<![CDATA[The application of machine learning and artificial intelligence is popular nowadays to improve data analytics in the oil and gas industry. A huge amount of data can be processed to gain insights about the subsurface conditions, even reducing time for manual review or interpretation. There are three cases to be discussed in this study that starts from porosity estimation of thin core image using Otsu's thresholding, estimation of oil production rate from sucker-rod pumping wells and sonic travel-time log generation. Two supervised learning algorithms are applied, XGBoost and Keras. These algorithms will capture all possible correlations between the input and output data. From data normalization, exploratory data analysis and model building, the workflow is built on Google Colab. The original dataset is split into training and testing. Tuning hyperparameters such as the number of hidden layers, neurons, activation function, optimizers and learning rates are captured to reduce the complexity of the model. The model is evaluated by error values and the coefficient of determination to estimate the model skill on unseen data.]]>
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