Ivan Kurnia
Institut Teknologi Bandung

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Investigation and Optimization of Enhanced Oil Recovery Mechanism by Sophorolipid Biosurfactant in Carbonate Reservoir Indah Widiyaningsih; Harry Budiharjo Sulistyarso; Ivan Kurnia; Taufan Marhaendrajana; Tutuka Ariadji
Scientific Contributions Oil and Gas Vol 48 No 3 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i3.1830

Abstract

The Remaining Oil in Place (ROIP) in carbonate rock reservoirs is often substantial. This is due to the tendency of carbonate rocks to be oil-wet in terms of wettability. The oil's inherent property of wetting the rock causes the residual oil to adhere to the rock's pores, making it challenging to extract to the surface. One method to enhanced oil recovery (EOR) is through biosurfactant injection, i.e., sophorolipid, a fungal biosurfactant that possesses the properties of surfactants in general. This study aims to evaluate the effectiveness of sophorolipid biosurfactant injection in enhancing oil recovery in carbonates, as well as to identify the dominant mechanism at work during the injection process and optimize it through coreflooding simulation. This research was conducted through laboratory testing and validation using a simulator, comprising two phases: coreflooding tests and coreflooding simulations. Coreflooding simulation was conducted to reduce the need for coreflooding experiments, which are time-consuming and costly. The simulator used in this research is CMG-GEM with sensitivity parameter and optimization using CMOST. The Sobol Analysis was conducted to assess the sensitivity parameters and identify the primary mechanism of sophorolipid. Then, optimization is achieved by adjusting the parameters, such as sophorolipid concentration, pore volume (PV) injection, and injection rate. Coreflooding sensitivity results show that the dominant parameter is the nonwetting trapping number (DTRAPN), which is closely related to the mechanism of wettability alteration and mix viscosity. The effectiveness of the Sophorolipid mechanism in modifying wettability, enhancing displacement efficiency, and facilitating emulsion formation, hence improving sweeping efficiency. The recovery factor (RF) increased from the coreflooding simulation optimization results, reaching 19%-33%.
Integrated Static–Dynamic Analysis for Sweet Spot Identification and Reserves Prediction in Low-Permeability Reservoirs Using Production Data and Geospatial Attributes Yuliani Yuniwati; Dedy Irawan; Ivan Kurnia; Alfian Gilang; M. Soleh Ibrahim
Scientific Contributions Oil and Gas Vol 49 No 2 (2026)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v49i2.2085

Abstract

The optimization of infill well placement and the reliable prediction of Estimated Ultimate Recovery (EUR) continue to represent critical challenges in low-permeability reservoir. Conventional permeability evaluation using Pressure Build-Up tests is often impractical due to prolonged shut-in requirements and operational constraints. Although geospatial tools such as Antelope Map effectively identify fracable zones associated with high initial production rates, sustained long-term recovery is not necessarily guaranteed. This study proposes an integrated static–dynamic framework that combines geospatial attributes with production-based analysis to improve reservoirs characterization and well placement decisions. In-situ permeability and flow capacity, expressed as √khXf, are extracted directly from routine production data of 14 hydraulically fractured wells using the Inverted Decline Curve method, thereby bypassing the limitations of pressure transient analysis. The Stretched Exponential Production Decline model is subsequently applied to generate bounded and realistic EUR predictions. Pearson correlation analysis shows a weak relationship between permeability derived from static logs and EUR. In contrast, the production-derived √khXf the parameter shows the strongest positive correlation, reflecting effective flow capacity and the influence of matrix heterogeneities such as trace fossils. By comparing the √kh distribution with the static Antelope Map, this dual-criteria approach helps identify sweet spots that support both favourable fracability and more sustained fluid delivery. In general, this data-driven workflow provides a practical alternative framework to reduce geological uncertainty and optimize well placement in low-quality reservoirs.