Measurement of Gas-Oil Two-Phase Flow Patterns by Using CNN Algorithm Based on Dual ECT Sensors with Venturi Tube

Zhuoqun Xu,Fan Wu,Yi Li,Xinmeng Yang

doi:10.3390/s20041200

Abstract

In modern society, the oil industry has become the foundation of the world economy, and how to efficiently extract oil is a pressing problem. Among them, the accurate measurement of oil-gas two-phase parameters is one of the bottlenecks in oil extraction technology. It is found that through the experiment the flow patterns of the oil-gas two-phase flow will change after passing through the venturi tube with the same flow rates. Under the different oil-gas flow rate, the change will be diverse. Being motivated by the above experiments, we use the dual ECT sensors to collect the capacitance values before and after the venturi tube, respectively. Additionally, we use the linear projection algorithm (LBP) algorithm to reconstruct the image of flow patterns. This paper discusses the relationship between the change of flow patterns and the flow rates. Furthermore, a convolutional neural network (CNN) algorithm is proposed to predict the oil flow rate, gas flow rate, and GVF (gas void fraction, especially referring to sectional gas fraction) of the two-phase flow. We use ElasticNet regression as the loss function to effectively avoid possible overfitting problems. In actual experiments, we compare the Typical-ECT-imaging-based-GVF algorithm and SVM (Support Vector Machine) algorithm with CNN algorithm based on three different ECT datasets. Three different sets of ECT data are used to predict the gas flow rate, oil flow rate, and GVF, and they are respectively using the venturi front-based ECT data only, while using the venturi behind-based ECT data and using both these data.

Highlights

Multiphase flow measurement technology is important in the exploitation of petroleum, the accurate measurement of gas and oil flow rate in the oil-gas two phase flow has been the current research issue
We propose the convolutional neural network (CNN) algorithm to realize the non-linear mapping of the flow rates and flow patterns in oil-gas two-phase flow
For oil flow rate prediction, the relative error of 90% is less than 11%; for gas flow rate prediction, the relative error of 95% is less than 3.6%, and that of 90% is less than 2.2%; and, for gas void fraction (GVF)

Summary

Introduction

Multiphase flow measurement technology is important in the exploitation of petroleum, the accurate measurement of gas and oil flow rate in the oil-gas two phase flow has been the current research issue. In the traditional flow measurement, multiple mixtures that were obtained in oil wells need to be separated in the well for single-phase measurements [1]. This method improves the accuracy of single-phase measurement, but separating multiphase flow is too complicated [2], the equipment is expensive, and the efficiency is low. Concerning the flow measurement of two-phase flow, there are many technologies that have been used. Mohmmed et al and Abbagoni and Yeung introduced high-speed cameras (with transparent tube segments) to take high-speed shooting records for convection type [4,5], while Dong et al used ultrasonic Doppler sensors to estimate the total surface speed of the oil-water two phase flow [6]

Methods

Findings

Discussion

Conclusion