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All Journal JOURNAL OF EARTH ENERGY ENGINEERING Indonesian Journal of Energy JUITECH: Jurnal Ilmiah Fakultas Teknik Universitas Quality Jurnal Offshore: Oil, Production Facilities, and Renewable Energy Jurnal Penelitian dan Karya Ilmiah Lembaga Penelitian Universitas Trisakti Jurnal AKAL: Abdimas dan Kearifan Lokal JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Journal of Earth Energy Science, Engineering, and Technology Jurnal Eksakta Kebumian
Mardiana, Dwi Atty
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Journal : Journal of Earth Energy Science, Engineering, and Technology

 1 
Modeling of Shrimp Chitosan Polymer Adsorption Using Artificial Neural Network Fathaddin, Muhammad Taufiq; Mardiana, Dwi Atty; Sutiadi, Andrian; Maulida, Fajri; Ulfah, Baiq Maulinda
Journal of Earth Energy Science, Engineering, and Technology Vol. 7 No. 2 (2024): JEESET VOL. 7 NO. 2 2024
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jeeset.v7i2.21134

Abstract

One phenomenon that can occur when a polymer solution is injected into an oil reservoir is adsorption. Adsorption occurs due to interactions between polymer molecules and the reservoir pore surface. Adsorption causes some polymer molecules to be removed from solution. So, this process results in a reduction in the polymer concentration in the solution. In this study, an artificial neural network (ANN) model is used to estimate the adsorption of shrimp chitosan polymer on the surface of 40 mesh and 60 mesh sand grains. The ANN model can estimate adsorption more accurately than previous models. This is because previous models only predicted certain adsorption patterns, while the ANN model is able to predict adsorption with complex relationships. The comparison of the mean absolute relative errors (MAREs) of the ANN, Langmuir, Freundlich, Henry, and Harkins-Jura models is 5.7%, 15.9%, 14.6%, 15.2%, and 14.5%, respectively.
Adsorption Modeling of Amorphophallus oncophyllus Prain Using Artificial Neural Network Sutiadi, Andrian; Mardiana, Dwi Atty; Fathaddin, Muhammad Taufiq
Journal of Earth Energy Science, Engineering, and Technology Vol. 7 No. 3 (2024): JEESET VOL. 7 NO. 3 2024
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/qagty424

Abstract

Adsorption is the process of interaction between a liquid and a solid surface. It happens because of physical forces or chemical bonds, which moves substance molecules dissolved in a liquid to the solid surface. As a result, the concentration of the substance in the solution drops. In this study, an artificial neural network (ANN) was applied to model the adsorption of Amorphophallus oncophyllus Prain and xanthan gum on sand grains with sizes of 40 mesh and 60 mesh. Two ANN models were developed. The first ANN model was used to predict the final concentration of the polymer solution after the adsorption process. This model had a correlation coefficient for the training, validation, and testing phases of 0.9968, 0.9982, and 0.9990, respectively. Meanwhile the second ANN model was used to predict the adsorbed polymer. This model had a correlation coefficient for the training, validation, and testing phases of 0.9984, 0.9996, and 0.9985, respectively. These models were capable of accurately predicting the final concentration and adsorbed polymer when compared to laboratory data.
Evaluation of Indonesia's Upstream Oil and Gas Fiscal Terms in Comparison to Malaysia's Enhanced Profitability Terms (EPT)Case Study of Block X Exploration Field Halim, Yosep; Rakhmanto, Pri Agung; Mardiana, Dwi Atty; Irawan, Sonny; Lalaina, Ramefivololona Hanitra; Aimé, Rajomalahy Julien; Fifaliana, Razakamampianina Valisoa; Harifenitra, Ravololoarimanana
Journal of Earth Energy Science, Engineering, and Technology Vol. 8 No. 1 (2025): JEESET VOL. 8 NO. 1 2025
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/hsn4wb37

Abstract

Oil and gas sector is one of the main drivers of Indonesia's economy. Thus, it is important to ensure the attractiveness of Indonesia's Production Sharing Contracts (PSC) fiscal terms for investment, especially in comparison with neighboring countries. In 2021, Malaysia introduced the Enhanced Profitability Terms (EPT) PSC, which is considered to provide a better and more reasonable return for oil and gas contractors. The purpose of this study is to analyze and compare the attractiveness of the Cost Recovery and Gross Split fiscal terms in Indonesia with the EPT fiscal terms in Malaysia, based on economic indicators, including their sensitivity. This study uses a quantitative approach by calculating the economic viability of fields (NPV, IRR, POT), their sensitivity, the range of %CT and %GT, and the profitability characteristics of an exploration block field (Block X). From the evaluation and comparison conducted (specific to the assumed case), it was concluded that the Indonesian Gross Split PSC and the Malaysian EPT PSC have improved economic indicators compared to the Indonesian Cost Recovery PSC. Therefore, the Indonesian Gross Split PSC and the Malaysian EPT PSC generally have better economic indicators, including sensitivity to changes in oil prices, operating costs, and production levels, compared to the Indonesian Cost Recovery PSC. To obtain a more complete picture and enrich the evaluation of these fiscal terms, further analysis can be conducted by considering business risks of contractors, simulations with the application of incentives, and other factors that can affect investment decisions.
Characterization of Addition Porang on Polyacrylamide Polymer for Enhanced Oil Recovery Siahaya, Jacob; Mardiana, Dwi Atty; Fathaddin, Muhammad Taufiq
Journal of Earth Energy Science, Engineering, and Technology Vol. 6 No. 3 (2023): JEESET VOL. 6 NO. 3 2023
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/jeeset.v6i3.17423

