钻井过程实时状态监测与诊断技术研究
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摘要
石油钻井系统构成复杂,工作环境复杂多变且工况恶劣,钻井系统钻进过程实时状态监测与诊断是钻井工程的重要研究方向。钻进过程实时信息的获取、处理及传输技术是钻进过程实时状态监测与诊断的关键技术。本文围绕自动化钻井过程的信息获取和信息处理开展研究工作。
自动化钻井是钻井技术未来重要的发展方向,在总结自动化钻井技术发展现状的基础上提出自动化钻井信息模型,认为自动化钻井系统应该包含地面和井下两个信息处理系统,地面信息处理系统是自动化钻井的核心,井下信息处理系统、井下随钻测量仪器以及井下工具组合构成的井下控制系统将起到越来越重要的作用。钻井过程实时状态监测与异常状态诊断是两个信息处理系统的核心内容之一,钻井系统及其工作环境的复杂性决定了钻井状态的判别不能只依靠单一的传感器信息,钻井过程反映状态的参数多,利用钻井过程地面及井下各种传感器信息的冗余性和互补性,提出采用多传感器信息融合原理对钻井过程进行状态监测与异常状态诊断,论证了基于多传感器信息融合方法改善钻井过程状态监测和诊断结果鲁棒性的可行性。
在总结多传感器信息融合方法的基础上,结合钻井系统复杂多变的特点,提出采用不依赖于精确数学模型的神经网络和证据理论多传感器信息融合方法。多传感器信息融合方法在刻画系统状态、系统行为等方面具有比单一传感器信息更强的能力,但是当多传感器信息存在矛盾或融合模型不完善时,信息融合的结果可能异常,在钻进过程中可能得出错误的状态判别,本文针对此种情形提出了冲突证据处理的基本框架,采用修改证据组合规则的方法对冲突证据进行分配,以提高证据融合处理结果的合理性。
钻井地面系统的信息获取系统技术尚不完善,提出建立基于无线数据传输的新型钻井地面数据采集监测系统,制订无线数据通讯协议,开发钻井地面数据采集系统。按照钻井专家知识,总结形成了钻井异常状态诊断判据,建立不同钻井过程状态测量参数的特征提取模型。
结合钻井专家经验建立钻井状态空间,钻井状态空间包括钻具刺穿、井涌、井漏等九种钻井异常状态和正常状态共十种钻井状态。采用神经网络方法建立钻井过程状态的多传感器信息融合模型,实现钻井状态参数特征到钻井状态空间的映射,建立神经网络训练样本和教师样本,提取钻井过程实际异常状态下的数据并形成异常样本,神经网络模型经过训练后进行异常状态辨识,模型的诊断结果表明,神经网络具有较好的诊断能力。
D-S证据理论是融合不确定性信息的有效方法,钻井系统中各传感器信息均存在不确定性,尝试采用D-S证据理论进行多传感器信息融合。证据理论采用基本信任指派对状态空间内的状态进行辨识,基本信任指派的获取是进行证据融合的前提,提出基于神经网络与D-S证据理论结合方法对钻井过程进行异常状态诊断的集成多传感器多层次融合诊断模型,将神经网络对异常状态辨识输出作为D-S证据理论融合输入,即把神经网络输出经归一化之后作为状态辨识的基本信任指派,运用D-S证据理论沿时间轴进行证据融合,产生决策输出。结果表明,集成多层次状态监测与异常状态诊断模型具有较好的诊断能力。多层次诊断模型不仅实现了基于神经网络的当前状态多传感器信息融合,还实现了基于D-S证据融合的当前状态与历史状态的证据融合,实现多层次多方位的综合融合过程。
针对传统D-S证据理论对于冲突证据处理存在的缺陷,提出一种解决冲突证据融合的模型用于钻井过程的状态监测,实现在证据冲突的情况下不影响证据融合的结果,融合结果的合理性、正确性得到保障,提高集成多层次融合模型鲁棒性。
Oil drilling are complicated, complex and changing work environment, poor working conditions, the drilling process real-time state monitoring and diagnosis is an important research direction. Drilling process real-time information acquisition, processing and transmission technology is key technologies for the drilling process real-time state monitoring and diagnosis. This paper focuses on information acquisition and information processing research work about automated drilling.
Automated drilling is an important development direction of drilling technology in the future. In conclusion, the development of automated drilling technology on the basis of the status quo,Proposed the automated drilling information model, that the automated drilling system should include both ground and underground information processing systems, ground information processing system is automated drilling core, the downhole control system that include underground information processing systems, downhole measurement while drilling equipment and Combination of downhole tools will play an increasingly important role. Real-time state monitoring during drilling and abnormal state diagnosis is the core of two information processing systems, the complexity of drilling system and its working environment determines that we can not just rely on a single sensor information, we have more than one parameter to describe drilling process state. using surface and underground drilling process information from various sensors redundancy and complementarity, we used multi-sensor information fusion theory for the drilling process state monitoring and diagnosis, Based on multi-sensor information fusion method to improve the drilling process state monitoring and diagnosis robustness is feasible.
