Index of /skoge/mer/subpro

	Name	Last modified	Size	Description
	Parent Directory		-
	Oppslag.docx	2019-05-16 08:00	15K
	README.html	2019-05-15 19:27	4.6K
	SUBPRO-PhD-Production-optimization.docx	2019-05-13 18:37	18K
	SUBPRO-PhD-Production-optimization.pdf	2019-05-13 18:46	131K

-------------------------------------------------------------------------------------------------------------
PhD position at NTNU on "Production optimization" 
Supervisor: Professor Sigurd Skogestad
Application deadline: 17 June 2019
--------------------------------------------------------------------------------------------------------------


NTNU is Norway's main institution for engineering, and in the Department of Chemical Engineering there is a PhD position in the field of "Field-wise production optimization".
The project is part of the SUBPRO center which is a large 8-year research-based innovation program in the field of subsea processing and production.
Industrial partners in SUBPRO include Equinor, Lundin, AkerBP, Neptune Energy, Aker Solutions, DNVGL and Kongsberg Digital.

The overall objectice of the PhD project is to achieve lost-cost production with low carbon footprint towards the final aim of achieving zero emissions oil and gas production. 
Scientifically, the PhD project focuses towards online process optimization using both existing software infrastructure and advanced optimization tools as well as machine learning and data analytics.

Daily production optimization is an important aspect throughout the production phase of any oil and gas field, where the objective is to maximize the operational profits on a day-to-day basis. 
With the recent focus on low carbon footprint of oil and gas operations, the daily production optimization must also aim to reduce the carbon footprint of day-to-day operations in addition to increasing production rates. 
In order to take into account the carbon footprint of the subsea production network, it is important to consider not just the subsea wells, 
but also the subsea processing equipment such as compact separators, subsea compressors and booster pumps in the daily production optimization problem. 
Therefore, this project will take a holistic view of the entire field including the subsea well network and the subsea processing equipment in the production optimization problem. 
Production optimization requires detailed model of the system in order to determine the optimal operation of the field. 
However, as the complexity of the systems increases, the models used may be uncertain or may not be able to capture the real production system accurately. 
In order to address this issue, we also propose to incorporate real time production data into real time decision making, by using machine-learning algorithms. 

This project will build upon the PhD work of Dinesh Krishnamoorthy on "Production optimization under uncertainty" where we developed different algorithms that can use such transient measurements 
efficiently for optimization. One of the deliverables of the new project is to apply these methods to industrial cases. 
This project also aims to develop new machine learning based algorithms for online production optimization, which when combined with the existing first-principle models result in what is called a grey-box model. 
Such a model is more flexible and has potential for further savings.
Furthermore, we want to develop further software tools and methods in order to optimize production from a field while reducing emissions.  

The project is financed by the Norwegian research council and SUBPRO through the SUBPRO program. 
The PhD student will be integrated in the SUBPRO program as well with the Process Systems Engineering Group at NTNU, which has about 30 Faculty, PhD students and Master students.

Requirements:
1.      The successful candidate should have a background in process systems engineering.
2.      Good written and oral English language skills 

PhD candidate salary is normally NOK 449400 before tax per year. From the salary, 2 % is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is 3 years with the possibility of until one year extension with 25% teaching duties. 

Questions about the position can be directed to Sigurd Skogestad, e-mail: sigurd.skogestad@ntnu.no

Further information: