scholarly journals Optimization of Long-distance Oil Pipeline Production Operation Scheme

2021 ◽  
Vol 772 (1) ◽  
pp. 012081
Author(s):  
Liming Gu
Keyword(s):  
Production Operation ◽  
Long Distance ◽  
Oil Pipeline

Simulation of Liquid-Gas Replacement in Commissioning Process for Large-Slope Crude Oil Pipeline

2012 ◽  
Author(s):  
Yuanyuan Chen ◽  
Jing Gong ◽  
Xiaoping Li ◽  
Nan Zhang ◽  
Shaojun He ◽  
...  
Keyword(s):  
Crude Oil ◽  
Flow Rate ◽  
Liquid Flow ◽  
Liquid Flow Rate ◽  
Fluid Model ◽  
Engineering Application ◽  
Computational Procedure ◽  
Control Measures ◽  
Production Operation ◽  
Oil Pipeline

Pipeline commissioning, which is a key link from engineering construction to production operation, is aim to fill an empty pipe by injecting water or oil to push air out of it. For a large-slope crude oil pipeline with great elevation differences, air is fairly easy to entrap at downward inclined parts. The entrapped air, which is also called air pocket, will cause considerable damage on pumps and pipes. The presence of it may also bring difficulties in tracking the location of the liquid head or the interface between oil and water. It is the accumulated air that needed to be exhausted in time during commissioning. This paper focuses on the simulation of liquid-gas replacement in commissioning process that only liquid flow rate exists while gas stays stagnant in the pipe and is demanded to be replaced by liquid. Few previous researches have been found yet in this area. Consequently, the flow in a V-section pipeline consisted of a downhill segment and a subsequent uphill one is used here for studying both the formation and exhaustion behaviors of the intake air. The existing two-fluid model and simplified non-pressure wave model for gas-liquid stratified flow are applied to performance the gas formation and accumulation. The exhausting process is deemed to be a period in which the elongated bubble (Taylor bubble) is fragmented into dispersed small bubbles. A mathematical model to account for gas entrainment into liquid slug is proposed, implemented and incorporated in a computational procedure. By taking into account the comprehensive effects of liquid flow rate, fluid properties, surface tension, and inclination angle, the characteristics of the air section such as the length, pressure and mass can be calculated accurately. The model was found to show satisfactory predictions when tested in a pipeline. The simulation studies can provide theoretical support and guidance for field engineering application, which are meanwhile capable of helping detect changes in parameters of gas section. Thus corresponding control measures can be adopted timely and appropriately in commissioning process.

scholarly journals An Improved High-Intelligence Method of Gas and Oil Pipeline Prewarning System in Real Soil Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fang Wang ◽  
Jichuan Xing ◽  
Jinxin Li ◽  
Feng Zhao ◽  
Shufeng Zhang
Keyword(s):  
Optical Fiber ◽  
Real Time ◽  
Recognition Accuracy ◽  
Signal Acquisition ◽  
Soil Environment ◽  
Long Distance ◽  
Vibration Signals ◽  
Oil Pipeline ◽  
Time Performance ◽  
High Intelligence

With the development of technology, the total extent of global pipeline transportation is also increased. However, the traditional long-distance optical fiber prewarning system has poor real-time performance and high false alarm rate when recognizing events threatening pipeline safety. The same vibration signal would vary greatly when collected in different soil environments and this problem would reduce the signal recognition accuracy of the prewarning system. In this paper, we studied this effect theoretically and analyzed soil vibration signals under different soil conditions. Then we studied the signal acquisition problem of long-distance gas and oil pipeline prewarning system in real soil environment. Ultimately, an improved high-intelligence method was proposed for optimization. This method is based on the real application environment, which is more suitable for the recognition of optical fiber vibration signals. Through experiments, the method yielded high recognition accuracy of above 95%. The results indicate that the method can significantly improve signal acquisition and recognition and has good adaptability and real-time performance in the real soil environment.

scholarly journals Safety Study on Wax Deposition in Crude Oil Pipeline

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1572
Author(s):  
Bin Yao ◽  
Deyin Zhao ◽  
Zhi Zhang ◽  
Cheng Huang
Keyword(s):  
Crude Oil ◽  
Cloud Point ◽  
Freezing Point ◽  
Development Trend ◽  
Wax Deposition ◽  
Scanning Calorimetry ◽  
Long Distance ◽  
Wax Precipitation ◽  
Oil Pipeline ◽  
Precipitation Characteristics

The Shunbei crude oil pipeline is prepared to use the unheated transportation process to transport waxy crudes. However, the wax formation in the pipeline is unknown. In order to predict the wax deposition of the pipeline, the physical property experiment of Shunbei crude oil was carried out through field sampling. The density, freezing point, hydrocarbon composition, and viscosity–temperature characteristics of crude oil are obtained. The cloud point and wax precipitation characteristics of the crude oil were obtained using the differential scanning calorimetry (DSC) thermal analysis method. Then, the wax deposition rate of the pipeline was predicted by two methods: OLGA software and wax deposition kinetic model. Finally, the optimal pigging cycle of the pipeline was calculated on this basis. The results show that: Shunbei crude oil is a light crude oil with low wax content, a low freezing point, and a high cloud point. Comparing the OLGA simulation results with the calculation results of the Huang Qiyu model, the development trend of wax deposition along the pipeline was the same under different working conditions. The relative error of the maximum wax layer thickness was 6%, proving that it is feasible for OLGA to simulate wax deposition in long-distance crude oil pipelines. Affected by the wax precipitation characteristics of Shunbei crude oil, there was a peak of wax precipitation between the pipeline section where crude oil temperature was 9.31–13.31 °C and the recommended pigging cycle at the lowest throughput was 34 days in winter and 51 days in spring and autumn.