Based on the complex characteristics of drilling system, Summarized multi-sensor information fusion, use multi-sensor information fusion method of neural network and evidence theory that does not rely on accurate mathematical model. Using multi-sensor information fusion to describe the system state and system behavior is better than a single sensor information, But when there is conflict or multi-sensor information fusion model imperfect, the information fusion results may be abnormal, in the drilling process may come to the wrong state estimation, this paper put forward such a case the basic framework for dealing with conflicting evidence, by amending the evidence combination rule, the allocation of the evidence of conflict can improve rationality of the evidence fusion results.
Drilling ground systems information acquisition technology is not perfect, established a new type of wireless data acquisition monitoring system, drafted wireless data communication protocols, developed drilling ground data acquisition system. In accordance with the drilling expert knowledge, summed up the drilling abnormal state diagnosis criterion, established the feature extraction model for different parameters. Combined with drilling expert knowledge, established the drilling state-space, including drill piercie, well kick, lost circulation etc. nine abnormal state and normal state that is 10 kinds of drilling state. Using neural network to establish the drilling process model of multi-sensor information fusion to achieve drilling characteristics of state parameters to the drilling state space mapping, established the neural network training samples and sample of teachers, extracted the drilling process data under abnormal conditions, established abnormal samples, trained neural network to identify abnormal state, the diagnostic results show that neural network has good diagnostic capabilities.
D-S evidence theory is an effective method for the fusion of uncertainty information, the multi-sensor information of the drilling system is uncertainty, try using D-S evidence theory to multi-sensor information fusion. In D-S evidence theory using basic belief assignment with the state space for the state identification, access to basic belief assignment is a prerequisite for evidence fusion, proposed based on neural networks combined with the D-S evidence theory method for drilling abnormal state diagnosis, that is a multi-sensor multi-level integration diagnostic model, the neural network recognition output is basic belief assignment for drilling state space, that is, the neural network output by the normalization is the input of D-S evidence theory fusion, along the timeline to produce decision-making output. The results show that the integration of multi-level state monitoring and diagnosis model has better diagnostic capabilities. Multi-level diagnosis model not only realize the current state of multi-sensor information fusion based on neural network, also achieved to fuse the historical status and the current state based on DS evidence, completed multi-dimensional and multi-level integrated fusion process.
When the evidence is conflict, the traditional D-S combination model have defects, A conflict digestion model for evidence fusion is proposed to monitor the drilling process, implement not to affect the fusion result when evidence conflict, the fusion results is reasonable and correct, and improving robustness of integration multi-level fusion model.
自动化钻井是钻井技术未来重要的发展方向,在总结自动化钻井技术发展现状的基础上提出自动化钻井信息模型,认为自动化钻井系统应该包含地面和井下两个信息处理系统,地面信息处理系统是自动化钻井的核心,井下信息处理系统、井下随钻测量仪器以及井下工具组合构成的井下控制系统将起到越来越重要的作用。钻井过程实时状态监测与异常状态诊断是两个信息处理系统的核心内容之一,钻井系统及其工作环境的复杂性决定了钻井状态的判别不能只依靠单一的传感器信息,钻井过程反映状态的参数多,利用钻井过程地面及井下各种传感器信息的冗余性和互补性,提出采用多传感器信息融合原理对钻井过程进行状态监测与异常状态诊断,论证了基于多传感器信息融合方法改善钻井过程状态监测和诊断结果鲁棒性的可行性。
在总结多传感器信息融合方法的基础上,结合钻井系统复杂多变的特点,提出采用不依赖于精确数学模型的神经网络和证据理论多传感器信息融合方法。多传感器信息融合方法在刻画系统状态、系统行为等方面具有比单一传感器信息更强的能力,但是当多传感器信息存在矛盾或融合模型不完善时,信息融合的结果可能异常,在钻进过程中可能得出错误的状态判别,本文针对此种情形提出了冲突证据处理的基本框架,采用修改证据组合规则的方法对冲突证据进行分配,以提高证据融合处理结果的合理性。
钻井地面系统的信息获取系统技术尚不完善,提出建立基于无线数据传输的新型钻井地面数据采集监测系统,制订无线数据通讯协议,开发钻井地面数据采集系统。按照钻井专家知识,总结形成了钻井异常状态诊断判据,建立不同钻井过程状态测量参数的特征提取模型。
结合钻井专家经验建立钻井状态空间,钻井状态空间包括钻具刺穿、井涌、井漏等九种钻井异常状态和正常状态共十种钻井状态。采用神经网络方法建立钻井过程状态的多传感器信息融合模型,实现钻井状态参数特征到钻井状态空间的映射,建立神经网络训练样本和教师样本,提取钻井过程实际异常状态下的数据并形成异常样本,神经网络模型经过训练后进行异常状态辨识,模型的诊断结果表明,神经网络具有较好的诊断能力。
D-S证据理论是融合不确定性信息的有效方法,钻井系统中各传感器信息均存在不确定性,尝试采用D-S证据理论进行多传感器信息融合。证据理论采用基本信任指派对状态空间内的状态进行辨识,基本信任指派的获取是进行证据融合的前提,提出基于神经网络与D-S证据理论结合方法对钻井过程进行异常状态诊断的集成多传感器多层次融合诊断模型,将神经网络对异常状态辨识输出作为D-S证据理论融合输入,即把神经网络输出经归一化之后作为状态辨识的基本信任指派,运用D-S证据理论沿时间轴进行证据融合,产生决策输出。结果表明,集成多层次状态监测与异常状态诊断模型具有较好的诊断能力。多层次诊断模型不仅实现了基于神经网络的当前状态多传感器信息融合,还实现了基于D-S证据融合的当前状态与历史状态的证据融合,实现多层次多方位的综合融合过程。
针对传统D-S证据理论对于冲突证据处理存在的缺陷,提出一种解决冲突证据融合的模型用于钻井过程的状态监测,实现在证据冲突的情况下不影响证据融合的结果,融合结果的合理性、正确性得到保障,提高集成多层次融合模型鲁棒性。
Oil drilling are complicated, complex and changing work environment, poor working conditions, the drilling process real-time state monitoring and diagnosis is an important research direction. Drilling process real-time information acquisition, processing and transmission technology is key technologies for the drilling process real-time state monitoring and diagnosis. This paper focuses on information acquisition and information processing research work about automated drilling.
