Surface Pipeline Leak Detection Using Machine Learning

Abstract: Petroleum, also known as "rock oil," is a material that has developed over many years beneath the earth's surface. The sea (both onshore and offshore) is one of the main suppliers of petroleum products. Transporting oil or petroleum products from the Middle Sea region to the shore region involves a number of steps that are successfully completed via pipeline transportation. One of the biggest problems with oil and gas production is pipeline leakage. Every time there is a leak, they pose a very serious harm to the environment. This study focusses on inspecting pipelines for leaks by looking for cracks or holes in them. This study offers a revolutionary machine learning-based method for pipeline leak detection. This study offers a unique method for detecting pipeline leaks that makes use of machine learning (ML) techniques. The goal of the suggested method is to improve leak detection procedures' accuracy and efficiency. A large dataset of sensor data gathered from a moving object, calling it as Rover, is used to train the machine learning model. By looking at these data, the model can learn to identify patterns and anomalies that point to leakage. In order to increase the reliability of leak detection, developing a verification framework and an in-pipe inspection robot, mentioned earlier as Rover, that allows the operator to validate their discovery of a leak by passing it via a neural network-based system. The proposed method has several advantages over traditional leak detection methods. This work can be used to compare the leak detection system with sensors on the rover and in the pipeline. These two approaches' accuracy can be compared to determine which is best.