测井解释参数的确定是脉冲中子-中子(PNN)测井剩余油饱和度定量解释的关键。首先分析了PNN饱和度定量解释标准岩石物理体积模型与改进模型形式上的统一性; 然后阐述了俘获截面解释参数的确定方法, 并基于PyQt工具包开发了图版法解释参数选择模块; 最后利用该模块中的增强图版法对实际测井资料的解释参数进行了确定, 并进行了饱和度计算。结果表明, PNN饱和度定量解释的关键为区域解释参数的选择, 而图版法解释参数选择模块能避免改进模型中区域特征因子的确定问题, 并能较准确得到不同组分的区域俘获截面解释参数。PNN测井饱和度计算结果与过套管电阻率饱和度计算结果一致性较好, 且与实际生产动态情况相吻合, 证明了俘获截面解释参数选取方法的可行性与准确性。该方法对PNN测井、热中子成像测井(TNIS)以及脉冲中子寿命测井(NLL)的饱和度定量解释具有指导意义和实际应用价值。
Assessment of logging interpretation parameters is a core issue in quantitatively interpreting residual oil saturation in Pulsed neutron-neutron (PNN) logging.In this study, the standard petrophysical volume model and modified model for the quantitative interpretation of PNN saturation were analyzed, the method used to determine the capture section of different components, namely matrix, shale, formation water, and hydrocarbons, was discussed, and a graphical interpretation parameter selection module was developed based on PyQt.The interpretation parameters were determined using the graphical-enhanced method of this module, and the actual logging data of the PNN were interpreted and analyzed.The results have shown that the standard petrophysical volume model and the modified model for the quantitative interpretation of PNN saturation have unity in form, and the essence was the selection of regional interpretation parameters.The interpretation parameter selection module developed based on PyQt meets the requirements of logging interpretation, avoids the determination of regional characteristic factors of the modified model, and accurately obtains the regional capture section parameters of different components.The saturation interpretation result of PNN logging is in line with the case-hole resistivity logging interpretation result and is consistent with the actual production performance data.This demonstrates the suitability and accuracy of the proposed interpretation parameter selection method.This method is important for quantitatively interpreting saturation in PNN, thermal neutron imaging logging (TNIS), and pulsed neutron lifetime logging (NLL).