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| “三高”油气井早期溢流在线监测与预警系统 |
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戴永寿1,岳炜杰1,2,孙伟峰1,李立刚1,张亚南3,程佳成4
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(1.中国石油大学信息与控制工程学院,山东青岛 266580;2.中国航空油料有限责任公司北京分公司,北京 100088;3.青岛鼎信通讯股份有限公司,山东青岛 266073;4.中国石油北京销售公司,北京 100107)
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| 摘要: |
| 为解决钻井过程中传统溢流监测实时性和可靠性差的问题,设计开发出一套“三高”油气井早期溢流在线监测与预警系统。该系统针对常规单一参数、单一手段溢流监测实时性低、可靠性差的问题,综合应用微流量、随钻压力(pressure while drilling, PWD)以及综合录井3类参数,通过多参数、多手段相互印证的方式提高溢流监测的实时性与可靠性。提出基于专家系统和改进的贝叶斯判别相融合的溢流等钻井事故判别方法:当缺少训练数据时,应用专家系统判别溢流等钻井事故;当训练数据充足时,应用专家系统和改进的贝叶斯判别相结合识别溢流等钻井事故。改进的贝叶斯判别模型对于因属性变量间不独立而引起的误判具有一定的抑制作用,能够提高判别准确度,且训练简单,应用灵活。通过两种算法的有机结合,优势互补,可以提高井控安全的智能化水平与现场适用性。测试实验结果表明,该系统能够实时、有效地监测溢流等钻井事故。 |
| 关键词: 井喷 溢流监测 专家系统 改进的贝叶斯判别 “三高”油气井 |
| DOI：10.3969/j.issn.1673-5005.2015.03.027 |
| 分类号::TE 28 |
| 基金项目:国家自然科学基金项目(40974072)；中石油“十二五”科技攻关项目(2014D-4601) |
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| Online monitoring and warning system for early kick foreboding on "three high" wells |
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DAI Yongshou1, YUE Weijie1,2, SUN Weifeng1, LI Ligang1, ZHANG Yanan3, CHENG Jiacheng4
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(1.College of Information and Control Engineering in China University of Petroleum, Qingdao 266580, China;2.China Aviation Oil Company Limited Beijing Branch, Beijing 100088, China;3.Qingdao Topscomm Communication INC, Qingdao 266073, China;4.China Petroleum Beijing Sales Company, Beijing 100107, China)
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| Abstract: |
| In order to solve the problem about the poor instantaneity and reliability of early kick detection during drilling process by traditional methods, a realtime monitoring and warning system for kick foreboding on "three high" wells was designed and developed. Regarding the above defects of kick detection by conventional means in which only one parameter or one mode was used, the method makes use of three parameters which include micro-flux, pressure while drilling (PWD) and comprehensive logging parameters, and applies the multi-parameter and multi-means to cross-verify and improve the instantaneity and reliability of kick detection. An identification method based on expert system in combination with the improved Bayes discriminant was proposed. If lacking of training data, the method applies expert system to identify kick and other accidents; otherwise it applies expert system combining with the improved Bayes discriminant to identify kick and other accidents. The improved Bayes discriminant model can reduce the misjudgment caused by the attribute variables which are not independent completely, and thus improve the identification accuracy. The application is more flexible, and the training is easier. The combination of the two kinds of intelligent algorithms, whose advantages are complementary can improve significantly the intelligent level and applicability. The verification tests show that the system can detect kick and other accidents instantaneously and effectively. |
| Key words: blowout kick detection expert system improved Bayes discriminant "three high" wells |
