## 3 Results
### Bibliometric Analysis
The figure provides a summary of recent articles that apply machine learning to various challenges in the oil and gas industry. These studies illustrate the growing role of modern AI techniques in solving specific problems ranging from flow measurement and production forecasting to the structural optimization of complex environments. The study by [PERSON] et al. [30] explores the application of machine learning techniques to analyze Magnetic Flux Leakage (MFL) data, a crucial method for pipeline inspection in the oil and gas sector. By employing methods such as support vector regression and kernelization, the research enhances the detection and severity evaluation of pipeline defects. These advancements enhance the reliability and safety of pipeline maintenance, thereby reducing risks such as environmental damage and financial losses resulting from pipeline failures. This contribution underscores the role of advanced computational techniques in maintaining the integrity of critical infrastructure. [PERSON] et al. [31] addressed the challenge of optimizing well placement in geologically complex reservoirs by incorporating spatial and geological data into the machine learning model. This research is significant because it reduces the computational costs associated with traditional full-physics simulations, which are time-intensive and resource-heavy. By enabling faster and more accurate decision-making in reservoir management and optimization, the proposed methodology helps operators maximize returns on investment while managing uncertainties in subsurface conditions. [PERSON] et al. [20] present the following two case studies: one on automated fault detection in seismic images and another on constructing elastic models for seismic inversion. The study demonstrates that synthetic data can effectively train CNNs to produce accurate predictions, even for real-world field data. The research is particularly relevant to the oil and gas industry because it automates seismic interpretation tasks, such as fault detection, which are traditionally time-consuming and labor-intensive. By enhancing efficiency and reducing subjectivity in geophysical analysis, the study contributes to accelerating exploration activities and lowering associated costs. [PERSON] et al. [32] address the challenges of dynamic risk assessment in safety-critical industries, including the oil and gas sector. Their study introduces machine learning-based tools to continuously monitor and adapt to risks in real time, a significant need in high-risk environments. By improving the capacity for ongoing analysis and dynamic response to evolving conditions, the study supports the optimization of safety protocols and risk management strategies. This ensures better decision-making and accident prevention, making it a valuable contribution to risk governance in the oil and gas industry. [PERSON] et al. [26] highlight the advantages of data-driven models, such as artificial neural networks, over traditional physics-based models, which struggle to capture the complex, nonlinear relationships between drilling variables. Accurate ROP prediction is crucial in reducing drilling time and costs, particularly in challenging environments such as offshore exploration. The paper also discusses the potential of real-time optimization systems based on artificial intelligence, encouraging the adoption of innovative drilling technologies in the oil and gas sector. [PERSON] et al. [19] present a novel deep learning-based approach for detecting and classifying external corrosion in pipelines, a critical issue in oil and gas operations. Using Convolutional Neural Networks (CNNs), their method automates corrosion monitoring, reducing reliance on manual inspections and intrusive techniques. This approach not only enhances the precision and efficiency of inspections but also lowers costs and minimizes operational disruptions. By enabling early and accurate identification of corrosion, this work significantly contributes to ensuring the safety and longevity of oil and gas infrastructure.