Quantum mechanics

Algorithm

Engineering

Computer science

Radiative transfer

Filter (signal processing)

Mechanical engineering

Physics

Finite element method

Parametrization (atmospheric modeling)

Pipeline (software)

Artificial intelligence

Pipeline transport

Structural engineering

Artificial neural network

Computer vision

Wavelet

Advances in Engineering Software

The assessment of the safety of corroded pipelines is considered a vital task in the oil and gas industry. This research aims to develop an efficient system to accurately predict the burst pressure of corroded pipelines with complex corrosion profiles through hybrid models using multiresolution analysis, numerical analysis and meta-models. The work addresses the parametrization of real corrosion shapes and its use as input to a neural network system that can predict the burst pressure accurately and quickly. Ultrasonic inspections provide the thicknesses in the corrosion area and with that data the river-bottom profile (rbp) can be constructed. A set of scripts creates finite element models from the rbp, which are then subjected to non-linear analyses to determine the failure pressure. In this work, the Finite Element models and analysis procedure are validated against experimental tests and are compared to semi-empirical assessment methods. A discrete wavelet transform is performed for the parametrization of the remaining thicknesses and it is used as a filter bank to reduce the amount of data that describes the defect. The coefficients obtained from the wavelet transform are used as inputs to feed a neural network to provide a quick evaluation of the failure pressure. This neural network is trained with the results of finite element analyses performed on synthetic models built with statistical properties similar to those of real corrosion defects The results from the neural networks are obtained very fast and accurate for all cases presented in this work.

Multiresolution analysis and deep learning for corroded pipeline failure assessment