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Abstract
Pipelines are like a lifeline that is vital to a nation’s economic sustainability; as such, pipelines need to be monitored to optimize their performance as well as reduce the product losses incurred in the transportation of petroleum chemicals. A significant number of pipes would be underground; it is of immediate concern to identify and analyse the level of corrosion and assess the quality of a pipe. Therefore, this study intends to present the development of an intelligent model that predicts the condition of crude oil pipeline cantered on specific factors such as metal loss anomalies (over length, width and depth), wall thickness, weld anomalies and pressure flow. The model is developed using Feed-Forward Back Propagation Network (FFBPN) based on historical inspection data from oil and gas fields. The model was trained using the Levenberg-Marquardt algorithm by changing the number of hidden neurons to achieve promising results in terms of maximum Coefficient of determination (R2) value and minimum Mean Squared Error (MSE). It was identified that a strong R2 value depends on the number of hidden neurons. The model developed with 16 hidden neurons accurately predicted the Estimated Repair Factor (ERF) value with an R2 value of 0.9998. The remaining useful life (RUL) of a pipeline is estimated based on the metal loss growth rate calculations. The deterioration profiles of considered factors are generated to identify the individual impact on pipeline condition. The proposed FFBPN was validated with other published models for its robustness and it was found that FFBPN performed better than the previous approaches. The deterioration curves were generated and it was found that pressure has major negative affect on pipeline condition and weld girth has a minor negative affect on pipeline condition. This study can help petroleum and natural gas industrial operators assess the life condition of existing pipelines and thus enhances their inspection and rehabilitation forecasting.
Highlights
In terms of the transportation of petroleum fluids around the globe, pipelines are becoming more critical
The main objective of the current study is to develop a predictive model for assessing the life condition of the crude oil pipeline using a Feed-Forward Back Propagation Network (FFBPN)
If the training criteria are met in the first stage, the model proceeds to the test stage where it is tested to measure its performance for a given testing data sample values; otherwise, it is recalled for retraining in the first stage
Summary
In terms of the transportation of petroleum fluids around the globe, pipelines are becoming more critical. El-Abbasy et al developed an ANN model based on various factors to predict the condition of oil and gas pipelines [12]. Based on survey results received from expert opinions from the oil and gas industries and analysing the available historical data, this work identified factors that have influenced the pipeline’s life conditions, viz., metal loss anomalies (over length, width and depth), wall thickness, welding girth and pressure flow. The main objective of the current study is to develop a predictive model for assessing the life condition of the crude oil pipeline using a FFBPN. Proposed Feed Forward Back Propagation Network (FFBPN) Approach robustness The contribution of this approach helps petroleum oil and gas industrial operators to take the necessary preventive actions and reduce product1.losses thestep oil and gas industries. Each parameter of this section had 85,519 reference points and the data was normalized, which is discussed in the further section in detail
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