Future Workforce Education Through Big-Data Analysis for Drilling Optimization

Fig. 1—Key components in delivering value from big data.

An operator partnered with the drilling-automation research group at The University of Texas at Austin to develop a work flow for big-data analysis and visualization. The objectives were to maximize the value derived from data, establish an analysis toolkit, and train students on data analytics. The operator provided data sets, business and technical objectives, and guidance for the project, while a multidisciplinary group of undergraduate and graduate students piloted an analysis work flow.


The project stakeholders agreed on three main objectives. The foremost objective was to maximize the value from the tens of gigabytes of data gathered during drilling operations. Several work streams were selected to help identify key drilling-­performance limiters and cost-saving opportunities. These work streams include assessment of the bottomhole-assembly (BHA) and directional-drilling performance by using measures such as wellbore tortuosity and time-based vibration data to create meaningful visualizations and implement standardized data structures.

The second objective was to establish a standardized data-analysis toolkit. The steps toward such a toolkit were to identify, streamline, and document the working process to establish work flows and to build software tools that automate these work flows (e.g., perform analysis or visualization of the data).

The third objective was to help educate undergraduate students and equip them with the skills necessary to tackle problems in a big-data world.

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE/IADC 184739, “Future Workforce Education Through Big-Data Analysis for Drilling Optimization,” by Y. Zhou, SPE, T. Baumgartner, G. Saini, SPE, P. Ashok, SPE, and E. van Oort, SPE, The University of Texas at Austin; M.R. Isbell, SPE, Hess Corporation; and D.K. Trichel, formerly of Hess Corporation, prepared for the 2017 SPE/IADC Drilling Conference and Exhibition, The Hague, The Netherlands, 14–16 March. The paper has not been peer reviewed.
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Future Workforce Education Through Big-Data Analysis for Drilling Optimization

01 February 2018

Volume: 70 | Issue: 2


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