Blar i NTNU Open på forfatter "Hotvedt, Mathilde"
-
Bayesian neural networks for virtual flow metering: An empirical study
Grimstad, Bjarne Andre; Hotvedt, Mathilde; Sandnes, Anders Thoresen; Kolbjørnsen, Odd; Imsland, Lars Struen (Peer reviewed; Journal article, 2021)Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells. Encouraging results and advantageous properties of ML models, such as ... -
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study
Hotvedt, Mathilde; Grimstad, Bjarne Andre; Imsland, Lars Struen (Peer reviewed; Journal article, 2021)Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based on first-principles are most common, however, ... -
Developing a hybrid, gray-box model of a production wellbore
Fredriksen, Morten (Master thesis, 2021)I produksjonssystemer for hydrokarboner er det en betydelig fordel å kunne måle multifaseflyten ut av brønnbanen. Tradisjonelt sett har man brukt brønntesting for å finne denne masseflyten, og i senere tid også fastmonterte ... -
Dynamic Real-Time Optimisation of a CO2 Capture Facility
Hotvedt, Mathilde; Hauger, Svein Olav; Gjertsen, Fredrik; Imsland, Lars Struen (Journal article; Peer reviewed, 2019)This work investigates economic optimisation of an energy-intensive amine regeneration process in a post-combustion CO2 capture plant, subject to a minimum CO2 capture ratio over 24 hours. A Dynamic Real-Time Optimisation ... -
Dynamic Real-Time Optimisation of an Amine-Based Post-Combustion CO2 Capture Facility using Single-Level Nonlinear Model Predictive Control
Hotvedt, Mathilde (Master thesis, 2018)A complete model of a CO2 capture facility has been optimised with the aid of Dynamic Real-Time Optimisation (DRTO) utilising single-level, Nonlinear Model Predictive Control to merge regulatory and economic objectives. ... -
Exploring the potential for improved performance of flow metering using hybrid modeling
Jacobsen, Petter Hjørungnes (Master thesis, 2022)Denne masteroppgaven utforsker anvendelsen av hybrid flytmodellering i olje- og gass-flytmåling. Ettersom beregningskraft og teknologisk forståelse har økt de siste årene, er virtuelle flytmålere (VFM) et voksende felt ... -
Identifiability and physical interpretability of hybrid, gray-box models - a case study
Hotvedt, Mathilde; Grimstad, Bjarne Andre; Imsland, Lars Struen (Journal article; Peer reviewed, 2021)Model identifiability concerns the uniqueness of uncertain model parameters to be estimated from available process data and is often thought of as a prerequisite for the physical interpretability of a model. Nevertheless, ... -
On a hybrid approach to model learning applied to virtual flow metering
Hotvedt, Mathilde (Doctoral theses at NTNU;2022:165, Doctoral thesis, 2022)Process modeling using first-principle equations has existed for centuries as a methodology to represent and analyze real-world processes. In time with increasing computing power and sensor data availability, data-driven ... -
On gray-box modeling for virtual flow metering
Hotvedt, Mathilde; Grimstad, Bjarne Andre; Ljungquist, Dag; Imsland, Lars Struen (Journal article; Peer reviewed, 2021)A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a petroleum asset. Gray-box modeling is an ... -
Passive learning to address nonstationarity in virtual flow metering applications
Hotvedt, Mathilde; Grimstad, Bjarne Andre; Imsland, Lars Struen (Peer reviewed; Journal article, 2022)Steady-state process models are common in virtual flow meter applications due to low computational complexity, and low model development and maintenance cost. Nevertheless, the prediction performance of steady-state models ... -
Real-Time Data-Driven and Hybrid Modeling of Two-Phase Flow in Oil and Gas Wells - A study on how data quality affects model accuracy and the application of neural networks for flow estimation
Sjulstad, Christine Foss; Almås, Ingeborg Victoria Aarsvold (Master thesis, 2020)Denne avhandlingen forsøker å estimere to-fase-flyt for olje- og gassbrønner i sanntid ved bruk av den nyeste teknologien innenfor datadrevne og hybride modeller. Siden fokuset er på enkeltbrønner, modellerer vi brønnspesifikke ... -
When is gray-box modeling advantageous for virtual flow metering?
Hotvedt, Mathilde; Grimstad, Bjarne André; Ljungquist, Dag; Imsland, Lars Struen (Peer reviewed; Journal article, 2022)Integration of physics and machine learning in virtual flow metering applications is known as gray-box modeling. The combination is believed to enhance multiphase flow rate predictions. However, the superiority of gray-box ...