大数据驱动的钻井时效提升与参数优化方法

doi:10.3969/j.issn.2096-1693.2026.03.009

石油科学通报 ›› 2026, Vol. 11 ›› Issue (2): 558-580. doi: 10.3969/j.issn.2096-1693.2026.03.009

大数据驱动的钻井时效提升与参数优化方法

高禹¹(), 罗鸣¹, 肖平¹, 傅琦¹, 李鑫¹, 黄洪林¹, 胡益涛², 姜波³^,^*()

¹ 中海石油(中国)有限公司海南分公司, 海口 570105
² 中法渤海地质服务有限公司湛江分公司, 湛江 524000
³ 中法渤海地质服务有限公司海南分公司, 海口 570312

收稿日期:2025-10-24 修回日期:2026-01-09 出版日期:2026-04-15 发布日期:2026-04-30
通讯作者: ^*姜波(1983年—)，本科，主要从事钻井时效与工程参数优化方法的研究，jiangbo@cfbgc.com。
作者简介:高禹(1993年—)，硕士研究生，主要研究方向为海上油气钻井技术研究， gaoyu5@cnooc.com.cn。
基金资助:
海南省重点研发项目“海上高温高压钻井关键技术研究”(ZDYF2022GXJS329)

Drilling time and parameter optimization technology development based on big data analysis

GAO Yu¹(), LUO Ming¹, XIAO Ping¹, FU Qi¹, LI Xin¹, HUANG Honglin¹, HU Yitao², JIANG Bo³^,^*()

¹ China National Offshore Oil Corporation Hainan Branch, Haikou 570105, China
² China France Bohai Geoservices Corporation Zhanjiang Branc, Zhanjiang 524000, China
³ China France Bohai Geoservices Corporation Hainan Branch, Haikou 570312, China

Received:2025-10-24 Revised:2026-01-09 Online:2026-04-15 Published:2026-04-30
Contact: ^*jiangbo@cfbgc.com

摘要/Abstract

摘要：

为实现地质工程一体化钻井综合提速，建立了钻井时效与参数优化系统。包含数据管理及配置模块、钻井时效管理模块、井筒力学分析及参数优化模块3个部分，实现了对钻井作业时效的可视化管理。首先，基于录井数据筛选指标建立了“虚拟时效最优井”及时效分析图版，以优化提升钻井时效，满足高钻速、低风险的工程需求。其次，通过对目标区块的地层滤失性、井身结构、钻具组合、钻井液性能进行充分调研和建模，建立了针对不同地层的最优钻井模型。同时，建立井筒环境监测评价模型，实现作业风险预警。应用结果表明，钻井工况识别准确率高达85%，缩短了钻井周期约20%。研究有利于实现钻井降本增速、安全生产，为钻井行业的数字化、智能化发展奠定基础。

关键词: 钻井时效优化, 钻井参数优化, 大数据, 钻井提速, 软件开发

Abstract:

To achieve integrated drilling acceleration in geological engineering, a drilling efficiency and parameter optimization system has been established. The system consists of three modules: data management and configuration, drilling efficiency management, and wellbore mechanical analysis with parameter optimization, enabling the visualized management of drilling operation efficiency. First, based on selected indicators from mud logging data, a “virtual best-performing well” model and an efficiency analysis chart were developed to optimize drilling efficiency and meet the engineering requirements of high penetration rates and low risks. Second, thorough investigation and modeling of formation fluid loss, wellbore structure, bottom-hole assembly, and drilling fluid properties in the target block were conducted to establish an optimal drilling model tailored to different formations. Meanwhile, a wellbore environment monitoring and evaluation model was built to provide early warning of operational risks. Application results demonstrate that the accuracy of drilling condition identification reaches 85%, reducing the drilling cycle by approximately 20%. This research contributes to cost reduction, efficiency improvement, and safe drilling operations, laying a foundation for the digital and intelligent development of the drilling industry.

