COURSE OVERVIEW:

Artificial Intelligence (AI) is transforming the oil and gas industry by enabling smarter, automated, and data-driven decisions across exploration, drilling, completions, production, reservoir management, and field operations. This course provides participants with a comprehensive understanding of how AI, machine learning (ML), deep learning, data analytics, and predictive modelling are applied to oilfield workflows to improve efficiency, reduce operational risks, enhance recovery, optimize maintenance, and support real-time decision making.

Participants will learn how AI algorithms are trained, validated, and deployed on subsurface, drilling, production, and facility datasets. The course covers digital oilfield frameworks, advanced analytics, anomaly detection, drilling automation, predictive maintenance, reservoir forecasting, autonomous field operations, and integration of AI with SCADA, instrumentation, and real-time data platforms. Case studies demonstrate how leading operators use AI technologies to significantly improve field performance and business value.

COURSE OBJECTIVES:

After completion of the course, the participants will be able to:

• Understand the role of AI, ML, and data analytics across oilfield value chains.
• Identify appropriate AI techniques for exploration, drilling, production, and reservoir workflows.
• Apply machine learning algorithms to classify, cluster, and predict subsurface properties.
• Utilize AI methods for real-time drilling optimization and dysfunction detection.
• Implement predictive maintenance solutions using sensor and equipment-health data.
• Use AI models to improve production forecasting and optimize well performance.
• Integrate AI with reservoir simulation for history matching and scenario analysis.
• Apply anomaly-detection models for early problem identification in wells and facilities.
• Evaluate AI-driven solutions for enhanced oil recovery and field-development planning.
• Leverage digital twins, automation, and robotics in field operations.
• Integrate AI with SCADA systems, IoT sensors, and cloud-based data platforms.
• Recognize data-quality challenges and best practices for AI model development.
• Assess risk, uncertainty, explainability, and validation requirements for AI models.
• Develop roadmaps for deploying AI at scale in oilfield environments.

TARGET AUDIENCE:

• Reservoir Engineers
• Drilling and Completions Engineers
• Production and Petroleum Engineers
• Data Scientists and Digital Transformation Teams
• Geoscientists and Subsurface Specialists
• Asset Managers and Decision-Support Teams
• Professionals integrating AI into oilfield workflows

TRAINING COURSE METHODOLOGY

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximise the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.