liquid unloading, include the main sources of methane emissions. Emissions are affected significantly by total production and consumption. Change in the overall supply and demand of natural gas affects change in methane emissions more significantly than adopting new technologies and strengthening facility maintenance, i.e., the overall supply and demand of natural gas are the dominant factors in controlling methane emissions. This study suggests that controlling the total demand for oil and gas should be at the core of the methane emission control policy for the local oil and gas industry. Moreover, equipment maintenance and emission reduction technologies should be used more effectively to reduce total emissions.