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基于数据模型协作的海上钻井溢流早期预测预警

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为防止海上钻井过程中井喷事故的发生,提出基于数据模型协作的海上钻井溢流早期预测预警方法.首先,建立基于粒子群优化(PSO)-最小二乘支持向量机(LSSVM)(PSO-LSSVM)的溢流风险预测模型,预测钻井监测参数未来时长内的趋势,并分析溢流事件与表征参数之间的关联关系;然后,建立基于朴素贝叶斯方法的钻井单参数溢流概率估算模型,并通过优化的D-S方法融合多个钻井参数的概率,分级预警溢流事件.结果表明:基于PSO-LSSVM的预测模型所得的溢流表征参数,预测误差较低;因对溢流事件的敏感度不同,单钻井参数所表征的溢流事件概率存在一定偏差;融合后的预警模型能够解决单参数的预警时间不一致的问题,排除误报警的可能.
Early prediction and warning of offshore drilling overflow based on data model collaboration
An early prediction and warning method of offshore drilling overflow based on data model collaboration was proposed to prevent blowout accidents during offshore drilling.Firstly,an overflow risk prediction model based on PSO-LSSVM was established to predict the trend of drilling monitoring parameters in the future,and analyze the correlation between overflow events and characterization parameters.Then,a single-parameter overflow probability estimation prediction model was proposed based on the Naive Bayesian method,and the probabilities of multiple drilling parameters were integrated through the optimized D-S method to realize a hierarchical early warning of overflow events.The results indicated that the overflow characterization parameters simulated by the PSO-LSSVM model had low prediction errors.The overflow event probability represented by a single drilling parameter showed discrepancies due to different sensitivities.The fused early warning model can address the issues of inconsistent early warning times of single parameters and eliminate the possibility of false alarms.

data model collaborationdrilling overflowearly predictionparticle swarm optimization(PSO)-least squares support vector machines(LSSVM)(PSO-LSSVM)early warning models

杨向前、张苹茹、武胜男、张来斌、李中、冯桓榰

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中海石油(中国)有限公司 北京研究中心,北京 100028

中国石油大学(北京) 安全与海洋工程学院,北京 102249

数据模型协作 钻井溢流 早期预测 粒子群优化(PSO)-最小二乘支持向量机(LSSVM)(PSO-LSSVM) 预警模型

国家重点研发计划中海石油(中国)有限公司北京研究中心科研项目

2022YFC2806504CCL2022RCPS2008XNN

2024

10.16265/j.cnki.issn1003-3033.2024.04.1390

中国安全科学学报
中国职业安全健康协会

中国安全科学学报

CSTPCD北大核心
影响因子：1.548
ISSN：1003-3033
年,卷(期)：2024.34(4)
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