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All Journal Journal of Geoscience, Engineering, Environment, and Technology JOURNAL OF EARTH ENERGY ENGINEERING Communications in Science and Technology Journal of Renewable Energy and Mechanics ARSY : Jurnal Aplikasi Riset kepada Masyarakat EcoNews LEMBARAN PUBLIKASI MINYAK DAN GAS BUMI
Putra, Dike Fitriansyah
Department Of Petroleum Engineering, Islamic University Of Riau, Jl. Kaharuddin Nasution No.113 Pekanbaru 28284, Indonesia
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences

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Sosialisasi Pelumas Bekas Terhadap lingkungan Serta Penanggulannya Pada Remaja Masjid Nurul Iman Marpoyan Damai Pekanbaru Khalid, Idham; Erlina, Erlina; Putra, Dike Fitriansyah; Ilhamsyah, Alvin; Hidayah, Feby Agung
ARSY : Jurnal Aplikasi Riset kepada Masyarakat Vol. 6 No. 1 (2025): ARSY : Jurnal Aplikasi Riset kepada Masyarakat
Publisher : Lembaga Riset dan Inovasi Al-Matani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/arsy.v6i2.1473

Abstract

Perkembangan industri dan padatnya mobilisasi masyarakat secara tidak langsung berpengaruh terhadap tingginya permintaan sehingga jumlah limbah yang dihasilkan juga meningkat. Oli Bekas merupakan salah satu jenis cairan kental yang berasal dari hasil pemakaian mesin motor, mobil, atau alat penggerak lainnya. Ditinjau dari komposisi kandungannya, terdiri dari campuran hidrokarbon, bahan kimia aditif, beberapa sisa hasil pembakaran yang bersifat deposit, asam korosif, dan logam berat yang bersifat karsinogenik. Adapun dampak-dampak negatif oli bekas jika dibuang sembarangan adalah sebagai berikut: Mengganggu kesehatan masyarakat, Pencemaran air, Pencemaran tanah, Mudah terbakar, Pencemaran udara dan Gangguan pada ekosistem Studi juga menyebutkan bahwa minyak goreng yang dipakai berkali-kali dapat menghasilkan berbagai senyawa, termasuk polisiklik aromatik hidrokarbon yang bersifat karsinogenik atau menyebabkan kanker. Adapun cara efektif pengelolaannya pengumpulan terpisah, penyimpanan yang aman, pemanfaatan kembali atau daur ulang, pengolahan oil sludge dan menggandeng penyedia jasa pengolahan limbah B3. Pengabdian Kepada Masyarakat (PKM) yang telah dilakukan oleh Tim Pelaksana pada Remaja Masjid Nurul Iman dengan tema “Sosialisasi Pelumas Bekas Terhadap Lingkungan Serta Penanggulannya Pada Remaja Masjid Nurul Iman Marpoyan Damai.” yang beralamat di Jln. Pahlawan Kerja, Kec. Marpoyan Damai, Kota Pekanbaru, Riau, tidak mengalami kesulitan atau hambatan dalam proses pelaksanan PKM tersebut.
Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm Putra, Dike Fitriansyah; Jaafar, Mohd Zaidi; Khalif, Ku Muhd Na’im; Siswanto, Apri; Lukman, Ichsan; Kurniawan, Ahmad
Communications in Science and Technology Vol 10 No 1 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.1.2025.1649

Abstract

Optimizing the Alkaline-Surfactant-Polymer (ASP) injection process remains a persistent challenge in Enhanced Oil Recovery (EOR), particularly in heterogeneous sandstone reservoirs where traditional reservoir simulators are constrained by high computational demands and limited flexibility. This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. Unlike previous approaches, our method blends high-fidelity CMOST simulation data with machine learning precision in which it enables real-time optimization with field-scale relevance. Using 500 simulation scenarios validated by laboratory input, the SL model achieved exceptional predictive performance (R² = 0.988, RMSE = 0.304), outperforming all individual learners. The optimal recovery factor (RF) of 79.49% was obtained with the finely tuned concentrations of surfactant (5483.29 ppm), polymer (2242.61 ppm), SO?²? (5610.15 ppm), CO?²? (7053.59 ppm), and Na? (9939.35 ppm). Remarkably, the SL approach could reduce optimization time from 10 hours (CMOST) to under 1 minute; this underscored its potential for real-time operational deployment. The novelty of this work lies in its integrated use of ensemble learning to capture the complex and non-linear interactions between ionic chemistry and oil mobilization behavior, offering a field-ready AI framework for rapid and adaptive EOR design. This approach paves the way for the intelligent optimization of ASP schemes by minimizing the reliance on computationally intensive simulations while ensuring chemical and economic efficiency in marginal or complex reservoirs.
Co-Authors Andiansyah Madani Nasution Apri Siswanto Aznil Arif Rahman Aznil Arif Rahman Cendra, Desma Cenk Temizel Claudia Anugerah Putri Dedek Julianto Desma Cendra Dody Yulianto Erlina Erlina Fitrianti Fitrianti Fitrianti Fitrianti, Fitrianti Hidayah, Feby Agung Ilhamsyah, Alvin Jaafar, Mohd Zaidi Khalif, Ku Muhd Na’im Kurniawan, Ahmad Lazuardhy Vozika Futur Lazuardhy Vozika Futur Lukman, Ichsan Masli Irwan Rosli Muhammad Ariyon Mursyidah Umar Putri, Claudia Anugerah Rahman, Aznil Arif Rycha Melysa Temizel, Cenk