Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells

High-frequency induction logging is a crucial technique in subsurface exploration, particularly in the oil and gas industry. It involves transmitting electromagnetic signals into the ground and analyzing their responses to determine the geological structure, lithology, and fluid content of the formation. However, one of the major technical challenges in processing digital sounding signals is the complexity of wave propagation paths, which can be influenced by multiple factors such as subsurface heterogeneity, noise interference, and signal attenuation. Traditional signal processing techniques often struggle to accurately interpret low-amplitude, high-frequency signals that contain valuable subsurface information. These signals can be obscured by noise or exhibit complex, non-stationary behavior, making them difficult to detect and analyze using conventional Fourier-based methods. Moreover, standard regression-based approaches for interpreting induction logging data may fail to capture intricate signal variations, limiting their effectiveness in real-time decision-making. To address these challenges, this research explores the application of machine learning and wavelet transform techniques for improving the accuracy and efficiency of digital signal processing in high-frequency induction logging. The wavelet transform, particularly the Daubechies, Morlet, and Galerkin wavelets, is employed to decompose signals into different frequency components while preserving time-domain information. This enables better identification of subtle variations and anomalies in the recorded data. Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. This advancement supports the implementation of smart field technology in Kazakhstan’s oil and gas industry, ultimately improving decision-making speed, operational efficiency, and resource optimization. The results were based on a set of three drilled oil wells, namely, West Kazakhstan region, Mangystau, publicly available well log dataset. The evaluation performed using R-square and root mean square error to validate the proposed approach revealed values of 0.887 and 0.0091. This implementation demonstrated the effectiveness of the proposed model, significantly outperforming traditional methods.