Exebenus
Stine Tuxen began their work experience in 2003 with a summer internship at Accenture. In 2004, they joined Bekk Consulting as a Consultant and remained in that role until 2013. From 2013 to 2017, they worked as a Consultant at Webstep. In 2017, they moved to Exebenus, where they held multiple roles including Test & QA Manager, Product Development Manager, and currently serves as the Vice President of Research and Development since December 2019.
Stine Tuxen's education history began in 1998 when they attended the University of Oslo (UiO). However, it is not specified whether they completed a degree or a field of study during their time there. In 1999, Stine Tuxen moved on to the University of Agder (UiA) where they obtained a Bachelor's degree in Computer Engineering, which they completed in 2002. From 2001 to 2002, Stine Tuxen attended the Waterford Institute of Technology, but no information is given about any degree or field of study. Lastly, from 2002 to 2004, Stine Tuxen studied Datateknikk (Computer Science) at the Norwegian University of Science and Technology (NTNU) and earned a degree in Sivilingeniør (Master of Science in Engineering).
This person is not in any teams
Exebenus
Exebenus is a US-Norwegian company with 40+ drilling engineers and data scientists, focused on developing real-time machine learning applications (agents) to address critical drilling NPT and ILT issues. Targeted machine learning algorithms each work on specific problems, such as mechanical sticking, differential sticking, hole cleaning, washouts, mud losses, bit degradation, motor/tool failure, ROP optimization and others. The offered solution is an automated plug-and-play web application that takes about 20 min to set it up on your next well. It is WITSML vendor-agnostic, with no need for the models to be pre-trained on offset data and no back-office engineering support required. Machine Learning we apply is based on proprietary, new generation and physics informed algorithms that consistently deliver over 90% precision metric on any wells: onshore or offshore, conventional or unconventional, horizontal or vertical. The Exebenus Current ML machine learning agents enable real-time predictive situational awareness during oilfield operations, providing drilling engineers with pragmatic, manageable and easy-to-use instruments to significantly reduce downtime and increase drilling performance. We are transforming Machine Learning from "rocket science" to an everyday "wrench" tool. It is easy, reliable, scalable, "false alarms" proof and affordable. "Why would you ever drill a well without an application that can predict getting a stuck pipe 1-4 hours in advance ? It's like driving a car without the ABS or any other safety system." - senior drilling engineer, US Permian