Reservoir Evaluation and Development ›› 2021, Vol. 11 ›› Issue (4): 613-620.doi: 10.13809/j.cnki.cn32-1825/te.2021.04.018

• Intelligent Evaluation • Previous Articles     Next Articles

Overview of oil and gas production forecasting by machine learning

HUANG Jiachen1(),ZHANG Jinchuan2,3,4   

  1. 1. Sinopec Petroleum Exploration and Production Research Institute, Beijing 100083, China
    2. China University of Geosciences(Beijing), Beijing 100083, China
    3. Key Laboratory of Shale Gas Exploration and Evaluation, Ministry of Land and Resources, Beijing 100083, China
    4. Beijing Key Laboratory of Unconventional Natural Gas Geology Evaluation and Development Engineering, Beijing 100083, China
  • Received:2021-05-18 Online:2021-08-26 Published:2021-08-19


The machine learning is not only an important tool for oil and gas big data analysis, but also a general data-driven analysis method. As an important field with a long history and a large data base, oil and gas exploration and development has a great potential for data mining. The use of big data analysis technology for oil and gas field can help decision makers to conduct investment analysis, risk assessment and production optimization, which brings significant economic benefits. The machine learning method has been tried by the researchers applying to the researches on oil and gas. Nowadays, many application scenarios have been proposed with the development of machine learning algorithms, but general solutions for specific scenario are still divided. So that, we introduces the procedure of a machine learning modeling upon the most basic principles, and summarizes the development history of the main three kinds of machine learning methods that can be applied to oil and gas big data analysis. And then based on the characteristics of oil and gas field big data, the core contents, goals and advantages of oil and gas field big data analysis and utilization are discussed, the main application scenarios of machine learning in oil and gas field are analyzed, and the existing problems and countermeasures in typical oil and gas production prediction are summarized.

Key words: big data of oil and gas field, data-drive model, production forecast, machine learning, intelligent oilfield

CLC Number: 

  • TE328