Application of Gamma Attenuation Technique and Artificial Intelligence to Detect Scale Thickness in Pipelines in Which Two-Phase Flows with Different Flow Regimes and Void Fractions Exist

Mohammed Alamoudi,Mohammad Amir Sattari,Osman Taylan,Ehsan Nazemi,El Mostafa Kalmoun,Ehsan Eftekhari-Zadeh,Mohammed Balubaid

doi:10.3390/sym13071198

Abstract

Scale deposits can reduce equipment efficiency in the oil and petrochemical industry. The gamma attenuation technique can be used as a non-invasive effective tool for detecting scale deposits in petroleum pipelines. The goal of this study is to propose a dual-energy gamma attenuation method with radial basis function neural network (RBFNN) to determine scale thickness in petroleum pipelines in which two-phase flows with different symmetrical flow regimes and void fractions exist. The detection system consists of a dual-energy gamma source, with Ba-133 and Cs-137 radioisotopes and two 2.54-cm × 2.54-cm sodium iodide (NaI) detectors to record photons. The first detector related to transmitted photons, and the second one to scattered photons. The transmission detector recorded two signals, which were the counts under photopeak of Ba-133 and Cs-137 with the energy of 356 keV and 662 keV, respectively. The one signal recorded in the scattering detector, total counts, was applied to RBFNN as the inputs, and scale thickness was assigned as the output.

Highlights

Scale deposits on the inside of surface production equipment can cause problems such as the reduction of the internal diameter of pipelines, perforation of equipment and pipelines due to corrosion, high energy consumption costs, reduced equipment life cycle, and decreased equipment efficiency in the petroleum industry
The present study will focus on determining scale thickness in pipelines in which two-phase flows with different flow regimes and void fractions exist
P, T, Goal, Spread, MN, and DF are R-by-Q matrix of Q input vectors, S-by-Q matrix of Q target class vectors, Mean squared error goal, Spread of radial basis functions, Maximum number of neurons, and number of neurons to add between displays, respectively [67]

Summary

Introduction

Scale deposits on the inside of surface production equipment can cause problems such as the reduction of the internal diameter of pipelines, perforation of equipment and pipelines due to corrosion, high energy consumption costs, reduced equipment life cycle, and decreased equipment efficiency in the petroleum industry. The research team used the internal diameter of the pipe and acquired gamma spectrum with the detector as the inputs to an artificial neural network, and the thickness of scale was the output. With this method, scale thickness was estimated with deviations below 10% for 70% of the cases. Existing gamma attenuation techniques for detecting scale layers in pipelines are usually considered a single-phase flow. The present study will focus on determining scale thickness in pipelines in which two-phase flows with different flow regimes and void fractions exist To achieve this goal, a dual-energy gamma attenuation method and RBFNN was proposed

Monte Carlo Simulation

Conclusions