Novel Intelligent Pigging Tool For Deposit Inspection Using Electrical Tomography with High Computational Efficiency Enabled Through Neural Networks

Deposition inspection sensor based on electrical tomography has been proposed recently. In this work, a next generation electrical tomography sensor is introduced and a novel mathematical approach for the estimation of the deposit thickness is described. It is essential for the pipeline operators to keep the lines open for smooth flow and high flow efficiency. Deposit thickness, deposit type and location of deposit is required for optimal pipeline cleaning. The usage of chemicals as well as number of cleaning pig runs can be optimized based on the information that intelligent pig is giving. In electrical tomography, electrodes are attached on the surface of the sensor and excitations are applied to some electrodes and responses are measured from other electrodes. The electrical properties of the medium are estimated based on these measurements. In pigging applications, the distribution of electrical properties between the PIG surface and metal pipe is estimated. The thickness and type of deposit (wax/scale) can be identified from the estimated electrical properties. In the proposed approach the estimation of the parameters is done by using a novel deep neural network based approach. In practice, number of measurements that are analyzed after each PIG run can be hundreds of thousands. The neural network based approach was chosen in order to achieve reasonable computational efficiency (computation time) in real applications with large amounts of data. The introduced sensor is for 12-inch lines and designed to be used when the oil line is filled with water. This sensor was tested in a laboratory test line with artificial deposit samples. After these tests and calibration, the sensor is deployed to be used in real pipe line inspections. The major challenges in pipe line runs include the movement of the sensor during measurements, electrical noise and changing excitations. In the neural network model, the position of the PIG is estimated simultaneously with the electrical properties and the effect of all these aforementioned uncertainties are also modelled. Based on the results conclusions can be drawn on the efficiency and performance using neural networks and the high suitability of electrical tomography for deposit mapping. In this study, it is shown that intelligent pig based on the electrical tomography can be used reliable for deposit inspection. Furthermore, the computation approach based on the deep neural network is computationally efficient and it is tolerable for measurement noise and other uncertainties in real measurements.