Exebenus
Mats Hermansen has extensive work experience in the technology industry, particularly in sales and business development roles. Mats has worked as a Senior Vice President of Sales at Exebenus, a drilling and well software company, where they focused on automating decision making in well operations through real-time machine learning applications. Prior to that, they served as a Sales Director at Sekal AS, a company specializing in real-time forecasting and trend analysis software for drilling personnel.
Hermansen has also held various sales and marketing positions at companies such as BI Builders and Sense Intellifield. Additionally, they have experience at Kongsberg Oil & Gas Technologies, where they played a pivotal role in starting and expanding the company's business in Asia and Oceania.
Earlier in their career, Hermansen worked as a Sales and Services Manager at InnerLogix and as a Software Training Consultant and Support Analyst at Landmark Graphics and Landmark. Mats was responsible for delivering training and support for various software applications in the E&P industry.
Hermansen began their career at PGS Datamanagement, where they worked as a Software Test Analyst, focusing on quality checking of front-end tools for the PetroBank software.
Throughout their career, Hermansen has demonstrated a strong background in technology sales, business development, and client support.
Mats Hermansen began their education at St. Svithun vgs from 1992 to 1996. Mats then attended Nord University from 1998 to 2000, where they earned a Bachelor of Science (BSc) degree in Information Technology. Following that, they enrolled at The Norwegian School of Information Technology, obtaining a Bachelor's degree in ICT from 2000 to 2001. Additional information is not available regarding their education beyond these experiences.
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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