introduction

Digital transformation is revolutionizing extractive and energy industries globally. In a context where operational efficiency, environmental sustainability and resource optimization have become strategic imperatives, Artificial Intelligence (AI, Artificial Intelligence AI) and Machine Learning (Machine Learning, ML) emerge as disruptive technologies capable of redefining traditional operational paradigms (Abikoye, 2025), (Gowekar, How oil and gas industry are transforming with AI and ML, 2024), (Supermec, 2025).

The oil and gas, mining, water and energy sectors face complex challenges that range from the exploration of resources in increasingly remote and challenging locations, to the need to comply with stricter environmental regulations and reduce the carbon footprint (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022), (Archilles, 2024), (Huawei, 2025). In this scenario, AI and ML technologies offer innovative solutions that allow us to process enormous volumes of operational data, predict equipment failures, optimize production processes and improve decision-making in real time (Viso.ai, 2025), (Parvathareddy and others, 2025), (Rojas and others, 2025).

The adoption of these technologies not only represents an opportunity to increase productivity and reduce operating costs, but it also constitutes a fundamental element for the transition to more sustainable and environmentally responsible operations (Frías, 2025), (Skibicky, 2025), (Gupta A., 2025). The ability of AI to analyze complex patterns in large data sets makes it possible to identify opportunities for improvement that would be impossible to detect using traditional methods (Jambol et al., 2024), (Arinze et al., 2024), (Gowekar, Artificial intelligence for predictive maintenance in oil and gas operations, 2024).

Definitions of AI and ML

Artificial Intelligence (AI) (Artificial Intelligence, AI) is defined as the ability of computer systems to perform tasks that traditionally require human intelligence, including pattern recognition, decision-making, natural language processing and complex problem solving (AI Superior, 2025), (Eadic, 2024). In the context of extractive industries, AI ranges from computer vision systems for equipment inspection to optimization algorithms for operations management (Corrigan & Ikonnikova, A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization, 2024), (AI Superior, 2024).

Machine Learning

Machine Learning (Machine Learning, ML) constitutes a subset of AI that focuses on the development of algorithms capable of automatically learning and improving through experience, without being explicitly programmed for each specific task (Donti & Kolter, 2021), (Zhenpeng and others, 2023). ML algorithms use historical data to identify patterns and make predictions about future events, being especially valuable for predictive maintenance and process optimization (Jambol et al., 2024), (Arinze et al., 2024), (Gowekar, Artificial intelligence for predictive maintenance in oil and gas operations, 2024).

Fundamental Technologies

Key ML technologies applicable to these industries include:

• Artificial Neural Networks (Artificial Neural Network, ANN): Used to model complex nonlinear relationships in operational data (Gulzar et al., 2022), (Alenezi & Alabaiadly, 2025), (Nagpal et al., 2024).
• Support Vector Machines (Support Vector Machines, SVM): Effective for classification and predictive analysis (Gulzar et al., 2022), (Nagpal et al., 2024).
• Genetic Algorithms: Applied to multiobjective optimization (Gulzar et al., 2022), (Alenezi & Alabaiadly, 2025).
• Deep Learning (Deep Learning, DL): Used in image processing and seismic data analysis (FlyPix AI, 2025), (BHP, 2024)
• Fuzzy Logic (Fuzzy Logic): Used for managing uncertainty in industrial processes (Nagpal et al., 2024).

Foundations of globally applicable AI and ML technologies

Machine Learning Architectures

The technological foundations of AI and ML applicable to extractive sectors are based on advanced computational architectures that allow the processing of large volumes of data in real time. Recurrent neural networks (Recurrent Neural Network, RNN) and neural networks for long-term and short-term memory (Long-and Short-Term Mamory Neural Networks, LSTM) have proven to be particularly effective for time series analysis in operational data (Parvathareddy et al., 2025), (Blfgeh & Alkhudhayr, 2024).

Computer Vision and Image Processing

Computer vision is a fundamental technology for automated equipment inspection, safety monitoring and the detection of visual anomalies (Viso.ai, 2025), (FlyPix AI, 2025). In mining, computer vision-based systems can automatically identify types of minerals, evaluate the quality of the extracted material and detect hazardous conditions in real time (Globaldata, 2024), (AI Superior, 2024).

Internet of Things (IoT) and Smart Sensors

The integration of IoT sensors with AI algorithms allows the creation of continuous monitoring ecosystems that generate real-time data on equipment status, environmental conditions and operational parameters (Max Group, 2025), (Omar and others, 2023), (Skibicky, 2025). These systems provide the necessary database to train effective ML models (Gupta A., 2025), (Gowekar, How oil and gas industry are transforming with AI and ML, 2024).

