Drilling and well

Drilling and well

The drilling process is characterized by fragmented operations with many parties involved and many interfaces and high uncertainty especially with respect to the underground conditions. BRU21 aims to develop systems and methods which result in reduced well construction time and improved safety. The long term goal is to optimize the drilling process by achieving a high degree of autonomy. To achieve this, we focus on digitalization of the drilling program, data-driven decision support, optimal use of measurements for decision support and optimization and automation technologies in general...read more

Program area team

Current projects

Current projects

Title

Digitalization/automation of life cycle well integrity 

Staff photo

PhD Candidate Andreas Teigland

Main Supervisor Sigbjørn Sangesland

Sponsor: NTNU

The main objective of this project is to build a model for predictive real-time casing wear estimation. Automating the procedure of recalibrating the simulations with realtime data continuously during operation is stage one of the project. Secondly, an algorithm for wear mitigation though control of operational parameters will be developed. Expected output of the project is a prototype software for estimation and mitigation of casing wear.

Project result: Real-time estimation of casing wear
New model that continuously estimates casing wear based on real-time drilling measurements

current projects

Safe drilling in karstified carbonates 

Staff photo

PhD Candidate Danil Maksimov

Main Supervisor Alexey Pavlov

Sponsor: Lundin Energy Norway AS

This project focuses on developing methods for detection and mitigation of risks while drilling in karstified carbonates. Drilling into a karst-like object can lead to losses of drilling fluid into the formation, including the most severe case of lost circulation. Detecting regions with high risks of karst-like objects or even predicting such objects in proximity of the drilling bit has a significant value for drilling safety. The project seeks such detection methods either based on available drilling data or based on novel measurement tools.

Project result: Drilling in karstified carbonates – early risk detection technique  
Detection of karstification objects from patterns in real-time drilling data

BRU21 Conference 2022

Title

Drilling data analytics 

Staff photo

PhD Candidate Magnus Nystad

Main Supervisor Alexey Pavlov

Sponsor: NTNU

The main objective of this project is the development and testing of data-based methods that automatically navigate the input variables of the drilling process (such as WOB, RPM, flow rate) to their optimal values in real time while adhering to constraints. The methodology is based on model-free automatic optimization methods that gather information about the current drilling situation through small variations in the input parameters and take optimization actions in accordance with the system response. The expected outcome of the project is an algorithm/prototype software to optimize drilling variables while adhering to operational constraints.

Project result: Real-time drilling optimization through continuous micro-testing
Automatic detection of formation properties and real-time optimization of the drilling process  

title

Real-time fault and symptoms detection in drilling operations with wired pipe

PhD Candidate Mostafa Gomar

Main Supervisor Behzad Elahifar

Sponsor: NTNU

The objective of this PhD project is to develop systems and methods to reduce drilling and well construction time. This includes improvements in safety. The focus is on the utilization of wired drill pipe, which provide means for high-speed communication of real-time downhole measurements. These measurements provide the basis for (big) data analytics tools for real time decision support. The solution to deal with big data during drilling includes both data science techniques and physics-based models for the prominent non-productive activities during drilling.

 

 

title

Automatic real-time surveillance of drillstring vibrations 
 

PhD Candidate Ivan Pirir

Main Supervisor Sigve Hovda

Sponsor: NTNU

The project deals with development, simulation and optimization of dynamic models that can accurately predict the motion of a drill string during a drilling operation. The main focus is on models that are fast enough to be used with real-time data. Extending such models to include the hydraulics of drilling fluids also allows for the estimation of the bottom hole pressure changes. The expected outcome of the project includes partially validated models that can be used to estimate axial and torsional drill string vibrations and are also suitable for real-time implementations.

1

Intelligent data analytics for offshore well integrity and life cycle management

 

PhD Candidate to be hired

Main Supervisor Sigbjørn sangesland

Sponsor: Petrobras

The objective of this project is to develop a system/method capable of monitoring and ensuring the functionality of the well safety barrier during all stages of its life cycle. The focus is on combined utilization of available models and (Big) data logged during prior drilling and well operations. The expected project outcome is an algorithm/prototype software for well condition monitoring and development of corresponding prototypes for the demonstration of the method.