Data Analytics Special Interest Group Luncheon: Data-Driven Oil and Gas Well Optimization

When:  Jun 12, 2018 from 11:30 AM to 01:00 PM (MT)
Associated with  Calgary Section

Data Analytics Special Interest Group Luncheon:
Data-Driven Oil and Gas Well Optimization

Date: Tuesday, 12 June 2018
Speaker: Luther Birdzell, OAG Analytics
Location: Calgary Petroleum Club
Time: 11:30 – 1:00 pm  
Cost: $45 SPE Members, $55 Non-members, $15 Students


Profitability of unconventional oil & gas wells is critically dependent on predictable well performance. The factory approach of drilling the same well across large acreage positions does not maximize profitability. Traditional data analysis and reservoir simulation are falling short with today’s complex and inter-related data. Digital transformation is bringing profound changes to these traditional well planning workflows and the use of Digital Twins is significantly improving well planning and production forecasting.

A Digital Twin is a software representation of an industrial machine – in this case, an oil or gas well. Realistic virtual representations of each well enable teams to explore many scenarios pre-drill, resulting in improved well profitability. AI and Machine Learning are core enabling technologies for upstream Digital Twins.

Several leading oil companies are using Digital Twins to improve well profitability throughout North America. A major operator realized significant improvement in workflow efficiency and asset performance in less than 6 months. You can also design better wells with higher IRR’s using insights beyond the reach of traditional methods. We’ll show you how.

 3 takeaways for attendees:
- What is a Digital Twin?
- Digital Twin best practices for upstream oil & gas
- How leading Oil and Gas companies are using Digital Twins to maximize profitability

Speaker Bio:

Luther is an entrepreneur, data scientist and engineer passionate about energy efficiency, AI and self-service machine learning.

From boardrooms, to oilfields, to data centers, Luther has been building tools that transform data from a cost to an asset for over 20 years. He founded OAG Analytics in 2013 to build an AI and self-service machine learning platform for the oil & gas industry.


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