Identification of vuggy intervals and understanding their connectivity are critical for predicting carbonate reservoir performance. Although core samples and conventional well logs have been traditionally used to classify vuggy facies, this process is labor intensive and often suffers from data inadequacies. Recently, convolutional neural network (CNN) algorithms have approached human-level performance at multiimage classification and identification tasks. In this study, CNNs were trained to identify vuggy facies from a well in the Arbuckle Group in Kansas, USA. Borehole-resistivity images were preprocessed into half-foot intervals; this complete data set was culled by removing poor-quality images to generate a cleaned data set for comparison. Core descriptions along with conventional gamma ray, neutron/density porosity, photoelectric factor (PEF), and nuclear magnetic resonance (NMR) T2 data were used to label these data sets for supervised learning. Hyperparameters defining the CNN network size (numbers of convolutional layers/filters and the numbers of fully connected layers/neurons) and minimize overfitting (dropout rates, patience, and minimum delta) were optimized. The median losses and accuracies from five Monte Carlo realizations of each hyperparameter combination were the metrics defining CNN performance. After hyperparameter optimization, median accuracy for vuggy/nonvuggy facies classification was 0.847 for the cleaned data set (0.813 for the complete data set). This study demonstrated the effectiveness of using microresistivity image logs in a CNN to classify facies as either vuggy or nonvuggy, while highlighting the importance of data quality control. This effort lays the foundation for developing CNNs to segment images to estimate vuggy porosity.

识别孔隙间隔并了解其连通性对于预测碳酸盐岩储层性能至关重要。尽管传统上已使用岩心样品和常规测井数据对松散相进行分类，但该过程耗费大量人力，并且经常遇到数据不足的问题。最近，卷积神经网络（CNN）算法已在多图像分类和识别任务上达到了人类水平的性能。在这项研究中，对CNN进行了训练，以从美国堪萨斯州的Arbuckle集团的一口井中识别出孔隙相。将井眼电阻率图像预处理为半英尺间隔；通过删除质量较差的图像来挑选出完整的数据集，以生成干净的数据集进行比较。核心描述以及常规的伽马射线，中子/密度孔隙率，光电因子（PEF）和核磁共振（NMR）T2数据用于标记这些数据集以进行监督学习。优化了定义CNN网络大小（卷积层/过滤器的数量和完全连接的层/神经元的数量）并最小化过拟合（丢失率，耐心和最小增量）的超参数。每个超参数组合的五个蒙特卡洛实现的中值损失和准确性是定义CNN性能的指标。经过超参数优化后，清理后的数据集的相洞/相洞相分类的中位准确度为0.847（完整数据集为0.813）。这项研究证明了在CNN中使用微电阻率图像测井将相分类为疏松或非疏松的有效性，同时强调了数据质量控制的重要性。