一个决策树模型,用于准确预测肘部几何中的沙蚀
Fahd Saeed Alakbari1,2, Mysara Eissa Mohyaldinn1,2, Mohammed Abdalla Ayoub1,2
1Petroleum Engineering Department, Universiti Teknologi PETRONAS, 32610, Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia.
Heliyon
|August 4, 2023
概括
预测石油生产中的沙蚀率 (ER) 对安全至关重要. 机器学习模型,特别是决策树,为ER预测提供了强大而准确的解决方案,节省了时间和成本.
科学领域:
- 石油工程是石油工程中的一个.
- 材料科学 是一种材料科学.
- 数据科学数据科学数据科学
背景情况:
- 由于石油生产中的沙流造成的管道部件侵蚀造成了重大安全风险.
- 现有的侵蚀率 (ER) 模型在广泛的数据中缺乏概括性,并且未能探索变量关系.
- 准确和强大的ER预测对于保持材料完整性和确保安全运营至关重要.
研究的目的:
- 开发和评估机器学习模型,以可靠地预测沙侵蚀率 (ER).
- 为了比较 ER 预测的线性回归和决策树 (DT) 模型的性能.
- 为石油工业建立一个可靠的工具,以减轻与沙蚀相关的风险.
主要方法:
- 机器学习技术的应用,特别是线性回归和决策树 (DT) 算法.
- 使用趋势分析和统计错误分析 (SEA) 评估模型性能,包括相关系数 (R).
- 在训练,验证,测试和整个数据集中验证决策树模型的准确性,稳定性和物理行为.
主要成果:
- 决策树 (DT) 模型表现出卓越的性能,相关系数 (R) 高达0.9975.5.
- 该DT模型显示了较低的平均绝对百分比相对误差 (AAPRE) 和根平均平方误差 (RMSE),表明了高准确性.
- 统计分析证实了独立变量的正确物理行为和DT模型的稳定性.
结论:
- 拟议的决策树 (DT) 方法为侵蚀率 (ER) 预测提供了一种有效,强大,准确和快速的工具.
- 这种机器学习方法大大降低了石油行业的成本和时间.
- 该研究强调了数据驱动方法在提高石油和天然气运营中的安全性和效率方面的潜力.
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