Febriansyah, Lucky Rizky (2018) Optimisasi Kondisi Operasi Pada Proses Injeksi CO₂ Enhanced Gas Recovery Dan Carbon Sequestration Menggunakan Stochastic Optimization Algorithm. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Salah satu solusi efektif untuk mengurangi emisi CO2 adalah dengan melakukan injeksi CO2 ke dalam lapisan bumi atau dikenal dengan carbon sequestration (CS). Injeksi CO2 ke dalam reservoar gas tidak hanya dapat digunakan untuk penyimpanan gas CO2 tetapi juga dapat meningkatkan produksi gas pada reservoar natural gas yang mengalami penurunan produksi disebut dengan Enhanced Gas Recovery (EGR). Pada penelitian ini CO2 EGR dan CS dimodelkan dengan membagi ke dalam tiga bagian yakni injection well, reservoar, dan production well. Gradien tekanan pada injection dan production well dimodelkan dengan persamaan Beggs-Brill sedangkan pada reservoar menggunakan persamaan Darcy. Gradien temperatur untuk setiap bagian dimodelkan dengan persamaan perpindahan panas. Validasi model gradien tekanan dan temperatur pada injection dan production well terhadap PIPESIM dihasilkan error yaitu masing-masing sebesar 0.85% dan 0.83%. Sedangkan validasi reservoar terhadap COMSOL dihasilkan error sebesar 0.192% dan 0.031%. Dari hasil permodelan, CO2 yang tersimpan adalah 19.14 ton/hari. Berdasarkan analisa sensitivitas, profit akan meningkat ketika laju aliran massa dan temperatur injeksi meningkat sementara akan menurun ketika tekanan injeksi meningkat. Hasil terbaik optimisasi pada ketiga variabel menggunakan metode stochastic optimization algorithm didapatkan ketika digunakan Killer Whale Algorithm (KWA), Genetic Algorithm (GA), dan Particle Swarm Optimization (PSO) dimana terjadi peningkatan profit dari 4453.872 USD/hari menjadi 12331.909 USD/hari dengan jumlah CO2 yang dapat tersimpan sebanyak 1486.01 ton. ======================================================================================================= One of the effective solution to reduce CO2 emissions is by injecting CO2 into the geologial formation or known as carbon sequestration (CS). CO2 injection into gas reservoir not only give advantage for CO2 gas storage but also can increase gas production in depleted reservoir or known as Enhanced Gas Recovery (EGR). In this research CO2 EGR and CS process modeled by dividing into three parts injection well, reservoir, and production well. Pressure gradient in injection and production well modeled using Beggs-Brill while in reservoir using Darcy equation. Temperature gradient for each part modeled using heat transfer equation. Validation of injection and production well pressure and temperature gradient model compared to PIPESIM show average mean error 0.85% and 0.83%, respectively. While, validation on reservoir model compared to COMSOL shows average mean error 0.192% and 0.031% respectively. From the model, CO2 stored per day is 19.14 tones / day. Based on the sensitivity analysis, the profit will increase when mass flow rate and temperature increase otherwise it will decrease when pressure increase. By using stochastic optimization algorithm, the best optimization results of these variables are obtained when using Killer Whale Algorithm (KWA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). Optimization result shows an increase in profit 176.88% from 4453.872 USD / day to 12331.909 USD/day with total CO2 storage about 1486.01 tones.
| Item Type: | Thesis (Undergraduate) |
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| Additional Information: | RSF 622 Feb o-1 3100018077283 |
| Uncontrolled Keywords: | Enhanced Gas Recovery (EGR), Carbon Sequestration, Optimization, Stochastic Algorithm, |
| Subjects: | Q Science > QA Mathematics > QA274.2 Stochastic analysis T Technology > TA Engineering (General). Civil engineering (General) T Technology > TN Mining engineering. Metallurgy > TN879.5 Petroleum pipelines T Technology > TP Chemical technology > TP350 Natural gas--Drying. |
| Divisions: | Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Lucky Rizky Febriansyah |
| Date Deposited: | 21 Oct 2020 06:47 |
| Last Modified: | 21 Oct 2020 06:47 |
| URI: | https://repository.its.ac.id/id/eprint/55861 |
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