Digitalization of hole cleaning process (study of cuttings transport) 

PhD Candidate Sartika Dwi Purwandari

Main Supervisor Sigve Hovda

Sponsor: NTNU

In Sartika’s research, she is going to predict the hole cleaning indicator such as cuttings bed height cuttings concentration and pressure loss using 2 approaches, physics-based model and machine learning model. In physics model, she build an empirical model based on SINTEF flow loop experiment results. She is investigating the effect of flow rate, inclination and eccentricity for two different fluids, water-based mud and oil-based mud using bed load transport model. Later on, she develops a mathematical model to include the effect of pipe rotation. Some of assumption are made such as fluid is incompressible, viscosity and density are constant during the test and the model is limited for steady state condition. The physics model will be compared with machine learning model based on several experiment result from other researcher. Since this research is based on the laboratory experiment, she has to find the correlation to scale it up for the industry and the model should be changed from steady state to real time model. For the future work, it might be helping to simulate the cuttings concentration and heights in wellbore and early detection for cuttings accumulation in wellbore which result to avoid and prevent the operational wellbore during drilling due to cuttings accumulation. 

Applications of bismuth alloys in well completion and well plugging 

PhD Candidate Lewaa Hmadeh

Main Supervisor Behzad Elahifar

Sponsor: NTNU

Lewaa’s research topic “Applications of bismuth alloys in well completion and well plugging” aims to evaluate and test bismuth-tin alloys as a potential alternative to cement which is currently the mostly used sealing material. Cement has shown weak sealing integrity over the long run and the urge of finding an alternative has become more important. Lewaa’s research will mainly focus on the behavior of bismuth-tin alloys as a sealing plug in representative wellbore conditions. The research study will propose and evaluate applications of bismuth alloys in well completion and P&A. The project’s goal is to qualify short bismuth alloys as safe, cost effective and environmentally friendly sealing materials. 

Well integrity and lifecycle management data analytics approach

PhD Candidate David Semwogerere

Main Supervisor Sigbjørn Sangesland

Sponsor: Petrobras

The project objective is to develop a system of systems for well integrity and lifecycle management on the Norwegian and Brazilian continental shelfs. The system will be used for well integrity monitoring and control, failure detection and prediction of critical faults as well as degradation mechanisms in the well lifecycle. In addition, by understanding degradation mechanisms, we can extend the life of existing wells, reducing costs of drilling new wells, while maintaining well integrity. We aim to achieve this by applying data analytics and available artificial intelligence models on big data collected during the entire lifecycle of the well from concept design, construction, operation up to plug and abandonment. This data ranging from well logs, sensor data and post well operations data.

Investigating the properties of bismuth alloys in well completion and well plugging

PhD Candidate Andriani Manataki

Main Supervisor Sigbjørn Sangesland

This  PhD research work focuses on the investigation of alternative - to cement - materials for P&A operations. Bismuth alloys seem to be promising materials for this area and therefore research is needed to support this claim. Investigation of the properties of the alloy, the effect of temperature and pressure on the alloy's microstructure and the interaction between the BiSn alloy, the casing steel and the surroundings/formations, are some of the topics of this Ph.D. work. 

Digitalization/automation of life cycle well integrity 

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

Safe drilling in karstified carbonates 

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

Drilling data analytics 

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  

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.

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.

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.