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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Journal of Geoscience, Engineering, Environment, and Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal Migasian
lukmana, allen haryanto
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Physics

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Perilaku Mol Komponen Mineral dan Akuatik dalam Penyimpanan Karbon (Carbon Capture Storage) dengan dan tanpa Sumur Injeksi Air Lukmana, Allen Haryanto; Kabul Pratiknyo, Avianto; Ragil Putradianto, Ristiyan; Putro Suryotomo, Andiko
Jurnal Migasian Vol 8 No 2 (2024): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jm.v8i2.321

Abstract

This research aims to analyze the changes in mineral and aqueous component moles during Carbon Capture Storage (CCS) with and without water injection in a reservoir field. Using GEM reservoir simulation software, the study models interactions between CO2, reservoir minerals (Anorthite, Calcite, Kaolinite), and aqueous components (Ca++, Al+++, SiO2(aq), HCO3-, CO3--, OH-) over 189 years a time period. The simulation reveals that water injection significantly accelerates mineral dissolution and precipitation, affecting reservoir porosity, permeability, and fluid chemistry. Key findings include enhanced Calcite stability and Kaolinite formation with water injection, alongside noticeable changes in aqueous chemistry. These results provide crucial insights for optimizing water injection strategies in CCS projects and improving reservoir management. The study concludes that water injection enhances mineral stability and impacts ionic concentration in the subsurface environment, aiding in more efficient carbon storage solutions.
Construction of fuzzy systems based on fuzzy c-means clustering and singular value decomposition for predicting rate of penetration in geothermal drilling Abadi, Agus Maman; Mansyaroh, Akhid Khirohmah; Lukmana, Allen Haryanto; Harini, Lusi; Sugiyarto, Aditya Wisnugraha
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2190-2198

Abstract

The potential for geothermal energy is very abundant, but its utilization is still minimal. Therefore, the utilization of geothermal energy facility that has been installed must be optimized. This study aims to predict drilling rate of penetration using the first-order Sugeno’s fuzzy system. Fuzzy c-mean and singular value decomposition were used to form the rules and determined the parameters respectively. This study used in total of 6738 data of geothermal wells drilling in Indonesia. The results show that the rate of penetration prediction has accuracy 85.76% for data training and 87.72% for data testing, and it is better than the radial basis function neural networks (RBFNN) and RBFNN-singular value decomposition (SVD) methods.
Prediksi Rate of Penetration pada Pengeboran Minyak Bumi dengan Elman Recurrent Neural Network AIZIYAH, ELSA; LUKMANA, ALLEN HARYANTO; ABADI, AGUS MAMAN
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.145-161

Abstract

ABSTRAKPenelitian ini bertujuan memprediksi laju penetrasi (ROP) guna mempercepat waktu pengeboran dan menekan biaya operasional. Metode yang digunakan adalah Elman Recurrent Neural Network (ERNN) dengan algoritma backpropagation, yang dipilih karena kemampuannya dalam mengenali pola data sekuensial pada data pengeboran. Data yang digunakan 2613 data ASCII Mudlogging minyak bumi dari PT Geotama Jogja dengan 5 variabel input, yaitu Kedalaman Vertikal Sejati atau TVD (m), Beban Mata Bor atau WOB (klbs), Kepadatan Sirkulasi Ekuivalen atau ECD (SG), Mud Weight in atau MWI (SG), dan Total Kecepatan Rotasi Pahat atau TRPM. Sedangkan variabel outputnya yaitu laju penetrasi atau ROP (m/hr). Data dihaluskan menggunakan Savitzky-Golay filter dan dibagi data training dan data testing yang sebesar 90% dan 10%. Model ERNN terbaik yang diperoleh yaitu 5 variabel input, 17 neuron tersembunyi, dan 1 variabel output. Nilai MAPE data training sebesar 16.18%, dengan akurasi 83.82%. Sedangkan nilai MAPE data testing sebesar 15.48%, sehingga akurasinya 84.52%.  Kata kunci: Elman Recurrent Neural Network, laju penetrasi, prediksi, minyak bumi, MAPE ABSTRACTThis study aims to predict the rate of penetration (ROP) to speed up drilling time and reduce operational costs. The method used is the Elman Recurrent Neural Network (ERNN) with the backpropagation algorithm, which was chosen because of its ability to recognize sequential data patterns in drilling data. The data used are 2613 ASCII Mudlogging data from PT Geotama Jogja with 5 input variables, namely True Vertical Depth or TVD (m), Drill Bit Load or WOB (klbs), Equivalent Circulation Density or ECD (SG), Mud Weight in or MWI (SG), and Total Tool Rotation Speed or TRPM. While the output variable is the rate of penetration or ROP (m/hr). The data is smoothed using the Savitzky-Golay filter and divided into training data and testing data of 90% and 10%. The best ERNN model obtained is 5 input variables, 17 hidden neurons, and 1 output variable. The MAPE value of the training data is 16.18%, so the accuracy is 83.82%. Meanwhile, the MAPE value for the testing data was 15.48%, resulting in an accuracy of 84.52%.  Keywords: Elman Recurrent Neural Network, penetration rate, prediction, petroleum, MAPE
Analysis Of Co2 Storage in A Saline Aquifer Using A Fully Implicit Integrated Network Modeling Approach in the 'AZ' Field Swadesi, Boni; Zayd, Ahmad; Buntoro, Aris; Kristanto, Dedi; Widiyaningsih, Indah; Lukmana, Allen Haryanto
Journal of Geoscience, Engineering, Environment, and Technology Vol. 10 No. 4 (2025): JGEET Vol 10 No 04 : December (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.4.25106

Abstract

The increasing carbon dioxide (CO2) emissions from industrial and energy activities have driven the development of Carbon Capture and Storage (CCS) technology as a key solution for climate change mitigation. Among various geological storage options, saline aquifers offer significant advantages due to their large storage capacity, wide distribution, independence from hydrocarbon value, and stable geological and geochemical conditions. The “AZ” Field, located near a power plant emitting 2.2 million tons of CO2 annually, was selected as the study site for CO2 storage. This study aims to analyze the trapping mechanisms and optimize the CO2 storage capacity (storativity) using a fully implicit integrated modeling approach. The methodology involves building a static and dynamic model of the Johansen Formation saline aquifer, and integrating well and surface facility models using the well designer and network designer features in tNavigator. A 140-year simulation was conducted, comprising 40 years of injection and 100 years of post-injection period. Simulation results show that the “AZ” Field can store up to 83.9 Mt of CO2, predominantly through solubility/residual trapping mechanisms, in addition to structural trapping. No leakage was observed to the surface, indicating that caprock integrity remained intact throughout the simulation period. The fully implicit integrated modeling approach effectively captured the dynamic interactions between the reservoir, wells, and surface facilities, supporting the feasibility of the “AZ” Field as a safe and sustainable CO2 storage site.
Co-Authors Agus Maman Abadi AIZIYAH, ELSA Andiko Putro Suryotomo Boni Swadesi Buntoro, Aris Harini, Lusi Kabul Pratiknyo, Avianto Kristanto, Dedi Mansyaroh, Akhid Khirohmah Ragil Putradianto, Ristiyan Sugiyarto, Aditya Wisnugraha Zayd, Ahmad