Key words: drilling time optimization, drilling parameter optimization, big data, drilling speed, software development

中图分类号:

TE2
TP311.13

高禹, 罗鸣, 肖平, 傅琦, 李鑫, 黄洪林, 胡益涛, 姜波. 大数据驱动的钻井时效提升与参数优化方法[J]. 石油科学通报, 2026, 11(2): 558-580.

GAO Yu, LUO Ming, XIAO Ping, FU Qi, LI Xin, HUANG Honglin, HU Yitao, JIANG Bo. Drilling time and parameter optimization technology development based on big data analysis[J]. Petroleum Science Bulletin, 2026, 11(2): 558-580.

https://sykxtb.cup.edu.cn/CN/Y2026/V11/I2/558

图/表 23

图1 软件数据收集需求

Fig. 1 Software data collection requirements

图2 数据接口开发示意图

Fig. 2 Data interface development diagram

图3 研发钻井动作识别算法

Fig. 3 Develop a drilling action recognition algorithm

表1 评价指标表

Table 1 Evaluation metrics table

模型	准确率/%	精确率%	召回率/%	F1分数/%
随机森林	85.2	84.7	85.5	85.1
支持向量机	81.3	80.8	81.5	81.1
逻辑回归	78.6	77.9	78.8	78.3

图4 系统框架示意图

Fig. 4 System framework diagram

图5 数据记录框架示意图

Fig. 5 Schematic diagram of data recording framework

表2 核心KPI指标及量化标准

Table 2 Core KPI indicators and quantitative standards

编号	KPI指标	定义	量化标准
1	机械钻速(ROP)	单位时间内钻进深度	按地层类型区分：如页岩地层≥8 m/h，砂岩地层≥12 m/h(参考区域最优井历史数据)
2	非生产时间占比 (NPT)	非有效操作时间/总作业时间	≥5%(区分“计划内NPT”如接单根、起下钻与“非计划NPT”如卡钻、井漏、仅后者计入考核)
3	井眼清洁度	环空岩屑浓度	基于实时返速计算：≤5%(通过水力模型模拟临界返速，确保岩屑床厚度<井眼直径1/3)
4	钻头寿命	单只钻头累计进尺	≥300 m(根据区域地层研磨性，结合PDC钻头选型标准)
5	井下风险发生率	复杂工况(如黏吸卡钻、井塌等)发生频次	0次/1000 m(基于历史井事故数据库，通过工况识别模型实时预警)

图6 虚拟时效最优井的建立

Fig. 6 Establishment of virtual time optimal well

图7 钻井时效分析图版

Fig. 7 Drilling time analysis chart

图8 多井时效分析结果

Fig. 8 Results of multi well time efficiency analysis

图9 区域钻头分析结果

Fig. 9 Regional drill bit analysis results

图10 钻井参数分析结果

Fig. 10 Drilling parameter analysis results

图11 井眼清洁分析结果

Fig. 11 Analysis results of wellbore cleaning

表3 实验井地质参数

Table 3 Geological parameters of experimental well

参数名称	数值
参数名称	深度/m	岩性
钻遇地层岩性	122	泥岩
	1200	砂质泥岩
	1500	砂岩
	2320	砂岩
补心海拔/m	30
设计井深/m	4800
设计垂深/m	4800
完钻井深/m	4352
完钻垂深/m	4352
完钻层位	望江楼组

图12 井身结构

Fig. 12 Wellbore structure

图13 日报解析结果

Fig. 13 Daily report analysis results

图14 工况识别结果

Fig. 14 Identification results of working conditions

图15 虚拟时效最优井生成

Fig. 15 Virtual time optimal well generation

图16 钻井周期计算

Fig. 16 Drilling cycle calculation

图17 钻井时效统计

Fig. 17 Drilling efficiency statistics

图18 钻具组合及钻井液设计结果

Fig. 18 Drilling tool assembly and drilling fluid design results

图19 钻头优选

Fig. 19 Optimal selection of drill bits

图20 井眼清洁分析

Fig. 20 Analysis of wellbore cleaning

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大数据驱动的钻井时效提升与参数优化方法

Drilling time and parameter optimization technology development based on big data analysis

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