Drilling Data Quality Control and Monitoring Using Artificial intelligence

Recorded On: 06/30/2021

In this presentation, the winning teams of the SPE-DUPTS Drilling Data Competition will present their solution to address one of the major challenges in the drilling domain - Data quality. In 6 months, 15 teams were tasked to develop an innovative approach to detect, correct and measure the uncertainty in drilling sensor data.

This webinar is categorized under the Drilling technical discipline.

All content contained within this webinar is copyrighted by Rayana Alshehri, Lucas Andres Miranda Pinto and James Omeke and its use and/or reproduction outside the portal requires express permission from Rayana Alshehri, Lucas Andres Miranda Pinto and James Omeke.

Rayana Alshehri

Data Science Specialist, Saudi Aramco

Rayana Alshehri is a Data Science specialist working with the Data Science Group in the Upstream Exploration organization at Saudi Aramco. She participated with her team in the development of many petroleum engineering projects. Rayana's main interest is in utilizing Machine Learning and Data Analytics techniques to provide a better understanding of the Data and to develop intelligent solutions for scientific problems. She graduated with a Bachelor degree in Computer Science from Imam Abdulrahman Bin Faisal University (IAU) in May 2020.

Lucas Miranda

Reservoir Engineering Intern, Tecpetrol

Lucas Miranda is a petroleum engineering student with an scholarship from Pan American Energy. Captain of the Petrobowl team of ITBA. Currently working in Tecpetrol as a Reservoir Engineering Intern. Interested in the use of Data Science to improve the Oil and Gas operations.

James Omeke

Reservoir Engineer

James Omeke is an experienced reservoir engineer passionate about integrating Artificial intelligence and machine learning techniques into existing workflows in the upstream oil and gas activities. He has been successfully embedding data science – analytics into engineering processes including assisted history-matching, supervised and unsupervised machine learning applied to reservoir characterization, virtual flow meter development and production optimization. James’s research interest has been in online/offline deep learning and physics-informed neural networks (PINN). He holds a bachelor’s in petroleum engineering, a postgraduate certificate in artificial intelligence and machine learning from University of Texas in Austin and currently a master’s student in reservoir geoscience and engineering at IFP school France.

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Drilling Data Quality control and monitoring using Artificial intelligence.
06/30/2021 at 12:00 PM (EDT)   |  90 minutes
06/30/2021 at 12:00 PM (EDT)   |  90 minutes
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0.15 CEU/1.5 PDH credits  |  Certificate available
0.15 CEU/1.5 PDH credits  |  Certificate available