COURSE OVERVIEW:

In the modern digital oilfield, the ability to extract actionable insights from vast amounts of raw data is a critical skill for engineers and technologists. This course provides a comprehensive introduction to the data analysis techniques used to optimize production, predict equipment failure, and improve reservoir characterization. Participants will learn how to transition from basic spreadsheet manipulation to advanced statistical and analytical methods specifically tailored for the petroleum industry.

The curriculum covers the entire data lifecycle, from quality control and cleaning of sensor data (SCADA) to the application of trend analysis and regression. Attendees will explore how to identify correlations between various production parameters, such as the relationship between pump speed and fluid levels, or the impact of injection rates on reservoir pressure. The course emphasizes the importance of data visualization in communicating complex technical trends to management and cross-functional teams.

A significant focus of the training is on predictive analytics and its application in proactive maintenance and production forecasting. Participants will examine the basics of machine learning and how it can be used for virtual flow metering, well test validation, and the identification of anomalous operating conditions. By the end of the program, attendees will be equipped with the analytical tools necessary to turn "big data" into better decisions, ultimately driving higher efficiency and lower operating costs.

COURSE OBJECTIVES:

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

• Explain the fundamental concepts of the digital oilfield and data driven engineering.
• Implement data cleaning and quality control procedures for production data.
• Utilize statistical techniques to identify outliers and non representative data.
• Perform regression analysis to correlate production variables.
• Apply trend analysis for early detection of equipment performance degradation.
• Understand the basics of machine learning and its role in petroleum analytics.
• Develop effective data visualizations and dashboards for operational monitoring.
• Utilize virtual flow metering techniques to supplement physical measurements.
• Conduct decline curve analysis using automated and statistical methods.
• Identify production bottlenecks through integrated system data analysis.
• Predict the "Mean Time Between Failures" for critical rotating equipment.
• Communicate technical data insights effectively to non technical stakeholders.
• Formulate a data strategy for a specific asset or production department.

TARGET AUDIENCE:

This course is intended for Production Engineers, Reservoir Engineers, Petroleum Technologists, and Data Analysts who work with upstream operational data.

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.