## 5 Conclusions  
This study aimed to present a comprehensive overview of how artificial intelligence (AI) methods have been applied to real-world problems in the oil and gas sector. The methodology adopted involved a detailed analysis of AI applications described in scientific articles and conferences, divided into two main stages. Initially, scientific articles were explored through the database Scopus, creating a co-occurrence map to identify relevant terms. In the second stage, the analysis focused on the OnePetro database, resulting in the selection and analysis of specific articles on production and drilling.  
The results demonstrate the growing impact of AI on production operations in the oil and gas sector, particularly in areas such as process optimization, predictive maintenance, reservoir management, image and video analysis, and operational safety [28]. For example, AI-based models using multilayer long short-term memory (LSTM) networks achieved high accuracy in predicting reservoir porosity, significantly reducing the need for costly field measurements [33]. Predictive models using support vector machines (SVMs) and multilayer perceptrons (MLPs) demonstrated strong performance in anticipating faults in centrifugal pumps, thereby helping to avoid unplanned shutdowns and reduce maintenance costs [34]. Convolutional neural networks (CNN) have also been successfully applied to corrosion detection [19; 36; 38], improving the speed and reliability of visual inspections in pipeline infrastructure.  
The bibliometric analysis revealed that the Society of Petroleum Engineers (SPE) serves as the central hub of dissemination, concentrating a significant portion of publications related to AI applications. This reflects the high level of interest and investment from both academia and industry in leveraging digital solutions to tackle persistent challenges.  
Overall, the integration of AI into production operations has led to measurable improvements in efficiency, accuracy, and safety. Deep learning algorithms for seismic interpretation [20; 35] and earthquake detection [35] have enabled faster and more accurate analysis of subsurface conditions, supporting safer and more informed decision-making processes. These examples confirm that AI is not only a theoretical promise but a practical and impactful tool already in use across multiple domains of the oil and gas production chain.  
Continued collaboration between oil companies, universities, and research centers is recommended, expanding the development and application of digital solutions that address the industry's emerging challenges. With the ongoing evolution of AI technologies and their expanded adoption, digital transformation in the oil and gas sector is expected to play an increasingly crucial role in optimizing operations and sustaining long-term production.  
Validation, S.V.M.N.; Investigation, J.F.M.; Investigation e Writing--original draft, M.d.S.P.; Writing--review & editing, C.A.d.S.C. and B.S.C.; Supervision, A.L.A.G.; Project administration, G.B.A.L. All authors have read and agreed to the published version of the manuscript.  
The author [PERSON] wishes to acknowledge his support by the Brazilian National Council for Scientific and Technological Development (CNPq) through grant 311961/2023-1. The author [PERSON] acknowledge the support by Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.