Data Analytics Breakfast Series:
Enabling Autonomous Well Optimization Using loT-Enabled Devices and Machine Learning in Bakken Horizontal Wells
Date: Thursday, 10th of January 2019
Speaker: Ryan Benoit, CTO of Ambyint
Location: Calgary Petroleum Club
Time: 7:30 AM - 9:30 AM
$45 Members, $55 Non-Members, $15 Student
Breakfast Series Bundle:
$144 for all 4 events
Purchase all 4 breakfast events in this series and save 20% off Member pricing
This series isn’t about the edge of your desk it’s about the edge of your work data universe. Edge computing pushes applications, data and computing power away from head office – away from 10 square blocks of dense fibre optic networks – away from the comfort of a Plus 15 connected office – away from reliable cell service. Data is gathered, devices monitor data streams, and decisions get made on the edge. This is a four-part series exploring different opportunities to use knowledge gained from the edge to further push the limits of our work data universe.
Session 1 of 4
During the mid-twentieth century, Dr. Sam Gibbs developed math that continues today to serve as the basis for the downhole monitoring and control of many wells. However, The Gibbs wave equation and fillage calculation do not consider key wellbore forces such as mechanical wear due to deviation in the well. Friction due to deviation can result in distorted calculations and poor downhole card analysis. Additionally, most rod pump control systems are based on Programmable Logic Control Systems (PLC). PLC systems are simple to program but are limited in their computation capabilities. Increased computational capabilities are required to execute higher-order mathematics that accurately calculates downhole parameters and enable well autonomy.
One approach to driving autonomous well classification and optimization of setpoints is the deployment of a system that is capable of real-time analysis and higher-order mathematics. An Industrial Internet of Things (IIoT) controller with high-performance computational capabilities and direct communication with a cloud-based analytics software platform was developed with the capabilities to execute higher-order mathematics, artificial intelligence and machine learning on high-resolution data, sampled in real-time from the rod pump control system.
Equinor deployed this technology on 50 wells in the Bakken resulting in significant improvements to their production. As a result of the successful launch, the multinational energy company expanded the deployment across its Bakken assets. Ambyint’s CTO, Ryan Benoit, will share his experience as part of this project.
Ryan Benoit is CTO of Ambyint, an optimization solutions company that leverages Artificial Intelligence with an open and secure Industrial Internet of Things technology to provide enhanced real-time automation and control for Every Well in Every Field. Ryan has worked in technology for over 15 years and has served in various technical roles where he advanced, mentored, and lead consulting, solution, and product development initiatives across a number of technology companies. These companies include Matrikon (acquired by Honeywell), Thoughtworks, NavNet, and Clarocity (formerly Zaio). Ryan holds a BSc in Electrical Engineering with a specialization in Biomedical Engineering from the University of Alberta.
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