3 research outputs found
Data Driven Model Discovery - Petroleum application
The SINDy algorithm is a data driven algorithm that discovers dynamical system
in data that evolves over time. The method can be utilized for every dataset that
evolves over time. In this study we have looked the Lorenz system, covid-19 data and
production data from two different oil fields on the Norwegian shelf.
The aim of the study was to investigate if SINDy can be used on the well data to extract
sparse and suitable well models. The complexity of the models are decided by the user
when using prior knowledge to choose the candidate function. If you have limited
knowledge about the system a handful of different models are tested and parameters
are optimized to fit the data.
Noisy and spiky data are an issue for the SINDy method due to its use of the differentiated data. Therefor filtering is needed on production data to minimize the large spikes
and smooth out the data.
The SINDy algorithm gives good results to the production data using polynomials to
describe the data. The results are good for data from Draugen and Statfjord Øst. And
the results from the covid-19 data are promising
Depalletering av produkter ved hjelp av robot
Denne bacheloroppgaven handler om depalletering av produkter ved hjelp av bildebehandling og robot. Det blir brukt OpenCV og RobotStudio til å løse oppgaven.This bachelor thesis is about depalletizing products using image processing and robotics. OpenCV and RobotStudio are used to solve the task
Depalletering av produkter ved hjelp av robot
Denne bacheloroppgaven handler om depalletering av produkter ved hjelp av bildebehandling og robot. Det blir brukt OpenCV og RobotStudio til å løse oppgaven