Artificial neural network models for predicting condition of offshore oil and gas pipelines

Pipelines daily transport and distribute huge amounts of oil and gas across the world. They are considered the safest method of transporting oil and gas because of their limited number of failures. However, pipelines are subject to deterioration and degradation. It is therefore important that pipelines be effectively monitored to optimize their operation and to reduce their failures to an acceptable safety limit. Numerous models have been developed recently to predict pipeline conditions. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. Hence, this paper presents the development of models that evaluate and predict the condition of offshore oil and gas pipelines based on several factors besides corrosion. The models were developed using artificial neural network (ANN) technique based on historical inspection data collected from three existing offshore oil and gas pipelines in Qatar. The models were able to successfully predict pipeline conditions with an average percent validity above 97% when applied to the validation data set. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize the planning of their inspection and rehabilitation. (C) 2014 Elsevier B.V. All rights reserved.