Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly available geological image datasets suitable for reproducible benchmarking remain limited. Here we describe a high-resolution dataset of reservoir-property image slices derived from the Groningen static geological model. The dataset contains aligned two-dimensional PNG images representing facies, porosity, permeability, and water saturation, generated from three-dimensional reservoir grids and prepared for downstream visualization, segmentation, and image-to-image translation tasks. In addition to the deposited original image corpus, we provide an archived software workflow for reproducing augmentation, mask generation, paired-image construction, and example baseline experiments. The resource is designed to support benchmarking of geological image analysis methods and the study of cross-domain relationships among reservoir properties. By separating the fixed image dataset from the reproducible processing workflow, this work provides a transparent foundation for reuse in geoscience, reservoir modeling, and machine-learning applications.

翻译：储层表征工作流程日益依赖基于图像的方法、机器学习/深度学习乃至生成式人工智能技术，但适用于可重复基准测试的开源地质图像数据集仍然有限。本文介绍了基于格罗宁根静态地质模型导出的高分辨率储层属性图像切片数据集。该数据集包含对齐的二维PNG图像，分别表征岩相、孔隙度、渗透率及含水饱和度，这些图像源自三维储层网格数据，并已为后续可视化、分割及图像到图像翻译任务完成预处理。除原始图像语料库外，我们还提供了一套可归档的软件工作流程，用于复现数据增强、掩码生成、配对图像构建及基线实验范例。本资源旨在支持地质图像分析方法基准测试以及储层属性跨域关系研究。通过将固定图像数据集与可复现处理工作流程分离，本研究为地球科学、储层建模及机器学习应用中的复用提供了透明化的基础。