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This paper presents diagnostic and prognostic analysis of oil and gas pipeline industries with allowable corrosion rate using artificial neural networks approach. The results revealed sand deposit, carbon dioxide (CO 2 ) partial pressure, pipe age, diameter and length, temperature, flow velocity of the fluid, fluid pressure, chloride contents and pH value of its environment as the relevant parameters affecting corrosion of oil and gas pipeline in this region. Condition prediction of steel pipes used for the transmission of oil and gas varies 0.02 mm/yr to 0.10 mm/yr. The training of the neural network was performed using Levenberg-Marquardt algorithm and optimal regression coefficient was equal to 0.99, for the network 10-40-1. Also, the results show a remarkable agreement with the field measurement. A corrosion severity level of two (0.01 mm/yr to 0.10 mm/yr) oil and gas pipelines was established from the analysis. Keywords : Artificial neural network, Levenberg-Marquardt algorithm, condition prediction, oil and gas data

Diagnostic and prognostic analysis of oil and gas pipeline with allowable corrosion rate in Niger Delta Area, Nigeria