Publications

Supervised learning methods from machine learning are starting to be widely used in oil & gas data management. The usage of the method is adjusted to the purpose of data processing, including data classification and regression. In this research, there are six classification methods to estimate the electrofacies shape, lithology type, and fluids, namely Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGB). This research compared those six methods qualitatively and quantitatively to obtain the best method. This research was conducted in the Maju Royal Field using one oil well data for training data and another one well as testing data. For validation purposes, 85% of the data was split for training and 15% for validation, aiming to evaluate the machine learning model through the correlation coefficient value. In the test data, qualitative and quantitative analyzes were also conducted. Qualitative analysis was performed by comparing the results of the electrofacies shape prediction with the original interpretation, lithology prediction with shale volume data, and prognosis of fluids with test zone data. Meanwhile, quantitatively, it is done by comparing the correct predictive data with the actual amount of data on each parameter. The training data evaluation result shows that KNN and XGB are suitable for electrofacies shape prediction. Meanwhile, lithology and fluid estimation are good with DT, KNN, and XGB methods. The qualitative and quantitative analysis result from the test data shows that the DT and GNB methods are suitable for estimating the electrofacies shape. In contrast, all methods are considered good at predicting and have good correlation values for calculating the lithology and fluids. Hence, both training and test data evaluation result has good correlation values