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arXiv · 2605.03942

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

Abdulrahman Al-Fakih·Nabil Sariah·Ardiansyah Koeshidayatullah·SanLinn I. Kaka
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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.

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Abdulrahman Al-Fakih, Nabil Sariah, Ardiansyah Koeshidayatullah, SanLinn I. Kaka. 2026-05-05. Reservoir property image slices from the Groningen gas field for image translation and segmentation. https://arxiv.org/abs/2605.03942

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