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HOW MACHINE LEARNING DRIVES ARTIFICIAL LIFT PERFORMANCE

  • JUNE 9, 12 PM CST
  • digital event

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ABOUT THIS EVENT

    • About this event: 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, reduced carbon footprint, and reduced lease operating expenses.

      This digital event will explain 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.

    • Why join: Join us on Wednesday, June 9th at 12 CST and hear from Mohamed Shawky, Business Development Principal for Energy at AWS, and David Benham, Senior Data Scientist at Laredo Petroleum, as they discuss the challenges of rotating equipment performance in the context of artificial lift. These energy experts will then share how using telemetry data analysis and machine learning modeling helps solve these issues.

      You’ll also hear from Yaroslav Svyryda, a data scientist, and Andrii Struk, Energy, Oil, and Gas SMEs at SoftServe. They will demonstrate the Proof-of-Concept (PoC) created in collaboration with Laredo Petroleum and AWS. The POC’s purpose was to optimize Laredo’s artificial lift compressors performance as well as predict any anomalies or failures. Learn how this POC utilized exploratory data analysis (EDA), data processing, and ML modeling using Amazon SageMaker, ultimately increasing Laredo’s equipment uptime and reliability.

AGENDA

  • • Introduction
  • • Amazon and Laredo Petroleum discuss the challenges of compressors and artificial lift systems behavior
  • • Laredo Petroleum dives into the broader Intelligent Well initiative
  • • SoftServe experts demo ML for predicting compressor failures Proof of Concept
  • • Q&A

OUR SPEAKERS

  • Rodion
    Myronov

    Data Architect and Consultant

    photo-rodion-myronovrmyro@softserveinc.com

     

    Rodion Myronov has nearly 20 years of data technology experience. Beginning with FoxPro2.6 for DOS, he followed the traditional route of becoming an MS SQL Server and Oracle developer, DBA, and trainer before becoming a team leader and independent consultant. Rodion has also worked on data warehousing and ETL projects based on Redshift, Hadoop, Big Query, and Netezza. He’s collaborated on multiple projects as an architect and consultant, including data lake and data warehouse systems design, assessments, and complex migrations as well as developing data governance frameworks. Today, Rodion manages the direction of AWS projects within SoftServe’s Big Data and Analytics Center of Excellence.

  • Roy 
    Hasson

    WW Analytics Specialist

    photo-roy-hasson

    Roy Hasson is WW Analytics Specialist leader at Amazon Web Services, where he helps transform organizations using data, analytics and machine learning. Roy serves as an expert advisor to customers across all industries to transform their business and become a data driven organization by building a cloud native modern data architecture on AWS. He is also a product leader driven by voice-of-the-customer to guide the development of new innovative services and user experiences for AWS. Prior to AWS, Roy spent 15 years working with tier 1 service providers to design and deploy large scale data systems used to serve today’s cable modem, voice over IP and wireless data services.

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