Pipelines are important facilities for the development of oil and gas, and it is of great significance to ensure their safe and efficient operation. The existing pipeline ultimate load evaluations and researches lack the consideration of pipe characteristics and failure mechanism, there are certain errors, and the types of defects involved are relatively single. In order to give full play to the potential of pipes, consider the strain hardening effect of pipeline steel during the true failure process of pipelines, and study the influence of different types of defects on the ultimate bearing capacity of the pipeline. This paper uses the twin shear stress yield criterion and based on the Ramberg-Osgood material strain hardening model to derive an equation which is used to predict the ultimate load of the defect-free X60 pipeline. Using finite element method to study different kinds of defects on the ultimate load of X60 pipeline. A model for predicting the ultimate load of the X60 pipeline with a flat-bottomed rectangular single-point external defect is fitted, and the accuracy of the model is verified by comparison with real data. The failure modes of pipelines with different defects are also studied. On the basis of the above prediction model, the ultimate load prediction models of X60 pipeline with axially adjacent double-point external defects were fitted. Besides, the critical distance of interaction between the double-point defects has been discussed. The above analysis provides a basis for the safety assessment of X60 pipeline.

LNG unloading system plays an important role in the transport and storage medium of the process. Once the unloading system occurs exceptions, such as pressure fluctuations, uneven pre-cooling and unexpected temperature rises, these phenomena will affect the safe operation of LNG terminals. In the process of LNG unloading, there is a coupling relationship between the changing of pressure, flow rate, temperature and other parameters. The accurate cause and development direction of abnormal working conditions are conducive to the safe operation and maintenance of the unloading system. According to the accident statistics of 38 LNG receiving stations and peak shaving stations by The LNG International Importer’s Group (GIIGNL), the accident rate of the unloading system was 27%. Therefore, the realization of the extraction and identification of the causality of abnormal working conditions of the LNG unloading system has great significance for the traceability and early warning of the accident. As a crucial type of relationship, causality plays a key role in many fields such as relational reasoning and so on. Therefore, extracting causality is a basic task in text mining. At present, in China, the analysis of abnormal working conditions of LNG unloading system adopts the methods of HAZOP and FMEA, and a large amount of causality is contained in the text data. To solve the problem of automatically extracting the causality of the LNG unloading system text data, this paper adopts the method of sequence labeling to extract the causal relationship entity and determine its direction. Sequence to sequence (Seq2Seq) architecture and bidirectional long short memory network (Bi-LSTM) are used to train input word vectors and predict the development of causal relationship. In order to solve the problem of unclear objects and inaccurate predictions caused by redundant causal nodes, attention mechanism is applied, and the prediction accuracy reaches 95.23%. In the sensitivity analysis, the recall rate and the granularity of causal relationship information perform well, which indicates that the algorithm has generalization and applicability in the establishment of the causal knowledge base of abnormal conditions in the unloading system.

With the development of oil and gas resources extending to the deep sea, more and more submarine pipelines are applied, and the importance of the structural and functional integrity of submarine pipelines is becoming more and more prominent. Dent has long been considered one of the potential factors affecting the integrity of submarine pipelines, which can be formed when pipelines are impacted by fallings objects from the sea surface. Due to the seabed subsidence, pipeline suspension and earthquake, mudslide activity and other factors, submarine pipelines may be under bending moment, seriously affecting the safe operation of submarine pipelines. The study of failure and limit load capacity of submarine pipelines under external pressure and bending moment is of great significance. In this paper, the nonlinear finite element method is used to study the collapse failure and local buckling failure of subsea pipelines under external pressure and bending moment respectively. The process of the pipeline being dented by the impact of objects is simulated, spherical denter and wedge-shaped denter are considered. The influence of D/t and dent depth on collapse capacity and bending capacity are investigated. The results can be referred in engineering practice.

