OPTIMASI ALOKASI LAJU INJEKSI WATERFLOOD MENGGUNAKAN PARAMETER DYNAMIC LORENZ COEFFICIENT DENGAN MATLAB RESERVOIR SIMULATION TOOLBOX
The use of waterflood which is less effective on heterogeneous porosity and permeability becomes a challenge for optimization. Optimization in regulating the injection rate allocation is chosen because it is very effective considering the lower price compared to drilling sequences and well placeme...
Saved in:
Main Author: | |
---|---|
Format: | Final Project |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/53767 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | The use of waterflood which is less effective on heterogeneous porosity and permeability becomes a challenge
for optimization. Optimization in regulating the injection rate allocation is chosen because it is very effective
considering the lower price compared to drilling sequences and well placement. The injection rate allocation is
carried out by considering the Dynamic Lorenz Coefficient value using flow diagnostics. Dynamic Lorenz
Coefficient is used because it can measure the distribution of water injection in the reservoir with very fast
optimization times using the open-source simulator MRST (MATLAB Reservoir Simulation Toolbox). This
study results in optimization by reducing the Dynamic Lorenz Coefficient value on the injection rate control for
each injector well can increase the sweep efficiency. Change in size of grid is not very sensitive to changes in
the Dynamic Lorenz Coefficient value. The magnitude of injection rate after optimization of the reservoir with
the normal distribution of porosity and permeability is directly proportional to its magnitude of pore volume.
Conversely, in a reservoir with a permeable streak, the magnitude of the injection rate is proportionally inverse
by the magnitude of permeability. Optimization of injection rate allocation by reducing the Dynamic Lorenz
Coefficient value is proven to be effective because it can increase oil recovery from increasing in cumulative oil
production using the initials scenario and the after-optimization scenario. These results have been validated in
the simulator commercial CMG. |
---|