33 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
6 papers
Multi-task neural networks by learned contextual inputs
Anders T. Sandnes, Bjarne Grimstad, Odd Kolbjørnsen
This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing traina…
On gray-box modeling for virtual flow metering
Mathilde Hotvedt, Bjarne Grimstad, Dag Ljungquist +1
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 pet…
Multi-task learning for virtual flow metering
Anders T. Sandnes, Bjarne Grimstad, Odd Kolbjørnsen
Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multiphase flow rates in petroleum assets. Inferences about flow rates are fundamental to…
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study
Bjarne Grimstad, Mathilde Hotvedt, Anders T. Sandnes +2
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…
Identifiability and physical interpretability of hybrid, gray-box models -- a case study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
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 i…
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based o…