As one of the main risks of long-distance oil and gas pipelines, the consequences of pipeline accidents caused by third-party damage (TPD) are usually catastrophic. At present, TPD prevention approaches mainly include manual line patrol, fiber-optical vibration warning, and unmanned aerial vehicle (UAV) line patrol, but there are some limitations such as untimely warning, false alarm, and the missed report. As the location technology of mobile device matures, the user group provides massive data sources for the collection of location information, with which the tracks and features of the third-party activity along the pipeline can be directly obtained. Therefore, this paper proposes a method to identify the TPD behavior based on the location data of mobile devices. Firstly, the characteristics of relevant destruction behaviors were extracted from the historical destruction events. Then, the location information of the third-party activity near the target pipeline is obtained and the data is processed to remove the influence of noise, to reduce the computational burden of the subsequent identification process. Finally, calculate the difference degree of neighborhood trajectory and the similarity with the TPD features based on the data feature grouping (Difference feature and Similarity feature) to classify the type of third-party activity. Taking a 10km pipeline segment as an example, the method of this paper is used to preprocess the collected data and calculate the difference degree and similarity, 232 suspected TPD events are identified. After the on-site verification of the suspected damage by the line patrol, the results show that the method can better identify the third-party activities near the pipeline.

Due to geographical restrictions, it is often necessary to lay two pipelines in the same ditch during pipeline construction. However, as developments in natural gas pipelines increasingly incorporate higher grades of steel, larger diameters, and higher pressures, the consequences of an accident caused by leakage, explosion, or ignition become progressively more severe. The explosion occurred in one pipe would affect the other pipe laid in the same ditch, resulting in subsequent explosion. In this study, a full-scale explosion test was conducted on two X80 steel pipes with diameters of 1422 mm and 1219 mm, laid in one ditch and set up in parallel arrangement, with a spacing of 36 m between one another, and a burying depth of 1.5 m. Some X80 steel samples were selected to assess materials performance in different areas of a given pipe in the same ditch by deploying selective explosions. The mechanical performance tests on a number of points including tensile strength, impact, and hardness were executed by the Mechanical Testing and Sensing (MTS) universal testing machine. Drop-Weight Tear Test (DWTT) of the selected samples were executed by the DWTT pendulum test machine. Metallography and microstructure analysis of the steel samples was performed by the MEF4M metallographic microscope and image analysis system. Results indicate that limited effects of jet fire on the macroscopic tensile properties and impact toughness of the pipelines, but a more obvious softening effect on the steel surface material. Drop-Weight Tear Test and microscopic observation revealed that jet fire can significantly reduce crack propagation resistance on the surface of the pipeline, pointing to deformation behavior and other concerns that could impact pipeline safety and efficacy. The results of this experimentation can help to improve pipeline construction, resulting in better safety.

Dents has long been regarded as a potential hazard for integrity of subsea pipelines in the oil and gas industry. The dents in subsea pipeline are usually caused by the collision and extrusion with other objects resulting in permanent plastic deformation in pipe wall. Meanwhile, seabed deformation or motion due to subsidence, mudslides, and seismic activities may load the pipeline to an ultimate state, where the pipeline is under combined external pressure and bending moment. It is of great importance to carry out the research on the buckling failure and bending capacity of dented subsea pipelines under these combined loads. Nonlinear finite element method is adopted to investigate the local buckling failure of the dented subsea pipeline under combined loads. The numerical results show that the buckling behavior and bending capacity of the pipeline can be largely reduced due to the existence of external pressure and axial force. Based on numerical results, a formula for predicting the bending capacity of dented subsea pipelines is proposed. Finally, the accuracy of propose solution is validated.

The subsea pipelines are usually located in a corrosive external and internal environment. Corrosion presents to be the most common defect type in subsea pipelines, and it is regarded to be one of the main causes of subsea pipeline failure. Due to subsea subsidence, mudslides, and seismic activities, the pipeline is presented under combined external pressure, bending moments and axial force combined loading cases. The accurate determination of the collapse pressure of corroded pipelines under combined loading is important in engineering practice. On the basis of the finite element method, collapse failure of subsea corroded pipelines under combined loads is investigated. The influence of corrosion length, corrosion width, corrosion depth and diameter-thick ratio on the collapse failure pressure is studied. It is observed that corrosion depth has the most significant impact on pipelines’ collapse capacity. Furthermore, regression equations for predicting the collapse pressure of subsea corroded pipelines are proposed based on numerical results. The solution can be referred to in structural integrity assessment of subsea corroded pipelines.

