极限学习机在黏土矿物分析中的应用

对鄂尔多斯盆地苏里格北部上古生界盒8段低渗透储层研究发现，成岩作用是控制气藏分布的主要因素之一，而黏土矿物又影响着成岩作用类型及强度，在成岩作用划分中具有重要指示作用.本研究尝试利用自然伽马能谱测井结合神经网络（极限学习机）准确计算储层中黏土矿物含量，为全井自动成岩作用识别提供支撑.在黏土矿物分析过程中，为避免测井信息受岩石骨架颗粒成分差异的影响，针对研究区沉积的岩屑砂岩和岩屑石英砂岩分别建立黏土矿物分析神经网络，提高计算精度.神经网络采用了参数不易陷入局部最优的极限学习机，保证了分析结果的速度和稳定性.在此基础上，利用苏里格北部地区上古生界盒8段15个X衍射分析样本，将分岩性计算结果与及未分岩性分析结果进行比较，证明了方法的有效性.

APPLICATION OF EXTREME LEARNING MACHINE IN ANALYSIS OF CLAY MINERALS

The study on low permeability reservoirs in the 8th member of Lower Shihezi Formation, Upper Paleozoic in northern Sulige of Ordos Basin show that diagenesis is one of the main factors controlling the distribution of gas reservoirs, while clay minerals affect the type and intensity of diagenesis and serve as an important indicator in the classification of diagenesis. This study attempts to use natural gamma-ray spectral logging combined with neural network (extreme learning machine, ELM) to accurately calculate the content of clay minerals in reservoir, providing support for automatic identification of diagenesis in the whole well. In the analysis of clay minerals, to avoid the influence of difference of rock skeleton and particle compositions on logging information, the neural network of clay mineral analysis is established respectively for lithic sandstone and lithic quartz sandstone in the study area to improve calculation accuracy. The neural network adopts the ELM with lower probability of trapping into low efficiency and local optimum to ensure the speed and stability of analysis result. On this basis, 15 X-ray diffraction analysis samples from the 8th member of Lower Shihezi Formation in northern Sulige area are used to compare the calculated results of differential and indifferential lithology, proving the effectiveness of the method.