an employee-owned company

Pipeline Pigging & Integrity Management Conference


Booth #:



February 18 – 21, 2020


George R. Brown Conv. Cntr. | Houston, TX

PPIM 2020
Trans-Alaska Oil Pipeline

Presentations and Papers

Using Deep Learning to Identify the Severity of Pipeline Dents

by Ishita Chakraborty and Brent Vyvial

Friday, February 21, 2020 | 8:00 AM

With the advent of machine learning, data based models can be used to increase efficiency and reduce cost for the characterization of various anomalies in pipelines. In this work, artificial intelligence is used to classify pipeline dents directly from the in-line inspection (ILI) data according to their risk categories. A deep neural network model is built with available ILI data, and the resulting machine learning model requires only the ILI data as an input to classify dents in different risk categories. Using a machine learning based model eliminates the need for conducting detailed engineering analysis to determine the effects of dents on the integrity of the pipeline. Concepts from computer vision are used to build the deep neural network using the available data. The deep neural network model is then trained on a sub set of the available ILI data and the model is tested for accuracy on a previously unseen set of the available data. The developed model predicts risk factors associated with a dent with 95% accuracy for a previously unseen data set.