With the oil and gas industry undergoing continuous evolution and transformation, there is a growing need to consolidate leadership capabilities, domain expertise, knowledge, and the multiple data silos still present within organizations. This course centers around developing a fundamental understanding of the petroleum industry and machine learning, along with data management practices. Its primary goal is to assist organizations within the industry in leveraging their data to achieve success while mitigating the pervasive risk and uncertainty inherent in the oil and gas sector. By harnessing their data effectively, organizations can drive informed decision-making and reduce uncertainty, thereby enhancing their overall performance.

Course Objectives:

This 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:

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

Who Should Attend?

The training course has been designed for professionals whose jobs involve the data gathering, data analysis, decision making.

This 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 Outlines:

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

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

Areas where machine learning can be implemented within oil and gas industry

Forecasting
Anomaly detection
Process control
Optimization
Maintenance
HSE
Other areas

Data collection and analysis using machine learning

Data from SCADA
Data from sensors
Data from ECM
Data visualization
Data Analytics techniques for immediate insights

Technologies in use

Digital core
Digital oilfield
Machine learning in predictive maintenance
Use of soft sensors
Example cases and way forward

Filters