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Aceh International Journal of Science and Technology
Published by Universitas Syiah Kuala
ISSN : 20889860     EISSN : 25032348     DOI : http://10.13170/aijst
Core Subject : Science, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences
Aceh International Journal of Science & Technology (AIJST) is published by the Graduate School of Syiah Kuala University (PPs Unsyiah) and the Indonesian Soil Science Association (Himpunan Ilmu Tanah Indonesia, Komda Aceh). It is devoted to identifying, mapping, understanding, and interpreting new trends and patterns in science & technology development, especially within Asian countries as well as other parts of the world. The journal endeavors to highlight science & technology development from different perspectives. The aim is to promote broader dissemination of the results of scholarly endeavors into a broader subject of knowledge and practices and to establish effective communication among academic and research institutions, policymakers, government agencies, and persons concerned with the complex issue of science & technology development. The Journal is a peer-reviewed journal. The acceptance decision is made based upon an independent review process supported by rigorous processes and provides constructive and prompt evaluations of submitted manuscripts, ensuring that only intellectual and scholarly work of the greatest contribution and highest significance is published. The AIJST publishes original conceptual and research papers, review papers, technical reports, case studies, management reports, book reviews, research notes, and commentaries. It will occasionally come out with special issues devoted to important topics concerning science & technology development issues. Scopes Starting in 2016, AIJST has focused on science and engineering aspects, and therefore now AIJST considers the topics but not limited to : Engineering (Mechanical, Chemical, Civil, Transportation) Geology and Geomorphology Environmental Science (Hydrology, Pollution, Water Treatment, Soil Science, Climatology) Physical Oceanography Mathematics Physics and Geophysics Geospatial and Information Technology
Arjuna Subject : Ilmu Lingkungan (semua ketegori) - Global dan Perubahan Planet
Articles 12 Documents
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Search results for , issue "Vol 10, No 1 (2021): April 2021" : 12 Documents clear
Utilization of Acoustic Wave Velocity for Permeability Estimation in Static Reservoir Modeling: A Field Case Prakoso*, Suryo; Burhannudinnur, Muhammad; Irano, Teddy; Herdiansyah, Firman
Aceh International Journal of Science and Technology Vol 10, No 1 (2021): April 2021
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.10.1.20328

Abstract

Several researches have shown that P-wave velocity carries information on the complexity of the rock's pore geometry and pore structure. Their complexity can be characterized by critical porosity. Therefore, the P-wave velocity is used to estimate permeability. This research uses data taken from the Tomori formation from Banggai-Sula basin, Central Sulawesi, which is a carbonate rock reservoir. Also, this research aims to obtain a 3D permeability model by using acoustic wave velocity cube data. The results show that permeability can be modeled well using acoustic wave velocity data. Furthermore, compared to the raw data log of permeability, the modeling results using wave velocity based on critical porosity show good results. This method is another alternative to permeability modeling if acoustic wave velocity cube data is available.
The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data Saroji*, Sudarmaji; Winata, Ekrar; Hidayat, Putra Pratama Wahyu; Prakoso, Suryo; Herdiansyah, Firman
Aceh International Journal of Science and Technology Vol 10, No 1 (2021): April 2021
Publisher : Graduate School of Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13170/aijst.10.1.18749

Abstract

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns.

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