The text below is a brief description of the project topics supervised by professor Lars Imsland. Contact lars.imsland@itk.ntnu.no for further information. Most topics can be continued in a master thesis project if the candidate and supervisor(s) so wishes. Some of the descriptions are rather advanced/involved for a single project, and should be considered descriptions for a combined project/master project.

Related to a cooperation project we have with two top Brazilian universities (in Florianapolis and Rio de Janeiro), we seek 1-2 adventurous students to do a combined project/master. The specialization project (fall) will be in Norway, while the master thesis work (spring) will be done in Brazil. The topic will be related to data-driven production optimization in offshore oil and gas production. Some financial support for the stay in Brazil is available.

Drillbotics (se drillbotics.com) is an international student competition in autonomous drilling. A student team (3-6 people) needs to design a robotic drilling rig that would drill through a given test rock with a given accuracy and at the least time. The rock may contain unknown layers and can cause significant operational challenges. NTNU has participated in the competition 3 times, resulting in the 2nd place in 2017 and the 1st place in 2018. Similar to previous years, we are collecting a team of cybernetics and petroleum (drilling) students to compete in 2019/2020 competition (against USA, Canada, Germany, Norway and other countries). This is a very practical project, with lots of challenges on all aspects including control, measurements, modelling, etc. According to the previous teams, this was the most interesting part of their education at NTNU. The winners will be invited to present at an international drilling conference organized by Society of Petroleum Engineers.

Information about of the flowrates of water, oil, and gas in the networks of wells and flowlines that produce into the processing facilities at the oil platforms is key to properly manage, monitor and optimize the production. Some of the decisions related to operation of the production systems require absolute values of the water, oil and gas with associated uncertainties. There are different types of existing measurements in the networks as well as in the processing facilities that can be utilized to estimate these flowrates with associated uncertainties without the need for dedicated physical meters. One way to this is to properly combine mechanistic models with optimization algorithms. The task of this project work is to analyze the properties of the associated optimization problem.

Co-supervisor: Jørgen Spjøtvold, ABB

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Offshore wind on the Norwegian Continental Shelf is foreseen a massive capacity expansion in the coming years. This capacity expansion may provide value opportunities to supply offshore petroleum installations with renewable electricity generation and thereby reduce CO2 emissions associated with Norwegian petroleum activity. Yet, integration of offshore wind is likely to be performed in combination with existing or newbuilt gas-turbines, possibly together with steam-bottoming cycle (GTCC) (exhaust-gas heat recovery), which have been shown to be a promising technology also for offshore installations to reduce platform CO2 emission rates. Integration of intermittent renewables and offshore GT(CC)s requires good control strategies to enable high energy efficiency, good complementarity and stable electric-power supply. To this end, model predictive control (MPC) may be a preferred control solution. This project will seek to develop adequate reduced-order models of offshore GTs, including GTCC together with predictive models for electricity supply from offshore wind mills. Furthermore, these models will be to study the potential for MPC of the electricity supply on an offshore platform.

The successful outcome of the project may be a prospect for improved integration of renewables and GTs on platforms at the NCS, contributing to reduced CO2 emissions from offshore operations.

Co-supervisor: Brage Rustad Knudsen, SINTEF.

This project focuses on safety of drilling operations in oil and gas industry. In particular, its objective is to find/develop/verify an automatic/automated method that would enable possibility of gas kick /hydrocarbons inflow into the wellbore as well as other drilling contingency situations (contingency - is a situation when the controlled process deviates from safety/desired limits). Predicting the possibility of the inflow and of other contingency situations can have essential economical benefits for drilling operations. In this project the student will implement in MATLAB a dynamic simulator for testing the method, implement the method in MATLAB (based on the initial suggestions from the supervisor), test it, improve, if needed, and provide suggestions for further development. This project is a part of BRU21 – NTNU Research and Innovation Program in Digital and Automation solutions for Oil and Gas industry: www.ntnu.edu/bru21 .

Co-supervisor: Professor Alexey Pavlov, IGP

Introduction/theory: The student will participate in a part of BRU21 PhD project dedicated to vugs/karst zones detection through automatic multivariate analyses of drilling data. Automated data analysis is the initial step to retrieve important information which may not be noticed by drilling operation support engineer. Automatic approach can provide a systematic process of data analytic, with specific focusing on drilling application. The goal of gathering necessary information in automatic manner is suggesting solutions and supporting decision making for further drilling operations. To analyze drilling data, tools need to be developed that can provide robust, reliable and capable solutions that can be automated. The project is devoted to the development of a solution that allows one to automatically analyze high resolution drilling data in order to continuously monitor changes in drilling conditions to minimize drilling related risks like stuck pipe, gas kick, high stick-slips occurrence etc. Another possible focus of the project can be developing of an algorithm for automatic tracking of formation property changes. A distinctive feature of this project is a working with high-density (Big) data, which is typically not available for real time analyses. This can be an additional challenge in terms of the effectiveness of the proposed approach and computing resources. From the other hand this may be an additional benefit in terms of the reliability of the proposed approach. Problem area: Manual analysis of real-time drilling data is slow and prone to human errors. High quality real time drilling data available nowadays for the analyses and risks detection on early stages. Manual analyses of this data is not efficient as some deviation from expected trends may not be noticed and recognized in advance by engineer when less action is needed and drilling situation is still controllable.

