# Multi-task and few-shot learning in virtual flow metering

arXiv: https://arxiv.org/abs/2309.15828 · HTML: https://mlsanity.com/paper/2309.15828/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Measuring the Intrinsic Dimension of Objective Landscapes](https://mlsanity.com/paper/1804.08838/) (2018)
- [Multi-task learning for virtual flow metering](https://mlsanity.com/paper/2103.08713/) (2021)
- [Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study](https://mlsanity.com/paper/2102.01391/) (2021)
- [Semi-supervised Learning for Data-driven Soft-sensing of Biological and Chemical Processes](https://mlsanity.com/paper/2107.13822/) (2021)
- [Passive learning to address nonstationarity in virtual flow metering applications](https://mlsanity.com/paper/2202.03236/) (2022)
- [Modeling Task Relationships in Multi-variate Soft Sensor with Balanced Mixture-of-Experts](https://mlsanity.com/paper/2305.16360/) (2023)
- [A Machine Learning Approach for Virtual Flow Metering and Forecasting](https://mlsanity.com/paper/1802.05698/) (2018)

## Built on by (most cited first, 1 of 1)

- [A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes](https://mlsanity.com/paper/2407.13310/) (2024)

## Abstract (arXiv)

Recent literature has explored various ways to improve soft sensors by utilizing learning algorithms with transferability. A performance gain is generally attained when knowledge is transferred among strongly related soft sensor learning tasks. One setting where it is reasonable to expect strongly related tasks, is when learning soft sensors for separate process units that are of the same type. Applying methods that exploit transferability in this setting leads to what we call multi-unit soft sensing. This paper formulates a probabilistic, hierarchical model for multi-unit soft sensing. The model is implemented using a deep neural network. The proposed learning method is studied empirically on a large-scale industrial case by developing virtual flow meters (a type of soft sensor) for 80 petroleum wells. We investigate how the model generalizes with the number of wells/units. We demonstrate that multi-unit models learned from data from many wells permit few-shot learning of virtual flow meters for new wells. Surprisingly, regarding the difficulty of the tasks, few-shot learning on 1-3 data points often leads to high performance on new wells.