溶液气油比率 (Rs) 的组成建模:对基于树的模型,神经网络和状态方程进行比较研究
Aydin Larestani1, Sara Sahebalzamani1, Abdolhossein Hemmati-Sarapardeh2
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Scientific reports
|March 12, 2025
概括
机器学习模型可以准确预测原油.
科学领域:
- 石油工程是石油工程中的一个.
- 计算化学的计算化学
- 数据科学数据科学数据科学
背景情况:
- 准确的原油压力-体积-温度 (PVT) 属性对于储工程至关重要.
- 试验确定溶液气油比 (Rs) 是耗时且昂贵的.
研究的目的:
- 开发先进的机器学习 (ML) 模型,以高效,可靠地预测Rs.
- 将ML模型性能与传统状态方程 (EoS) 进行比较.
主要方法:
- 利用一个包含1,154个数据点的综合数据库进行模型开发.
- 实施和评估各种ML算法,包括额外树木 (ET).
- 评估了七种不同的状态方程 (EoS),包括施密特-温泽尔 (SW).
主要成果:
- 额外树木 (ET) 算法实现了Rs预测的~3%的高级平均绝对百分比相对误差 (AAPRE).
- 施密特-温泽尔 (SW) EoS显示了传统方法中最高的准确性,平均误差为~11%.
- ET模型在各种温度范围内表现出强度,并准确地捕获了Rs-压力关系.
结论:
- 先进的ML模型,特别是ET,为Rs预测提供了一个高度可靠和具有成本效益的替代方案.
- 机器学习模型为学术和工业应用提供了可扩展的工具,尽管存在潜在的部署限制.
- 这项研究推进了Rs预测建模,提高了石油工程应用的效率.
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