Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Earth Energy Science, Engineering, and Technology Advance Sustainable Science, Engineering and Technology (ASSET)
Bharoto
Unknown Affiliation
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Effectiveness of MES Palm Oil Surfactant using Core Flooding and Spontaneous Imbibition in EOR methods Setiati, Rini; Haryono, Muhammad Furqon; Ristawati, Arinda; Samsol; Akbar, Fahrurrozi; Bharoto; Sumirat, Iwan; Ramadhan, Ranggi Sahmura
Journal of Earth Energy Science, Engineering, and Technology Vol. 9 No. 1 (2026): JEESET VOL. 9 NO. 1 2026
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/mvpkat05

Abstract

Enhanced Oil Recovery (EOR) is one of the methods developed to optimize oil extraction from wells that still have reserve potential. This study focuses on the application of surfactant injection techniques using vegetable-based surfactants derived from Methil Ester Sulfonate (MES) from palm oil. The objective of this study is to evaluate the effectiveness of MES surfactants in increasing the recovery factor through two main testing methods, namely core flooding test and spontaneous imbibition. The tests were conducted under two variations of conditions, namely salinity of 5,000 ppm at a concentration of 0.5% and salinity of 10,000 ppm at a concentration of 2%. The test results showed that in the core flooding method, a salinity of 5,000 ppm with a concentration of 0.5% produced the highest recovery factor of 84.74%, while a salinity of 10,000 ppm with a concentration of 2% produced 62.63%. Meanwhile, in spontaneous imbibition testing, the recovery factor achieved was 51.94% for a concentration of 0.5% and 45.24% for a concentration of 2%. Based on these results, it can be concluded that the most optimal conditions for increasing oil recovery with palm oil MES surfactant are achieved in the core flooding test method with a salinity of 5,000 ppm and a concentration of 0.5%..
Viscosity Modeling of MES and SLS Using Machine Learning Method Fathaddin, Muhammad Taufiq; Setiati, Rini; Akbar, Fahrurrozi; Sumirat, Iwan; Bharoto; Ramadhan, Ranggi Sahmura; Onnie Ridaliani Prapansya; Ristawati, Arinda
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.2304

Abstract

Viscosity is crucial to improve the efficiency of injected fluids for oil displacement in reservoirs. Traditionally, research has focused on polymers that help reduce the mobility of injected fluids, while surfactant viscosity has received less consideration. This research investigated the viscosity behavior of methyl ester sulfonate (MES) and sodium lauryl sulfate (SLS) surfactant solutions using a machine learning method—adaptive neurofuzzy inference system (ANFIS). This study aimed to predict the viscosity of surfactant solutions. Experimental data included viscosity measurements of 36 MES and SLS samples at various concentrations and temperatures, obtained by digitizing viscosity curves. These data served as input and validation for the ANN and ANFIS models. The results showed that ANFIS predicted viscosity values ​​reliably, yielding only 1.33% and 0.43% differences for MES and SLS, respectively. Comparison of viscosity prediction with Artificial Neural Network (ANN) showed that ANFIS prediction was better, because ANN yielded two deviating predictions.
Co-Authors Akbar, Fahrurrozi Haryono, Muhammad Furqon Iwan Sumirat Muhammad Taufiq Fathaddin, Muhammad Taufiq Onnie Ridaliani Prapansya Ramadhan, Ranggi Sahmura Rini Setiati Ristawati, Arinda Samsol