Development of Predictive Model for Corrosion Management of Oil and Gas Pipelines, Validatiion of the Model Using Artificial Neural Network (Ann) and Optimization Using Genetic Algorithm in the Niger Delta Area of Nigeria

Abstract

The potential rupture of pipelines poses significant threats to the environment, human safety and infrastructure integrity. To mitigate these risks, pipelines require constant monitoring and maintenance to detect and rectify defects such as corrosion before they lead to failure. However, the regular monitoring and non-destructive testing of pipelines incur substantial costs. Consequently, there is a growing interest in research focused on predictive corrosion monitoring of pipelines based on easily measurable operational parameters. This study was aimed to develop predictive model for corrosion management in oil and gas pipelines in the Niger Delta area of Nigeria.

Secondary data on mean corrosion rates, mean pH levels, mean temperatures, mean pressures, and mean aqueous CO2 partial pressures were obtained from an oil and gas multinational company spanning the years 2007 to 2011. Polynomial regression and Artificial Neural Network (ANN) methodologies were chosen as suitable methods for data analysis. Polynomial regression and ANN models were developed and subsequently optimized using a genetic algorithm. The models' validity was assessed using Goodness of Fit Indices (GFI).

For the full second-degree quadratic polynomial model yielded the following results for both training and testing data sets: Coefficient of Determination (R2) was 0.9869/0.9361, Root Mean Square Error (RMSE) was 0.0007/0.0012, Mean Biased Error (MBE) was 0.0000/0.0002, Mean Absolute Biased Error (MABE) was 0.0004/0.0008, Mean Percentage Error (MPE) was 0.0006/0.0636, and correlation coefficient (r) was 0.9934/0.9689. The goodness of fit for the reduced second-degree model for both training and testing datasets provided the following results: R2 was 0.9859/0.9341, RMSE was 0.0007/0.0012, MBE was 0.0000/0.0001, MABE was 0.0004/0.0008, MPE was 0.0006/0.0390, and r was 0.9929/0.9676. Hypothesis testing of the full second-degree polynomial model for significance revealed that all model parameters were significant at the 95% confidence interval, except for the coefficients related to the interaction of mean pressure and mean aqueous CO2 partial pressure (x_(2) x_3) and the square of mean aqueous CO2 partial pressure (x_4^2). Comparatively, the ANN model exhibited slightly higher accuracy than the polynomial model, reinforcing the validity of the polynomial modelling and interaction analyses. Furthermore, the goodness of fit indices for the ANN model during both training and testing phases were as follows: R2 was 0.9965/0.9158, RMSE was 0.0004/0.0014, MBE was 0.0000/-0.0003, MABE was 0.0003/0.0011, MPE was 0.0003/-0.0905, and r was 0.9982/0.9608. Through optimization with a genetic algorithm, it was determined that the minimal corrosion rate occurred at mean pH of 8.446, mean temperature was 23.692°C, mean pressure was 15.725 bar, and mean aqueous CO2 partial pressure was 2.022 bar. This research contributes valuable insights into the technological applications and policy implications of predictive corrosion monitoring for pipelines. It also highlights avenues for further research in this critical field.