Publikasjoner
- Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models
- An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations
- Drilling Parameter Optimization in Real-Time
- A Hybrid Approach to Detect Bad Hole Cleaning
- Drilling Parameter Optimization in Real-Time
- Dynamic Reservoir Behaviour and Production Due to Periodic Supply of Wind Power
- Well Control Incident in the North Sea as Interpreted with Advanced Gas Influx Modelling
- An Experimental Study of Gas Influx in Oil-Based Drilling Fluids for Improved Modelling of High-Pressure, High-Temperature Wells
- Study targets potential for gas loading in drilling fluids to improve influx detection, management
Annen formidling
- Real-time Automated Pore Pressure And Wellbore Stability Updates in A Digital Twin, Using Smart Agents And Log Predictions Ahead of Bit, Example From Norwegian Continental Shelf"
- An Intrusive Hybrid-Analytics and Modelling with Deep-Learning for Efficient and Accurate Predictions of Hole-Cleaning Process during Wellbore Drilling Simulations
- Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models
- A Hybrid Approach to Detect Bad Hole Cleaning
- PressureAhead
- Pilot field trial of real-time pore pressure and well stability updates in a digital twin
- LowEmission - Integrated topside - downhole modelling (webinar)
- Pilot test for automated mud management leverages real-time monitoring, hydraulic modeling
- New methodology for case studies of well control incidents
- Drilling Mud Process Control – A Step Change in Automatic Mud Management