## 4 Discussion
[37], data governance and model explainability are crucial to ensure that critical decisions, whether automated or assisted by AI, align with the principles of justice, equity, and social responsibility. Thus, in addition to technical efficiency, it is essential to integrate ethical principles into the development and implementation of AI solutions, particularly in sectors with significant environmental and social impacts. The results indicated that the AI-based model was able to reduce computational time by up to 95% while maintaining an average error of less than 1% compared to conventional methods. This type of gain is critical in large-scale compositional simulations, where response time directly influences the feasibility of sensitivity and uncertainty studies [37]. Another example is presented by [PERSON] et al. [33], who used LSTM networks to predict reservoir porosity. The study demonstrated that the AI-based model achieved a coefficient of determination (R\({}^{2}\)) exceeding 0.95, whereas methods based on multivariate linear regression (MLR) yielded values below 0.80. Additionally, the LSTM model reduced prediction time by more than 90%, underscoring its suitability in environments that require rapid responses with a high degree of confidence. Still, in the area of prediction and optimization, [PERSON] et al. [26] reported that techniques such as Random Forest and SVM applied to drilling penetration rate (ROP) obtained improvements of up to 18% in predictive accuracy when compared to adjusted physical models in addition to requiring less manual calibration and less computational effort for new simulations. The oil and gas sector plays a crucial role in the global energy economy, supplying a substantial portion of the energy demand, particularly in sectors such as transportation, petrochemicals, and thermal power generation [16]. The increasing complexity of exploration, production, and distribution operations has driven the adoption of artificial intelligence (AI) and machine learning techniques to increase efficiency, reduce costs, and mitigate risks. For example, machine learning techniques have been used to predict failures in centrifugal pumps, optimize predictive maintenance, and reduce unscheduled shutdowns [34], as well as to estimate reservoir porosity with greater accuracy and lower cost [33]. The economic impact of these solutions is reflected in improvements in well productivity, operational safety, and the reduction in losses associated with pipeline corrosion [38]. In addition, the application of deep neural networks for seismic interpretation and location of induced earthquakes has allowed faster and more informed decisions about safe and sustainable exploration [35]. Taken together, these advances indicate that the strategic use of AI not only transforms internal processes but also strengthens the role of the oil and gas sector in the global energy matrix through greater reliability, productivity, and resilience. The results of this research underscore the importance of digital technologies, particularly artificial intelligence (AI), as essential tools for overcoming the complex challenges of E&P operations. Digital transformation is driving a structural shift in operational practices, providing a safer, more efficient, and data-driven approach that can meet the demands of a competitive and constantly evolving market.