Research and Application of the Big Data Analysis Platform of Oil and Gas Production

2019 ◽  
Author(s):  
Ruidong Zhao ◽  
Junfeng Shi ◽  
Xishun Zhang ◽  
Jinya Li ◽  
Yi Peng ◽  
...  
2019 ◽  
Author(s):  
Ruidong Zhao ◽  
Junfeng Shi ◽  
Xishun Zhang ◽  
Jinya Li ◽  
Yi Peng ◽  
...  

2018 ◽  
Author(s):  
Zhao Ruidong ◽  
Xiong Chunming ◽  
Shi Junfeng ◽  
Zhang Yufeng ◽  
Peng Yi ◽  
...  

Author(s):  
Zhenhua Zhang ◽  
Longbin Tao

Slug flow in horizontal pipelines and riser systems in deep sea has been proved as one of the challenging flow assurance issues. Large and fluctuating gas/liquid rates can severely reduce production and, in the worst case, shut down, depressurization or damage topside equipment, such as separator, vessels and compressors. Previous studies are primarily based on experimental investigations of fluid properties with air/water as working media in considerably scaled down model pipes, and the results cannot be simply extrapolated to full scale due to the significant difference in Reynolds number and other fluid conditions. In this paper, the focus is on utilizing practical shape of pipe, working conditions and fluid data for simulation and data analysis. The study aims to investigate the transient multiphase slug flow in subsea oil and gas production based on the field data, using numerical model developed by simulator OLGA and data analysis. As the first step, cases with field data have been modelled using OLGA and validated by comparing with the results obtained using PIPESYS in steady state analysis. Then, a numerical model to predict slugging flow characteristics under transient state in pipeline and riser system was set up using multiphase flow simulator OLGA. One of the highlights of the present study is the new transient model developed by OLGA with an added capacity of newly developed thermal model programmed with MATLAB in order to represent the large variable temperature distribution of the riser in deep water condition. The slug characteristics in pipelines and temperature distribution of riser are analyzed under the different temperature gradients along the water depth. Finally, the depressurization during a shut-down and then restart procedure considering hydrate formation checking is simulated. Furthermore, slug length, pressure drop and liquid hold up in the riser are predicted under the realistic field development scenarios.


2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.


2018 ◽  
Vol 1060 ◽  
pp. 012023
Author(s):  
Zhixiang Wang ◽  
Yao Bu ◽  
Demeng Bai ◽  
Bin Wu ◽  
Jiafeng Qin

2014 ◽  
Vol 484-485 ◽  
pp. 922-926
Author(s):  
Xiang Ju Liu

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.


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