Multi-task learning for virtual flow metering
| Autor: | Odd Kolbjørnsen, Bjarne Grimstad, Anders T. Sandnes |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Decision support system Information Systems and Management Artificial neural network Computer science Multiphase flow Multi-task learning Small sample Industrial engineering Machine Learning (cs.LG) Management Information Systems Artificial Intelligence Robustness (computer science) Baseline (configuration management) Flow metering Software |
| Zdroj: | Knowledge-Based Systems |
| ISSN: | 0950-7051 |
| Popis: | 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. Comment: 23 pages, 11 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect