石油钻井钻速预测研究现状与未来展望

doi:10.3969/j.issn.2096-1693.2025.02.032

石油科学通报 ›› 2025, Vol. 10 ›› Issue (6): 1252-1266. doi: 10.3969/j.issn.2096-1693.2025.02.032

石油钻井钻速预测研究现状与未来展望

裴志君¹^,²(), 宋先知²^,^*(), 李根生²

1 西南石油大学石油与天然气工程学院油气藏地质及开发工程全国重点实验室，成都 610500
2 中国石油大学(北京)石油工程学院油气资源与工程全国重点实验室，北京 102249

收稿日期:2024-11-26 修回日期:2025-05-06 出版日期:2025-12-30 发布日期:2025-12-30
通讯作者: *宋先知(1982年—)，工学博士，教授、博士生导师，主要从事智能钻完井工作，songxz@cup.edu.cn。
作者简介:裴志君(1994年—)，工学博士，博士后、讲师，主要从事智能钻完井、二氧化碳封存、井筒完整性评估与管控等工作，pzj0227@163.com。
基金资助:
国家杰出青年科学基金项目“油气井流体力学与工程”(52125401);国家资助博士后研究人员计划(GZC20251947);中国石油—西南石油大学创新联合体支持交叉学科发展“揭榜挂帅”项目“深井减振与提速智能协同作用理论研究”(2024CXJB03)

Research status and future prospect of penetration rate prediction for petroleum drilling

PEI Zhijun¹^,²(), SONG Xianzhi²^,^*(), LI Gensheng²

1 State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
2 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, China

Received:2024-11-26 Revised:2025-05-06 Online:2025-12-30 Published:2025-12-30

摘要/Abstract

摘要：

机械钻速预测对钻井工程意义重大，可为钻井优化、资源分配、安全保障等提供重要参考依据。近年来，人工智能掀起新一轮智能化变革热潮，推动了钻井工程的智能化转型升级，涌现出了一大批新兴钻速预测方法，但当前尚缺乏对这些钻速预测新方法的系统性总结和分析。本文通过对国内外主要钻速预测模型和方法的系统调研和提炼，归纳总结了显式钻速方程、数值模拟仿真和人工智能模型3类钻速预测方法的发展背景和理论原理，深入剖析了各类钻速预测方法在实际应用中面临的关键技术难题和挑战，并指出机理与数据融合的钻速预测方法是突破现有瓶颈的重要方向，也是未来技术发展的主流趋势。在此基础上，结合智能钻井发展趋势和钻速预测模型主要卡点，提出了5个未来的发展方向：①自动化、智能化钻井装备；②钻速预测专用的机理与数据融合模型；③基于具身智能、群体智能、强化学习和在线学习的钻速预测模型环境响应机制；④基于大模型和迁移学习算法的通用钻速智能预测模型；⑤基于科学知识发现的钻速预测模型闭环优化。

关键词: 机械钻速, 智能钻井, 神经网络, 知识约束, 混合模型

Abstract:

Rate of penetration (ROP) prediction is of great significance to drilling engineering and can provide important reference basis for drilling optimization, resource allocation, safety guarantee. In recent years, artificial intelligence has sparked a new round of intelligent transformation, promoting the intelligent transformation and upgrading of drilling engineering and giving rise to a large number of new ROP prediction methods. However, there is still a lack of systematic summary and analysis of these new ROP prediction methods at present. This paper, through systematic research and refinement of the main ROP prediction models and methods at home and abroad, summarizes and expounds the development background and theoretical principles of three types of ROP prediction methods: explicit ROP equation, numerical simulation, and artificial intelligence model. It also deeply analyzes the key technical problems and challenges faced by various ROP prediction methods in practical applications at present. It is also pointed out that the ROP prediction method integrating mechanism and data is an important direction to break through the existing bottlenecks and also the mainstream trend of future technological development. Based on this, combined with the development trend of intelligent drilling and the main bottlenecks of the ROP prediction model, five future development directions are proposed: ① Automated and intelligent drilling equipment; ② A dedicated mechanism and data fusion model for ROP prediction; ③ Environmental response mechanism of ROP prediction model based on embodied intelligence, swarm intelligence, reinforcement learning and online learning; ④ A general ROP intelligent prediction model based on large models and transfer learning algorithms; ⑤ Closed-loop optimization of ROP prediction model based on scientific knowledge discovery.

