Abstract:

It is important to a pre-warning system for oil-drilling accidents to determine abnormal changes in engineering parameters during the course of oil drilling. A predictive model based on the hierarchical fuzzy system was designed in accordance with high complexities and large numbers of input variables in oil drilling and it could judge the abnormality of parameters by differences between the predicted model output and the actually measured value. A method called ‘fuzzy curve’ was utilized to select and reduce the model inputs from a mass of variables. ANFIS (adaptive neural-fuzzy inference system) was employed to determine the structure of the predictive model and to optimize the parameters of membership functions and fuzzy rules. The introduction of the ‘dynamic fuzzy domain’ concept could solve the problems arising from the slow time-variance of parameter norms in fuzzification. Experimental results indicated that this model showed the superiority both in strong stability of prediction and in high satisfaction for real-time utilization, it responded accurately to varying trends of engineering parameters in oil drilling by taking full advantage of human experience and knowledge. The model was validated with true data of a falling accident for drilling tools and the results suggested that the model could detect abnormal changes in the parameters in time and lay a dependable foundation for the early warning of oil-drilling accidents.

Key words: oil drilling, hierarchical fuzzy system, predictive model, dynamic fuzzy domain, input selection, ANFIS

摘要：

确定钻井过程中工程参数的异常变化是石油钻井事故预警系统的重要内容。针对钻井过程高度复杂和输入变量众多的特点，建立了一种基于分层模糊系统的工程参数预测模型，通过参数预测模型输出与其实际值的误差来判断参数是否异常。通过模糊曲线法对预测模型的众多输入变量进行合理筛选，利用ANFIS学习算法对预测模型的结构以及隶属函数、模糊规则等参数进行训练和优化。引入动态论域概念解决模糊化时参数基准缓变产生的问题。实验表明，这种预测模型具有良好的预测稳定性，满足实时性要求，并可充分利用经验知识，准确反映工程参数的变化趋势。用钻具掉落的事故数据进行检验，结果显示该模型能够及时发现参数的异常变化，从而为事故预警奠定基础。

关键词: 石油钻井, 分层模糊系统, 预测模型, 动态模糊论域, 输入选择, 自适应模糊推理系统