Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.

如今，管道已广泛用于长距离输送石油和天然气。因此，他们的风险评估可以帮助识别相关的危害并采取必要的措施来消除或降低风险。在本研究中，使用人工神经网络（ANN）和模糊推理系统（FIS）为管道风险评估准备了一个新模型，具有更高的准确性。为了达到这个目标，使用Muhlbauer方法作为油气管道风险评估的常用方法，以确定管道性能中的重要因素和影响因素。通过考虑专家知识，在Matlab软件中开发了Mamdani模糊模型。该模型的结果用于开发人工神经网络。为了验证开发的模型，以南帕尔斯气田的9-10期炼油厂的相间岸管为例。结果表明，该模型在管道风险评估方面具有更高的准确性，准确性和可靠性。