Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Abdullah K Alanazi,John William Grimaldo Guerrero,Igor M Narozhnyy,Hala M Abo-Dief,Karina Shamilyevna Nurgalieva,Seyed Mehdi Alizadeh,Ehsan Nazemi,Ehsan Eftekhari-Zadeh,Slavko Nesic

doi:10.3390/app12031336

Abstract

One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

Highlights

In addition to the gamma radiation efficiency technique, which is the basis of this study, there are other techniques, such as hydrostatic, ultrasonic, and hydrometric techniques, that are used to distinguish the flow regime and volume fraction of multiphase flow
(B) Understanding the type of flow pattern along with determining the volume fraction of gas and oil phases is a requisite of transfer processes because it is straightforwardly related to a large part of the project economy
(C) The efficiency of the separation process is highly influenced by the type of flow regime. (D) Whether the drilling process should continue or stop at any time can only be determined by understanding the volume fraction of each component

Summary

Introduction

In addition to the gamma radiation efficiency technique, which is the basis of this study, there are other techniques, such as hydrostatic, ultrasonic, and hydrometric techniques, that are used to distinguish the flow regime and volume fraction of multiphase flow. They performed two independent GMDH neural networks to detect volume percentages and flow regimes In the beginning, they passed the photon spectrum extracted from the detector through a Savitzky–Golay filter to eliminate the existing high-frequency noise, extracted 7 time-domain characteristics from this spectrum to give them as input of the neural network. They passed the photon spectrum extracted from the detector through a Savitzky–Golay filter to eliminate the existing high-frequency noise, extracted 7 time-domain characteristics from this spectrum to give them as input of the neural network This approach could eventually achieve the type of flow regimes and volume percentage prediction with a root mean square error of less than 1.11.

Simulation Setup

MLP Neural Network

Result and Discussion

Conclusions

Methods