Deep Learning Technique for Oil and Gas Pipeline Surveillance - H, Alalibo; ND, Nwiabu (International Journal of Computer Sciences and Engineering, 2019)
This research presents a model for detecting pipeline vandalism in oil and gas sector. Feed-forward deep learning technique was applied. The methodology adopted the Rational Unified Process (RUP), Convolutional neural network and UML tools where applied for the system design. The architectural design consists of three input parameters stored in the hidden neurons, and one output. A back-propagation Convolutional neural network was used to train the parameters. The system was implemented using Hypertext Pre-processor (PHP) programming language. An input interactive interface was generated for predicting parameters threshold values for pipeline intrusion threat ranging from (0-18) pound by square inch(Psi) for threat while (19 and above Psi) for normal. Comparison has been carried out on the outcome between existing system and the proposed system. Results shown in the graph, denoting manual digging, pipeline leakage, walking on pipeline, and pressure. The intrusion point is indicated at line six in the result table where the pressure drops as a result of manual digging. The use of Convolutional neural network in pipeline surveillance system has shown that oil and gas pipeline intrusion can be monitored and controlled.