UNCERTAINTY ANALYSIS OF ECONOMIC EVALUATION FOR CO2 INJECTION USING DETERMINISITC-STOCHASTIC PARTICLE SWARM OPTIMIZATION (PSO) COMBINATION
The utilization of CO2 gas from the oil and gas fields or industry for the purposes of EOR has a dual purpose which to increase reserves and also to help deal with the greenhouse effect. The source of CO2 supply in Field X are from Merbau Gas Gathering Station and Bukit Asam Power Plant with rate 15...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/52078 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The utilization of CO2 gas from the oil and gas fields or industry for the purposes of EOR has a dual purpose which to increase reserves and also to help deal with the greenhouse effect. The source of CO2 supply in Field X are from Merbau Gas Gathering Station and Bukit Asam Power Plant with rate 150 tons/day. The objective is to analyze economic uncertainty in decision making based on stochastic method analysis with the objective function of Net Present Value (NPV).
This research was conducted by two methods, deterministic and stochastic method. The deterministic method optimization study is carried out by directly analyzing the results of optimization using software for technically and economically by using Gross Split production sharing contract. In this field development, 3 producer were converted to injector with injection rate 3,92 MMSCFD, cummulative production of this scenario reaches 1,99 mmbbl, recovery factor (RF) increase for about 20,37% from 4,51% to 24,88%, and the economic parameter such as Net Present Value (NPV) 6,62 MMUSD, Internal Rate of Return (IRR) 23%, and Pay Out Time (POT) 4,89 years.
Then an analysis of the optimal cases applied to Field X both technically and economically with the stochastic method, in this study used the Particle Swarm Optimization (PSO) with an objective function Net Present Value (NPV) and limit parameters for the number of injection wells and CO2 injection volume. There are 3 scenario using inertia weight for 0,4 to 0,9 and learing factor of 1 to 1,8. The changes in the main parameters of PSO (inertia weight and learning factor) indicate that inertia weight 0,9 provides the fastest optimum solution search velocity 400 experiment ID with NPV 3,5 – 4 MMUSD. Based on Design of Experiment and static analysis, inertia weight is the main effect with recommendation on higher value.
|
|---|