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

Nesi Syafitri; Tomi  Erfando; Widya  Lestari; Niken  Karina Rinaldi

doi:10.25299/itjrd.2022.8640

Authors

Nesi Syafitri
Tomi Erfando Department of Petroleum Engineering, Universitas Islam Riau
Widya Lestari Department of Petroleum Engineering, Universitas Islam Riau
Niken Karina Rinaldi Department of Petroleum Engineering, Universitas Islam Riau

DOI:

https://doi.org/10.25299/itjrd.2022.8640

Keywords:

Petroleum, Industry, EOR method, Fuzzy, Sensitivity

Abstract

The petroleum industry is developing technology to increase oil recovery in reservoirs. One of the technologies used is Enhanced Oil Recovery (EOR). Selecting an EOR method for a specific reservoir condition is one of the most challenging tasks for a reservoir engineer. This study tries to build a fuzzy logic-based screening system to determine the EOR method. It created the system intending to be able to assist in selecting and determining the appropriate EOR method used in the field. There are nine input criteria used to screen the EOR criteria, namely: API Gravity, Oil Saturation, Formation Type, Net Thickness, Viscosity, Permeability, Temperature, Porosity, Depth criteria. The output criteria generated from the calculation of the EOR screening criteria are 14 outputs, namely: CO2 MF Miscible Flooding, CO2 IMMF Immiscible Flooding, HC MF Miscible Flooding, HC IMMF Immiscible Flooding, N2 MF Miscible Flooding, N2 IMMF Immiscible Flooding, WAG MF Miscible Flooding , HC+WAG IMMF Immiscible Flooding, Polymer, ASP, Combustion, Steam, Hot Water, Microbial. In this system, 512 rules are generated to produce 14 different outputs of the EOR method, with Mamdani's Fuzzy Inference reasoning. This fuzzy-based screening system has an accuracy rate of 80.95%, so this system is suitable to be used to assist reservoir engineers in determining the appropriate EOR method to be used according to the conditions in the reservoir. The sensitivity level of the system only reaches 53.1%, while the specificity level reaches 94%.

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