Due to its capital-intensive nature, the Oil and Gas industry requires high operational standards to meet safety and environmental requirements, while maintaining economical returns. In this context, maintenance policies play a crucial role in the avoidance of unplanned downtimes and enhancement of productivity. In particular, Condition-Based Maintenance is an approach in which maintenance actions are performed depending on the assets’ health state that is evaluated through different kinds of sensors. In this paper, Deep Learning methods are explored and different models are proposed for health state prognostics of physical assets in two real-life cases from the Oil and Gas industry: a Natural Gas treatment plant in an offshore production platform where elevated levels of CO₂ must be predicted, and a sea water injection pump for oil extraction stimulation, in which several degradation levels must be predicted. A general methodology for preprocessing the available multi-sensor data and developing proper models is proposed and apply in both case studies. In the first one, a LSTM autoencoder is developed, achieving precision values over 83.5% when predicting anomalous states up to 8 h ahead. In the second case study, a CNN-LSTM model is proposed for the pump’s health state prognostics 48 h ahead, achieving precision values above 99% for all possible pump health states.

由于其资本密集型性质，石油和天然气行业要求高运营标准以满足安全和环境要求，同时又要保持经济回报。在这种情况下，维护策略在避免计划外停机和提高生产率方面起着至关重要的作用。特别地，基于条件的维护是一种方法，其中维护操作取决于通过不同类型的传感器评估的资产的健康状态。本文探讨了深度学习方法，并针对石油和天然气行业的两个实际案例提出了针对物理资产健康状态预测的不同模型：在离岸生产平台中的天然气处理厂，其中CO ₂含量升高必须预测，以及用于增产石油的海水注入泵，其中必须预测几个降解水平。提出了一种预处理可用多传感器数据并开发适当模型的通用方法，并将其应用于两个案例研究中。在第一个中，开发了一种LSTM自动编码器，当预测最多8小时的异常状态时，其精度值达到83.5％以上。在第二个案例研究中，提出了一个CNN-LSTM模型用于48小时后的泵健康状态预测，对于所有可能的泵健康状态，其精度值均达到99％以上。