The Application of Neural Networks to Forecast Radial Jet Drilling Effectiveness

Sergey Krivoshchekov, Alexander Kochnev and Evgeny Ozhgibesov
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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

Energies, 2022, vol. 15, issue 5, 1-17

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.

Keywords: radial jet drilling; reservoir flow simulation; technology effectiveness; machine learning; neural network; carbonate reservoirs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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