Dissolution is a common diagenetic effect in carbonate formations. Vugs caused by dissolution significantly impact carbonate reservoir quality by affecting the porosity and permeability of the reservoir. However, without core and image logs, the identification and classification of vugs using wireline logs only is challenging, because logging tool responses reflect a mixed effect of changing mineral/fluid composition and diagenetic features. This paper presents a data-driven approach using neural networks to identify vugs and classify vug facies based on vug size. The purpose is to predict wells/intervals with limited measurements by machine learning models trained with core data from key wells. The input features for vug identification are conventional well logs (i.e., gamma ray, resistivity, neutron/density porosity, photoelectric factor, and acoustic slowness) from the Cambrian-Ordovician Arbuckle formation, Kansas. Two classification labels are used as the prediction target for the neural networks: (1) a binary vuggy index derived from nuclear magnetic resonance (NMR) measurements using a cutoff on T2 distribution, which presents the proportion of large pores over the total porosity, and (2) vug size labels from depth-by-depth core visual descriptions. A one-hidden-layer shallow neural network is compared against deep neural networks, including structures such as one-dimensional convolutional layers (1-D CNN) and long short-term memory (LSTM) layers. Results suggest that using a combination of multi-mineral analysis results and original well logs will increase the prediction accuracy of vug facies. Shallow and deep neural networks show a similar ability to identify vugs, with average accuracy of around 80%. However, to predict vug-size-based facies labels, deep neural networks outperform shallow neural networks, with overall accuracy improved by as much as 10%. The proposed method shows that deep neural networks (1-D CNN and LSTM) are reliable tools for vug facies prediction.

溶解是碳酸盐岩层中常见的成岩作用。溶解引起的孔洞通过影响储层的孔隙度和渗透率，显着影响碳酸盐岩储层的质量。但是，在没有岩心和图像测井的情况下，仅使用电缆测井来对孔洞进行识别和分类就具有挑战性，因为测井工具的响应反映出矿物/流体成分和成岩特征变化的混合作用。本文提出了一种数据驱动的方法，该方法使用神经网络来识别容器并根据容器大小对容器相进行分类。目的是通过使用来自关键井的核心数据训练的机器学习模型来预测测量值有限的井/井间隔。孔洞识别的输入特征是常规测井曲线（即伽马射线，电阻率，中子/密度孔隙率，光电系数，和声慢度）来自堪萨斯州的寒武纪-奥陶纪Arbuckle组。两个分类标签被用作神经网络的预测目标：（1）使用T2分布的临界值从核磁共振（NMR）测量中得出的二元松散指数，该指数表示大孔隙在总孔隙度中所占的比例，以及（2）来自各个核心核心视觉描述的容器尺寸标签。将一隐藏层浅层神经网络与深层神经网络进行了比较，其中包括一维卷积层（1-D CNN）和长短期记忆（LSTM）层等结构。结果表明，综合使用多矿物分析结果和原始测井资料，将可以提高孔洞相预测的准确性。浅层神经网络和深层神经网络具有相似的识别口哨的能力，平均准确率约为80％。但是，要预测基于孔洞大小的相标记，深层神经网络要优于浅层神经网络，总体准确性提高了多达10％。所提出的方法表明，深层神经网络（1-D CNN和LSTM）是用于孔隙相预测的可靠工具。