ANALYSIS OF RESERVOIR COMPLEXITY INDEX (RCI) TOWARDS VALUE OF RECOVERY FACTOR (RF) IN OIL FIELD ON SANDSTONE RESERVOIR WITH CHEMICAL SURFACTANT INJECTION

• Main Author: Made Dalam Saputra Jagadita, I
• Format: Theses
• Language: Indonesia
• Subjects: Pertambangan dan operasi berkaitan
• Online Access: https://digilib.itb.ac.id/gdl/view/69972
• Institution: Institut Teknologi Bandung

The recovery technique using the chemical enhanced oil recovery (CEOR) method, especially surfactant injection, is receiving special attention because it is technically capable of significantly increasing the recovery factor. However, there are common problems that often occur, such as the low realization of the recovery factor (RF) value in the implementation of field trials or on a field scale, this RF value is out of range from the prediction of the core test on the laboratory. On the laboratory, RF predictions represent only core scale which indicated the reservoir characteristics tend to be homogeneous and there is no element of complexity in the core sample. Meanwhile, on a field scale, the element of reservoir complexity has major impact. The complexity referred to here is how the distribution and combination of reservoir properties spread both rock properties, fluid properties to the dynamic response of surfaktan injection so as to produce different RF for each condition. Based on the explanation above, an assessment tool is needed on how this reservoir complexity index value influences the RF value by using a calculation approach using the RCI (reservoir complexity index) method that accommodates reservoir and surfaktan properties data with reservoir simulation. The more complex a reservoir is indicated by an increase in the RCI index, the smaller the RF value generated from the reservoir. Parameter properties analyzed in this study include 12 parameters, namely: oil density, viscosity oil, horizontal permeability, corey parameter, porosity, horizontal to vertical permeability (Kv/Kh), residual oil saturation (Sor), pore volume injection, salinity injection, surfactant adsorption, surfactan concentration, and capillary number. Then running reservoir simulation and analysis RCI. The value of the RCI equation RCI = 0.05(API) + 0.343(Viscosity) + 0.611(Permeability XY) + 0.097(Corey) + 0.112(Porosity) + 0.05(Kv/Kh) + 0.242(Sor) + 0.1(PV Injection) + 0.05(Water Salinity) + 0.132(Surf. Adsorption) + 0.112(Surf. Concentration) + 0.103(Capillary Number). The value of the RCI vs RF relationship is RF = 0.0968(RCI)4-2.1172 (RCI)3 +14.735 (RCI)2 -41.472 (RCI) +117.05 with R2 = 0.9555. The results of the validation of the RCI for the testing case, the actual value of the field and REV case generate good average % difference RF results at 3.47% ; 7.9% ; 9%; and 9.1% respectively. So it can be concluded that the RCI vs RF model can be used as an assessment tool in predicting RF values in accordance with the properties of the analyzed parameters.