Research and Application of the Big Data Analysis Platform of Oil and Gas Production
- Ruidong Zhao (RIPED, CNPC) | Junfeng Shi (RIPED, CNPC) | Xishun Zhang (RIPED, CNPC) | Jinya Li (China University of Petroleum) | Yi Peng (RIPED, CNPC) | Chunming Xiong (RIPED, CNPC) | Meng Liu (RIPED, CNPC) | Feng Deng (RIPED, CNPC) | Shenggang Song (Daqing Oilfield Company, CNPC) | Guojing Miao (Daqing Oilfield Company, CNPC)
- Document ID
- International Petroleum Technology Conference
- International Petroleum Technology Conference, 26-28 March, Beijing, China
- Publication Date
- Document Type
- Conference Paper
- 2019. International Petroleum Technology Conference
- 7 Management and Information, 7.6 Information Management and Systems, 7.4 Energy Economics, 7.4.3 Market analysis /supply and demand forecasting/pricing, 7.6.4 Data Mining
- Big Data, Oil & Gas Production, data mining
- 10 in the last 30 days
- 131 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 6.00|
|SPE Non-Member Price:||USD 20.00|
With the continuously progressing of building the intelligent oilfield and the rapid development of the Internet of Things and big data technology, the emerging technologies such as the information acquisition, distributed computing and data mining lead to unceasing innovation in the research and development and management approaches of enterprises. A technical revolution also occurs in the petroleum industry. At present, one of the challenges we are facing is how to keep the advantages of the conventional oil and gas production and combine them with the big data analysis to better serve the petroleum industry. Given the complexity and uncertainty and thus difficulty endowed in the conventional oil and gas production, one potential solution, based on the big data mining, is proposed. The big data clustering analysis can be used to evaluate the oil well, and the multi-variable sensitivity analysis can be visualized through the dimensionality reduction. Data processing, operation condition diagnosis, forecasting and warning, and multi-dimension multi-variable visualization analysis are the main functions that are achieved in our preliminarily-built big data analysis platform for the production of oil and gas wells. In the future, this platform will gradually transform form the "business model" into the "data model", which adopts various methods such as the statistical analysis, pattern recognition, clustering analysis and visualization to enable the intelligent diagnosis, forecasting and warning, and decision-making optimization and evaluation for the oil and gas well production, on the basis of the multi-dimension multi-source data. At last, it is anticipated that this platform should serve the oil and gas production and evolve into an effective method to improve the production, reduce energy consumption and decrease cost for the oilfield.
|File Size||761 KB||Number of Pages||8|
World Economic Forum, Big Data, Big Impact: New Possibilities for International Development. http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf. 2012.
White House. Big Data Research and Development Initiative. http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf. 2012.
Data for Development: Challenges & Opportunities. http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf. 2012
BP Globa. Number crunching with Big Data. http://www.bp.com/en/global/corporate/press/bp-magazine/innovations/number-crunching-with-big-data.html.2014.12.22.
Lucas Mearian. Shell Oil targets hybrid cloud as fix for energy-saving, agile IT. ComputerWorld. http://www.computerworld.com/s/article/9225827/Shell_Oil_targets_hybrid_cloud_as_fix_for_energy_saving_agile_IT. 2014.
Chevron Explores Open Source Using Hadoop. The Wall Street Journal. http://blogs.wsj.com/cio/2012/06/05/chevron-explores-open-source-using-hadoop/?mod=wsjcio_hps_cioreport. 2012.