CN108388921A - 一种基于随机森林的溢流漏失实时识别方法 - Google Patents
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Abstract
Description
根数 | 单根总长(m) | 内径(mm) | 外径(mm) | 接头长度(m) | 接头内径(mm) | 接头外径(mm) |
1 | 0.27 | 70 | 168.28 | 0 | 0 | 0 |
1 | 6.32 | 54 | 127 | 0 | 0 | 0 |
1 | 7.64 | 57.15 | 120.7 | 0 | 0 | 0 |
1 | 9.3 | 70.21 | 88.9 | 0 | 0 | 0 |
33 | 9.6126 | 52.4 | 88.9 | 0.79 | 50 | 127 |
356 | 9.6126 | 76 | 88.9 | 0.51 | 50 | 127 |
底深(m) | 内径(mm) | 扩大率 |
5938 | 179.9 | 1 |
6300 | 168.28 | 1.02 |
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Cited By (19)
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CN109322660A (zh) * | 2018-08-13 | 2019-02-12 | 中国石油集团工程技术研究院有限公司 | 一种水平主地应力方向随钻测量系统信号激励装置 |
CN109472285A (zh) * | 2018-09-29 | 2019-03-15 | 北京中油瑞飞信息技术有限责任公司 | 井漏识别方法、装置及计算机设备 |
CN109779604A (zh) * | 2018-12-17 | 2019-05-21 | 中国石油大学(北京) | 用于诊断井漏的建模方法及诊断井漏的方法 |
CN110134113A (zh) * | 2019-05-20 | 2019-08-16 | 中国石油大学(华东) | 一种海洋石油井控装备安全保障方法及系统 |
CN110795853A (zh) * | 2019-11-01 | 2020-02-14 | 西南石油大学 | 一种油气钻井过程中早期溢流层位随钻识别方法 |
CN111396025A (zh) * | 2020-03-19 | 2020-07-10 | 成都维泰油气能源技术有限公司 | 控压钻井智能钻进控制、钻进异常识别和处理方法及系统 |
CN111652253A (zh) * | 2019-03-04 | 2020-09-11 | 中石化石油工程技术服务有限公司 | 一种基于大数据的井漏事故检测预警方法 |
CN111827982A (zh) * | 2019-04-17 | 2020-10-27 | 中国石油天然气集团有限公司 | 钻井溢流漏失工况预测方法及装置 |
CN111853555A (zh) * | 2020-07-07 | 2020-10-30 | 杭州电子科技大学 | 一种基于动态过程的供水管网暗漏识别方法 |
CN112329804A (zh) * | 2020-06-30 | 2021-02-05 | 中国石油大学(北京) | 基于特征随机的朴素贝叶斯岩相分类集成学习方法及装置 |
CN112926839A (zh) * | 2021-02-05 | 2021-06-08 | 中国石油大学(华东) | 一种用于油气井钻井过程的溢漏风险协同监测方法及系统 |
CN113417588A (zh) * | 2021-07-29 | 2021-09-21 | 雷彪 | 一种油气钻井过程中溢流情况评价方法 |
CN113449417A (zh) * | 2021-06-17 | 2021-09-28 | 中国海洋石油集团有限公司 | 一种注水井溢流层段预测方法 |
CN113482595A (zh) * | 2021-08-04 | 2021-10-08 | 中海石油(中国)有限公司 | 一种钻井溢流预警方法、系统、设备和存储介质 |
CN113919422A (zh) * | 2021-09-30 | 2022-01-11 | 西南石油大学 | 一种使用综合模式识别增强井涌检测的方法 |
CN114184154A (zh) * | 2021-11-29 | 2022-03-15 | 浙江大学 | 一种基于随机森林和直流磁场的油气井套管内径检测方法 |
CN114897225A (zh) * | 2022-04-22 | 2022-08-12 | 清能艾科(深圳)能源技术有限公司 | 钻井作业的事故预测方法和装置、电子设备、存储介质 |
CN114943361A (zh) * | 2022-03-15 | 2022-08-26 | 水利部交通运输部国家能源局南京水利科学研究院 | 一种估算缺资料地区参考作物蒸散量的方法 |
WO2024001061A1 (zh) * | 2022-06-29 | 2024-01-04 | 中国石油天然气集团有限公司 | 一种溢流识别处理方法及装置 |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101696627A (zh) * | 2009-10-30 | 2010-04-21 | 湖北工业大学 | 一种钻井过程事故监测方法 |
CN103470202A (zh) * | 2013-05-10 | 2013-12-25 | 中国石油大学(华东) | 油气井钻井过程中溢流在线综合监测与预警方法 |
