AR谱估计法在感应测井中的应用

APPLICATION OF AR SPECTRAL ESTIMATION TO CALIBRATE THE INDUCTION LOGGING DATA

应用AR谱估计法求取感应测井的纵向传递函数,可用于感应测井数据反褶积校正。在地层电导率分布统计模型的基础上,确定感应测井传递函数的两个约束条件是,H(0)=1和H(f)=H(-f)。文中应用不同地区的两口井的感应测井数据,采用AR谱估计法估算其感应测井传递函数,再同共轭梯度法进行反褶积校正。把校正后的数据,与油基泥浆岩心数据、七侧向测井数据、原始的感应测井数据进行对比,表明校正明显地提高了分辨率。校正前后感应数据的散点图的对比也说明校正使感应测井数据在数值上更接近岩心数据。

This paper demonstrates the application of AR spectral estimation to obtain the vertical transfer function of induction log, and to deconvolve the induction logging data with it for calibration. With a statistical model of formation conductivity distribution, this paper determines two constraints of the transfer function as: H (0) =1 and H (f) =h ( f) . The vertical transfer functions of induction logging data in two wells located in different district are estimated by means of AR spectral estimation. Then, deconvolution as a calibration, are made by conjugate gradient method. Comparison of the data calibrated with core conductivity data from a well with oil base mud,7 late-rolog data and original induction data indicates that this calibration improves obviously the resolution and accurary of inducation logging data. In addition, scatter diagrams of induction logging data before and after the calibration shows that the calibration makes induction logging data closer to core data than the original ones.