Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation

Andrei Baraian, Vili Kellokumpu, Räty Tomi, Leena Kallio

2023

Abstract

Fracture analysis represents one of the key investigations that needs to be carried in borehole logs. Identifying fractures, as well as other similar features (like breakouts or foliations) is essential for characterizing the reservoir where the drilling took place. However, identifying and characterizing the fractures from borehole images is a very time and resource consuming task, that require extensive knowledge from geological experts. For this reason, developing semi-automated or automated tools would facilitate and increase the productivity of fracture analysis, since even for one reservoir, experts need to analyze and interpret hundreds of meters of borehole images. This paper presents a deep learning based approach for application of automatic fracture detection and characterization in borehole images, relying on state-of-the-art convolutional neural network for accurate semantic segmentation of fractures. Target images consists of color borehole images, as opposed to acoustic or drill-core images, and uses real world data, both for training the deep learning model and testing the whole system. The system is evaluated by using multiple metrics and the final outputs of the system are the parameters of the sinusoids that define the predicted fractures.

Paper Citation

in Harvard Style

Baraian A., Kellokumpu V., Tomi R. and Kallio L. (2023). Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 856-863. DOI: 10.5220/0011673100003417

in Bibtex Style

@conference{visapp23,
author={Andrei Baraian and Vili Kellokumpu and Räty Tomi and Leena Kallio},
title={Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={856-863},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011673100003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Automatic Fracture Detection and Characterization in Borehole Images Using Deep Learning-Based Semantic Segmentation
SN - 978-989-758-634-7
AU - Baraian A.
AU - Kellokumpu V.
AU - Tomi R.
AU - Kallio L.
PY - 2023
SP - 856
EP - 863
DO - 10.5220/0011673100003417
PB - SciTePress