Review of application of artificial intelligence techniques in petroleum operations

被引:0
作者
Saeed Bahaloo [1 ]
Masoud Mehrizadeh [2 ]
Adel NajafiMarghmaleki [3 ]
机构
[1] Department of Petroleum Engineering, Amirkabir University of Technology
[2] Department of Petroleum Engineering, School of Science and Engineering, Khazar University
[3] Department of Petroleum Engineering, Ahwaz Faculty of Petroleum, Petroleum University of
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论]; TE34 [油田开发(油藏工程)];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the last few years, the use of artificial intelligence(AI) and machine learning(ML) techniques have received considerable notice as trending technologies in the petroleum industry. The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data. Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation.Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes. This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies, drilling and production engineering. The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented. Moreover, possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.
引用
收藏
页码:167 / 182
页数:16
相关论文
共 5 条
  • [1] Application of artificial intelligence to forecast hydrocarbon production from shales
    Palash Panja
    Raul Velasco
    Manas Pathak
    Milind Deo
    [J]. Petroleum, 2018, 4 (01) : 75 - 89
  • [2] Estimation of water saturation by using radial based function artificial neural network in carbonate reservoir:A case study in Sarvak formation
    Hamid Heydari Gholanlo
    Masoud Amirpour
    Saeid Ahmadi
    [J]. Petroleum, 2016, 2 (02) : 166 - 170
  • [3] Big Data analytics in oil and gas industry: An emerging trend.[J].Mehdi Mohammadpoor;Farshid Torabi.Petroleum.2018, 4
  • [4] Application of adaptive neuro-fuzzy inference system and data mining approach to predict lost circulation using DOE technique (case study: Maroon oilfield).[J].Farough Agin;Rasool Khosravanian;Mohsen Karimifard;Amirhosein Jahanshahi.Petroleum.2018, 4
  • [5] Novel Approaches to the Determination of Archie Parameters II: Fuzzy Regression Analysis.[J].Chen; H.C.;Fang; J.H.;Kortright; M.E.;Chen; D.S..SPE Advanced Technology Series.1995, 01