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In this paper we propose new real time architectures for monitoring underwater oil and gas pipelines by using underwater wireless sensor network (UWSN). New monitoring architectures for underwater oil/gas pipeline inspection system combine a real time UWSN with nondestructive In Line Inspection (ILI) technology. These architecture will help in reducing or detecting the pipeline’s defects such as cracks, corrosions, welds, pipeline’s wall thickness ...etc by improving data transfer from the pipeline to the processor to extract useful information and deliver it to the onshore main station. Hence, decreasing delays in default detection.

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.