Reservoir property image slices from the Groningen gas field for image translation and segmentation

TL;DR

This paper introduces a high-resolution geological image dataset from the Groningen gas field, supporting benchmarking and analysis of reservoir properties using machine learning and image translation techniques.

Contribution

It provides a publicly available, well-annotated dataset and reproducible workflow for geological image analysis, facilitating benchmarking and cross-domain studies.

Findings

Dataset includes aligned images of facies, porosity, permeability, and water saturation.

Reproducible workflow enables augmentation, mask creation, and baseline experiments.

Supports benchmarking and cross-property analysis in reservoir modeling.

Abstract

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.