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基于PCA-Informer建模的ROP预测方法
Yefeng Wang1,2, Yishan Lou1,2, Yang Lin1,2
1School of Petroleum Engineering, Changjiang University, Wuhan 430100, China.
ACS omega
|June 10, 2024
概括
本研究介绍了一种主要组件分析 (PCA) 优化的Informer模型,用于预测油田钻探中的透率 (ROP). 与现有方法相比,PCA-Informer模型显著提高了预测准确性和效率.
科学领域:
- 石油工程是石油工程中的一个.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 在石油和天然气行业中,提高钻探效率至关重要.
- 目前用于预测透率 (ROP) 的智能方法需要提高准确性和效率.
研究的目的:
- 开发一个更准确,更有效的ROP预测模型.
- 增强钻井操作中的ROP智能预测方法.
主要方法:
- 利用主要组件分析 (PCA) 来提取特征和减少维度.
- 开发了一个用PCA优化的Informer模型,用于ROP预测.
- 与循环神经网络 (RNN) 和长期短期记忆 (LSTM) 基线的性能比较.
主要成果:
- PCA-Informer模型的平均平均绝对误差 (MAE) 为9.402,根平均平方误差 (RMSE) 为0.172,确定系数 (R2) 为0.858.
- 与基线模型相比,通过更高的R2和较低的RMSE和MAE表现出卓越的性能.
- 使用台北盆地块油田的数据验证了模型的有效性.
结论:
- PCA-Informer模型在ROP预测准确性和效率方面提供了显著的改进.
- 这种方法为优化钻井操作提供了一种新的解决方案.
- 这些发现表明,在现实世界钻井场景中提高透率的新方法.
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