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All Journal Bionatura Bulletin of Scientific Contribution : Geology Journal of Geoscience, Engineering, Environment, and Technology Jurnal Elektronika dan Telekomunikasi
Irwan Ary Dharmawan
Department Of Geophysics, Faculty Of Mathematics And Natural Science, Universitas Padjadjaran, Jatinangor, Indonesia.
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Physics

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Journal : Journal of Geoscience, Engineering, Environment, and Technology

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Machine Learning Application of Two-Dimensional Fracture Properties Estimation Ardian Nurcahya; Aldenia Alexandra; Satria Zidane Zainuddin; Fatimah Az-Zahra; M. I. Khoirul Haq; Irwan Ary Dharmawan
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from “The 1st International Conference on Upstream Energy Te
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2023.8.02-2.13874

Abstract

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.
Machine learning prediction of tortuosity in digital rock Fadhillah Akmal; M. Cisco Ramadhan Dzulizar; Muhammad Faizal Rafli; Fatimah Az-Zahra; M. I. Khoirul Haq; Irwan Ary Dharmawan
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from “The 1st International Conference on Upstream Energy Te
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2023.8.02-2.13875

Abstract

Physical rock property measurement is an important stage in energy exploration, both for hydrocarbons and geothermal sources. The value of physical rock properties can provide information about reservoir quality, and one of these properties is tortuosity. Tortuosity is an intrinsic property of porous materials that describes the level of complexity of the porous arrangement when a fluid passes through it. Conventionally, tortuosity values are measured through laboratory analysis and numerical simulation, but these measurements can take a long time. An alternative method for measuring tortuosity is using machine learning with a convolutional neural network (CNN). A CNN is a type of deep neural network designed to analyze multi-channel images and has been applied successfully to classification and non-linear regression problems. By training a CNN on a dataset of digital rock samples that have been simulated using numerical computation to obtain their tortuosity values, it is possible to demonstrate that CNNs can accurately predict the tortuosity of digital rock. The result is that the CNN model can predict tortuosity values with the Xception model being the most accurate with the lowest RMSE value of 0.90962.
Co-Authors Aldenia Alexandra Ardian Nurcahya Asep Harja Ayi Bahtiar Budi Adiperdana Fadhillah Akmal Fatimah Az-Zahra Fatimah Az-Zahra Kartika Hajar Kirana Kusnahadi Susanto M. Cisco Ramadhan Dzulizar M. I. Khoirul Haq M. I. Khoirul Haq Muhammad Faizal Rafli Muhammad Zharfan Azzam Nadya Larasati Kartika Satria Zidane Zainuddin Yayah Yuliah - Zhafirah Nurul Syarafina