Machine Learning Application of Two-Dimensional Fracture Properties Estimation
DOI:
https://doi.org/10.25299/jgeet.2023.8.02-2.13874Keywords:
Fracture, Mean aperture, Surfaces roughness, Machine learning, CNNAbstract
Fractures are substantial contributors to solute transport sedimentary systems that form pathways. The pathway formed in a fracture has two physical parameters, there are mean aperture and surface roughness. Mean aperture is the thickness of the pathway that the fluid will pass through, and surface roughness is the roughness of the fracture pathway. The two physical parameters of the fracture are important to determine since they affect the permeability value in petroleum reservoir analysis. We developed a machine learning algorithm based on the Convolutional Neural Network (CNN) to predict those two parameters. Furthermore, image processing analysis is performed to generate the datasets. The results show that the CNN algorithm shows good agreement with the reference results. In addition, the algorithms showed efficient performance in terms of computational time. CNN is a type of deep neural designed to perform analysis on multi-channel images that can classify fracture geometry. The best model was determined using a benchmark dataset with a CNN model provided by Keras. The results of experiments conducted on fracture geometry images show that the machine learning model created is able to predict the mean aperture and surface roughness values.
Downloads
References
Dharmawan, I. A., Ulhaq, R. Z., Endyana, C., Aufaristama, M., 2016. Numerical simulation of non-Newtonian fluid flows through fracture network. In IOP conference series: earth and environmental science 29(1), 012030. doi: 10.1088/1755-1315/29/1/012030.
ESDM., 2019. Outlook Energi Indonesia (OEI) 2019. Ministry of Energy and Mineral Resources Republic of Indonesia. Retrieved from https://www.esdm.go.id/assets/media/content/content-outlook-energi-indonesia-2019-bahasa-indonesia.pdf
Gao, Y., Mosalam, K. M., 2018. Deep transfer learning for image based structural damage recognition. Computer-Aided Civil and Infrastructure Engineering 33(9), 748–768.
Herdiansyah, H., Negoro, H. A., Rusdayanti, N., Shara, S., 2020. Palm oil plantation and cultivation: Prosperity and productivity of smallholders. Open Agriculture 5(1), 617-630.
Huang, G., Liu, Z., Der, V., Weinberger, K. Q., 2017. Densely connected convolutional networks. 2261–2269. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Doi: 10.1109/CVPR.2017.243
Koesoemadinata, R.P., 1980. Geologi Minyak dan Gas Bumi (1st Ed). Penerbit ITB, Bandung.
Naufal, M.F., Kusuma, S.F., 2022. Weather image classification using convolutional neural network with transfer learning. AIP Conference Proceedings 2470(1), 050004.
Talo, M., 2019. Convolutional neural networks for multi-class histopathology image classification. arXiv:1903.10035.
Wang, D., de Boer, G., Neville, A., Ghanbarzadeh, A., 2021. A new numerical model for investigating the effect of surface roughness on the stick and slip of contacting surfaces with identical materials. Tribology International 159, 106947. doi: 10.1016/j.triboint.2021.106947
Downloads
Published
Issue
Section
License
Copyright @2019. This is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License which permits unrestricted use, distribution, and reproduction in any medium. Copyrights of all materials published in JGEET are freely available without charge to users or / institution. Users are allowed to read, download, copy, distribute, search, or link to full-text articles in this journal without asking by giving appropriate credit, provide a link to the license, and indicate if changes were made. All of the remix, transform, or build upon the material must distribute the contributions under the same license as the original.