Digital Twin For Offshore Pipeline Corrosion Monitoring: A Deep Learning Approach

Abstract

Pipeline corrosion is a major identified threat in the offshore oil and gas industry. In this paper, a novel computer vision-based digital twin concept for real-time corrosion inspection is proposed. The Convolution Neural Network (CNN) algorithm is used for the automated corrosion identification and classification from the ROV images and In-Line Inspection data. Predictive and prescriptive maintenance strategies are recommended based on the corrosion assessment through the digital twin. A Deep-learning Image processing model is developed based on the pipeline inspection images and In-Line Inspection images from some previous inspection data sets. During the corrosion monitoring through pipeline inspection, the digital twin system would be able to gather data and, at the same time, process and analyze the collected data. The analyzed data can be used to classify the corrosion type and determine the actions to be taken (develop predictive and prescriptive maintenance strategy). Convolution Neural Network, a well known Deep Learning algorithm, is used in the Tensorflow framework with Keras in the backend is used in the digital twin for corrosion inspection. CNN algorithm will first detect the corrosion and then the type of corrosion based on image classification. The deep-learning network training is done using 4000 images taken from the inspection video frames from a subsea pipeline inspection using ROV. The performances of both the methods are compared based on result accuracy as well as processing time. Deep Learning algorithm, CNN has approximately 81% accuracy for correctly identifying the corrosion and classify them based on severity through image classification. The processing time for the deep-learning method is significantly faster, and the digital twin generates the predictive or prescriptive strategy based on the inspection result in real-time. Deep-learning based digital twin for Corrosion inspection significantly improve current corrosion identification and reduce the overall time for offshore inspection. The inspection data loss due to the communication interference during real-time assessment can be eliminated using the digital twin. The image data can recover the required features based on other features available through the previous inspection. Furthermore, the system can adapt to the unrefined environment, making the proposed system robust and useful for other detection applications. The digital twin makes a recommended decision based on an expert system database during the real-time inspection. The complete corrosion monitoring process is performed in real-time on a cloud-based digital twin. The proposed pipeline corrosion inspection digital twin based on the CNN method will significantly reduce the overall maintenance cost and improve the efficiency of the corrosion monitoring system.