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使用基于树的机器学习方法,模拟原油氧化过程中的残留物形成
Mohammad-Reza Mohammadi1, Seyyed-Mohammad-Mehdi Hosseini1, Behnam Amiri-Ramsheh1
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Scientific reports
|July 19, 2025
概括
机器学习通过建模热氧化特征和残留物形成,准确地预测原油在现场燃烧 (ISC) 过程中的原油行为. CatBoost 模型非常出色,有助于有效的燃料管理,以提高石油回收率.
科学领域:
- 石油工程是石油工程中的一个.
- 化学工程是化学工程的重要组成部分.
- 机器学习应用 机器学习应用
背景情况:
- 在现场燃烧 (ISC) 对于重油回收至关重要,它依赖于碳化合物氧化和热解.
- 了解热氧化特征和残留物形成是优化ISC效率的关键.
- 现有的模型可能无法完全捕捉影响残留物产生的复杂相互作用.
研究的目的:
- 开发准确的原油热氧化特征和ISC期间残留物形成的预测模型.
- 为了比较先进的机器学习算法对这个预测任务的性能.
- 确定影响剩余原油含量的关键因素.
主要方法:
- 使用了来自18种原油 (API重力5-42) 的热重度测量分析 (TGA) 数据.
- 使用了四种基于树的机器学习算法 (CatBoost,LightGBM,RF,XGBoost).
- 使用平均绝对相对误差 (MARE) 和确定系数 (R2) 评估模型性能.
主要成果:
- CatBoost 模型表现出卓越的性能,其 MARE 为 4.95% ,R2 为 0.9993.
- 温度是影响剩余原油含量的最重要因素 (负相关性).
- 青,树脂和加热速度与残留含量正相关,而API重力显示出负面影响.
结论:
- CatBoost模型提供了一种可靠和准确的方法,用于预测ISC中的原油行为.
- 有了这些预测能力,可以有效地管理燃料消耗和残留物形成.
- 开发的模型支持优化ISC流程,以提高石油回收效率.
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