摘要
随着页岩气的大力开发,页岩气钻井过程中的机械钻速低、钻井周期长以及钻头磨损严重等问题日益突出,因此,预测地层岩石可钻性和建立地层岩石可钻性剖面有着十分重要的意义。文章结合分形理论对分形维数进行推导与分析,确定出上返岩屑的分形维数与岩屑质量和岩屑尺寸之间的联系。并以岩屑分形维数和测井数据作为输入参数,可钻性极值作为输出参数,利用BP神经网络建立涪陵页岩气区块输入参数与输出参数之间的映射关系。最后,进行了实例验证,结果与现场实际吻合,表明该方法具有现场实用性,对实际生产开发有一定指导意义。
With the shale gas development,it is becoming important to predict formation drillability and set up the rock drillability profile. In this article the fractal theory is briefly introduced,and the fractal dimensions D is derived and analyzed,the relationship between the cuttings dimensions and quality,and sizes is determined. The fractal dimension and log data are used as input parameters and the drillability extreme values are the output parameters. The mapping relationship between input parameters and output parameters of Fuling shale gas block is established using BP neural network. The model has been proved with high fitting degree by cases,which will be a guide for actual development.
引文
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