Fuzzy-Based Screening System for Determination of Enhanced Oil Recovery (Eor) Method in Reservoir

ABSTRACT


INTRODUCTION
Currently, the oil industry is developing technology to increase oil recovery in reservoirs. According to Aladasani [1]- [3], increasing oil recovery is presently focusing on research and development of the proper Enhanced Oil recovery method in a field. Screening Criteria can be used as a guide or the first step in implementing Enhanced Oil Recovery (EOR). If the Screening IT Jou Res and Dev, Vol. 6 Criteria are successfully implemented, selecting the following stage method becomes easier [4]- [6]. Screening Criteria is a step to identify known parameters of a reservoir. Meanwhile, Enhanced Oil Recovery is a method used to increase the recovery of oil reserves [7]- [12]. Of the 15 parameters that exist in the EOR Screening Criteria such as: API Gravity, Oil Saturation, Formation Type, Net Thickness, Viscosity, Permeability, Temperature, Salinity, Depth, and so on, a minimum of two parameters is required to determine the method in Enhanced Oil Recovery (EOR). Namely, the degree of API and reservoir depth [13]- [15].
Based on these problems, this research will build an EOR filtering system based on fuzzy logic that can help and simplify reservoir work carried out by reservoir engineers or students in the oil sector in determining the EOR method suitable for use in a reservoir.
In the screening system to be built, nine input criteria will be used to screen EOR criteria, namely: API Gravity, Oil Saturation, Formation Type, Net Thickness, Viscosity, Permeability, Temperature, Porosity, Depth criteria.
Nageh conducted similar research, Mohamed. et al., regarding applications using fuzzy logic on the screening criteria of EOR technology. Screening tool developed with Matlab programming language [16]- [19].

RESEARCH METHOD
According to Trujilo [14], the filtering criteria is the step of identifying the known parameters of a reservoir. Meanwhile, Enhanced Oil Recovery (EOR) is a method used to increase the recovery of oil reserves based on the input and output parameters produced. Table 1 describes the units used for each parameter [20], [21]. The domain set of each input parameter used to screen the EOR criteria is as follows: This fuzzy-based screening system consists of 9 (nine) fuzzy input parameters. Each input has 3 (three) fuzzy sets, as shown in table 2. The output of this system is the screening criteria of the EOR method, which consists of 14 categories. The number of fuzzy sets from each type consists of 3 (three) groups, as shown in table 3.

RESULTS AND ANALYSIS
System capability testing in determining the EOR method will be carried out with 65 test data obtained from several research sources, namely:  Table 5 shows the results of comparing outputs between those generated from the fuzzybased EOR screening system and the actual data from the research conducted by P Sang Kang and J (2014) shown in Maritime Korea, Brashear, and Kuuskraa fields.

CONCLUSION
From the results of the design and manufacture of an intelligent application system based on Mamdani fuzzy logic, it can conclude that the accuracy of the screening system based on Mamdani fuzzy logic from 65 test data, only reached 80.95%.