COURSE OVERVIEW:

Digital Oilfield Fundamentals: Data Quality, Dashboards, and KPIs is a specialized program designed to introduce professionals to the convergence of petroleum engineering and information technology. In the modern energy landscape, the ability to harness real-time data from downhole sensors and surface facilities is a critical competitive advantage. This course provides a comprehensive roadmap for transforming raw operational data into actionable intelligence, ensuring that digital initiatives result in measurable improvements in production and safety.

The scope of this training focuses on the "Data-to-Decision" pipeline, beginning with the fundamental requirement of data integrity. Participants will explore the technical challenges of data acquisition, including sensor calibration, signal transmission, and the elimination of data silos through integrated databases. The curriculum emphasizes that a dashboard is only as good as the underlying data, teaching attendees how to identify and remediate "bad data" before it influences management decisions or automated control systems.

Coverage also includes the design and implementation of effective visualization tools and Key Performance Indicators (KPIs). Attendees will learn how to build intuitive dashboards that highlight production variances, equipment health, and operational efficiency. By focusing on the human element of digital transformation, the course ensures that technical teams can collaborate effectively using shared data environments. Participants will leave with the skills to define meaningful KPIs that drive performance, optimize asset uptime, and support the shift toward proactive rather than reactive field management.

COURSE OBJECTIVES:

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

• Define the core components and architecture of a Digital Oilfield (DOF) system.
• Identify the primary sources of data in upstream operations including SCADA and DCS.
• Evaluate the impact of poor data quality on production forecasting and reserves.
• Implement automated data validation and "cleansing" workflows.
• Distinguish between descriptive, predictive, and prescriptive analytics.
• Design effective operational dashboards for different organizational levels.
• Select and define meaningful Key Performance Indicators (KPIs) for asset management.
• Explain the role of the "Real-Time Operations Center" (RTOC) in field optimization.
• Analyze the security risks associated with interconnected oilfield data systems.
• Use data visualization best practices to communicate technical insights to management.
• Link digital initiatives to specific business outcomes such as reduced OPEX or increased recovery.
• Troubleshoot common data transmission and latency issues in remote field locations.
• Develop a roadmap for scaling digital pilot projects to full asset implementation.

TARGET AUDIENCE:

Production Engineers, IT Professionals, Asset Managers, Data Analysts, and Field Supervisors involved in digital transformation and operational excellence.

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.