Predictive analytics and digital twins

Digital twins represent an advanced AI application that creates virtual models of physical assets, allowing real-time simulations and accurate predictions about the future behavior of equipment and processes (Rojas and others, 2025), (Huawei, 2025), (Huawei, 2025), (Huawei, 2025). This technology is especially valuable for the optimization of complex operations and predictive maintenance (BHP, 2024), (Yokogawa, 2025).

Natural Language Processing

Natural Language Processing (Natural Language Processing, NLP) facilitates the extraction of knowledge from technical documents, operational reports and equipment manuals, automating organizational knowledge management (AI Superior, 2025), (Bi Technology, 2025). This technology is crucial for the digitization of tacit knowledge in industries with a large amount of technical documentation (Kaira, 2025).

Trends in the use of AI and ML

Predictive maintenance and asset management

Predictive maintenance has established itself as one of the most successful applications of AI in extractive sectors (Jambol et al., 2024)], (Arinze et al., 2024), (Gowekar, How oil and gas industry are transforming with AI and ML, 2024). ML algorithms analyze sensor data to predict equipment failures before they occur, reducing unplanned downtime by up to 30% and extending the useful life of assets (Supermec, 2025), (Jambol and others, 2024).

In the oil sector, companies such as Shell and BP have implemented AI-based predictive maintenance systems that have generated savings of tens of millions of dollars annually (Supermec, 2025). These systems use vibration analysis, infrared thermography and acoustic analysis to detect anomalies in pumps, compressors and turbines (Jambol et al., 2024), (Arinze et al., 2024).

Optimization of operational processes

Process optimization through AI has transformed operational efficiency in multiple aspects (Skibicky, 2025), (Gupta A., 2025), (Gupta N., 2023). In mining, ML algorithms optimize crushing and grinding processes, which represent the most energy intensive operations, achieving significant reductions in energy consumption (Parvathareddy et al., 2025), (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022).

AI systems also optimize production planning, inventory management and transportation logistics (Supermec, 2025), (Gupta A., 2025). In the water sector, AI improves the efficiency of treatment plants by automatically optimizing operational parameters such as pH, chemical dosing and retention times (Gulzar et al., 2022), (Alenezi & Alabaiadly, 2025), (Nagpal et al., 2024).

Automation and intelligent robotics

AI-driven automation is revolutionizing operations in hazardous or remote environments (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022), (Ericsson, 2024), (Globaldata, 2024). In mining, autonomous trucks and robotic drilling equipment operate continuously without the need for breaks, increasing productivity by 15% and significantly improving worker safety (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022), (AI Superior, 2024).

Intelligent network and distribution management

In the energy sector, AI is transforming the management of smart grids (Smart grids), allowing the efficient integration of renewable energy sources and the optimization of the balance between supply and demand (Frías, 2025), (Skibicky, 2025), (Gupta A., 2025). Predictive algorithms analyze consumption patterns and weather conditions to optimize energy distribution and minimize losses (Pooley & Thompson, 2024), (Mexico Energy Partners LLC, 2025), (LLC, Mexico Energy Partners, 2025).

Advanced exploration and prospecting

AI has revolutionized the exploration of natural resources through the advanced analysis of geological and geophysical data (Corrigan & Ikonnikova, A review of the use of AI in the mining industry: Insights and ethical considerations for multi-objective optimization, 2024), (BHP, 2024), (Dam, 2025). In the oil sector, ML algorithms analyze seismic data to identify promising subsurface structures with greater precision than traditional methods (Drafting, 2025), (Cortes, 2000), (Dam, 2025).

Companies like Barrick Gold use AI systems to analyze exploration data and accelerate the discovery of new mineral deposits, significantly reducing exploration times and costs (Globaldata, 2024), (AI Superior, 2024).

Challenges and opportunities of AI and ML applicable in indicated sectors in Mexico

Context of the Mexican energy sector

Mexico is in a strategic position to take advantage of AI technologies in the energy sector. With significant potential in renewable energy, especially solar and wind, the country faces the challenge of integrating these intermittent sources into the national electricity grid (Frías, 2025), (LLC, Mexico Energy Partners, 2025), (BNamericas, 2019). AI can facilitate this integration through advanced weather forecasting systems and intelligent network management (Mexico Energy Partners LLC, 2025), (LLC, Mexico Energy Partners, 2025).

Opportunities in the Oil Sector

Petrleos Mexicanos (PEMEX), as a state hydrocarbons company, faces particular challenges related to the decline of mature fields and the need to improve operational efficiency (Archilles, 2024), (Editorial Office, 2025). The implementation of AI for predictive maintenance, optimization of refining processes and advanced reservoir analysis represents a strategic opportunity to revitalize the productivity of the sector (Editorial Office, 2025), (Cortes, 2000).

