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The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

Author

Listed:
  • Sergey Krivoshchekov

    (Petroleum Geology Department, Perm National Research Polytechnic University, Komsomolsky Prospekt, 29, 614990 Perm, Russia)

  • Alexander Kochnev

    (Petroleum Geology Department, Perm National Research Polytechnic University, Komsomolsky Prospekt, 29, 614990 Perm, Russia)

  • Evgeny Ozhgibesov

    (Petroleum Geology Department, Perm National Research Polytechnic University, Komsomolsky Prospekt, 29, 614990 Perm, Russia)

Abstract

This paper aims to study the applicability of machine-learning algorithms, specifically neural networks, for forecasting the effectiveness of Improved recovery methods. Radial jet drilling is the case operation in this study. Understanding changes in reservoir flow properties and their effect on liquid flow rate is essential to evaluate the radial jet drilling effectiveness. Therefore, liquid flow rate after radial jet drilling is the target variable, while geological and process parameters have been taken as features. The effect of various network parameters on learning quality has been assessed. As a result, conclusions on the applicability of neural networks to evaluate the radial jet drilling potential of wells in various geological conditions of carbonate reservoirs have been made.

Suggested Citation

  • Sergey Krivoshchekov & Alexander Kochnev & Evgeny Ozhgibesov, 2022. "The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness," Energies, MDPI, vol. 15(5), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1917-:d:765140
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