## 3 Results
### Bibliometric Analysis
[PERSON] et al. [33] introduced a machine learning framework utilizing a multilayer Long Short-Term Memory (LSTM) network to predict reservoir porosity. Their study addresses the high costs and technical challenges associated with traditional coring methods, which become increasingly complex as drilling depths increase. The model uses well-log data to predict porosity with higher accuracy and lower costs compared to conventional methods. [PERSON] et al. [34] explored the application of machine learning models, specifically Multilayer Perceptron (MLP) and Support Vector Machine (SVM), for fault prediction in centrifugal pumps within the oil and gas industry. The study highlights the importance of predictive maintenance in preventing equipment failures and minimizing unplanned downtime. Using real-world data from operating machinery, the authors demonstrated the efficacy of these algorithms in identifying potential faults early and accurately. [PERSON], [PERSON] et al. [35] proposed a deep learning-based method to locate small earthquakes induced by industrial activities, such as fluid injection, in Oklahoma. Using a fully convolutional neural network (FCN) and data from a network of seismic stations, the study demonstrated the ability to predict the location of seismic events in real-time accurately. This capability is significant for events with low magnitudes (ML < 3), which are often overlooked by traditional methods due to their low signal-to-noise ratio. [PERSON] et al. [36] introduce a hybrid algorithm that integrates support vector regression, principal component analysis, and chaos particle swarm optimization to improve prediction accuracy. The findings are crucial for maintaining pipeline integrity, reducing operational risks, and preventing costly failures. By enhancing the ability to foresee corrosion issues, this work supports safer and more efficient pipeline operations. The oil and gas industry generates vast amounts of data across various operations, creating challenges in efficiently processing and analyzing data. [PERSON] et al. [37] review the application of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. By highlighting AI's potential to enhance data interpretation and decision-making in exploration, drilling, and production, the authors emphasize the role of technology in optimizing processes, mitigating risks, and reducing costs. The study illustrates how AI-driven solutions can transform industry practices, making operations more intelligent and adaptive. External corrosion of oil and gas pipelines is a critical threat to their structural integrity and operational reliability. [PERSON] and [PERSON] [38] comprehensively examine external corrosion mechanisms, including hydrogen-induced cracking, stress corrosion cracking, and microbiologically influenced corrosion. By exploring predictive prevention strategies and assessing the effectiveness of current monitoring tools, the study provides valuable insights for improving pipeline longevity and safety. Its emphasis on transitioning from corrosion protection to predictive and preventive measures marks a significant advancement in pipeline management practices. Table 1, demonstrate the importance articles about the ML and oil and gas industry. The reduction of computational costs is essential to demonstrate the practical impact of AI models objectively. Several recent studies have advanced in this direction. For example, [PERSON] et al. [19] reported an accuracy of above 94% in the automatic detection of external corrosion in pipelines using convolutional neural networks (CNN), validating the model's potential to replace time-consuming manual inspections. Similarly, [PERSON] et al. [36] developed a hybrid model to predict the internal corrosion rate in multiphase pipelines, achieving a root mean square error (RMSE) of only 0.112, which demonstrates high predictive capacity despite the nonlinear complexity of the problem. In the context of predictive maintenance, [PERSON] et al.