物理约束多源数据驱动机械钻速智能预测方法

廖华林; 刘建胜; 裴学良; 许军富; 程丙方; 王华健

doi:10.7623/syxb202609010

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物理约束多源数据驱动机械钻速智能预测方法

石油工程 | 更新时间：2026-10-17

- 物理约束多源数据驱动机械钻速智能预测方法
- An intelligent prediction method for rate of penetration driven by the physical constraint and multi-source data
- 石油学报 2026年47卷第9期页码：1874-1887
- 作者机构：
  
  1. 中国石油大学（华东）石油工程学院,山东,青岛,266580
  2. 怀柔实验室新疆研究院,新疆,乌鲁木齐,830000
  3. 中石化胜利石油工程有限公司,山东,东营,257000
  4. 中石化经纬有限公司,山东,青岛,266071
- 作者简介：
- 基金信息：
- DOI：10.7623/syxb202609010
  中图分类号： TE242
- 网络首发：2026-10-14，
  
  纸质出版：2026
- 稿件说明：
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廖华林, 刘建胜, 裴学良, 许军富, 程丙方, 王华健. 物理约束多源数据驱动机械钻速智能预测方法[J]. 石油学报, 2026, 47(9): 1874-1887.

Liao Hualin, Liu Jiansheng, Pei Xueliang, et al. An intelligent prediction method for rate of penetration driven by the physical constraint and multi-source data[J]. ACTA PETROLEI SINICA, 2026, 47(9): 1874-1887.
廖华林, 刘建胜, 裴学良, 许军富, 程丙方, 王华健. 物理约束多源数据驱动机械钻速智能预测方法[J]. 石油学报, 2026, 47(9): 1874-1887. DOI： 10.7623/syxb202609010.

Liao Hualin, Liu Jiansheng, Pei Xueliang, et al. An intelligent prediction method for rate of penetration driven by the physical constraint and multi-source data[J]. ACTA PETROLEI SINICA, 2026, 47(9): 1874-1887. DOI： 10.7623/syxb202609010.

摘要

钻井机械钻速智能预测是油气井工程领域关键研究方向，但现有数据驱动模型多基于单一来源数据，未能有效融合多源数据与物理先验知识，导致模型在不同井间的泛化能力有限。结合深度学习理论和经典钻速方程，提出了一种融合多源信息和物理规律的数据—物理耦合驱动机械钻速预测方法。该方法采用并联的双通道多层感知机模块，分别提取地层信息和钻井工程参数特征，并利用Transformer模型注意力机制实现地质—工程多源数据融合。通过在损失函数中引入修正杨格钻速方程作为物理约束，增强模型的物理一致性，有效抑制了过拟合。结果表明:引入物理约束后，模型的泛化性能和预测精度均显著提升，相较于纯数据驱动模型，平均绝对误差降低约14.6%，均方误差降低约61.9%，均方根误差降低约38.3%，

提高约19.8%；双通道多层感知机特征提取模块显著改善了预测精度与稳定性，与单通道框架相比，

提升约5.5%，均方根误差降低约30.2%；所提出的物理约束多源数据驱动钻速智能预测方法在不同区块井中均表现出良好的稳定性和泛化能力，相较于传统数据驱动模型具有明显优势。

Abstract

Intelligent prediction of rate of penetration (ROP) is a critical research direction in oil and gas well engineering. However

existing methods are predominantly based on single-source data-driven models

which fail to effectively integrate multi-source data with physical knowledge

resulting in limited generalization capability across different wells. In this study

a data-physics-coupled ROP prediction method integrating multi-source information and drilling physics is proposed by combi

ning deep learning theory with the classical ROP equation. The proposed method employs a parallel dual-channel multilayer perceptron (MLP) architecture to independently extract features from formation information and drilling engineering parameters

while the attention mechanism of the Transformer model is utilized to achieve the fusion of geological and engineering multi-source data. Furthermore

a modified ROP equation of the F. S. Young model is embedded in the loss function as a physical constraint

enhancing the physical consistency and effectively mitigating overfitting of the model. The results show that the incorporation of the physical constraint significantly enhances both the generalization capability and prediction accuracy of the model. Compared with the purely data-driven model

the proposed method reduces mean absolute error (MAE)

mean squared error (MSE) and root mean square error (RMSE) by approximately 14.6%

61.9% and 38.3%

respectively

while increasing

by approximately 19.8%. The dual-channel MLP feature extraction module further improves prediction accuracy and model stability; compared with the single-channel framework

it increases

by approximately 5.5% and reduces RMSE by approximately 30.2%. The proposed ROP prediction method

which integrates multi-source data with the physical constraint

exhibits excellent stability and generalization capability across wells from different blocks and demonstrates clear advantages over conventional data-driven models.

关键词

Keywords

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