Neural network analysis of safe life of the oil and gas industrial structures

Abstract

Purpose is to study safe life of industrial (metal) structures under long-time operation in the corrosive-active media of oil and gas wells with the help of neural network analysis. Methods. The MATLAB system (MATrix LABoratory) was selected as the tool environment for interface modelling; the system is developed by Math Works Inc. and is a high-level programming language for technical computations. Of the three existing learning paradigms, we used the “with teacher” learning process, as we believed that a neural network had correct answers (network outputs) for each input example. The coefficients were adjusted so that the network gave answers being as close as possible to the known correct answers. Findings. An artificial neural network has helped obtain a generalized diagram of the expected areas of high viscoplastic characteristics of carbon steels used to manufacture metal structures in the oil and gas industry. While applying the trained neural networks, generalized dependences of the corrosion rates of structural steels on the parameters of media with different concentrations of chlorine ions, sulphate ions, hydrogen sulphide, carbon dioxide, carbon dioxide, and oxygen ions were obtained; they were the basis to predict corrosion behaviour of steels. Originality. For the first time, the possibility of applying neural network modelling to predict local corrosion damage of structural pipe steels has been shown in terms of the “steel 20 – oxygen and chloride-containing medium” system. For the first time, the technological possibility has been demonstrated to use neural network analysis for engineering predictive assessment of corrosion activity of binary systems of simulated solutions, which are most often found under industrial conditions of the oil and gas sector of the economy. Practical implications. The proposed technology of using the neural network analysis will make it possible to expand a range of predicted values beyond experimental data, i.e. to predict the value of Vcor in very dilute or concentrated salt solutions within the acidified and neutral pH ranges. It should be noted that the error of the prediction results shown by the neural network will increase along with distancing from the scope of experimental data.