Long-distance oil and gas pipelines buried in permafrost areas will inevitably encounter typical geological disasters, such as frost heave and thaw settlement and sliding, which easily cause pipeline displacement, bending, or deformation. When there are certain defects in the pipeline, additional complex, external stress will further lead to the failure of the pipeline or weld and can even lead to serious accidents such as pipeline leakage, pipe burst, or fracture. This paper introduces in detail the typical defects and risks of buried pipelines in permafrost areas and summarizes the in-line inspection technologies, off-line inspection technologies, and integrated monitoring systems for pipelines in the pipeline industry. Regarding pipelines in permafrost areas, in-line inspection methods may be employed. These include magnetic flux leakage, electromagnetic eddy current, ultrasonic, IMU, and electromagnetic acoustic transducer inspections. Off-line inspection is also one of the important means of inspecting a pipeline in a permafrost area. Indirect inspection is combined with verification by direct inspection to check and evaluate the integrity of the anticorrosive coating and the effectiveness of the cathodic protection for the pipeline. Meanwhile, considering the external environment of a pipeline in a permafrost area, a monitoring system should be developed and established. This paper discusses and projects the future development of related technologies, which provides reference for the construction and operation of pipelines in permafrost areas.

Abnormal formation of girth weld is a major threat to the safe operation of pipelines, which may lead to serious accidents. Therefore, regular inspection and maintenance of girth weld are essential for accident prevention and energy security. This paper presents a novel method for inspecting abnormal girth weld formation in oil and gas pipelines using alternating excitation detection technology. The method is based on the analysis of the microscopic magnetic variations in the welded area under alternating magnetic fields. An internal inspection probe and electronic system for detecting abnormal girth weld formation were designed and developed. The system’s capability to identify misalignment, undercutting, root concavity, and abnormal formation height of girth weld was tested by numerical simulation and experimental study. The results show that the detection system can effectively identify a minimum misalignment of 0.5 mm at a lift-off height of 15 mm. The proposed method offers several advantages, such as rapid response, low cost, non-contact operation, and high sensitivity to surface flaws in ferromagnetic pipelines.

External corrosion dent is a common type of compound dent. On the one hand, this type of compound dent reduces the bearing area and bearing capacity of the pipeline. On the other hand, it leads to an increase in the stress–strain concentration in the dent and reduces the anti-fatigue load capacity of the pipeline, which is more harmful to the service safety of the pipeline than the simple dent. In this study, the reliability of the modeling method was verified by the numerical inversion of the full-size dented pipe test. A three-dimensional finite element model for a pipe with a small corrosion dent was established by analyzing the internal detection data on corrosion defects of pipes with a diameter of 813 mm. The failure criterion of the corrosion dent pipe and the calculation method of the critical dent depth were determined. The influence of corrosion depth, length, width, internal pressure load, curvature radius of indenter, and diameter–thickness pipeline ratio on critical dent depth was investigated. Finally, a critical dent-depth prediction formula was developed based on the numerical results. This study provides a reference and significant guidance for the applicability evaluation of corroded sunken pipelines.

Compressed sensing (CS) is a popular data compression theory for many computer vision tasks, but the high reconstruction complexity for images prevents it from being used in many real-world applications. Existing end-to-end learning methods achieved real time sensing but lack theory guarantee for robust reconstruction results. This paper proposes a neural network called RootsNet, which integrates the CS mechanism into the network to prevent error propagation. So, RootsNet knows what will happen if some modules in the network go wrong. It also implements real-time and successfully reconstructed extremely low measurement rates that are impossible for traditional optimization-theory-based methods. For qualitative validation, RootsNet is implemented in two real-world measurement applications, i.e., a near-field microwave imaging system and a pipeline inspection system, where RootsNet easily saves 60% more measurement time and 95% more data compared with the state-of-the-art optimization-theory-based reconstruction methods. Without losing generality, comprehensive experiments are performed on general datasets, including evaluating the key components in RootsNet, the reconstruction uncertainty, quality, and efficiency. RootsNet has the best uncertainty performance and efficiency, and achieves the best reconstruction quality under super low-measurement rates.

With the robust growth of the national economy, the demand for oil and natural gas continues to rise, heightening the significance of pipeline transportation in the energy sector. However, long-term pipeline operations are often subjected to factors such as corrosion, aging, and damage, which can result in leaks and safety incidents, posing significant threats to life, property, and environmental integrity. Consequently, timely and precise detection of pipeline defects and estimation of their sizes hold paramount practical importance. This research endeavors to employ advanced information technology and artificial intelligence to explore methods for pipeline defect detection and size estimation grounded in prior knowledge. The aim is to enhance the accuracy and reliability of pipeline defect diagnosis and ensure the safe operation of pipelines. The primary innovative work includes the development of a preprocessing method based on prior knowledge, the design of an adaptive algorithm for estimation of defect size, and the creation of an algorithm for estimation of deep cascade pipeline defect size. These methods effectively combine traditional mechanisms and data-driven approaches, leveraging the strengths of both to improve performance, accuracy, and robustness. The proposed methodology demonstrates superior accuracy and stability in defect inversion, providing strong technical support for the quantitative assessment of pipeline defects, which is significant for fault diagnosis and the precise maintenance of long-distance pipelines.

In this paper, the effect of nozzle height on the combustion dynamics of jet fires in rotating flow fields (JFRFFs) is systematically investigated through experimental and numerical simulations. As the nozzle height increases, the JFRFF flame state transitions from stable rotation (SR) to unstable rotation (USR), and eventually to non-rotation (NR), indicating a weakening interaction between the vortex flow and the jet flame. The radial distribution of tangential velocity gradually deviates from the Burgers vortex model as the nozzle height increases, providing a criterion for distinguishing different flame states. Both vortex intensity and flame length are found to decrease with increasing nozzle height, whereas the maximum flame diameter increases. The relative position of the maximum flame diameter to the whole flame length firstly increases and then decreases to match that of the free jet fires, as the flame evolves from SR to USR and NR. In addition, the air entrainment near the nozzle exit decreases with increasing nozzle height, as evidenced by the gradual rise in lift-off height. These findings establish a theoretical basis for the fire performance design of flares in pipeline retrofitting and process industries.