Abstract

The main purpose of polymer injection is to reduce the water-oil mobility ratio. By increasing the viscosity of the injection fluid, polymer injection can increase sweep efficiency, thereby increasing oil recovery. This study aims to determine the viscosity and adsorption effects of adding porang to polyacrylamide polymers. The method used in this research is a laboratory experiment. The salinity of the formation water used in this study was 6000 ppm, 12000 ppm, and 18000 ppm, with variations in polymer concentrations of 2000 ppm, 4000 ppm, and 6000 ppm on the polymer without a mixture of porang and with a mixture of porang. The result of measuring the viscosity of polyacrylamide by adding porang at the salinity of 6000 ppm and polymer concentration of 6000 ppm was 21.82 cp. With the addition of porang to the polyacrylamide polymer at 2000 ppm concentration and 18000 ppm salinity, the adsorption value of the polymer decreased from 2.708 mg/gr to 1.748 mg/gr for 40 mesh sand and from 3.333 mg/g to 2.358 mg/g for 60 mesh sand.  
Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Fathaddin, Muhammad Taufiq; Ridaliani, Onnie; Rakhmanto, Pri Agung; Mardiana, Dwi Atty; Septianingrum, Wydhea Ayu; Irawan, Sonny; Abdillah, Ridho
Journal of Earth Energy Science, Engineering, and Technology Vol. 8 No. 3 (2025): JEESET VOL. 8 NO. 3 2025
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/fh90e382

Abstract

This study examines the viscosity behavior of Kappaphycus alvarezii polymer solutions enhanced with TiO2 nanoparticles under varying concentrations, salinity, and temperature. Predictive models were developed using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) approaches. The experimental work involved preparing Kappaphycus alvarezii solutions with polymer concentrations ranging from 2,000 to 6,000 ppm and TiO2 nanoparticle concentrations from 2,000 to 4,000 ppm at salinities of 6,000–30,000 ppm and temperatures between 30 °C and 80 °C. Results showed that increasing Kappaphycus alvarezii concentration enhanced viscosity by 1.04–21.12%, while TiO2 nanoparticles improved viscosity stability, especially under high-salinity conditions. In contrast, higher salinity and temperature reduced viscosity due to ionic screening and increased molecular motion, although a slight rise was observed at 30,000 ppm salinity. The optimized ANN model (18 neurons, one hidden layer) achieved a superior correlation coefficient (r = 0.9980) compared to ANFIS (r = 0.8769), confirming higher predictive accuracy. These findings demonstrate the potential of Kappaphycus alvarezii–TiO2 nanofluids as sustainable viscosity modifiers for enhanced oil recovery (EOR) and verify the reliability of ANN and ANFIS models in predicting viscosity under complex multivariable interactions.
Co-Authors Abdillah, Ridho Aimé, Rajomalahy Julien Arkaan, Muhammad Dzaki Asri Nugrahanti, Asri Dardjat, Izumi Wicaksono Fathaddin, Muh. Taufiq Fifaliana, Razakamampianina Valisoa Halim, Yosep Harifenitra, Ravololoarimanana Havidh Pramadika Kartoatmodjo, RS. Trijana Kosasih, Andri Lalaina, Ramefivololona Hanitra Maulida, Fajri Muhammad Taufiq Fathaddin, Muhammad Taufiq Onnie Ridaliani, Onnie Permata Sari, Dianie Rachmatika, Danella Rahkmanto, Pri Agung Rakhmanto, Pri Agung Rini Setiati Ristawati, Arinda Riswati, Shabrina Sri Sagarmatha, Erly Amai Samsol Sariallo, Gabriela Martines Septianingrum, Wydhea Ayu Siahaya, Jacob Sonny Irawan Sugiatmo Kasmungin, Sugiatmo Suparwoto, Sugeng Suryo Prakoso, Suryo Sutiadi, Andrian Ulfah, Baiq Maulinda Utama, Yeriandi Valentino, Boy Candra Ziad Ziad