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Found 11 papers, 1 papers with code
Flow Fusion, Exploiting Measurement Redundancy for Smarter Allocation
In this paper, we highlight that a combination of statistical tests and supporting logic for gross error detection and elimination can be beneficial in obtaining a more justifiable production allocation.
Multi-unit soft sensing permits few-shot learning
Using a large industrial dataset, we demonstrate that, when the soft sensor is learned from a sufficient number of tasks, it permits few-shot learning on data from new units.
Sequential Monte Carlo applied to virtual flow meter calibration
This paper presents a calibration method based on the measurement provided by the production separator, and the assumption that the observed flow should be equal to the sum of flow rates from each individual well.
Multi-task neural networks by learned contextual inputs
It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters.
Adjustment formulas for learning causal steady-state models from closed-loop operational data
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 by control are accounted for.
Passive learning to address nonstationarity in virtual flow metering applications
Nevertheless, the prediction performance of steady-state models typically degrades with time due to the inherent nonstationarity of the underlying process being modeled.
On gray-box modeling for virtual flow metering
Nevertheless, the results are promising and indicate that gray-box VFMs may reduce the prediction error of a mechanistic VFM while remaining scientifically consistent.
Multi-task learning for virtual flow metering
Virtual flow metering (VFM) is a cost-effective and non-intrusive technology for inferring multiphase flow rates in petroleum assets.
Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study
Recent works have presented promising results from the application of machine learning (ML) to the modeling of flow rates in oil and gas wells.
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization.
ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs
To this end, we devise and study several bound tightening procedures that consider both input and output bounds.
