Velocity Prediction of a Pipeline Inspection Gauge (PIG) with Machine Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (23), p.9162
• Main Authors: Freitas, Victor Carvalho Galvão De, Araujo, Valbério Gonzaga De, Crisóstomo, Daniel Carlos de Carvalho, Lima, Gustavo Fernandes De, Neto, Adrião Duarte Dória, Salazar, Andrés Ortiz
• Format: Article
• Language: English
• Subjects: Acceleration; Accuracy; artificial neural networks; Differential pressure; Embedded systems; Errors; Evaluation; Fault diagnosis; Gas pipelines; Gas transmission industry; Inspection; Machine Learning; Multilayer perceptrons; Natural gas; Neural networks; Neural Networks, Computer; Petroleum pipelines; Pigging; pipeline inspection gauge (PIG); Quality control equipment; raspberry Pi; Reproducibility of Results; Sensors; Signal processing; Topography; Velocity
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22239162

A device known as a pipeline inspection gauge (PIG) runs through oil and gas pipelines which performs various maintenance operations in the oil and gas industry. The PIG velocity, which plays a role in the efficiency of these operations, is usually determined indirectly from odometers installed in it. Although this is a relatively simple technique, the loss of contact between the odometer wheel and the pipeline results in measurement errors. To help reduce these errors, this investigation employed neural networks to estimate the speed of a prototype PIG, using the pressure difference that acts on the device inside the pipeline and its acceleration instead of using odometers. Static networks (e.g., multilayer perceptron) and recurrent networks (e.g., long short-term memory) were built, and in addition, a prototype PIG was developed with an embedded system based on Raspberry Pi 3 to collect speed, acceleration and pressure data for the model training. The implementation of the supervised neural networks used the Python library TensorFlow package. To train and evaluate the models, we used the PIG testing pipeline facilities available at the Petroleum Evaluation and Measurement Laboratory of the Federal University of Rio Grande do Norte (LAMP/UFRN). The results showed that the models were able to learn the relationship among the differential pressure, acceleration and speed of the PIG. The proposed approach can complement odometer-based systems, increasing the reliability of speed measurements.