Big data intelligent platform and application analysis for oil and gas resource development

• Published in: 工程科学学报
• Main Authors: Hong-qing SONG, Shu-yi DU, Yuan-chun ZHOU, Yu-he WANG, Jiu-long WANG
• Format: Article
• Language: Chinese
• Published: Science Press 2021-02-01
• Subjects: petroleum; natural gas; big data; artificial intelligence; deep learning; machine learning
• Online Access: http://cje.ustb.edu.cn/article/doi/10.13374/j.issn2095-9389.2020.07.21.001

With the rapid improvement of exploration and monitoring technologies, the oil and gas industry has accumulated a large amount of data in the fields of seismic exploration, logging, production, and development. How to transform the huge “data resources” into “data assets” and fully utilize data and tap their real value to better serve society is a main concern in the oil and gas industry today. Therefore, the oil industry needs to complete the industrial upgrading of “Smart Oilfield” through digital and intelligent transformation. In recent years, the rise of big data technology and artificial intelligence have allowed international oil companies and oil service giants to accelerate the construction of digital and intelligent oil fields. The overall framework of the big data intelligent platform of oil and gas resources should be based on data resources with big data platform computing power as the support and artificial intelligence algorithms as the core. To meet the production needs of the oil and gas industry, it is of great urgency to build an oil and gas data resource pool that integrates exploration, development, and production data. The data quality can be improved via data cleaning and fusion. Physical simulations, data mining, and other approaches should be combined to achieve the modularization of service functions. Additionally, the goals of intelligent monitoring, early warning, and display on multi-dimensional platforms such as PC, control screen, and mobile apps can also be achieved. The analysis of artificial intelligence methods such as deep learning in the context of the oil and gas industry shows that these methods have good application prospects. In the future, oil companies should work together with scientific research institutes to tap the huge potential of oil industry data, achieve cost reduction and efficiency increase, and build a new smart oil and gas industrial ecosystem to complete industrial upgrading.