An intelligent oil accident predicting and classifying system using deep learning techniques

Yasmen Samhan Abd ElWahab, Mona Mohamed Nasr, Fahad Kamal Al Sheref


This study discusses the problem of oil and gas faults that lead to spills or explosions that lead to a lot of losses in human life, oil field extraction, and costs. Petrol is an important field in our lives because it controls all aspects of human life and their way of life, so our research focused on petrol and its problems in order to introduce a better way of life. The data used in this research was taken from the 3w database that was prepared by Petrobras, the Brazilian oil holding. The 9 classes classified in that work include the normal state that indicates the factors that will not lead to a problem. Deep learning classification techniques were used in this study. 99% accuracy was obtained in that model, and it refers to a successful prediction and classification of each class. Different results were observed when different hidden layers, optimizers, neurons, epochs, and activation functions were used. 99% was achieved when using Adam's optimizer and Tanh's activation function.


3W database; Adam; Deep learning classification; Optimizers; Petrol; Tanh

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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