WO2016209230A1 (en) * | 2015-06-25 | 2016-12-29 | Tde Petroleum Data Solutions, Inc. | Method for standardized evaluation of drilling unit performance |
CN106382102A (zh) * | 2016-11-24 | 2017-02-08 | 西南石油大学 | 一种基于聚类算法的溢流早期预警方法 |
CN106761699A (zh) * | 2017-03-13 | 2017-05-31 | 中国石油集团钻井工程技术研究院 | 一种用于控压钻井的井漏实时检测系统 |
CN106837310A (zh) * | 2016-12-15 | 2017-06-13 | 中法渤海地质服务有限公司 | 基于质量流量综合检测的石油井涌井漏预报方法及装置 |
-
2018
- 2018-03-05 CN CN201810179878.4A patent/CN108388921B/zh active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101696627A (zh) * | 2009-10-30 | 2010-04-21 | 湖北工业大学 | 一种钻井过程事故监测方法 |
CN103470202A (zh) * | 2013-05-10 | 2013-12-25 | 中国石油大学(华东) | 油气井钻井过程中溢流在线综合监测与预警方法 |
WO2016209230A1 (en) * | 2015-06-25 | 2016-12-29 | Tde Petroleum Data Solutions, Inc. | Method for standardized evaluation of drilling unit performance |
CN106382102A (zh) * | 2016-11-24 | 2017-02-08 | 西南石油大学 | 一种基于聚类算法的溢流早期预警方法 |
CN106837310A (zh) * | 2016-12-15 | 2017-06-13 | 中法渤海地质服务有限公司 | 基于质量流量综合检测的石油井涌井漏预报方法及装置 |
CN106761699A (zh) * | 2017-03-13 | 2017-05-31 | 中国石油集团钻井工程技术研究院 | 一种用于控压钻井的井漏实时检测系统 |
Non-Patent Citations (3)
Title |
---|
LIANG HAIBO 等: "Application of an intelligent early-warning method based on DBSCAN clustering for drilling overflow accident", 《CLUSTER COMPUTING,》 * |
孙合辉 等: "基于录井参数的溢流预警模型研究", 《录井工程》 * |
张钰 等: "随机森林在滚动轴承故障诊断中的应用", 《计算机工程与应用》 * |
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US11360235B2 (en) | 2018-12-17 | 2022-06-14 | China University Of Petroleum (Beijing) | Modeling method and method for diagnosing lost circulation |
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CN114943361A (zh) * | 2022-03-15 | 2022-08-26 | 水利部交通运输部国家能源局南京水利科学研究院 | 一种估算缺资料地区参考作物蒸散量的方法 |
CN114897225A (zh) * | 2022-04-22 | 2022-08-12 | 清能艾科(深圳)能源技术有限公司 | 钻井作业的事故预测方法和装置、电子设备、存储介质 |
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Effective date of registration: 20221221 Address after: 100120 Xicheng District six paw Kang in Beijing City Patentee after: CHINA NATIONAL PETROLEUM Corp. Patentee after: CNPC ENGINEERING TECHNOLOGY R & D Co.,Ltd. Patentee after: BEIJING PETROLEUM MACHINERY Co.,Ltd. Address before: 100120 Xicheng District six paw Kang in Beijing City Patentee before: CHINA NATIONAL PETROLEUM Corp. Patentee before: CNPC ENGINEERING TECHNOLOGY R & D Co.,Ltd. |
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