A MATHEMATICAL MODEL FOR ENHANCED OIL RECOVERY FACTOR USING SILICA OXIDE
Due to the increase in demand for energy, Enhanced Oil Recovery (EOR) method has received considerable interest. In the oil and gas industries, common techniques in EOR are thermal injection, gas injection, carbon dioxide injection and chemical injection. However, these techniques have limitation...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | http://utpedia.utp.edu.my/22665/1/NurFarihaMohdIsa_16001824.pdf http://utpedia.utp.edu.my/22665/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Due to the increase in demand for energy, Enhanced Oil Recovery (EOR) method
has received considerable interest. In the oil and gas industries, common techniques in
EOR are thermal injection, gas injection, carbon dioxide injection and chemical
injection. However, these techniques have limitations due to harsh operating conditions,
such as high temperature and high pressure in oil well. Injection of nanofluids is an
alternative solution since it can sustain such operating conditions with promising results
for improving the oil recovery. Nanofluids can form adsorption layers on the top of
grain surface, alter the rock wettability and reduce oil rock interfacial tension and hence
increase the Recovery Factor (RF). These mechanisms are due to the properties of the
nanofluids such as particle size, its concentration and viscosity. Various types of
nanofluids have been studied by previous researchers for this purpose. Currently, RF
for nanofluids is determined through core flooding experiments. The experimental
approaches are not only time consuming, but also expensive. Hence, a mathematical
model for oil recovery prediction is required to assist in designing the core flooding
experiments. This study focused on development of a mathematical model for RF using
Silica Oxide (SiO2) nanofluids based on the properties such as particle size,
concentration, viscosity, density of the fluid and injection rate. The model is developed
by using MATLAB. Subsequently, sensitivity index is carried out to determine the
parameter that has the most influence on RF. Density of nanofluids (NFs) is found as
the most sensitive parameter followed by injection rate, particle size, concentration, and
viscosity of nanofluids. The optimization result from response surface methodology
using Design Expert10 suggests that an optimum RF of 12.99% is obtained for particle
size of 37nm, 0.4wt% concentration, 1.17cp viscosity, 1.00 g/cm³ density and 0.8
ml/min injection rate. |
|---|