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Hadziqul Abror
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Program Studi Teknik Perminyakan, Fakultas Teknik, Universitas Jember Jl. Kalimantan Tegalboto No.37, Krajan Timur, Sumbersari, Kec. Sumbersari, Kabupaten Jember, Jawa Timur 68121.
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Journal of Sustainable Energy Development
Published by Universitas Jember
ISSN : -     EISSN : 30482585     DOI : -
Core Subject : Science, Engineering,
Energy Engineering
The Journal of Sustainable Energy Development is the official scientific journal of Petroleum Engineering, Faculty of Engineering, University of Jember for the dissemination of information on research activities, technology engineering development and laboratory testing in sustainable energy development. The focus and scope of JSED as follows: Oil and Gas Technology: Production, Reservoir, and Drilling Technology, Enhance Oil Recovery Geothermal Technology: Reservoir Characterization and Modeling, Development of Productivity-Enhancing Methods, Plan of Vevelopment Earth Science: Geology, Geophysics, Geochemical Renewable Energy: Wind energy, Hydro energy, Solar cell energy, Biomass
Arjuna Subject : Energi (semua kategori) - Energi Terbarukan, Keberlanjutan dan Lingkungan
Articles 23 Documents
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Optimasi Produksi Menggunakan Injeksi CO2 dan Penerapan Sistem Carbon Pricing Reservoir X Wulan, Nanda; Eklezia Dwi Saputri, Eriska; Laksmita Sari, Riska
Journal of Sustainable Energy Development Vol. 3 No. 1 (2025): Journal of Sustainable Energy Development (JSED)
Publisher : Petroleum Engineering, Faculty of Engineering, University of Jember

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Abstract

Indonesia tercatat sebagai salah satu negara penyumbang emisi gas CO₂ terbesar di dunia dengan total emisi mencapai 1,3 Gt di mana 50,6% berasal dari sektor industri migas. Oleh karena itu, Pemerintah Indonesia berkomitmen untuk menurunkan emisi GRK sebesar 29%. Penelitian ini bertujuan untuk mengurangi emisi GRK dengan menerapkan sistem carbon pricing pada perhitungan keekonomian dan penggunaan metode injeksi gas CO2 pada reservoir X. Selain itu, metode injeksi CO2 diharapkan nantinya dapat mengoptimalkan produksi minyak pada reservoir. Injeksi CO₂ di reservoir X dan penerapan sistem carbon pricing menggunakan skema Production Sharing Contract (PSC) Gross Split dirancang dengan data asumsi yang memiliki karakteristik minyak ringan (°API 35) dan batuan sandstone dengan kedalaman 10.000 ft. Pada awal produksi, reservoir X mengalami penurunan yang signifikan akibat aquifer support yang lemah, sehingga diterapkan Enhanced Oil Recovery (EOR) dengan injeksi CO₂ secara miscible dan immiscible. Penelitian ini menggunakan 3 skenario yang nantinya disimulasikan dan dibandingkan hasil perolehan terbaik. Skenario 3 merupakan skenario terbaik dengan menginjeksikan 1 sumur produksi dan 2 sumur injeksi yang menunjukkan peningkatan kumulatif produksi minyak lebih besar dari simulasi basecase, diperoleh nilai sebesar 7,6 MMBBL dengan recovery factor sebesar 55% dan penurunan water cut hingga 91%. Selain itu, hasil perhitungan keekonomian dengan menerapkan sistem carbon pricing menghasilkan NPV sebesar 786.678,21 USD, IRR sebesar 11%, dan Pay Out Time (POT) selama 7,4 bulan yang mengindikasikan kelayakan ekonomi proyek bagi kontraktor. Penelitian ini memberikan triple-win solution dengan meningkatkan produksi minyak, mendukung target nasional pengurangan emisi karbon, dan memberikan keuntungan ekonomi.
Studi Simulasi: Pengaruh Soaking Time dan Injection rate Terhadap Peningkatan Recovery Factor dalam Injeksi CO2 Huff & Puff Pada Sumur X Sahtria panjaitan, sahtria panjaitan; Triono, Agus; Eklezia Dwi Saputri, Eriska
Journal of Sustainable Energy Development Vol. 3 No. 1 (2025): Journal of Sustainable Energy Development (JSED)
Publisher : Petroleum Engineering, Faculty of Engineering, University of Jember

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Abstract

CO₂ Huff & Puff injection is an effective enhanced oil recovery (EOR) method to boost production in declining reservoirs. This study focuses on simulating CO₂ Huff & Puff injection in Well X, SKW Field, by analyzing injection parameters such as soaking duration and injection rate. The simulation was conducted without history matching but included sensitivity analysis to evaluate reservoir performance. Seven soaking time variations (5–35 days) were tested, with 20 days yielding the highest Recovery Factor (RF) of 4.064%. Injection rate variations ranged from 1×10⁶ to 1.6×10⁶ ft³/day, with 1.4×10⁶ ft³/day achieving the highest RF increase of 4.070%. Soaking time and injection rate significantly impact oil recovery; however, excessive soaking leads to gravity segregation, reducing oil displacement efficiency. The optimal combination for maximizing recovery in Well X is a 20-day soaking time with a CO₂ injection rate of 1.4×10⁶ ft³/day. Keywords: CO2 Injection; Huff &Puff, Soaking Time, Injection rate
Studi Prediksi Porositas Dengan Menggunakan Metode Deterministik dan Machine Learning Pada Lapangan “X” Hafwandi, Babas Samudera
Journal of Sustainable Energy Development Vol. 3 No. 1 (2025): Journal of Sustainable Energy Development (JSED)
Publisher : Petroleum Engineering, Faculty of Engineering, University of Jember

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Abstract

Porosity is one of the most critical parameters in reservoir characterization, as it directly influences hydrocarbon storage capacity. Accurate porosity prediction becomes even more essential in fields with limited core data, such as Field “X”, located in the South Sumatra Basin. This study compares two different porosity prediction approaches: a deterministic method based on well log interpretation using NPHI and RHOB logs, and various Machine Learning (ML) algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GBR), AdaBoost (ADA), Support Vector Machine (SVM), and Decision Tree (DT). Data preprocessing involved feature selection using Pearson, Spearman, and Kendall correlation coefficients to identify the most influential log parameters. The dataset was then divided into training (70%) and testing (30%) subsets. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The deterministic method yielded an MAE of 0.0658 and RMSE of 0.0906, while the best ML model, Random Forest, achieved an MAE of 0.0329 and RMSE of 0.0434 on the testing dataset. In conclusion, Machine Learning, especially the Random Forest model, proves to be a more reliable and accurate tool for porosity prediction in geologically complex fields, offering significant potential for enhancing reservoir modeling and field development planning.

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