Feb. 3, 2021


Description

Equipment failures result in costly unplanned downtime of operations and can also lead to serious safety and environmental incidents. Therefore, predicting failures in advance and pinpointing the source unlocks significant value. Application of machine learning to real-time measurements and physics simulations provides a strong predictive maintenance framework for avoiding catastrophic failures and reducing costs.

 

SPE-GCS Data Analytics Study Group is pleased to collaborate with MathWorks and offer a hands-on workshop on Predictive Maintenance using Machine Learning. In this four-hour, instructor led online workshop, participants will tackle an oilfield equipment failure problem. Participants will build data-driven models in MATLAB using time series data and predict faults. 

 


Featured Speakers

Speaker: Inho Kim
Speaker Inho Kim

Inho Kim is a senior application engineer specializing in the energy industry. Prior to joining MathWorks, he worked at Halliburton in Houston as a principal R&D engineer in corporate automation CoE and Sperry drilling automation team for three years. His focus at MathWorks is modeling multi-physics systems related to the …

Inho Kim is a senior application engineer specializing in the energy industry. Prior to joining MathWorks, he worked at Halliburton in Houston as a principal R&D engineer in corporate automation CoE and Sperry drilling automation team for three years. His focus at MathWorks is modeling multi-physics systems related to the energy industry, predictive maintenance and reinforcement learning applications. Inho holds a Ph.D in Mechanical engineering from the Arizona State University specializing in Structural Health Monitoring.

Full Description



Organizer

SPE GCS Data Analytics Study Group

Pushpesh Sharma (s.pushpesh@gmail.com)


Date and Time

Wed, Feb. 3, 2021

noon - 4 p.m.
(GMT-0500) US/Central

Event has ended

Location

Webinar



Group(s): Data Analytics