Generative Adversarial Neural Network and Genetic Algorithms To Predict Oil and Gas Pipeline Defect Lengths

Estimation of expected failure in an oil and gas pipeline system is challenging due to large uncertainties in the parameters associated with burst failure predictive models. The development of machine learning (ML) algorithms for reliability and risk assessment applications has attracted considerable attention from the scientific and research community in recent years. Working on the automation, efficiency, and optimization of underground oil and gas pipeline networks demands open access to extensive databases, which may not be possible. Oil and gas databases are confidential assets of specific countries, and no one can access these databases easily. As a result, training ML models is a big challenge, since it needs large data. To address this data shortage, in this paper, we have generated synthetic training datasets using a tabular generative adversarial neural network (TGAN). The generated synthetic data and real data (when available) were combined to train an artificial neural network (ANN). To further enhance the performance of the proposed system, the application of a genetic algorithm (GA) has been introduced to optimize the weights and biases of the ANN automatically. The results show superior performance results when compared with the previously reported algorithms in the literature. The proposed methodology succeeds to predict Oil and Gas pipeline defects with robust results and low error rates.