Recurrent neural network-based model for estimating the life condition of a dry gas pipeline

The use of expansive pipeline networks guarantees domestic and industrial users for accessing a continuous flow of valuable liquids and gases. These pipeline systems were considered the most economical and safest pipeline of transport for oil and gas and are of great strategic importance. The risks during operating conditions need to be controlled to handle the pipeline safely and smoothly in the complex globalized economy. Accordingly, life prediction is an essential issue in the pipeline network of the maintenance systems. Recently proposed data-driven and statistical approaches for pipeline's life prediction continue to rely on previous information to create health indicators and define thresholds, which is inefficient in the significant data era. Thus, a recurrent neural network (RNN)-based method for predicting the life of equipment with corrosion dimension classes exposed to critical condition monitoring is proposed in this research. The RNN model uses the multiple condition monitoring observations at the current and previous inspection points as its inputs and the pipe life condition and corrosion type as its outputs. Further, to improve the predictability of the proposed model, a validation mechanism is introduced during the training process. The proposed RNN method is validated using the data from oil and gas fields that have been collected in real-time. A critical sensitivity analysis with missing parameters is performed to evaluate the model's effectiveness in forecasting the life situation when inputs are absent. A comparative study is also carried out between the proposed RNN method and an adapted version of the reported method. The results show the advantage of the proposed method in achieving an actual life expectancy which can reduce the annual maintenance costs and help take necessary actions for better protection and safety of a pipeline. © 2022 The Institution of Chemical Engineers