Porosity and Permeability Prediction of Oil and Gas Reservoirs Using Artificial Neural Networks and Support Vector Machines

This paper focuses on the prediction of porosity and permeability of oil and gas formations using support vector machine and artificial neural networks. The goal is to improve the accuracy and reliability of oil and gas formation porosity and permeability predictions by comparing the performance of these models with traditional statistical methods using oil and gas petrophysical well data. Also in the study, processing methods for extracting data features of well log data consisting of various petrophysical and geological parameters are considered. Research results are presented through detailed graphical visualizations, error distributions, scatter plots, and performance metrics. The performance and accuracy of each model were evaluated using statistical measures such as mean squared error (MSE), R2, and mean absolute error (MAE). The artificial neural network model gave better accuracy than support vector machine and traditional regression methods. The reduction in prediction errors demonstrated by these models provides greater confidence in operational decision-making. Accurate predictions from these models help increase confidence in oil and gas decision-making and reduce operational costs and resource wastage.

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