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机构地区:[1]同济大学岩土及地下工程教育部重点实验室 [2]中国石油大学石油天然气工程学院,北京102249
出 处:《岩石力学与工程学报》2008年第1期102-107,共6页Chinese Journal of Rock Mechanics and Engineering
基 金:国家自然科学基金资助项目(40772179);博士后科学基金资助项目(20060390655)
摘 要:为实现可钻性的钻前预测,需要结合地层沉积层序研究成果,对地层可钻性剖面序列特征进行深入分析。在对中国大陆科学钻探预先导孔地层可钻性序列研究的基础上,提出可钻性时序特征的2个表征:时序几何形态特征和内在趋势变化特征,分别用关联维数和Hurst指数表示。通过与实际地层可钻性的比较分析表明,这2个参数反映可钻性时序的内外特征。因此,根据这2个表征参数,结合神经网络技术,可建造地层可钻性的预测模型。The analysis and prediction of deep formation drillability are important for drilling and exploring engineering.In general,the formation drillability index is calculated based on well acoustic logging after the well being completed or at some stages.Using the well logging data,some methods can be employed to calculate the stratum drillability.For the sake of improving prediction technique,the characteristics of formation drillability time series(DTS) are needed.Based on the study of strata drillability time-series of pilot drilling of Chinese continental scientific drilling(CCSD),two typical indices were put forward.They are characteristics of time-series geometrical form and intrinsic change tendency.These two indices are expressed with correlation dimension(D2) and Hurst(H) exponent,respectively.Based on the analysis of 32 actual well formation drillability profile curves of different oil fields in China,a conclusion was drawn that most of the curves show fractal characteristics.For a drillability time-series,its correlation dimension(D2) can describe the degree of variety and it can be used to express the curve′s outside geometrical characteristic.The Hurst exponent can be used to estimate the time-series′ development tendency.The application results show that D2 and H can describe the main characteristics of a DTS perfectly.With these two characteristics,a drillability prediction model based on intelligent neural network was developed.This model was used to predict the formation drillability of CCSD,and it was proven to be practicable.
关 键 词:地质力学 钻探 可钻性 时间序列 分形几何 预测
分 类 号:P55[天文地球—构造地质学]
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