公开了格罗宁根气田高分辨率储层属性图像数据集，支持地质图像分析基准测试。
Reservoir property image slices from the Groningen gas field for image translation and segmentation
- 基于三维模型生成四类储层属性的二维对齐图像
- 包含岩相、孔隙度、渗透率、含水饱和度等4种属性图像
- 适合做图像分割与跨域图像转换研究，支持方法复现
储层表征工作日益依赖图像化与机器学习/深度学习甚至生成式AI方法，但可用于可复现基准测试的公开地质图像数据集仍十分有限。本文介绍一个来自格罗宁根静态地质模型的高分辨率储层属性图像切片数据集。该数据集包含从三维储层网格生成的四类二维PNG图像：岩相、孔隙度、渗透率和含水饱和度，已对齐，适用于下游可视化、分割与图像到图像翻译任务。除原始图像库外，还提供归档的软件工作流，可复现增强、掩码生成、成对图像构建及基线实验。该资源旨在支持地质图像分析方法的基准测试以及储层属性间跨域关系的研究。通过将固定图像数据集与可复现处理流程分离，本工作为地球科学、储层建模与机器学习应用提供了透明可复用的基础。
原文摘要 · Abstract (English)
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.
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