Key words: rate of penetration, intelligent drilling, neural network, knowledge constraint, intelligent hybrid model

中图分类号:

TE242
TE249

裴志君, 宋先知, 李根生. 石油钻井钻速预测研究现状与未来展望[J]. 石油科学通报, 2025, 10(6): 1252-1266.

PEI Zhijun, SONG Xianzhi, LI Gensheng. Research status and future prospect of penetration rate prediction for petroleum drilling[J]. Petroleum Science Bulletin, 2025, 10(6): 1252-1266.

https://sykxtb.cup.edu.cn/CN/Y2025/V10/I6/1252

图/表 8

表1 基于数理统计的钻速方程对比表

Table 1 Comparison of rate of penetration equations based on mathematical statistics

主要作者	年份	钻头适用性	考虑参数
Siman	1949	仅适用牙轮钻头	只考虑钻头尺寸和钻头功率
Cunningham	1960	仅适用牙轮钻头	考虑钻压与转速，提出在常压下，钻速正比于转速的结论
William	1990	仅适用牙轮钻头	从井下马达的角度入手，考虑钻压与转速
Galle&Woods	1963	仅适用牙轮钻头	将地质参数纳入钻速方程，考虑钻头重量、转速和钻头类型
Walker	1986	仅适用牙轮钻头	考虑岩石的内摩擦角、内聚力、岩石孔隙度和岩石抗压强度
巨满成	1992	仅适用牙轮钻头	考虑钻压、转速、牙齿磨损、岩石性质和钻井深度
Maldia	1991	仅适用牙轮钻头	考虑地层压实、压差、转速、牙齿磨损、钻压和钻头水力参数等
Al Betair	1988	牙轮和PDC钻头	同时考虑可控参数与不可控参数
郭永锋	1994	牙轮和PDC钻头	考虑喷嘴直径、钻压、转速、排量、泵压、比水功率和钻井液密度等

图1 基于数值模拟的机械钻速计算示意图。(a)破岩过程多模型耦合作用^[25]；(b)岩石模型^[26];(c)钻头模型^[27];(d)钻头和岩石相互作用模型^[27]

Fig. 1 Schematic diagram of ROP calculation based on numerical simulation. (a) Multi-model coupling effect in the rock-breaking process^[25]; (b) Rock mode^[26]; (c) Drill bit model^[27]; (d) Model of interaction between drill bit and rock^[27]

图2 PDC钻头破岩多物理场耦合示意图^[49]

Fig. 2 Schematic Diagram of multi-physics field coupling for PDC bit rock breaking^[49]

图3 钻速智能预测模型搭建流程示意图

Fig. 3 Schematic diagram of the construction process of the intelligent ROP prediction model

表2 钻速预测场景下机器学习算法对比分析表

Table 2 Comparison and analysis of machine learning algorithms in the ROP prediction scenario

算法类别	典型算法	特点
常规机器学习	随机森林、K近邻、支持向量回归、极端梯度增强、浅层人工神经网络	样本需求小，部分可解释，但易过拟合，复杂环境精度低
深度学习	深层神经网络、RNN、LSTM	强拟合能力，复杂环境精度高，但训练成本高，可解释性弱
在线学习	增量式学习、在线聚类	实时调整模型，预测精度高，但难适用于大数据和多变环境。
超参数优化	贝叶斯优化、遗传算法、灰狼算法	快速搜索最优的超参数组合

图4 钻速智能预测混合模型的结构示意图

Fig. 4 Schematic diagram of the structure of the intelligent hybrid model for ROP prediction

图5 神经网络的物理约束示意图 (a)常规深层全连接神经网络结构；(b)软约束原理示意图；(c)硬约束原理示意图

Fig. 5 Schematic diagram of physical constraints of neural networks (a) Conventional deep fully connected neural network structure; (b) schematic diagram of the soft constraint principle; (c) schematic diagram of the hard constraint principle

图6 钻速智能预测模型的部分依赖分析图

Fig. 6 Partial dependency analysis Diagram of the intelligent ROP prediction model

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