| dc.contributor.author | Gustavsen, Stig Harald | |
| dc.date.accessioned | 2019-09-30T10:31:20Z | |
| dc.date.available | 2019-09-30T10:31:20Z | |
| dc.date.issued | 2019 | |
| dc.identifier.uri | http://hdl.handle.net/11250/2619328 | |
| dc.description.abstract | The Alvheim field suffers from significant production deferrals of oil and gas, during
calibration of multiphase flow meters used in ownership allocation. This thesis has
developed an algorithm solving a new method, which effectively negates these deferrals.
This is done through an object-oriented data science approach, in creating a framework
for performing these calibrations in an elegant and efficient manner. The algorithm has
been tested and compared to real world data and shows promising results. The tests
during April 2019 showed an increase of 15000bbl of oil production during parallel
calibration compared to a normal calibration. The Cognite Data Fusion repository
helped in streamlining the development process with easy and swift access to process
data. The algorithm was implemented and developed in the programming language
Python. Additionally, this thesis covers the purpose and technical background of
ownership allocation measurements and the systems and sensors involved in
measurement and calibration. The details of the developed algorithm, and the calibration
results are presented and discussed. | nb_NO |
| dc.language.iso | eng | nb_NO |
| dc.publisher | University of South-Eastern Norway | nb_NO |
| dc.subject | parallel calibrations, cognite data fusions, multiphase flow meters, fiscal oil and gas metering and allocation, scientific computing, computational engineering, python, data fusion, digital twin, object oriented data science, condition based maintenance | nb_NO |
| dc.title | Parallel calibration of multiphase flow meters (MPFM) based on measurements of phase streams in separators | nb_NO |
| dc.type | Master thesis | nb_NO |
| dc.rights.holder | Copyright of the Author | nb_NO |
