Novel In-Line Inspection Tool for Deposit Characterization in Pipelines
Abstract Deposition formation inside pipelines is a major and growing problem in the oil and gas industry. The optimal use of prevention and remediation tools such as chemical inhibitors and cleaning processes could lead to major savings due to minimized production problems and optimized pipe cleaning costs. This requires characterization and quantification of the actual deposits inside pipelines and downholes. Recently, a novel deposition inline inspection sensor moving inside the pipeline has been proposed based on "inside-out" electrical tomography. In this sensor, the distribution of electrical properties between the sensor and the pipe wall are estimated based on measurements carried out using electrodes around the sensor. In this study, the next generation sensor moving inside the pipeline is described and a deep neural network based approach to deposit estimation is introduced. Test results from a 70 m long semi-industrial scale flow loop containing paraffin wax and calcium carbonate deposits of different thicknesses are shown. Challenges include the changing position and orientation of the sensor during the low. The results show that the sensor is able to measure both deposit thickness and type with good accuracy which indicates that the sensor is suitable for industrial use. Accurate knowledge about deposits allows future blockage prevention, detecting build-up locations in the early phase, increasing accuracy of multi-phase flow and deposition models, optimization of chemical use and validation of deposit cleaning tools before integrity campaigns leading to overall reduced pipeline operation costs.