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Drilling Optimization, Risk and Uncertainty Reduction, and Future Workforce Education Using Big Data Analysis

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Course Credit: 0.15 CEU, 1.5 PDH

Oil and gas operators are progressively adopting novel sensor and data-streaming technologies and their powerful data analytics capabilities in their continuous quest to improve drilling efficiency and remove risk and uncertainty. In this presentation, it is shown how operators partnered with the drilling automation research group (RAPID) at the University of Texas at Austin to develop a workflow for big data analysis and visualization. The objectives were to maximize the value derived from surface and downhole data, establish an analysis toolkit, and train students on data analytics—a necessary job function of any future drilling engineer. The operators provided data sets, business and technical objectives, and guidance for the project, while a multi-disciplinary group of undergraduate and graduate students piloted an analysis workflow. The students developed methods to: 1) understand and clean the data; 2) structure, combine, and condense information; 3) visualize, benchmark, and interpret the data, as well as derive key performance indicators (KPIs); and 4) automate these processes. Students investigated bottom hole assembly (BHA) and directional drilling performance using a combination of auto-generated visuals (e.g., BHA designs, annotated time vs depth curves) and newly developed tools (e.g., tortuosity, 3D well trajectory plots combined with operational data). Methods for ‘push a button’ investigations of mechanic specific energy (MSE), vibration, torque and drag were also developed by calculating specific KPIs from the raw measurement data. The analysis work itself coupled with the attempt to improve the workflow processes served as a meaningful and highly effective way to identify significant improvement opportunities for the operators, and at the same time educate students and prepare them to be the “drilling engineers of the future” with proficiency in data analytics.

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Course Chapters

  • 1Drilling Optimization, Risk and Uncertainty Reduction, and Future Workforce Education Using Big Data Analysis - Chapter 1
    Media Type: Video


Earn credits by completing this course0.15 CEU credit1.5 PDH credits


Dr. Eric Van OortDr. Eric van Oort became Professor in Petroleum Engineering at the University of Texas at Austin in 2012, after a 20-year industry career with Shell Oil Company. He holds a PhD degree in Chemical Physics from the University of Amsterdam. He has (co-)authored more than 190 technical papers, holds 14 patents, is a former SPE Distinguished Lecturer, a SPE Distinguished Member, and the 2017 winner of the prestigious international SPE Drilling Engineering Award. At UT Austin, he directs drilling-related R&D in two industry consortia (RAPID and CODA) with over 25 industry company sponsors, covering drilling automation & control, sensor design, big data analytics, complex well construction challenges, and well abandonment & decommissioning. Recently, he has become involved in deep closed-loop geothermal drilling through UT’s new GEO initiative, for which he is the technical lead. In addition, he is the co-founder of 3 start-up companies, including SPYDR dedicated to drilling automation technologies, and is the CEO of his own consulting company.