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Hattu, Valentyn Paul Bodywein
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PENGARUH PENCAMPURAN RUMPUT LAUT DENGAN POLIMER XANTHAN GUM TERHADAP VISKOSITAS DAN ADSORPSI Hattu, Valentyn Paul Bodywein; Pellu, Deny Ismail
Journal Mechanical Engineering Vol. 2 No. 1 (2024): April
Publisher : Jurusan Teknik Mesin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31959/jme.v2i1.2583

Abstract

Polymer injection is an EOR method using a polymer solution to increase oil recovery by decreasing the oil-water mobility ratio by increasing the viscosity of the injection water. The purpose of this study was to analyze the effect of adding seaweed to xanthan gum polymer at various concentrations. The influence that is analyzed in this study is the viscosity test and the adsorption of the solution. From the research that has been carried out, it can be concluded that the best polymer viscosity is a polymer mixture of xanthan gum and seaweed, while based on the adsorption test, xanthan gum polymer is the best.. Keywords: Polymer Flooding, EOR, Viscosity, Adsorption
OPTIMASI PARAMETER RESERVOIR MENGGUNAKAN PEMODELAN NUMERIK DAN ANALISIS REGRESI UNTUK MENINGKATKAN RECOVERY FACTOR Hattu, Valentyn Paul Bodywein; Pellu, Deny Ismail
JURNAL SIMETRIK Vol 15 No 2 (2025): Desember
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31959/js.v15i2.3483

Abstract

Reservoir parameter optimisation is a critical aspect of enhancing the hydrocarbon recovery factor and requires systematic, integrated approaches. This research develops an integrated framework combining numerical reservoir modelling with multivariate regression analysis to optimise key reservoir parameters. The research methodology employs quantitative techniques, including three-dimensional numerical simulation, implementation of a multi-objective optimisation algorithm, and development of machine learning models for recovery factor prediction. Research data encompasses reservoir petrophysical parameters, including effective porosity ranging from 12.5% to 28.7%, horizontal permeability from 15 mD to 450 mD, and initial oil saturation from 52.3% to 84.6%. Optimisation analysis using three algorithms demonstrates that the Multi-Objective Grey Wolf Optimiser achieves superior performance, with an optimal recovery factor of 46.3% using only eight parameters. The third-order polynomial regression model yields a coefficient of determination of 0.89 in predicting nonlinear relationships between reservoir parameters and recovery factor. An artificial neural network implementation achieves 94.2% training and 89.3% test prediction accuracy, with a mean absolute error of 2.1%. Development scenario simulation indicates the five-spot injection pattern configuration produces the highest recovery factor, 48.2% with a present value of 187.3 million USD. The developed integrated framework demonstrates the ability to handle reservoir heterogeneity, with validation showing deviations of less than 7% relative to field data.Keywords: numerical modeling, parameter optimization, recovery factor
Co-Authors Pellu, Deny Ismail