Applications of Artificial Intelligence and Big Data Analytics in Petroleum Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 June 2025 | Viewed by 30
Special Issue Editor
Special Issue Information
Dear Colleagues,
In the rapidly evolving field of petroleum engineering, data-driven decision-making is becoming essential for improving efficiency, reducing costs, and enhancing safety. This Special Issue delves into the transformative role of Artificial Intelligence (AI) and big data analytics within the petroleum sector. It explores innovative applications that address complex challenges in exploration, drilling, reservoir management, and production optimization, among other important aspects of this industry. The integration of big data analytics into the practice of petroleum engineers is essential to establishing a vision for the oil and gas industry to move toward data-based decisions in the production and operations arena. By leveraging advanced machine learning models, predictive analytics, knowledge discovery and real-time data processing, researchers and practitioners can make informed decisions that drive operational improvements and environmental sustainability.
This Issue invites original research and review articles on topics such as predictive maintenance, anomaly detection, process optimization, key performance indicator monitoring, digital twins and intelligent data processing, and management frameworks tailored for the unique needs of petroleum engineering. Contributions are expected to demonstrate the practical implementation of AI and big data analytics techniques, discuss challenges, and suggest pathways for future developments.
We aim to provide a comprehensive overview of how AI and big data are reshaping petroleum engineering, offering valuable insights for academics, industry professionals, and policymakers alike.
Dr. Karin Becker
Guest Editor
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Keywords
- petroleum engineering
- big data analytics
- predictive maintenance
- digital twins
- intelligent reservoir management
- drilling optimization
- anomaly detection
- production optimization
- intelligent event management
- intelligent KPI monitoring
- data-driven decision-making
- IA for energy sustainability
- intelligent exploration technologies
- frameworks for data intelligence
- predictive analytics
- operational efficiency
- environmental impact analytics
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