The private sector in Mexico also presents significant opportunities, especially in the development of local capacities for AI services applied to hydrocarbon exploration and production (Archilles, 2024), (Drafting, 2025).

Challenges in the mining sector

The Mexican mining industry, which represents 2.75% of the national GDP according to INEGI (Ericsson, 2024), faces challenges related to the adoption of advanced digital technologies. The collaboration between Epiroc and Ericsson to implement private cellular networks in Mexican mines exemplifies the type of infrastructure needed to support AI applications (Ericsson, 2024), (Keysight, 2025).

Key challenges include:

• Connectivity infrastructure in remote locations (Ericsson, 2024), (Keysight, 2025).
• Technical training of local personnel in AI technologies (Lebdioui and others, 2025), (Malamud, 2024).
• Investment in the digitalization of traditional processes (Archilles, 2024), (Huawei, 2025).
• Integration of legacy systems (Legacy) with new technologies (Keysight, 2025), (Kaira, 2025).

Water Management and Sustainability

Mexico faces significant challenges in water management, especially in regions with water scarcity such as Querétaro (McGovern & Branford, 2024), (Malamud, 2024). The implementation of data centers for AI has generated controversy due to the high consumption of water needed for cooling (McGovern & Branford, 2024), (Malamud, 2024).

However, AI also offers opportunities to optimize water use by:

• Real-time leak detection systems (Max Group, 2025), (Omar et al., 2023).
• Optimization of wastewater treatment processes (Gulzar et al., 2022), (Alenezi & Alabaiadly, 2025), (Nagpal et al., 2024).
• Intelligent management of water resources in mining operations (Max Group, 2025), (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022).

Regulatory framework and public policies

The development of an adequate regulatory framework constitutes both a challenge and an opportunity for Mexico (LLC, Mexico Energy Partners, 2025), (Lebdioui et al., 2025). The lack of specific regulations for the implementation of AI in critical sectors can create uncertainty, but it also allows the development of innovative regulatory frameworks that encourage the responsible adoption of these technologies (Lebdioui and others, 2025), (Malamud, 2024).

Human Capital Development

The training of human capital represents one of the greatest challenges for the adoption of AI in Mexico (Mexico Energy Partners LLC, 2024), (Inmersys, 2025). It is necessary to develop specialized educational programs that combine technical knowledge in AI with specific sector experience (Inmersys, 2025), (Bi Technology, 2025).

Institutions such as Tecnológico de Monterrey have started implementing certification programs in AI, but a significant expansion of these initiatives is required to meet market demand (Inmersys, 2025).

Main AI and ML products applicable to the indicated sectors

Enterprise AI platforms

• IBM Watson is one of the most robust AI platforms for industrial applications, offering advanced natural language processing, predictive analysis and knowledge management capabilities (AI Superior, 2025), (Writing, 2025). In the hydrocarbon sector, Watson is used for geological data analysis, optimization of drilling operations and predictive equipment maintenance (AI Superior, 2025), (Writing, 2025).
• Microsoft Azure AI provides a complete ecosystem of AI services in the cloud, including ML tools, computer vision and real-time data analysis (Drafting, 2025), (Huawei, 2025). Azure has been adopted by companies like ExxonMobil to predict maintenance in shale fields (Drafting, 2025).
• Google Cloud AI Platform offers specialized solutions for the analysis of large volumes of data and the implementation of industrial-scale ML models (AI Superior, 2025). Its strength in geospatial data processing makes it particularly valuable for mining exploration applications (AI Superior, 2024).

Specialized solutions by sector

Oil and Gas Sector

• Palantir Foundry: Data integration platform that allows advanced analysis of operational information and process optimization (Drafting, 2025).
• NVIDIA: Accelerated Computing Solutions for Seismic Processing and Reservoir Modeling (Drafting, 2025).
• Schlumberger Petrel: Integrated software with AI capabilities for reservoir characterization (Cortes, 2000).

Mining Sector

• KoBold Metals: Uses AI to explore mineral deposits, especially copper and cobalt, critical to the energy transition (AI Superior, 2024).
• Veracio: Digital sensing and AI platform for mineral deposit analysis (AI Superior, 2024).
• Earth AI: Autonomous exploration systems that integrate geophysical data with ML algorithms (AI Superior, 2024).
• Rio Tinto: Proprietary AI solutions for optimizing mining operations (AI Superior, 2024), (BHP, 2024).

