Machine Learning and  Data Management in the Oil and Gas Industry

An Intensive 5-day Training Course

Machine Learning and
Data Management in the Oil and Gas Industry

Extracting the Data as the New Oil

Machine Learning and Data Management in the Oil and Gas Industry

Scheduled Dates


21 - 25 Oct 2024 Dubai - UAE $5,950 RESERVE A SEAT
19 - 23 May 2025 Dubai - UAE $5,950 RESERVE A SEAT
27 - 31 Oct 2025 Dubai - UAE $5,950 RESERVE A SEAT


21 - 25 Oct 2024 Online $3,950 RESERVE A SEAT
27 - 31 Oct 2025 Online $3,950 RESERVE A SEAT

Are the scheduled dates matching with your needs?

We provide a wider range of training options. Tailored and customized, we can deliver your organisation’s training needs anytime, anywhere.

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Why Choose this Training Course?

This PetroKnowledge 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

What are the Goals?

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 is this Training Course for?

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

How will this Training Course be Presented?

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.

Organisational Impact

Data is now main element of sustainability and improvement. When the companies properly acquire, analyze, manage, store and secure their data they gain incredible competitive advantage, however it is not enough only to collect and store the data as the data itself does not guarantee success, the important element is to discover meaning within the data, correlation and causational paths which will help organizations improve their operations and planning by limiting and mitigating the risks of uncertainty.

This Machine Learning and Data Management in the Oil and Gas Industry training course will highlight:

  • Optimal principles of data management
  • Creation of data centric organization and adequate management of company data
  • Machine learning techniques and algorithms
  • Implementation of machine learning in anomaly detection
  • Principal Component Analysis and other techniques used

Personal Impact

The delegates will learn from the experiences of real projects, get the insight into the success stories, problems and even failures to be able to avoid mistakes, identify adequate use cases and implement proper methods and algorithms.

The delegates will acquire:

  • Full knowledge of data management, analysis and interpretation
  • Anomaly detection and risks mitigation measures related to data quality and data security
  • Use of available software and applications
  • Insight into the ways of removing data silos within the organization
  • Knowledge of modern Machine Learning algorithms and techniques used

Daily Agenda

Day One: 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 Two: 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 Three: Areas where machine learning can be implemented within oil and gas industry
  • Forecasting
  • Anomaly detection
  • Process control
  • Optimization
  • Maintenance
  • HSE
  • Other areas
Day Four: 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 Five: Technologies in use
  • Digital core
  • Digital oilfield
  • Machine learning in predictive maintenance
  • Use of soft sensors
  • Example cases and way forward


  • On successful completion of this Training Course / Online Training Course, a PetroKnowledge Certificate / E-Certificate will be awarded to the delegates.

In Association With

The Energy Training Centre

Our collaboration with The Energy Training Centre aims to provide the best training services and benefits for our valued clients.

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Frequently Asked Questions

  • To register online through our website, please click “Enroll Now” on the course page, complete and submit the form. A confirmation e-mail and instructions will be sent to the participant’s e-mail.
  • You may also get in touch with our Registration Team on
    +971 50 981 7386 | +971 2 557 7389 or send an email to [email protected]

  • Payments can be made in USD or UAE local currency AED (Arab Emirates Dirhams) either by Bank Transfer or by Credit Card. Our Bank Account details will be provided on the invoice.
  • Course fees are payable upon booking unless a valid, authorized Purchase Order is provided and accepted.
  • Invoices will be sent via email/courier to the ID/name and address provided.
  • The course fee shall be settled prior to course start date. Corporate payments with existing payment policy shall be relayed to us in advance.

Upon successful registration online, enrolment on the respective training course will be confirmed by Registration Team by e-mail along with the invoice and joining instruction.

For corporate fees and group registration, please send your query to [email protected].

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