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All Journal Jurnal Daulat Hukum Journal of Geoscience, Engineering, Environment, and Technology ABDIMAS SILIWANGI JURNAL PENDIDIKAN TAMBUSAI Dirasah : Jurnal Studi Ilmu dan Manajemen Pendidikan Islam JURNAL PEMBANGUNAN HUKUM INDONESIA Jurnal Laot Ilmu Kelautan Indonesian Journal of Sociology, Education and Development (IJSED) Jurnal Hukum Lex Generalis CIVICS: Jurnal Pendidikan Pancasila dan Kewarganegaraan Jurnal Eurekamatika JTechLP Demak Universal Journal of Islam and Sharia Media Hukum Indonesia (MHI) JIS: Journal ISLAMIC STUDIES
Az-Zahra, Fatimah
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Education Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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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 Nurcahya, Ardian; Alexandra, Aldenia; Zainuddin, Satria Zidane; Az-Zahra, Fatimah; Haq, M. I. Khoirul; Dharmawan, Irwan Ary
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from The 1st International Conference on Upstream Energy Techn
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 Akmal, Fadhillah; Dzulizar, M. Cisco Ramadhan; Rafli, Muhammad Faizal; Az-Zahra, Fatimah; Haq, M. I. Khoirul; Dharmawan, Irwan Ary
Journal of Geoscience, Engineering, Environment, and Technology Vol. 8 No. 02-2 (2023): Special Issue from The 1st International Conference on Upstream Energy Techn
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 Abdul Kholiq Adlim, Faza Fawwaz Fauzil Agustin, Fitri Aisiyah, Iseu Aisyah, Afifah Nurani Kamilia Akmal, Fadhillah Aksar Aksar, Aksar Alexandra, Aldenia Ambarwati Anggraini, Triya Anzani, Luthfi Ashari, Mahatir Muhamad Az Zahra, Fathimah Azzahra, Khalita Putri Boang Manalu, Vivin Silvia Br Bangun, Iren Br Bangun, Jacklyn Alona Ciek Julyati Hisyam Dafa, M Naufal Abiyi Damayanti, Intania Putri Destri, Asti Fitria Dewi Anggraeni, Salsa Bella Dinata, Umar Dwi Desi Yayi Tarina Dzulizar, M. Cisco Ramadhan Fachri, Amanda Maharani Febrian, Matthew Hadi, Alya Fatma Hadiningrum, Sri Hamidah, Hawarizmy Nurul Hanifah, Khusna Haq, M. I. Khoirul Hidayat, Febryan Irwan Ary Dharmawan Islami, Husnul Jelita, Dinda LATIFAH Loustiawati, Lousy Lumban batu, Rouli Maharani, Mutiara Nasjwa Marhamah, Sakinah Maryam Maryam Mediana, Topan Septiadi Mulyadi M, Mulyadi Mursida Nababan, Ramsul Najma, Siti Najla Nur Nur Hayati, Syahidah Nur Najma, Siti Najla Nurcahya, Ardian Nurholilah, Ayi Purba, Desri Ronay Fiona Putri, Mediana Rachma, Nabila Rafli, Muhammad Faizal Rahma, Intan Aulia Ridwandhani, Rifki Ahmad Rini Marwati Ririn Sispiyati Ryan Rayhana Sofyan, Ryan Rayhana Sa'adah, Miftahus Safier, Ginola Muhammad Sally, Fauzan Hari Sudding Salsabila, Nadiya Salsabila, Tengku Satriya, Raisa Nadia Bunga Shaleh, Ali Ismail Siahaan, Parlaungan Gabriel Sihombing, Albert Hasea Samuel Sihombing, Amalia Rahma Dini Sihotang, Josua Ferdinand Simamora, Tesalonika Amazia Situmorang, Dandi Abram Situmorang, Lennai Sophia, Fransisca Steffanie, Josephine Wahyuni, Devi Sri Wulandari, Hania Yesica S, Shely Yesica, Shely Yuli, Yuliana Zainuddin, Satria Zidane