Water Management

• Ayyeka: IoT and ML-based leak detection systems (Max Group, 2025).
• WINT: Intelligent water management platforms with predictive capabilities (Max Group, 2025).
• IBM Water Management: Predictive analytics solutions for water demand (Max Group, 2025).
• CropX and Arable: Intelligent irrigation systems for water use optimization (Max Group, 2025).

Energy Sector

• Quartux: Mexican company that uses AI and energy storage for energy optimization (Frías, 2025).
• Atlas Renewable Energy: Implements AI for the optimization of solar and wind power plants (Frías, 2025), (Atlas Renewable Energy, 2024).
• Honeywell Forge: Industrial AI platform for energy asset management (Huawei, 2025).

Technology infrastructure providers

Huawei offers comprehensive solutions for the digitalization of extractive industries, including 5G networks, edge computing and industrial AI platforms (Huawei, 2025), (Huawei, 2025). Its solutions have been successfully implemented in oil fields such as Changqing Oilfield (Huawei, 2025).

Ericsson provides critical connectivity infrastructure for AI applications in remote locations, as evidenced by its collaboration with Epiroc for mining operations in Mexico (Ericsson, 2024).

Yokogawa offers integrated industrial automation solutions with AI capabilities, particularly strong in asset operations management (AOM) that combine composability, AI/ML and digital twins (Yokogawa, 2025).

Applicable use cases in Mexico

Oil and Gas Sector: Refinery Optimization

PEMEX can implement AI systems to optimize refining processes, similar to the cases developed by YPF in Argentina (Voceros Ingeniería, 2024). Automated detection of anomalies in torches using infrared video analysis can significantly improve operational safety and reduce emissions (Voceros Ingeniería, 2024).

The application of AI-based petrophysical models for understanding the subsoil in Mexican formations, following the example of the development in Vaca Muerta, can optimize the location of wells and improve recovery rates (Voceros Ingeniería, 2024), (Editorial Office, 2025).

Mining: Implementing private 5G networks

The case of the alliance between Epiroc and Ericsson in Mexico demonstrates the potential of private cellular networks to enable AI applications in mining (Ericsson, 2024). This infrastructure allows:

• Real-time monitoring of security conditions using wearable devices (Globaldata, 2024).
• Operation of autonomous equipment with low latency communication (Ericsson, 2024).
• Predictive analysis of geological conditions during operation (Keysight, 2025).

Water Management: Intelligent Distribution Systems

Mexico can develop intelligent water management systems inspired by initiatives such as Waterplan in Argentina, which uses AI and remote sensors to prevent water disasters and optimize water conservation (Malamud, 2024). Specific applications include:

• Early detection of leaks in urban distribution systems (Max Group, 2025), (Omar et al., 2023).
• Optimization of irrigation in arid agricultural areas in the north of the country (Max Group, 2025).
• Intelligent wastewater treatment in metropolitan areas (Gulzar et al., 2022), (Alenezi & Alabaiadly, 2025), (Nagpal et al., 2024).

Renewable Energies: Optimization of wind and solar farms

Mexico can expand AI applications in renewable energy, following the Atlas Renewable Energy model (Frías, 2025), (Atlas Renewable Energy, 2024). Specific use cases include:

• Advanced weather forecasting for generation optimization (Skibicky, 2025), (Atlas Renewable Energy, 2024).
• Predictive maintenance of wind turbines and solar panels (Skibicky, 2025), (Gupta A., 2025).
• Intelligent energy storage management (Mexico Energy Partners LLC, 2025), (Blfgeh & Alkhudhayr, 2024).

Manufacturing: Intelligent Automation

The case of ArcelorMittal Mexico in Lázaro Cárdenas, with its investment of USD 1 billion in modernization, can serve as a model for the integration of AI in industrial processes (AcerlorMittal, 2024). The new hot rolling plant can incorporate:

• Automated quality control using computer vision (FlyPix AI, 2025).
• Energy optimization of production processes (Gupta N., 2023), (Bi Technology, 2025).
• Predictive maintenance of rolling mill equipment (AcerlorMittal, 2024).

Energy Sector: Smart Grids

The implementation of Intelligent Networks (Smart Grids) in Mexico can benefit from successful experiences documented in other countries (Frías, 2025), (Mexico Energy Partners LLC, 2025), (LLC, Mexico Energy Partners, 2025). Specific applications include:

• Integration of renewable energy into the national grid (Frías, 2025), (Mexico Energy Partners LLC, 2025).
• Real-time demand management (Skibicky, 2025), (Gupta A., 2025).
• Blackout prevention through predictive analysis (LLC, Mexico Energy Partners, 2025).

