Modeling of Crude Oil Types Classification Using the Naive Bayes Classifier Method

Authors

  • Harry Budiharjo Sulistyarso Universitas Pembangunan Nasional Veteran Yogyakarta
  • Dyah Ayu Irawati Universitas Pembangunan Nasional Veteran Yogyakarta
  • Joko Pamungkas Universitas Pembangunan Nasional Veteran Yogyakarta
  • Indah Widiyaningsih Universitas Pembangunan Nasional Veteran Yogyakarta

DOI:

https://doi.org/10.31098/ess.v1i1.126

Keywords:

Crude oil, EOR, Naïve Bayes, Prediction, Viscosity

Abstract

This research is part of previous research regarding the prediction of petroleum's physical properties to help the user get the prediction value of crude oil's physical properties from field test data, which was carried out from Enhanced Oil Recovery Research Laboratory, Petroleum Engineering UPN “Veteran” Yogyakarta. The field data that is measured in the laboratory that has been done is by adding biosurfactants and increasing the temperature. Various steps have been taken to reduce crude oil's viscosity value so that it can be diluted could flow. It is necessary to calculate the viscosity of crude oil in this process to determine the extent of the viscosity level as expected by adding biosurfactants or increasing the temperature that has been carried out in the EOR process. Naïve Bayes Classifier is used to classify oil data into three categories: light oil, medium oil, and heavy oil, based on the prediction of the viscosity value. The Naïve Bayes Classifier is a robust algorithm for performing machine learning-based predictive modeling that applies the Bayes theorem. This predictive modeling for the physical properties of crude oil was built using the Python programming language and the PyQt5 library to build desktop-based applications. The classification of oil has arrived at labeling the prediction results of crude oil's viscosity into three categories, namely light oil, medium oil, and heavy oil. The test results with testing data produce accurate data; the predicted value is within the specified range.

References

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Published

2020-10-27

Issue

Vol. 1 No. 1 (1): October 2020

Section

Articles

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