## 1 Introduction  
The importance of artificial intelligence (AI) in the oil and gas sector has been widely recognized, prompting a series of studies that discuss the challenges and opportunitiesassociated with its adoption. Several systematic reviews have identified recurring issues such as data privacy, model explainability, and the shortage of qualified professionals [1; 2; 3]. In this context, the literature emphasizes the need for robust algorithms that can accurately detect operational failures and adapt to highly complex environments. Studies have also investigated the role of AI in industry-specific applications, such as production forecasting, vulnerability assessment, and incident response. [PERSON] et al. [4] and [PERSON] et al. [5] present comprehensive overviews of these applications, emphasizing the use of techniques such as machine learning (ML) and artificial intelligence. [PERSON] et al. [6] and [PERSON] et al. [7] focus on domains such as intrusion detection, seismic analysis, and risk assessment, highlighting the role of AI in operational safety. In line with these efforts, [PERSON] et al. [8] and [PERSON], [PERSON] et al. [9] discuss limitations, including algorithmic bias and ethical challenges, and propose technical and regulatory solutions. Other studies reinforce the application of AI as a mechanism for automation and response to emerging threats. [PERSON] [10] and [PERSON] et al. [11] highlight how the combination of AI and ML has the potential to strengthen the security of cyber-physical systems. [PERSON] [12], [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] et al. [15] address techniques such as deep learning and natural language processing applied to anomaly detection network security, and user authentication. These works also discuss risks associated with the adoption of AI, such as lack of standardization and transparency in models. In the field of advanced applications, the use of AI for phase equilibrium modeling and history matching has gained prominence. [PERSON] et al. [1] proposed an approach based on deep neural networks to predict the thermodynamic behavior of complex mixtures in vapor-liquid equilibrium (VLE), significantly accelerating calculations compared to conventional methods. In the context of history matching. A hybrid system that combines particle swarm optimization with neural networks, reducing the calibration time of reservoir models and improving convergence in scenarios with geological uncertainties [16; 17]. Currently, the exploitation of oil and natural gas is widespread and diverse, encompassing activities in complex environments such as deep-sea reservoirs, salt mines, and unconventional strata. These operations often face challenges related to high-pressure and high-temperature conditions, as well as heterogeneous geological formations [1]. In this context, the application of artificial intelligence (AI) has become increasingly relevant for supporting real-time decision-making, optimizing drilling paths, and enhancing reservoir characterization [4]. AI techniques such as deep learning and reinforcement learning are instrumental in complex extraction environments due to their ability to process large volumes of unstructured data and learn nonlinear relationships in subsurface modeling [18]. As noted by [PERSON] et al. [12], AI applications have demonstrated strong potential in automating critical processes in offshore and deep-water operations, reducing human error, and enhancing operational safety. Furthermore, initiatives to integrate AI with physics-based models have enabled hybrid approaches that preserve physical interpretability while enhancing computational efficiency [1]. In particular, the treatment of nonlinearity in reservoir modeling has been enhanced with the use of deep neural networks. [PERSON] et al. [19] demonstrated the effectiveness of supervised models, such as artificial neural networks (ANNs), in predicting productivity in environments with multiple correlated variables.