How Artificial Intelligence is Reinventing Drilling in Oil and Gas

Includes a Live Event on 11/17/2020 at 9:00 AM (EST)

Oil and Gas is currently suffering an unprecedented downturn, but the companies that will emerge the strongest during these times are the ones who adopt innovative technologies to promote growth. These companies are now embracing technologies like machine learning and artificial intelligence to optimize operations and to predict the likelihood of catastrophic or downtime-related events. What if you had the ability to predict events like kicks or a blowout long before they occur? This sort of predictive maintenance has already been employed for drilling by major O&G companies. Artificial intelligence algorithms ingest asset operational data, pressures, mud properties, temperature, and more to understand exactly what is happening downhole at a specific well. These algorithms then use this data to predict bit wear, pore pressure, stuck pipe events, formation washouts, uncontrolled release of hydrocarbons to the atmosphere, and many other drilling dysfunctions. In this session, we will consider how AI can identify all drilling dysfunction and propose a path forward to address drilling optimization.

This webinar is categorized under the Drilling technical discipline.

All content contained within this webinar is copyrighted by Philippe Herve and its use and/or reproduction outside the portal requires express permission from Philippe Herve.

Philippe Herve

Vice President of Oil and Gas Solutions, SparkCognition

Philippe Herve joined SparkCognition as the VP of Solutions. He is a multilingual executive with U.S. and international success in operations, technology, business development, marketing, sales, and client relations. Herve started working with artificial intelligence in 1985.

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Webinar
11/17/2020 at 9:00 AM (EST)   |  90 minutes
11/17/2020 at 9:00 AM (EST)   |  90 minutes
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20 Questions
Certificate
0.15 CEU/1.5 PDH credits  |  Certificate available
0.15 CEU/1.5 PDH credits  |  Certificate available