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.

Keywords

• three-phase flow
• scale layer thickness
• volume fraction independent
• MLP neural network