You are probably familiar with AAPG’s highly regarded machine learning, deep learning, and Big Data in exploration in development in our conferences and events.
We are also eager to help you have opportunities to broaden your base in business management and other areas. So, we will announce discounts and special opportunities when they become available.
So, we are excited to announce our support for the 4th Annual Machine Learning in Oil & Gas Conference (www.machinelearning-oilandgas.com) which will take place in Houston on April 17th & 18th. The conference, which you can register at a 20% discount, will bring together leading professionals from upstream, midstream, and downstream companies to exchange insights on the latest developments in Machine Learning. In short, it comprises a carefully curated mix of case studies and interactive panel discussions to enable delegates benchmark against industry best practice.
Agenda highlights include:
- Understanding the needs of your business and identifying viable use cases for Machine Learning
- Optimize maintenance processes and eliminate down time
- Best practices for achieving employee buy in and gaining management trust
- Find and improve workflow bottlenecks
- Creating a technology road map for the short- and long- term in line with business objectives
- Examples of successful outcomes from Machine Learning in oil and gas operations (including an emphasis on tying ROI back to business objectives)
Some of the organizations that have already confirmed attendance include:
Hess Corporation | Halliburton | Kinder Morgan | Noble Drilling Services| Enable Midstream Partners| Shell | Devon Energy | Schlumberger | BP | Chevron | CNX Resources | Marathon Oil | Pioneer Natural Resources | Frontera Energy
Register your spot today and save 20% with our promo code: AAPG20 or click here: https://www.machinelearning-oilandgas.com/page/1360161/register?promo=AAPG20
If you have further questions about this event, or want to purchase one of the last exhibition booth spaces, please contact: