Abstract
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 (CO2) 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
Highlights
This paper presents diagnostic and prognostic analysis of oil and gas pipeline industries with allowable corrosion rate using artificial neural networks approach
The raw measured datasets of pipes sampled for the development and confirmation of the artificial neural network (ANN) model used for the multiphase interactions analysis of factors affecting corrosion of the pipes are shown in Table 1 and Table 2 respectively
The optimal function fitting neural network architecture developed from this data set is shown in Fig. 1 while Eqn 8 constitutes the model generated from it for prediction and interactions analysis of corrosion of steel pipes used for oil and gas transmission
Summary
This paper presents diagnostic and prognostic analysis of oil and gas pipeline industries with allowable corrosion rate using artificial neural networks approach. Crude oil (petroleum) is usually extracted and associated with natural gas as a mixture of other impurities such as sulfur, nitrogen, oxygen, heavy metals and salt producing water (Roberge, 2008; Lusk et al, 2008). Unexpected pipe failure resulting to accident, waste of products, environmental pollution, and high maintenance/production cost due to down time constitutes direct effect of oil and gas pipeline corrosion. Unexpected pipe failure and high maintenance cost resulting from replacement of pipes that are not due for replacement still persists in this energy sector especially in Nigeria even though the oil and gas companies in this country uses computer simulation in the analyzing and management of pipeline corrosion. It is of economic sense that this study re-examines the significant of these factors in the initiation and development oil and gas pipeline corrosion process using artificial neural network technique because (Bassam et al, 2009) revealed that this technique provides the best/tightest model fit for this type of complex nonlinear system process than regression methods
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