Machine Learning and Data Management in the Oil and Gas Industry
| Start Date | End Date | Venue | Fees (US $) | ||
|---|---|---|---|---|---|
| Machine Learning and Data Management in the Oil and Gas Industry | 09 Aug 2026 | 12 Aug 2026 | Dubai, UAE | $ 3,900 | Register |
| Machine Learning and Data Management in the Oil and Gas Industry | 25 Oct 2026 | 29 Oct 2026 | Riyadh, KSA | $ 3,900 | Register |
Machine Learning and Data Management in the Oil and Gas Industry
| Start Date | End Date | Venue | Fees (US $) | |
|---|---|---|---|---|
| Machine Learning and Data Management in the Oil and Gas Industry | 09 Aug 2026 | 12 Aug 2026 | Dubai, UAE | $ 3,900 |
| Machine Learning and Data Management in the Oil and Gas Industry | 25 Oct 2026 | 29 Oct 2026 | Riyadh, KSA | $ 3,900 |
Introduction
This training course on Machine Learning and Data Management in the Oil and Gas Industry focuses on the fact that energy and technological worlds are witnessing drastic changes that are influenced by intertwined causes of growth: energy demand, transition of energy systems, technological evolution and revolution. As the oil and gas industry is evolving and changing the necessity of bringing together leadership power, domain expertise, knowledge, and many data silos that still exist within the organizations. This course focuses on fundamental understanding of the petroleum industry and machine learning, as well as the data management, helping the organizations within the industry to achieve success by using the data they possess and reduce the risk and uncertainty which is omnipresent in oil and gas industry.
This Machine Learning and Data Management in the Oil and Gas Industry training course will highlight:
- Importance of Data analysis and removal of obstacles for unified data flow
- Data cleaning practices and techniques
- Interpretation of data, as well as machine learning techniques within the oil and gas industry
- CDMP® Data Management Fundamentals examination
- Components of an enterprise data / information management framework
- Main application areas of machine learning and data management in oil and gas industry
Objectives
- Learn to identify the impact of data quality and data management on success of oil and gas enterprise
- Acquire the knowledge about data management framework across the enterprises
- Identify the machine learning algorithms applied within the oil and gas industry
- Learn how to gather, transform and use the spatial, seismic, production and other data
- Identify the relations between the master data management process optimization
This training course focuses on presenting the delegates with the opportunity to learn the essentials of data governance, data collection and management, data security, data analysis, Machine Learning algorithms and their implementation within oil and gas industry.
By the end of this Machine Learning and Data Management in the Oil and Gas Industry training course, participants will learn to:
Training Methodology
The training course uses a number of proved adult learning techniques, theoretical presentations with practical work of the delegates on how to properly use the data in their enterprise, how to prepare and implement the retention schedule and how to define the data architecture applicable for their enterprise. The course also teaches the delegates how to use available software as well as combine it with R, Python and other available software to achieve improvement in exploration, production, operation as well as other areas where the data and machine learning combination can help organization achieve improvements and optimization.
Who Should Attend?
The training course has been designed for professionals whose jobs involve the data gathering, data analysis, decision making. This Machine Learning and Data Management in the Oil and Gas Industry training course is suitable to a wide range of professionals but will greatly benefit:
- Petroleum Data Analysts
- CEOs, CIOs, COOs
- Systems Analysts
- Programmers
- Data Analysts
- Database Administrators
- Project Leaders
- Software Engineers
Course Outline
Day 1: Data gathering and data quality within oil and gas industry
- Data sources
- Data rules for well identification and classification
- PPDM data model
- Geospatial data storage, analysis and use
- Machine learning in geospatial data
Day 2: Machine learning in oil and gas industry
- Machine learning algorithms
- Python programming
- R programming
- Use of existing software and its combination with Python and R
- Tensorflow
Day 3: Areas where machine learning can be implemented within oil and gas industry
- Forecasting
- Anomaly detection
- Process control
- Optimization
- Maintenance
- HSE
- Other areas
Day 4: Data collection and analysis using machine learning
- Data from SCADA
- Data from sensors
- Data from ECM
- Data visualization
- Data Analytics techniques for immediate insights
Day 5: Technologies in use
- Digital core
- Digital oilfield
- Machine learning in predictive maintenance
- Use of soft sensors
- Example cases and way forward

