Is your business taking full advantage of AI and ML to optimize production operations?
It is becoming increasingly clear that artificial intelligence and machine learning are revolutionizing every industry they touch, and the O&G sector is no exception. However, while the potential benefits of these new technologies are seemingly limitless, setting to the task of full adoption can be onerous, and potentially even harmful to a company's reputation if it is done incorrectly. Whether it be poor data quality or improperly structured architecture, the road to autonomy is littered with the pitfalls of poor implementation.
This event is intended to address the basics of AI and ML, along with some of the most commonly encountered challenges O&G executives face when implementing these new data science techniques into their organizations. We will then use this foundation to explore a number of "best practice" approaches and solutions to these problems, from the perspective of experienced practitioners with in-depth knowledge of AI enablement.
Topic: Artificial Intelligence & Machine Learning: Challenges and Solutions in Oil & Gas
Date: August 2nd, 2018, 7:30 AM - 9:30 AM (CT)
Venue: Petroleum Club of Midland
- Ryan Benoit, Ambyint, CTO
- Jesse Filipi, Ambyint, Technical Director
- Arno van den Haak, AWS, Global Business Development, Oil & Gas
Amazon Web Services (AWS) is a secure cloud services platform, offering compute power, database storage, content delivery and other functionality to help businesses scale and grow. AWS provides its customers with products and solutions to build sophisticated applications with increased flexibility, scalability and reliability.
Ambyint is building the "self-driving" oil well. We're leveraging best-in-class technology tools like artificial intelligence, machine learning, and IoT devices, in combination with physics-based analytics, deep domain expertise, and a proprietary data lake equivalent to 100 million operating hours. Our solution increases production, reduces labor costs, and reduces maintenance costs, and can ultimately deliver production improvements of up to 10% and operational expenditure savings of up to 20%.