AI 中文总结
本研究针对海上石油平台能源低效问题，采用机器学习构建柴油消耗预测模型，结合搜索算法优化发电机负载，实现日均27%的柴油消耗降低，为平台能效提升提供可行方案。
AI 中文摘要
能源需求增长、化石燃料枯竭及气候变化凸显了高效能源生产与消耗的必要性。海上油气平台面临能源利用低效、系统故障、可达性差及环境影响等挑战。机器学习（ML）为提升这些系统的安全性、可持续性和效率提供了机遇；然而，过往研究大多聚焦于提高石油产量，而非降低平台的能源消耗。本研究探讨了利用机器学习与搜索算法提升某海上石油平台的柴油效率。分析了从苏格兰某平台采集的18个月数据，重点关注四台柴油发电机作为主要柴油消耗设备。在完成探索性数据分析与异常值检测后，开发了回归模型以预测不同发电机功率负载下的每日柴油消耗量。与极端随机树回归、极端梯度提升及随机森林相比，多元线性回归与人工神经网络取得了最佳预测性能。随后使用搜索算法确定可最小化每日柴油消耗的发电机功率负载组合。结果显示，与最差的每日功率负载组合相比，平均每日柴油节省量达27%，相当于约24000升/天。这些发现表明，利用基于机器学习的优化方法提升海上石油平台能效具有重大潜力。
英文摘要
Rising energy demand, fossil fuel depletion and climate change highlight the need for more efficient energy production and consumption. Offshore oil and gas platforms face challenges related to inefficient energy use, system failures, accessibility and environmental impact. Machine learning (ML) offers opportunities to improve the safety, sustainability and efficiency of these systems; however, previous research has largely focused on increasing oil production rather than reducing energy consumption on platforms. This study investigates the use of ML and search algorithms to improve diesel efficiency on an offshore oil platform. Data collected over 18 months from a platform in Scotland were analysed, focusing on four diesel generators as the primary diesel-consuming equipment. Following exploratory data analysis and outlier detection, regression models were developed to predict daily diesel consumption for different generator power loads. Multiple Linear Regression and Artificial Neural Networks achieved the best predictive performance compared with Extra Trees Regression, Extreme Gradient Boosting and Random Forest. Search algorithms were then used to identify combinations of generator power loads that minimised daily diesel consumption. The results showed an average diesel saving of 27% per day compared with the worst daily power-load combinations, equivalent to approximately 24,000 litres/day. These findings demonstrate significant opportunities for improving energy efficiency on offshore oil platforms using ML-based optimisation.
