Application status and prospect of big data and artificial intelligence in oil and gas field development

被引：0
|
作者：
Li Y. [1 ]
Lian P. [2 ]
Xue Z. [1 ]
Dai C. [2 ]
机构：
[1] China Petroleum & Chemical Company Limited, Beijing
[2] Exploration and Production Research Institute, SINOPEC, Beijing
来源：
Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) | 2020年 / 44卷 / 04期
关键词：
Artificial intelligence; Big data; Development planning; Oil and gas field development;
D O I：
10.3969/j.issn.1673-5005.2020.04.001
中图分类号：
学科分类号：
摘要：
The application of big data and artificial intelligence (AI) technology has caused subversive changes to society and industry. As the life-blood of our industry, petroleum is an important material basis for promoting national economy and industrial modernization. However, with the continuous exploitation of mature resources, it is more and more difficult to exploit the remaining petroleum reserve. There are many challenges, such as poor quality of oil produced and with high water cut, low oil price and environmental pollutions. Therefore, the big data and AI technology are expected to play an important role in oil and gas exploitation and development. In this paper, the current application of the big data and AI in oil and gas field development was reviewed, and the existing problems and challenges were analyzed, in which the construction objectives and modes, the key links and core contents of intelligent oil and gas fields were discussed. The key technologies need to be developed based on the current circumstances of the China petroleum & chemical company are put forward for further research. © 2020, Editorial Office of Journal of China University of Petroleum(Edition of Natural Science). All right reserved.
引用
收藏
页码：1 / 11
页数：10
相关论文
共 66 条
  • [31] LIN Niantian, ZHANG Dong, ZHANG Kai, Et al., Predicting distribution of hydrocarbon reservoirs with seismic data based on learning of the small-sample convolution neural network, Chinese Journal of Geophysics, 61, 10, pp. 4110-4125, (2018)
  • [32] AL-ANAZI A, GATES I., A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs[J], Engineering Geology, 114, 3, pp. 267-277, (2010)
  • [33] LI Qingyuan, ZHANG Luoyi, CAO Daiyong, Et al., Usage, status, problems, trends and suggestions of 3D geological modeling, Geology and Exploration, 52, 4, pp. 759-767, (2016)
  • [34] KORJANI M, POPA A, GRIJALVA E, Et al., A new approach to reservoir characterization using deep learning neural networks, (2016)
  • [35] LI Q, AGUILERA R., Unsupervised statistical learning with integrated pattern-based geostatistical simulation, (2018)
  • [36] AGBALAKA C C, OLIVER D S., Automatic history matching of production and facies data with nonstationary proportions using EnKF, (2009)
  • [37] DENNEY D., Advanced reservoir-management work flow-EnKF-based assisted-history-matching method[J], Journal of Petroleum Technology, 62, 4, pp. 81-85, (2010)
  • [38] ZHOU Feng, LIU Junrong, HU Xiangyun, Numerical simulation of intelligent well feedback control production strategies, Journal of China University of Petroleum(Edition of Natural Science), 38, 4, pp. 110-116, (2014)
  • [39] MORADI M P, MORADI H M, KANTZAS A., Data-driven production forecasting of unconventional wells with apache spark, (2018)
  • [40] CRNKOVIC-FRIIS L, ERLANDSON M., Geology driven EUR prediction using deep learning, (2015)
1234567