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

Tommaso Barbariol,Enrico Feltresi,Gian Antonio Susto

doi:10.3390/en13123136

Tommaso Barbariol, Enrico Feltresi + Show 1 more

Open Access

https://doi.org/10.3390/en13123136

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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.

Highlights

A Multiphase Flow Meter (MPFM) is a complex instrument employed by oil and gas companies to monitor their production
In the AD4MPFM pipeline, the goal of the preprocessing block is to decouple the observed dynamics of the underlying process from the behavior of the metrology instrument; in order to demonstrate such capacity, we report in Figure 5 an example of the time series evolution of the MPFM raw data
We have proposed a novel approach for the self-diagnosis and anomaly detection of Multiphase Flow Meters, named AD4MPFM

Summary

Introduction

A Multiphase Flow Meter (MPFM) is a complex instrument employed by oil and gas companies to monitor their production. It is usually placed on top of an oil well, and it is continuously crossed by a mixture of gas, oil, and water. It allows to measure the flow in real-time, without separating the phases. The increased complexity is associated with a bigger set of failure types that the system can experience. The reliability of this system is crucial for every customer that consumes the supplied data for monitoring, decision-making, and control the oil production. To achieve the required reliability, the MPFM must be able to self-diagnose its sensors in an autonomous fashion

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