Wintershall Dea and Exebenus Joint Live LinkedIn Webinar
Invited experts from Wintershall Dea and Exebenus will discuss “Enhancing Stuck Pipe Risk Detection in Exploration Wells Using AI/ML Based Tools: A Gulf of Mexico Case Study”
Stay connected:
Invited experts from Wintershall Dea and Exebenus will discuss “Enhancing Stuck Pipe Risk Detection in Exploration Wells Using AI/ML Based Tools: A Gulf of Mexico Case Study”
Dalila Gomes, Ph.D., Project Manager, will present at SPE Asia Pacific Wells Week Workshop in Kuala Lumpur, Malaysia
At the SPE Norway Subsurface Conference, Exebenus’s own Dr. Serafima Schaefer will present and show several examples of successful implementation of AI/ML products in different fields.
Jan Kare Igland, Exebenus Principal Product Manager, will present at the DEA(e) Q1 2024 on March 8, 2024.
Dive into the future with AI/ML: Discover how Exebenus Spotter is transforming decision-making in O&G with engaging case studies and smart task outsourcing, enabling engineers to focus on what truly matters.
Based on Darcy Partner’s unique data set from the activity of >10,000 users on the DP platform – combined with analysis on investments and market traction – Exebenus was identified as one of the Top Emerging Technology Companies in Drilling from 2023.
Exebenus’ latest release of Spotter R2.5- market-leading real-time stuck pipe prediction and ROP optimisation solution driven by physics-informed machine learning technology – further refines already existed multi-aspect risk detection.
Exebenus AS, a leading provider of AI-driven solutions for the oil and gas industry, and Kongsberg Digital, a leading industrial software company, are thrilled to announce a strategic partnership that will deliver significant cost savings and efficiency to oil and gas operations.
In this free webinar, Jan Kare Igland will explore the ML-driven ROP optimization solution capabilities and field test results from two offshore drilling operations in West Africa and Southeast Asia, where live recommendations by ML application were applied by rig crews to assess real-world effectiveness in improving ROP. Register for the webinar here!
On the conference 2nd day, on the 15th of September, Jan Kåre Igland will present “Case Studies and Results from 2.5 Years of Using Targeted Machine Learning Models to Predict Stuck Pipe Incidents”.