Crude oil and gas products transported using pipelines systems is safe and economical all over the the world. Nonetheless, such pipelines can still be subject to various degrees of failure and degradation generating hazardous consequences and severe environmental damages. As a result, it is important for these pipelines to be effectively monitored and assessed for optimal operation. Many models have been developed to predict pipeline failures and conditions. However, most of these models were limited to use corrosion features as the only factor to assess the condition of pipelines which can lead to inaccurate condition prediction. Therefore, the main aim of this paper is to develop models that predict the condition of offshore oil and gas pipelines based on several other factors including corrosion. Regression analysis and artificial neural network (ANN) techniques were used to develop condition prediction models based on historical inspection data of three existing pipelines in Qatar. In addition, a condition assessment scale for pipelines was built based on experts' opinion. All necessary statistical diagnosis have been checked showing sound results for the developed models. The models have been validated and the results showed their robustness with an average validity percentage from 96 to 99%. The models are expected to help pipeline operators to assess and predict the condition of existing oil and gas pipelines and hence prioritize their inspections and rehabilitation planning.


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