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Data Exploration and Analytics for Artificial Lift and Production Engineers

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Course Credit: 0.1 CEU, 1 PDH

Artificial lift (AL) and production engineers envisage, design, operate, and optimize well-installations, production equipment (inside the wellbore and on the surface) that collectively generate significant amounts of real-time and offline data streams vital for the digital transformation underway in the oil and gas upstream. As our data sensing and acquisition capabilities continue to expand, a related challenge is analyzing the data streams and converting them into actionable information. Data exploration and analysis means the process of capturing, cleaning, inspecting, transforming, and modeling data to discover new, useful information for decision-making. Once the models are placed in the service, model diagnostics and maintenance are equally important. This presentation reviews a few examples of the data-captures and data-driven analytics workflows applied in artificial lift applications. A couple of key takeaways from the presentation are:

While ML/AI approaches promise new pathways to solving the operational challenges, AL and the production community needs to actively pay attention to and participate in the data management lifecycle from cleaning through modeling,
In most ML projects, AL and the production community are mostly used for data-labeling as subject matter experts (SMEs) while the remaining analytics workflows are managed by a team of data/ML/AI scientists. The low-code/no-code platforms offer the possibility of direct involvement of ‘SMEs.’

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Course Chapters

  • 1Data Exploration and Analytics for Artificial Lift and Production Engineers - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.1 CEU credit1 PDH credit

Speakers

Victoria Pons
Dr. Rajan Chokshi