150 citations · 242 across the 15 of their papers we have counts for
17 papers
Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance
Kristoffer Hjelmen Nesland, Bjarne Grimstad, Antorweep Chakravorty +1
In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, wh…
On a closed-loop identification challenge in feedback optimization
Kristian Lindbäck Løvland, Lars Struen Imsland, Bjarne Grimstad
Feedback optimization has emerged as an effective strategy for steady-state optimization of dynamical systems. By exploiting models of the steady-state input-output sensitivity, me…
An updated look on the convergence and consistency of data-driven dynamical models
Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Deep sequence models are receiving significant interest in current machine learning research. By representing probability distributions that are fit to data using maximum likelihoo…
A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes
Bjarne Grimstad, Kristian Løvland, Lars S. Imsland +1
In many industrial processes, an apparent lack of data limits the development of data-driven soft sensors. There are, however, often opportunities to learn stronger models by being…
Flow Fusion, Exploiting Measurement Redundancy for Smarter Allocation
Christine Foss Sjulstad, Danielle Monteiro, Bjarne Grimstad
In petroleum production systems, continuous multiphase flow rates are essential for efficient operation. They provide situational awareness, enable production optimization, improve…
Multi-task and few-shot learning in virtual flow metering
Kristian Løvland, Bjarne Grimstad, Lars S. Imsland
Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge i…
Sequential Monte Carlo applied to virtual flow meter calibration
Anders T. Sandnes, Bjarne Grimstad, Odd Kolbjørnsen
Soft-sensors are gaining popularity due to their ability to provide estimates of key process variables with little intervention required on the asset and at a low cost. In oil and…
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…
Adjustment formulas for learning causal steady-state models from closed-loop operational data
Kristian Løvland, Bjarne Grimstad, Lars Struen Imsland
Steady-state models which have been learned from historical operational data may be unfit for model-based optimization unless correlations in the training data which are introduced…
Passive learning to address nonstationarity in virtual flow metering applications
Mathilde Hotvedt, Bjarne Grimstad, Lars Imsland
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 pre…
When is gray-box modeling advantageous for virtual flow metering?
M. Hotvedt, B. Grimstad, D. Ljungquist +1
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 predict…
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…
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
Bjarne Grimstad, Henrik Andersson
We consider the embedding of piecewise-linear deep neural networks (ReLU networks) as surrogate models in mixed-integer linear programming (MILP) problems. A MILP formulation of Re…