Methane (CH₄) is the second-largest greenhouse gas contributing to global climate warming. As of 2022, methane emissions from the oil and gas industry amounted to 3.586 million tons, representing 13.24% of total methane emissions and ranking second among all methane emission sources. To effectively control methane emissions in oilfield regions, this study proposes a multi-source remote sensing data fusion method based on the concept of data fusion, targeting high-emission areas such as oil and gas fields. The aim is to construct an XCH₄ remote sensing dataset that meets the requirements for high resolution, wide coverage, and high accuracy. Initially, XCH₄ data products from the GOSAT satellite and the TROPOMI sensor are matched both spatially and temporally. Subsequently, variables such as longitude, latitude, aerosol optical depth, surface albedo, digital elevation model (DEM), and month are incorporated. Using a local random forest (LRF) model for fusion, the resulting product combines the high accuracy of GOSAT data with the wide coverage of TROPOMI data. On this basis, ΔXCH₄ is derived using GF-5. Combined with the GFEI prior emission inventory, the high-precision fusion dataset output by the LRF model is redistributed grid by grid in oilfield areas, producing a 1 km resolution XCH₄ grid product, thereby constructing a high-precision, high-resolution dataset for oilfield regions. Finally, the challenges that emerged from the study were discussed and summarized, and it was envisioned that, in the future, with the advancement of satellite technology and algorithms, it would be possible to obtain more accurate and high-resolution datasets of methane concentration and apply such datasets to a wide range of fields, with the expectation that significant contributions could be made to reducing methane emissions and combating climate change.

Since industrialization, global greenhouse gas emissions have gradually increased. Storage tanks, as industrial facilities for storing fossil energy, are one of the main sources of greenhouse gas emissions. Using remote sensing images to detect and locate storage tank targets over a large area can provide data support for regional air pollution prevention, control, and monitoring. Due to the circular terrain on the ground and the circular traces caused by human activities, the target detection model has a high false detection rate when detecting tank targets in large-scale remote sensing images. To address the above problems, a YOLOv7-OT model for tank target detection in large-scale remote sensing images is proposed. This model proposes a data pre-processing method of edge re-stitching for large-scale remote sensing images, which reduces the target loss caused by the edge of the image without losing the target information. In addition, to address the problem of small target detection, the CBAM is added to the YOLOv7 backbone network to improve the target detection accuracy under complex backgrounds. Finally, in response to the model’s misjudgment of targets during detection, a data post-processing method combining the spatial distribution characteristics of tanks is proposed to eliminate the misdetected targets. The model was evaluated on a self-built large-scale remote sensing dataset, the model detection accuracy reached 90%, and the precision rate reached 95.9%. Its precision rate and detection accuracy are better than those of the other three classic target detection models.

Range-resolved CH₄ concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH₄ concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH₄ concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH₄ profile by 85.54%. The standard deviation of CH₄ concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH₄ concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH₄ concentration at observation sites.

In deep-sea oil and gas development, the problem faced by traditional materials is to consider the driving force of new material application. Soft matter science involves many fields such as mechanics, physics, biology, materials, chemistry and so on. It is regarded as a science in the 21st century and has received widespread attention in the world in recent years. The composition of soft matter is complex, and its motion does not depend on the atomic or molecular scale quantum mechanical action in the constituent units. During installation and service of deep water oil and gas transmission pipelines, the stress conditions are complicated, which not only meet the conditions of single load such as external hydrostatic pressure and bending moment, but also meet the conditions of combined action of external hydrostatic pressure and bending moment. The research and large-scale application of the physical mechanism of intelligent materials will lead to a major revolution in the development of material physics science. This paper analyzes the application and performance of intelligent soft materials in oil and gas exploitation.

Amid global climate change, rising atmospheric methane (CH₄) concentrations significantly influence the climate system, contributing to temperature increases and atmospheric chemistry changes. Accurate monitoring of these concentrations is essential to support global methane emission reduction goals, such as those outlined in the Global Methane Pledge targeting a 30 % reduction by 2030. Satellite remote sensing, offering high precision and extensive spatial coverage, has become a critical tool for measuring large-scale atmospheric methane concentrations. However, traditional physical inversion models face challenges, including high computational complexity, low processing efficiency, and inadequate incorporation of spatial distribution information, limiting their effectiveness. To address these shortcomings, this study proposes a high-precision XCH₄ inversion method that integrates the Convolutional Block Attention Module (CBAM) with the ResNet18 neural network (CBAM-ResNet18). By leveraging shortwave infrared spectral data from the Sentinel-5P satellite and the CAMS reanalysis dataset, this approach achieves rapid and accurate XCH₄ inversion. Experimental results demonstrate that the method outperforms both conventional physical models and existing mainstream techniques in terms of inversion accuracy and computational efficiency. It achieves an error of less than 2 %, meeting the stringent precision requirements for XCH₄ in atmospheric remote sensing and providing a robust tool for methane monitoring.