# Multi-task learning for virtual flow metering

arXiv: https://arxiv.org/abs/2103.08713 · HTML: https://mlsanity.com/paper/2103.08713/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Which Tasks Should Be Learned Together in Multi-task Learning?](https://mlsanity.com/paper/1905.07553/) (2019)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Learning Internal Representations (COLT 1995)](https://mlsanity.com/paper/1911.05781/) (2019)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Multi-task Learning for Speaker Verification and Voice Trigger Detection](https://mlsanity.com/paper/2001.10816/) (2020)
- [Bayesian Neural Networks for Virtual Flow Metering: An Empirical Study](https://mlsanity.com/paper/2102.01391/) (2021)

## Built on by (most cited first, 6 of 6)

- [Passive learning to address nonstationarity in virtual flow metering applications](https://mlsanity.com/paper/2202.03236/) (2022)
- [Multi-task and few-shot learning in virtual flow metering](https://mlsanity.com/paper/2309.15828/) (2023)
- [Multi-task neural networks by learned contextual inputs](https://mlsanity.com/paper/2303.00788/) (2023)
- [Sequential Monte Carlo applied to virtual flow meter calibration](https://mlsanity.com/paper/2304.06310/) (2023)
- [A deep latent variable model for semi-supervised multi-unit soft sensing in industrial processes](https://mlsanity.com/paper/2407.13310/) (2024)
- [Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance](https://mlsanity.com/paper/2610.19860/) (2026)

## Abstract (arXiv)

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 decision support systems that operators extensively rely on. Data-driven VFM, where mechanistic models are replaced with machine learning models, has recently gained attention due to its promise of lower maintenance costs. While excellent performances in small sample studies have been reported in the literature, there is still considerable doubt about the robustness of data-driven VFM. In this paper, we propose a new multi-task learning (MTL) architecture for data-driven VFM. Our method differs from previous methods in that it enables learning across oil and gas wells. We study the method by modeling 55 wells from four petroleum assets and compare the results with two single-task baseline models. Our findings show that MTL improves robustness over single-task methods, without sacrificing performance. MTL yields a 25-50% error reduction on average for the assets where single-task architectures are struggling.