## 2 Materials and Methods  
The primary objective is to provide a comprehensive overview of how artificial intelligence methods have been applied to real-world problems in the oil and gas sector. The methodology followed a process based on the analysis of AI applications, as described in journals and conference proceedings.  
* Step 1: The first step involved preliminary exploration of scientific articles using the Scopus database. Keywords related to artificial and computational intelligence techniques were used (the search query for the SCOPUS database is: (TITLE-ABS-KEY (\"oil\" AND \"gas\") OR TITLE-ABS-KEY (\"O&G\" OR \"Petroleum\" OR \"OIL & GAS\" OR \"KDD\" OR \"knowledge-discovery\" OR \"knowledge discovery\") AND TITLE-ABS-KEY (\"crisp\" OR \"crisp-dm\" OR \"crisp dm\" OR \"Cross Industry Standard Process for Data Mining\" OR \"Cross Industry Standard Process\") AND TITLE-ABS-KEY (\"Artificial intelligence\" OR \"Deep Learning\" OR \"Machine Learning\" OR \"Neural Networks\" OR \"Convolutional Neural Network\" OR \"Artificial Neural Networks\")) for the search and the criteria adopted included: publication period between 2010 and 2024, English language and document types restricted to peer-reviewed articles and conferences. A total of 11,296 articles were selected, and a co-occurrence map was created with VOS Viewer software, identifying key terms and emerging trends in artificial intelligence that would guide the next phase of research.
* Step 2: Based on the relevant terms and insights identified in Step 1, the focus shifted to a more specific domain: the oil industry. The OnePetro database, a key resource for petroleum engineering research, was explored using keywords such as \"production system\", \"machine learning\", and \"neural network.\" This step aimed to connect broader AI applications with practical use cases in production optimization and well drilling.  
Machine learning has emerged as a cornerstone of technological advancement, bridging the gap between theoretical computation and practical applications across numerous fields. The relationships uncovered in Steps 1 and 2 highlight the role of machine learning in transforming industries, including oil and gas, through interdisciplinary collaboration and data-driven decision-making. The thematic word map (Figure 1) illustrates these connections. It sets the stage for understanding how machine learning integrates techniques, tools, and applications to solve complex problems in production and drilling processes.  
The image illustrates a word map emphasizing the centrality of \"machine learning\" and its connections to various subtopics and application areas, such as deep learning, forecasting, neural networks, and the oil and gas industry.  
Each color cluster represents a thematic group, highlighting the relationships between specific topics in artificial intelligence and data science., in the figure:  
* Blue: Indicates areas related to algorithms and techniques, such as neural networks and supervised learning.
* Red: Represents practical applications, including computer vision and natural language processing.
* Green: Relates to forecasting and statistical analysis.
* Yellow: Suggests intersections with big data, cloud computing, and optimization.
* Purple: Includes oil, satellite, and data fusion.  
These connections demonstrate that machine learning is an interdisciplinary field with applications ranging from specific tasks to strategic areas, such as automation and business intelligence. In summary, the map highlights the pivotal role of machine learning in driving the development of emerging technologies and their integration across various industries.