Application of machine learning methods in oil and gas geology and geophysics: from theoretical foundations to practical implementation

Abstract

This paper presents a systematic analysis of modern machine learning methods and their practical applications in solving key problems in petroleum geology and geophysics. This study discusses the advantages and limitations of major neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep feedforward networks (RNNs), and deep feedforward networks (DNNs). The authors of this paper paid special attention to the integration of various types and scales of data, ranging from seismic surveys to core samples and borehole geophysical measurements. The open-source machine learning platform, Orange, is highlighted as a very effective tool for geological data analysis and visualization tasks. Real-world examples illustrate how machine learning can significantly enhance interpretation accuracy, reduce time costs, and minimize subjective bias. The paper concludes that neural network technologies are transitioning from experimental tools to binding instruments for improving the economic efficiency of geological exploration activities.