Introduction to Machine Learning for Oil and Gas Professionals

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Disciplines: Completions | Data Science and Engineering Analytics | Drilling | Management | Production and Operations | Projects, Facilities, and Construction | Reservoir

Course Description

Machine learning (ML) algorithms are transforming workflows in the oil and gas industry. The democratization and access to advanced computational tools is helping organizations exponentially accelerate their decision making using these powerful tools. This course introduces the basic principles of machine learning approaches, introduces different ML algorithms, provides a framework on how to identify if these AI/ML algorithms are useful for your data and finally lists key focus areas for successful business outcomes with focus on subsurface.

 

• Topics:
• Basic concept of AI/ML – what does this try to achieve and what is required?
• Introduction to different types of algorithms
• Framing the correct AI/ML problem for the data (Classification vs Regression)
• Framework to address typical issues – data quality, data sufficiency, testing and final use in production (based on previous successful implementations)
• Successful business cases that drive framework and understanding

Learning Level

Introductory – Intermediate

Course Length

1 day

Why Attend

Upon completion of this course, participants are expected to:

• Identify areas in their organization where AI/ML can help
• Evaluate whether AI/ML can help independently or supplement existing workflows
• Frame AI/ML projects for successful business outcomes to include –
  • Preliminary evaluation (does my data lend towards AI/ML?),
  • Ask the right questions (classification vs regression) to obtain insights
  • Accelerate workflows for quicker decision making. 
• Set expectations what successful AI/ML project outcome looks like
  • Formulate right test cases to evaluate results and
  • Plan deployment for use at scale

Who Attends

Professionals that are responsible in the following functional areas should attend:

• Business Unit Head
• Data and Business Analysts
• Data Managers
• Data Mining
• Data Science Analysts
• Drilling and Completions
• Information Technology
• Operations
• Production Technology
• Project Management
• Reservoir Engineering

CEUs

0.8 CEUs are awarded for this 1-day course

Cancellation Policy

All cancellations must be received no later than 14 days prior to the course start date. Cancellations made after the 14-day window will not be refunded. Refunds will not be given due to no show situations.

Training sessions attached to SPE conferences and workshops follow the cancellation policies stated on the event information page. Please check that page for specific cancellation information.

SPE reserves the right to cancel or re-schedule courses at will. Notification of changes will be made as quickly as possible; please keep this in mind when arranging travel, as SPE is not responsible for any fees charged for cancelling or changing travel arrangements.

We reserve the right to substitute course instructors as necessary.

Instructor

Other courses by this instructor