Industrial cybersecurity

The case of the Mexican mining company that implemented OT cybersecurity solutions with Keysight (Keysight, 2025) illustrates the importance of protecting critical infrastructures in the era of digitalization. AI can strengthen cybersecurity by:

• Anomaly detection in industrial networks (Keysight, 2025).
• Behavioral analysis of IoT devices (Keysight, 2025), (Huawei, 2025).
• Automated threat response (Keysight, 2025).

Conclusions

The implementation of Artificial Intelligence and Machine Learning in the oil and gas, mining, water and energy sectors represents a transformative opportunity for Mexico. Global evidence shows that these technologies can generate significant improvements in operational efficiency, cost reduction, increased safety and advances towards environmental sustainability (Abikoye, 2025), (Gowekar, How oil and gas industry are transforming with AI and ML, 2024), (Supermanc, 2025), (Skibicky, 2025), (Gupta A., 2025).

Demonstrated transformational impact

International success stories show that AI can reduce operational costs by up to 30%, increase productivity by 15%, and significantly reduce unplanned downtime (Supermec, 2025), (Jambol et al., 2024), (Nagpal et al., 2024). In the Mexican context, these improvements are particularly relevant considering the productivity challenges faced by sectors such as PEMEX and the national mining industry (Archilles, 2024), (Drafting, 2025).

Strategic opportunities for Mexico

Mexico has unique competitive advantages for the adoption of AI in extractive sectors, including abundant natural resources, a strategic geographical location for data centers, and a significant domestic market (Lebdioui et al., 2025), (Malamud, 2024), (BNamericas, 2019). The combination of lithium resources, renewable energy potential and proximity to North American markets positions the country as a relevant player in the global digital economy (Lebdioui et al., 2025), (Malamud, 2024).

Critical Challenges to Overcome

The main obstacles to the mass adoption of AI include the need to develop connectivity infrastructure in remote locations, the formation of specialized human capital, and the establishment of regulatory frameworks that promote responsible innovation (Ericsson, 2024), (Keysight, 2025), (Lebdioui and others, 2025), (Malamud, 2024). The experience of other countries suggests that these challenges can be overcome through coordinated public policies and strategic alliances between the public and private sectors (Frías, 2025), (LLC, Mexico Energy Partners, 2025), (Inmersys, 2025).

Sustainability and environmental responsibility

AI offers powerful tools to address the sustainability challenges faced by extractive industries (Corrigan & Laye, The Use of AI in the Mining Industry - Insights and Ethical Considerations, 2022), (Lebdioui et al., 2025), (Montalvan-Chávez et al., 2024). From optimizing water use to reducing carbon emissions and improving waste management, these technologies can accelerate the transition to more environmentally responsible operations (Max Group, 2025), (Frías, 2025), (Skibicky, 2025), (Atlas Renewable Energy, 2024), (Montalvan-Chávez et al., 2024).

Recommendations for implementation

To maximize the benefits of AI in these sectors, Mexico must:

1. Develop innovation ecosystems that connect universities, companies and government to promote applied research (Inmersys, 2025), (Bi Technology, 2025).
2. Invest in robust digital infrastructure that supports AI applications in remote locations (Ericsson, 2024), (Keysight, 2025), (Huawei, 2025).
3. Establish specialized training programs to train the required technical talent (Mexico Energy Partners LLC, 2024), (Inmersys, 2025).
4. Create regulatory frameworks that balance innovation with data protection and operational security (LLC, Mexico Energy Partners, 2025), (Lebdioui et al., 2025).
5. Promote public-private partnerships to accelerate the adoption of emerging technologies (Frías, 2025), (Archilles, 2024), (AcerlorMittal, 2024).

Future Perspective

The future of the extractive and energy sectors in Mexico will be inexorably linked to the country's capacity to adopt and adapt AI technologies effectively and responsibly (LLC, Mexico Energy Partners, 2025), (Lebdioui and others, 2025), (Armaah, 2023), (Montalvan-Chávez et al., 2024). Global trends towards digitalization, sustainability and intelligent automation represent both an opportunity for technological leadership and a competitive imperative (Skibicky, 2025), (Gupta A., 2025), (Huawei, 2025), (Atlas Renewable Energy, 2024).

The evidence collected suggests that Mexico has the potential to become a regional reference in the application of AI to extractive sectors, provided that it can articulate a coherent national strategy that takes advantage of its natural strengths and develops the necessary technical capacities (Frías, 2025), (LLC, Mexico Energy Partners, 2025), (Lebdioui et al., 2025), (Malamud, 2024), (BNamericas, 2019). The current moment represents a critical window of opportunity that requires decisive and coordinated action by all relevant actors.

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