基于扩径对密度及核磁共振测井资料校正方法的探讨

Base on the Expanding of Density and Nuclear Magnetic Resonance(NMR) Logging Data Correction Method is Discussed

井径扩径时将会造成密度值和核磁测井信号发生畸变,影响储层参数建模和流体性质识别。为了解决上述问题,本文提出了神经网络法和核磁共振测井井眼校正法来消除扩径对密度曲线和核磁共振信号的影响,前者是选用受井眼影响较小的自然伽马、原状地层电阻率作为输入,采用径向基函数神经网络法重构密度曲线;后者可以在时域内通过估算井眼泥浆信号的大小,由原始回波串中剔除泥浆信号影响,从而得到来自地层流体的核磁共振信号。结果表明:在扩径段,使用神经网络法可以消除井眼的影响,提高储层参数建模精度;使用核磁共振测井井眼校正方法可以消除束缚流体孔隙度高、可动流体孔隙度小、渗透率低的难题,为后期流体性质识别打下了基础。

When the borehole diameter expanding will cause density value and nuclear magnetic logging signal distortion,influence of reservoir parameter modeling and fluid property recognition.In order to understand the above problem,the paper presents a neural network and nuclear magnetic resonance(NMR)logging borehole correction method to eliminate the hole diameter to the influence of density curve and nuclear magnetic resonance(NMR)signal,the former is affected by the hole smaller natural gamma,virgin zone resistivity as input,by using radial basis function neural network to reconstruct density curve;the later can in time domain by estimating the size of the borehole mud impact signal,by the original echo string to remove the mud signal,nuclear magnetic resonance(NMR)signal is obtained from the formation fluid.Results show that in the expanding period,using the neural network can eliminate the influence of borehole and improve the accuracy of reservoir parameter modeling;using nuclear magnetic resonance(NMR)logging borehole correction method can eliminate high bound fluid porosity,movable fluid porosity,low permeability,late for fluid property recognition to lay the foundation.