Application of Neural Network and Dual-Energy Radiation-Based Detection Techniques to Measure Scale Layer Thickness in Oil Pipelines Containing a Stratified Regime of Three-Phase Flow

Abstract

Over time, oil pipes are scaled, which causes problems such as a reduction in the effective diameter of the oil pipe, an efficiency reduction, waste of energy, etc. Determining the exact value of the scale inside the pipe is very important in order to take timely action and to prevent the mentioned problems. One accurate detection methodology is the use of non-invasive systems based on gamma-ray attenuation. For this purpose, in this research, a scale thickness detection system consisting of a test pipe, a dual-energy gamma source (241Am and 133Ba radioisotopes), and two sodium iodide detectors were simulated using the Monte Carlo N Particle (MCNP) code. In the test pipe, three-phase flow consisting of water, gas, and oil was simulated in a stratified flow regime in volume percentages in the range from 10% to 80%. In addition, a scale with different thicknesses from 0 to 3 cm was placed inside the pipe, and gamma rays were irradiated onto the pipe; on the other side of the pipe, the photon intensity was recorded by the detectors. A total of 252 simulations were performed. From the signal received by the detectors, four characteristics were extracted, named the Photopeaks of 241Am and 133Ba for the first and second detectors. After training many different Multi-Layer Perceptron(MLP) neural networks with various architectures, it was found that a structure with two hidden layers could predict the connection between the input, extracted features, and the output, scale thickness, with a Root Mean Square Error (RMSE) of less than 0.06. This low error value guarantees the effectiveness of the proposed method and the usefulness of this method for the oil and petrochemical industry.

An Intelligent Approach to Determine Component Volume Percentages in a Symmetrical Homogeneous Three-Phase Fluid in Scaled Pipe Conditions

“…The data from each simulation's received signals were then analysed to find three features: the 241 Am and 133 Ba photopeaks from the first detector as well as the total count from the second detector. In fact, the characteristics used in this study have been used in previous studies to determine other parameters of multiphase flows [45,46]. In this research, the performance of the mentioned characteristics to determine volume percentages in three-phase flows in the homogeneous regime and in the presence of the scale layer has been investigated.…”

A Diagnostic Method Based on Active Thermography for the Degradation Assessment of Power Plant Boiler Tubes

“…It would also have minimal costs associated with the preparation of the surfaces for measurement. Using dual-energy radiation and neural networks to measure scale layer thickness in oil pipelines was shown in article [ 13 ]. Determining the exact value of the diameter inside the pipe was very important to minimize the energy required for pumping.…”

Increasing the Accuracy and Optimizing the Structure of the Scale Thickness Detection System by Extracting the Optimal Characteristics Using Wavelet Transform

“…[11]), Balubaid et al [12] used wavelet transform for feature extraction, which resulted in an effective reduction of computational load and increased accurateness. Numerous studies have been conducted to gauge scale thickness and examine various features [13,14] with more detectors or higher errors. In this study, inspired by the existing history of similar studies, an attempt was made to design a high-precision system to acquire the scale value inside the pipe.…”