Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection

Tommaso Barbariol, Enrico Feltresi and Gian Antonio Susto
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Tommaso Barbariol: Department of Information Engineering, University of Padova, 35131 Padova (PD), Italy
Enrico Feltresi: Pietro Fiorentini S.p.A., 36057 Arcugnano (VI), Italy
Gian Antonio Susto: Department of Information Engineering, University of Padova, 35131 Padova (PD), Italy

Energies, 2020, vol. 13, issue 12, 1-24

Abstract: Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.

Keywords: anomaly detection; data fusion; data mining; edge analytics; Machine Learning; Measuring Systems; oil and gas; process monitoring; Root Cause Analysis; self-diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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