This paper examined the erosion characteristics of the curved core surface of the ball valve when subjected to the two-phase flow of sand-carrying high-pressure gas. The Computational Fluid Dynamics (CFD) method for joint analysis was used to establish the erosion simulation model, the critical factors, such as the ball opening degree, ball diameter affecting the flow field and erosion distribution of the gas-solid two-phase flow were analyzed. The flow field simulation results show that the gas velocity changed violently after entering the ball valve due to the reduction of the cross-sectional area, and the maximum velocity at the turning position increased gradually in conjunction with the decrease in opening degree, while remain unchanged with the increase of ball diameter. Moreover, the particle trajectory show that particles flowing in from the right straight pipe section mainly colliding with three areas including outer wall surface (zone 1), upstream wall surface (zone 2) and the outlet pipe section near the lower wall of the ball valve (zone 3), which were consistent with the location of that particle wall collision happens. The comparative analysis of the maximum erosion rate under different influencing factors shows that decreases with the increase of the ball diameters the opening degree and ball diameter. This research provided theoretical guidance to the erosion analysis of the ball valve core by the two-phase flow of high-pressure sand-carrying gas subjected to the changes in a variety of crucial parameters. This paper also presented a field application value for the design and maintenance of the ball valve in the natural gas pipeline.

With the rapid development of the national economy, the demand for oil is increasing. In order to meet the increasing energy demand, China has established a number of oil depot in recent years, whose largest capacity reaching up to tens of millions of cubic meters. Due to the flammable and explosive nature of the stored medium, the risk of fire in the oil depot area has increased dramatically as the tank capacity of the storage tank area has increased. The intensification of the oil depot and the development of large-scale oil storage tanks have brought convenience to the national oil depot, but also brought many catastrophic consequences. In recent years, there have been many fires and explosions in the oil depot, causing major casualties and property losses, which seriously endangered the ecological environment and public safety. Based on the constructed oil depot fire risk index system, the fuzzy C-means algorithm (FCM) and fuzzy maximum support tree clustering algorithm is introduced. Through the two fuzzy clustering mathematical models, key factors in the established index system are identified. Firstly, the expert scoring method is used to evaluate the indicators in the oil depot fire risk index system, and the importance degree evaluation matrix of oil depot fire risk factors is constructed through the fuzzy analysis of expert comments. Then, the fuzzy C-means algorithm (FCM) and the fuzzy clustering tree algorithm are used to cluster the various risk indicators, and the key factors of the oil depot fire risk are identified. Through the comparative analysis and cross-validation of the results of the two fuzzy clustering methods, the accuracy of the recognition results is ensured. Finally, using an oil depot as a case study, it is found that passive fire prevention capability and emergency rescue capability are key factors that need to be paid attention to in the oil depot fire risk index. The fuzzy clustering algorithm used in this paper can digitize the subjective comments of experts, thus reducing the influence of human subjective factors. In addition, by using two fuzzy clustering algorithms to analyze and verify the key factors of the oil depot fire risk, the reliability of the clustering results is guaranteed. The identification of key factors can enable managers to predict high-risk factors in advance in the fire risk prevention and control process of the oil depot, so as to adopt corresponding preventive measures to minimize the fire risk in the oil depot, and ensure the safety of the operation of the oil depot.

With the increasing of pipe diameter and operation pressure, the severity of the accident consequences has been increased, especially for the impact on the high consequence area. The safety of oil and gas pipeline is very important. At the same time, a lot of data were produced during the process and the amount of detection data signal has also reached the TB level. However, because the relationship between these data sets has not been established, most part of the “Big Data” in which the safety information of pipeline hidden was ignored and discarded. In order to effectively use the relevant pipeline data of the defects, the mutual information method was adopted to establish a correlation analysis model. Its main purpose was extracting all the factors that lead to pipeline defects from the “Big Data” and determined the crucial factors from them. A pipe segment on a long-distance pipeline with the length of 100km was taken as a case. Based on the correlation analysis model, the crucial factors which had great correlation relationship with pipeline defect were extracted, so as to provide reference to accident prevention. It is a new way of pipeline safety management.