How Machine Learning Drives Artificial Lift Performance

DURATION:

51 min

FORMAT:

AWS,Presentation

Unplanned downtime in the oil and gas industry leads to costly problems like production delays. With artificial lift downtime being a primary source of such deferred production, identifying potential problems before they occur is crucial.

By applying advanced analytics and machine learning (ML) at scale in the cloud, oil and gas companies can improve their field performance. Using asset diagnostics and acting on real-time monitoring insights, you’ll create data-driven maintenance flows, leading to an increase in production and reduced lease operating expenses.

This video explains how AWS production monitoring solutions and services like Amazon SageMaker reduce unscheduled maintenance and deferred production. These tools help you to predict suboptimal equipment performance and potential failures so you can make data-driven operational decisions.

Our speakers:

Andrii Struk, Business Analyst, SME, Energy, SoftServe

Andrii is an energy, oil, and gas business analyst and subject matter expert at SoftServe. As an experienced engineer and manager, Andrii has worked on a variety of oil and gas projects around the world.

Andrii provides clients with domain and technical assessments to solve their business challenges ranging from exploration, production, and manufacturing processes optimization to assets management and monitoring, performance development, and digital transformation.

Yaroslav Svyryda, Data Scientist, SoftServe

Yaroslav Svyryda is a data scientist at SoftServe, with years of experience in the field. He studied quantitative methods in economics and information systems at the Warsaw School of Economics and has worked on solutions involving price optimization, demand forecasting, NLP, predictive maintenance, and anomaly detection.

Mohamed Shawky, Principal, Business Development, Energy, AWS

Mo is with AWS in Business Development. His focus is on the Energy sector where he supports the industry in driving innovation. Prior to joining AWS, Mo spent 16 years in the Energy and Specialty Chemical sectors where he led Commercial, R&D and Operations functions. Mo holds a bachelor’s degree in Electrical Engineering from Queen’s University and an MBA from Georgetown University.

David Benham, Senior Data Scientist, Vital Energy

His expertise lies in unlocking the power of data to accelerate Vital Energy’s digital transformation. Prior to joining Vital Energy, David spent an additional eight years in the energy sector and another two years in the manufacturing sector. His focus has primarily been initiatives that solved various business challenges in the well planning, performance, and production domains.