Automated drilling is an important development direction of drilling technology in the future. In conclusion, the development of automated drilling technology on the basis of the status quo,Proposed the automated drilling information model, that the automated drilling system should include both ground and underground information processing systems, ground information processing system is automated drilling core, the downhole control system that include underground information processing systems, downhole measurement while drilling equipment and Combination of downhole tools will play an increasingly important role. Real-time state monitoring during drilling and abnormal state diagnosis is the core of two information processing systems, the complexity of drilling system and its working environment determines that we can not just rely on a single sensor information, we have more than one parameter to describe drilling process state. using surface and underground drilling process information from various sensors redundancy and complementarity, we used multi-sensor information fusion theory for the drilling process state monitoring and diagnosis, Based on multi-sensor information fusion method to improve the drilling process state monitoring and diagnosis robustness is feasible.
Based on the complex characteristics of drilling system, Summarized multi-sensor information fusion, use multi-sensor information fusion method of neural network and evidence theory that does not rely on accurate mathematical model. Using multi-sensor information fusion to describe the system state and system behavior is better than a single sensor information, But when there is conflict or multi-sensor information fusion model imperfect, the information fusion results may be abnormal, in the drilling process may come to the wrong state estimation, this paper put forward such a case the basic framework for dealing with conflicting evidence, by amending the evidence combination rule, the allocation of the evidence of conflict can improve rationality of the evidence fusion results.
Drilling ground systems information acquisition technology is not perfect, established a new type of wireless data acquisition monitoring system, drafted wireless data communication protocols, developed drilling ground data acquisition system. In accordance with the drilling expert knowledge, summed up the drilling abnormal state diagnosis criterion, established the feature extraction model for different parameters. Combined with drilling expert knowledge, established the drilling state-space, including drill piercie, well kick, lost circulation etc. nine abnormal state and normal state that is 10 kinds of drilling state. Using neural network to establish the drilling process model of multi-sensor information fusion to achieve drilling characteristics of state parameters to the drilling state space mapping, established the neural network training samples and sample of teachers, extracted the drilling process data under abnormal conditions, established abnormal samples, trained neural network to identify abnormal state, the diagnostic results show that neural network has good diagnostic capabilities.
D-S evidence theory is an effective method for the fusion of uncertainty information, the multi-sensor information of the drilling system is uncertainty, try using D-S evidence theory to multi-sensor information fusion. In D-S evidence theory using basic belief assignment with the state space for the state identification, access to basic belief assignment is a prerequisite for evidence fusion, proposed based on neural networks combined with the D-S evidence theory method for drilling abnormal state diagnosis, that is a multi-sensor multi-level integration diagnostic model, the neural network recognition output is basic belief assignment for drilling state space, that is, the neural network output by the normalization is the input of D-S evidence theory fusion, along the timeline to produce decision-making output. The results show that the integration of multi-level state monitoring and diagnosis model has better diagnostic capabilities. Multi-level diagnosis model not only realize the current state of multi-sensor information fusion based on neural network, also achieved to fuse the historical status and the current state based on DS evidence, completed multi-dimensional and multi-level integrated fusion process.
When the evidence is conflict, the traditional D-S combination model have defects, A conflict digestion model for evidence fusion is proposed to monitor the drilling process, implement not to affect the fusion result when evidence conflict, the fusion results is reasonable and correct, and improving robustness of integration multi-level fusion model.
引文
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