Geological carbon storage (GCS) is one of the key technologies to achieve Carbon peak and carbon neutrality goals. Since industrial-source CO₂ usually contains various impurity gases, and its purification is costly and technically challenging, these impurities are often injected into the subsurface together with CO₂ in practical engineering applications. Based on the geological characteristics of the Shiqianfeng Formation in a CCS demonstration project area, a two-dimensional geological model was established, and reactive transport simulations of impure CO₂ were conducted using the CMG-GEM compositional simulator. N₂ and H₂S were selected as representative impurity components to investigate the migration pathways, occurrence forms, and spatial distribution characteristics of impure CO₂ in the saline aquifer. The study systematically analyzed the dominant trapping mechanisms, including structural trapping, residual gas trapping, and solubility trapping, at different storage stages, and explored the role of capillary pressure in the geological storage process of impure CO₂. The simulation results indicate that during the early injection stage, CO₂ predominantly accumulates in the supercritical state at the top of the reservoir. Over time, the capillary trapping effect gradually emerges, more CO₂ is retained in the reservoir pores, promoting the continuous conversion of supercritical CO₂ into bound and dissolved forms, thereby significantly enhancing the overall storage stability and safety. CO₂ migration exhibits pronounced spatiotemporal heterogeneity: the injected gas rapidly rises under buoyancy and accumulates beneath the caprock, then gradually migrates downward under density and concentration gradients, promoting dissolution. After injection ceases, the gas spreads laterally along the base of the caprock, forming a tongue-shaped migration front with a maximum diffusion distance of approximately 650 m. The transport behaviors of the impurity components differ significantly. Due to its low solubility, N₂ tends to accumulate at the leading edge of the gas-liquid displacement front, whereas CO₂ and H₂S, with higher solubility, form dissolution-enriched zones near the injection well, reaching peak solubilities of 1.4 mol/kg-H₂O and 0.53 mol/kg-H₂O, respectively. Capillary pressure plays a crucial role by suppressing the rate of gas-phase migration, enhancing dissolution trapping efficiency, and inducing reverse imbibition of formation water during the post-injection stage, thereby promoting greater retention of CO₂ in pore spaces in the form of residual gas and effectively increasing the proportion of residual trapping. Comprehensive analysis demonstrates that the storage behavior of impure CO₂ in saline aquifers is jointly governed by impurity properties, trapping mechanisms, and capillary pressure effects. The findings provide scientific support for optimizing injection strategies in impure CO₂ geological storage projects and offer important guidance for ensuring long-term storage security and improving storage efficiency.

Jet fire may cause damage to people and facilities, which will lead to the escalation of accident. Jet fire caused by natural gas pipeline leakage could cause not only the waste of resources, but also the environmental pollution and serious damage to life and property. In order to study the hazard of jet fire in long distance natural gas pipeline with high pressure, CFD simulation based on gas combustion science and fluid mechanics mechanism was carried out to investigate the influence of leakage diameter, leakage pressure and ignition delay time on the length of flame and the damage range of thermal radiation. The results showed that at the initial stage of jet fire, the length of jet flame increases linearly with the combustion process under the action of initial ignition energy. There will be an unstable state for a period of time due to heat diffusion and insufficient supply, jet fire reaches an equilibrium state until the heat generated by the diffusion and the combustion of the jet fire reaches an equilibrium state. The front of the gas pipeline jet fire forms a spherical flame firstly, and then develop into a stable flame gradually. The length of jet flame has experienced three stages of straight rise, dynamic fluctuation and stability. The flame length increases with the increasing of leakage diameter and leakage pressure, and the increase amplitude decreases gradually. The flame profile becomes more obviously with the increase of leakage pressure. The damage range of thermal radiation level increases with the increase of leakage aperture and leakage pressure, and the influence of leakage aperture is higher than that of leakage pressure, and the growth of light injury range is the fastest. The ranges of minor injury and death at 10 MPa were 1.55 times and 1.72 times of those at 2 MPa. When the leakage diameter is 100 mm, the ranges of minor injury and death are 2.03 times and 3.08 times of those at 20 mm, respectively. At the initial stage of ignition, flame length and damage range increase significantly with the increasing of ignition delay time. When the combustion reaches stability, ignition delay time has little influence on flame length and damage range.

Oil and gas gathering station is the core part of oilfield ground engineering construction. As the important link of gathering and transportation system, the block station has the production characteristics of centralized equipment and successional production chain, and it is also prone to severe fluctuation of the inflow proportion and equipment operation faults. The diagnosis of the operation condition for block station is crucial to the oil and gas production system, for the abnormal data of simple equipment, the station staff can make a preliminary diagnosis, but for a large number of real-time SCADA monitoring data of the whole station, it is difficult to realize rapid analysis and processing only by experience and knowledge. Compared with the existing threshold alarm method in oil field, data-driven diagnostic approach is more accurate and intelligent. Among the data-driven methods, deep learning method which is good at processing massive high-dimensional data, can automatically extract the nonlinear features of data. Aimed at multiple time series characteristics of data (SCADA) in block station, a fault diagnosis method is proposed by use deep residual network (DRN). In order to identify and classify the abnormal working conditions of block station, a diagnostic model was established by taking 36 monitoring variables of the SCADA system in block station as model input and 5 working conditions as model out. The noise of field data will reduce the ability of the model to identify the working conditions with fewer samples, wavelet decomposition is used to de-noise the data of the block station to reduce the interference of equipment acquisition, enhance model diagnostic performance. Naive resampling is used to enlarge the data capacity to alleviate the difficulty in training the model caused by insufficient sample size of field data. The regularization method is used to punish the weight vector with large values to avoid the dependence of the model on individual variables. On this basis, eight different DRN architectures has proposed to select the optimal diagnostic model for the block station, and the correction between various samples is quantified according to the mutual information method, ensured the validity of the diagnosis results. Verification of real data in field shows that the method can be used quickly and accurately diagnose process status of block station. The average accuracy is 97.3%, which are significantly higher than other machine learning method like support vector machine (93%) and multilayer perceptron (65%). The method has guiding significance for fault diagnosis and anomaly identification of other oil and gas stations.

The flowability of waxy crude oil is poor at normal temperatures, resulting in serious flow assurance issues. This affects the economical and safe operation of crude oil pipelines. Magnetic treatment technology is an effective method to improve the flowability of waxy crude oil, which is in line with the current green and low-carbon development requirements. The research progress of waxy crude oil viscosity reduction technology by magnetic treatment was investigated here. The methods used by domestic and foreign researchers to explore the viscosity variation law of waxy crude oil after using magnetic treatment were summarized. The characteristics of relevant experimental devices were explained. The viscosity reduction effect and time-dependence of the magnetic treatment of waxy crude oil were analyzed. The impacts of influencing factors, such as crude oil composition and magnetic treatment conditions, on the viscosity reduction effect and time-dependence of waxy crude oil after magnetic treatment were investigated. The current research progress of the viscosity reduction mechanism of waxy crude oil by magnetic treatment was described. The existing quantitative prediction models of viscosity reduction effect were compared and evaluated. In view of the problems existing in the existing problems of waxy crude oil viscosity reduction technology by magnetic treatment, the research direction of the technology has been prospected.