Reservoir property image slices from the Groningen gas field for image translation and segmentation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866913091363536896 |
|---|---|
| author | Al-Fakih, Abdulrahman Sariah, Nabil Koeshidayatullah, Ardiansyah Kaka, SanLinn I. |
| author_facet | Al-Fakih, Abdulrahman Sariah, Nabil Koeshidayatullah, Ardiansyah Kaka, SanLinn I. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03942 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Reservoir property image slices from the Groningen gas field for image translation and segmentation Al-Fakih, Abdulrahman Sariah, Nabil Koeshidayatullah, Ardiansyah Kaka, SanLinn I. Computer Vision and Pattern Recognition Geophysics 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. |
| title | Reservoir property image slices from the Groningen gas field for image translation and segmentation |
| topic | Computer Vision and Pattern Recognition Geophysics |
| url | https://arxiv.org/abs/2605.03942 |