Development and Validation of a Scenario-Based Drilling Simulator for Training and Evaluating Human Factors. Chan, H., Lee, M. M., Saini, G. S., Pryor, M., & van Oort, E. IEEE Transactions on Human-Machine Systems, February, 2020. Conference Name: IEEE Transactions on Human-Machine Systems
doi  abstract   bibtex   
Drilling and completing an oil/gas well is a time-sensitive and high-value operation, in which environment/system parameters change in unseen, unpredictable environments. Safety issues arise at every stage. Drilling principles can be taught using traditional methods, but safety and event response are difficult to teach in such formats. Here, in this article, we integrate a hardware-in-the-loop simulator, downhole physics, and auxiliary touchscreen interfaces (similar to a rig's add-on equipment) to develop a realistic, real-time drilling simulator for well control operation training. Realistic operational data are supplied to the simulator representative of downhole operations, including unplanned well events. The well plan accounts for drilling parameter changes, the pore-pressure fracture-gradient drilling window, mud weights, etc., which occur in response to the unplanned events. The developed simulator is used for hands-on training, human factor studies, model verification, and evaluating new auxiliary equipment and/or operational procedures. A critical research objective was evaluating the accuracy/realism of the developed system. To do so, eight petroleum engineering students and 11 certified drillers were trained and asked to complete a comprehensive (\textgreater6 h) drilling operation. System accuracy was measured by comparing how new versus experienced operators learned to operate the simulator, execute mission-critical tasks, and respond to unplanned events. The results validate the realism of the developed simulator and scenarios, since personnel with prior drilling experience took significantly less time to master the system.
@article{chan_development_2020,
	title = {Development and {Validation} of a {Scenario}-{Based} {Drilling} {Simulator} for {Training} and {Evaluating} {Human} {Factors}},
	issn = {2168-2305},
	doi = {10.1109/THMS.2020.2969014},
	abstract = {Drilling and completing an oil/gas well is a time-sensitive and high-value operation, in which environment/system parameters change in unseen, unpredictable environments. Safety issues arise at every stage. Drilling principles can be taught using traditional methods, but safety and event response are difficult to teach in such formats. Here, in this article, we integrate a hardware-in-the-loop simulator, downhole physics, and auxiliary touchscreen interfaces (similar to a rig's add-on equipment) to develop a realistic, real-time drilling simulator for well control operation training. Realistic operational data are supplied to the simulator representative of downhole operations, including unplanned well events. The well plan accounts for drilling parameter changes, the pore-pressure fracture-gradient drilling window, mud weights, etc., which occur in response to the unplanned events. The developed simulator is used for hands-on training, human factor studies, model verification, and evaluating new auxiliary equipment and/or operational procedures. A critical research objective was evaluating the accuracy/realism of the developed system. To do so, eight petroleum engineering students and 11 certified drillers were trained and asked to complete a comprehensive ({\textgreater}6 h) drilling operation. System accuracy was measured by comparing how new versus experienced operators learned to operate the simulator, execute mission-critical tasks, and respond to unplanned events. The results validate the realism of the developed simulator and scenarios, since personnel with prior drilling experience took significantly less time to master the system.},
	journal = {IEEE Transactions on Human-Machine Systems},
	author = {Chan, Hong-Chih and Lee, Melissa M. and Saini, Gurtej Singh and Pryor, Mitch and van Oort, Eric},
	month = feb,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Human-Machine Systems},
	keywords = {Drilling, Human factors, Industries, Personnel, Physics, Safety, Training, education, simulation-based learning (SBL), simulator, training},
	pages = {1--10},
}

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