Goal: Design, implement and evaluate a semi-automatic analysis of drilling data for early detection of potential risks or formation properties changes tracking.

Thesis work:

• Literature review of multivariate analytics tools;
• Multivariate analytics tools applicability to manage big data, measure current trends and estimate where there might be a risk for further drilling operations;
• Focus on algorithms that can analyze data quickly and efficiently enough for real-time drilling application;
• Work with different sources of data – wired pipe data, surface sensors data, logging while drilling and wireline data, drilling mechanics data;
• Verification of suggested approach based on field data;
• Adaptation of new methodology to real well-drilling conditions for further practical application;

References Wong, R., Liu, Q., Ringer, M., Dunlop, J., Luppens, C., Yu, H., & Chapman, C. D. (2013, March 5). Advances in Real-Time Event Detection While Drilling. Society of Petroleum Engineers. doi:10.2118/163515-MS Zhao, J., Shen, Y., Chen, W., Zhang, Z., & Johnston, S. (2017, October 4). Machine Learning–Based Trigger Detection of Drilling Events Based on Drilling Data. Society of Petroleum Engineers. doi:10.2118/187512-MS Ritchie, G. M., Hutin, R., Aldred, W. D., & Luppens, J. (2008, January 1). Development and Testing of a Rig-Based Quick Event Detection System to Mitigate Drilling Risks. Society of Petroleum Engineers. doi:10.2118/111757-MS

This project is a part of BRU21 – NTNU Research and Innovation Program in Digital and Automation solutions for Oil and Gas industry: www.ntnu.edu/bru21

Co-supervisor: Professor Alexey Pavlov, IGP

Current trend for offshore oil and gas operations is to reduce offshore manning either by automation or by moving the operators onshore. The consequence of this is that the operators can lose part of their feeling of the process that they get through various perception channels (e.g. tactile, vibration, sound, etc) apart from visual, which can be easily implemented onshore with computer screens. These additional perceptions of the controlled process reconstructed artificially from sensors are called haptic feedback. It can significantly increase the quality of control by the operator and in some cases can have a major impact on reduction of safety incidents. In this project the student will focus on the possibilities of implementation of haptic feedback for drilling operations. The student will analyze the drilling operations, identify potential candidates for haptic feedback, and implement them in a lab with a dynamic simulator and simple haptic feedback hardware.

Co-supervisor: Professor Alexey Pavlov, IGP

Flow rate measurements are not always available in oil and gas operations (drilling, production). At the same time it is important to have accurate measurements not only of the flow rate, but also of the composition of the fluid (e.g. different phases, like water, oil, gas, solids). In this project the student will analyze the possibility to identify some of the flow parameters through advanced acoustic measurement system that can be clamped on the pipe. The sensor provides a spectrum of the measured signals which, can be utilized for analysis of the flow parameters. The student will develop and build a laboratory setup for testing this measurement method. The essential part of the project is automated analysis and processing of (Big) data streaming from the sensor, that would allow one to conclude on the flow properties.

This project is a part of BRU21 – NTNU Research and Innovation Program in Digital and Automation solutions for Oil and Gas industry: www.ntnu.edu/bru21

Co-supervisor: Professor Alexey Pavlov, IGP

In this project the student will investigate the possibility of determining flow properties of a fluid (e.g. flow rate, viscosity, density, water percentage, gas/liquid ratio, etc) by intelligent control/excitation of the flow through the pipe and/or other equipment. The work consists of implementing a simulator for flow properties (in MATLAB), analyzing observability/identifiability of desired flow properties through available measurements (pressure, temperature, etc) and developing smart control/identification techniques to identify the desired properties from the measurements.

Co-supervisor: Professor Alexey Pavlov, IGP

Resource allocation problem consists of distributing a given amount of work between a number of processing units in such a way that the overall efficiency of the processing is maximal. This problem arises in petroleum industry, in distributed computations, in production factories, etc. The difficulty appears when the efficiency/characteristics of each individual processing unit is not known, or uncertain due to varying external conditions. In this case model-based optimization methods are not applicable and other approaches are needed. In this project the student will focus on data-driven optimization methods (a variation of extremum seeking control), implement them in MATLAB for a given process, test and improve them according to practical needs.

This project is a part of BRU21 – NTNU Research and Innovation Program in Digital and Automation solutions for Oil and Gas industry: www.ntnu.edu/bru21

Co-supervisor: Professor Alexey Pavlov, IGP

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Co-supervisor: Leif Erik Andersson.

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Co-supervisor: Leif Erik Andersson/Konstanze Kölle.

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Co-supervisor: Leif Erik Andersson.

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Co-supervisor: Leif Erik Andersson.

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If you are interested in statistical methods, machine learning and optimization, you can do your project on Stochastic MPC. Two tasks are here: Cautious optimization-based control with machine learning and MPC using Multi-stage stochastic programming.