Defect characterization from magnetic field leakage signals in petroleum and natural gas pipelines

Pipelines transport invaluable energy resources such as crude oil and natural gas over long distances. The integrity of the piping system in terms of safety of the process is then of high importance. However, pipes are prone by time to defects that may degrade their properties and lead to failures. In this paper, we study the effect of defect parameters on the magnetic field leakage captured by Hall sensors operating along the pipe. In fact, the obtained results show that the defect parameters influence directly the MFL amplitude and shape. For this reason, the inversion problem allowing us to reconstruct the defect from the MFL signals became fast and easier in comparison to the deterministic and probabilistic algorithm inversion procedure. However, the simplified system cannot describe the real defects and the three-dimensional numerical study became necessary. In tank floor inspection domain, as our recent published work, we have studied the performance of defect shape reconstruction from MFL array sensor imaging and depth estimation while using an iterative inversion method. Indeed, the first stage consists of determining the defect width and length from magnetic flux leakage mapping reconstructed from the recorded signals of the micro-integrated magnetic sensors. Then, after coupling Comsol and Matlab software, the defect depth is obtained by coupling the 3D finite elements method and a fast iterative algorithm recently developed. Consequently, the defect shape and size are obtained after a few iterations with high precision. Furthermore, this method of defect reconstruction and seizing can be extended for irregular defect shapes encountered in pipeline such as cracks and corrosion.