人工智能方法模拟等效循环密度,以改善钻井泥管理
Mohammad-Saber Dabiri1, Reza Haji-Hashemi1, Abdolhossein Hemmati-Sarapardeh1,2
1Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman 76169-141111, Iran.
ACS omega
|May 19, 2025
概括
本研究介绍了先进的机器学习模型,用于预测钻井操作中的等效循环密度 (ECD). 草优化算法支持向量回归 (GOA-SVR) 模型在ECD预测中表现出卓越的准确性和稳定性.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
- 钻井优化 钻井优化
背景情况:
- 精确管理等效循环密度 (ECD) 对于防止井控制问题,如流失循环和形成裂纹至关重要.
- 使用深孔工具或复杂模型的传统ECD计算方法可能是低效的.
- 这项研究侧重于使用更少的输入变量以提高效率的简化方法.
研究的目的:
- 开发和评估先进的机器学习模型,以提高简单性和效率来预测ECD.
- 将各种机器学习算法的性能与现有的实证模型进行比较.
- 确定影响ECD预测的关键输入变量,并评估模型的操作范围.
主要方法:
- 使用基于水的流体利用伊朗油田的两个井的2367个现场测量的数据集.
- 应用了七种先进的机器学习算法:CFNN,GRNN,WNN,PSO-SVR,FFA-SVR,GOA-SVR和GMDH用于相关性开发.
- 使用70%的培训和30%的测试数据分割,分析关键变量:SPP,ROP和MW.
主要成果:
- 所有应用的模型都在ECD预测方面表现出高准确度.
- 该GOA-SVR算法产生了最可靠的结果,最小的平均绝对百分比相对误差 (AAPRE).
- 该GMDH模型的表现优于现有的实证模型,特别是在三个关键输入变量方面;表面泥重量是最有影响力的因素.
结论:
- 先进的机器学习模型,特别是GOA-SVR,为ECD预测提供了强大而准确的框架.
- 开发的GMDH模型为ECD估计提供了一个卓越的经验替代方案.
- 杆分析证实了拟议模型的高操作范围,只有很少有可疑或异常数据点.
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