Oil Temperature Prediction of Long-Distance Hot-Crude-Oil Pipeline Based on GA-BP Optimization

2020 ◽  
Author(s):  
Tao Yu ◽  
Peng Dong ◽  
Yang Yu ◽  
Jinzhou Song ◽  
Jie Zhang
Keyword(s):  
Prediction Model ◽  
Crude Oil ◽  
Pour Point ◽  
Theoretical Formula ◽  
Production Data ◽  
Influence Coefficient ◽  
Long Distance ◽  
Temperature Prediction ◽  
Oil Pipeline ◽  
Actual Production

Abstract Due to the high pour point of the oil products transported in the long-distance high wax crude oil pipeline, in order to ensure the operation safety, it is necessary to adopt heating transmission technology, so as to ensure that the oil temperature along the pipeline is 3–5 °C higher than the pour point, that is to say, the oil temperature is the most important operation parameter of the long-distance hot oil pipeline, and the accurate prediction and control of the oil temperature is the premise of the pipeline safety optimization. Aiming at the problems of large prediction error and poor applicability of the previous theoretical formula, this paper studies the establishment of oil temperature prediction model by using data mining algorithms such as Back Propagation (BP) neural network, and improves the prediction efficiency and accuracy of the model by using Genetic Algorithm (GA) optimization. The correlation coefficient formula is used to calculate the influence coefficient of oil temperature, ground temperature, pipeline transportation and other parameters on the inlet oil temperature of the downstream station, so as to obtain the input parameters of the model. The actual production data training model is downloaded through SCADA system, and the prediction accuracy of the control model is ±0.5 °C. Compared with BP model and other theoretical formulas, the accuracy and efficiency of GA-BP oil temperature prediction model are greatly improved, and the adaptability is better. The GA-BP oil temperature prediction model trained according to the actual production data can be effectively applied to the future pipeline big data platform, which lays a theoretical foundation for the intelligent control of the pipeline.

Analysis of Hot Oil Pipeline Stress Influencing Factors

2014 ◽  
Vol 887-888 ◽  
pp. 899-902
Author(s):  
Xiao Nan Wu ◽  
Shi Juan Wu ◽  
Hong Fang Lu ◽  
Jie Wan ◽  
Jia Li Liu ◽  
...  
Keyword(s):  
Crude Oil ◽  
High Viscosity ◽  
Analysis Model ◽  
Long Distance ◽  
Oil Pipeline ◽  
Software Analysis ◽  
Key Points ◽  
Temperature And Pressure ◽  
Pressure Changes ◽  
The Impact

In order to reduce the viscosity of crude oil for transport, we often use the way of heating delivery for high pour point, high wax, and high viscosity oil. Crude oil at high temperature, through long-distance transmission, the temperature and pressure changes on the piping stress greater impact. In this paper, in order to explore the main factor of hot oil pipeline stress and the location of key points, we build the XX hot oil pipeline stress analysis model used CAESAR II software, analysis of the impact of changes in temperature and pressure on piping stress when hot oil pipeline running, draw hot oil pipeline stress distribution, clearly identifies the location of key points of stress concentration, and we have come to that temperature is a major factor in generating pipe stress.

Research on Closed-Loop Safety Production System of Hot Oil Pipeline Based on Big Data Mining

2019 ◽  
Author(s):  
Yu Tao ◽  
Li Chuanxian ◽  
Liu Lijun ◽  
Chen Hongjun ◽  
Guo Peng ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Big Data ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Long Distance ◽  
Oil Pipeline ◽  
Big Data Mining ◽  
Actual Production

Abstract The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as large error and complex application. This research relies on actual production data and uses big data mining algorithms such as BP neural network, ARMA, seq2seq to establish oil temperature prediction model. The prediction result is less than 0.5 C, which solves the problem of accurate prediction of dynamic oil temperature during pipeline operation. Combined with pigging, the friction prediction model of standard pipeline section is established by BP neural network, and then the economic pigging period of 80 days is given; and after the friction database is established, the historical friction data are analyzed by using the Gauss formula, and 95% of the friction is set as the threshold data to effectively monitor the variation of the friction due to the long period of waxing in pipelines. The closed loop operation system of hot oil pipeline safety and optimization was formed to guide the daily process adjustment and production arrangement of pipeline with energy saving up to 92.4%. The prediction model and research results based on production big data have good adaptability and generalization, which lays a foundation for future intelligent control of pipelines.

Leakage of Long-Distance Pipeline by FIOEFD Simulation and Indoor Experiment

2013 ◽  
Vol 310 ◽  
pp. 362-367
Author(s):  
Rong Zun Yang ◽  
Xin Hua Wang ◽  
Dong Hu ◽  
Li Wei Wang
Keyword(s):  
Heat Transfer ◽  
Fluid Dynamics ◽  
Theoretical Model ◽  
Experimental Model ◽  
Simulation Experiment ◽  
Analysis Software ◽  
Long Distance ◽  
Oil Pipeline ◽  
Experimental Platform ◽  
Transfer Pressure

Based on the established indoor experimental platform for the leakage of buried oil pipeline, the characteristics of leakage under varied transfer pressure have been studied and simulated according to the experimental model by using FLOEFD—an analysis software of fluid & heat transfer. By comparing and analyzing the result of simulation, experiment and applying correlate theory of fluid dynamics，the theoretical model is established.

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