Optimasi Produksi Crude Oil, Energi, Dan Air Limbah Pada Steam Flood Enhanced Oil Recovery Menggunakan Genetic Algorithm

Andriyanto, Sony (2016) Optimasi Produksi Crude Oil, Energi, Dan Air Limbah Pada Steam Flood Enhanced Oil Recovery Menggunakan Genetic Algorithm. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Heavy oil memiliki jumlah akumulasi yang banyak. Minyak jenis ini memiliki viskositas yang tinggi, sehingga sulit untuk diangkat ke permukaan. Steam flood adalah salah satu jenis Enhanced Oil Recovery yang mampu mengangkat heavy oil. Steam flood menggunakan 2 sumur, yaitu sumur injeksi dan sumur produksi. Sumur injeksi digunakan untuk menginjeksi uap. Uap kemudian akan melepas panas pada reservoir sehingga dapat menurunkan viskositas minyak. Minyak mentah kemudian akan terdorong dan terangkat ke production well. Namun steam flood membutuhkan energi yang besar untuk menghasilkan uap, dan jumlah air limbah berbahaya yang tinggi. Sehingga produksi steam flood operation perlu dioptimasi dengan meminimalkan kebutuhan energi dan air limbah. Metode Beggs-Brill digunakan untuk memodelkan gradien tekanan aliran vertikal dua fasa pada injection well dan production well. Rata-rata error pemodelan metode Beggs-Brill terhadap hasil simulasi PIPESIM adalah 2.369%. Aliran uap pada reservoir dapat dimodelkan sebagai aliran fluida dalam media berporos dengan persamaan Darcy. Rata-rata error pemodelan dengan persamaan Darcy adalah 0.39%. Ketiga model ini dirangkai dan digunakan untuk menghitung profit dari steam flood operation. Genetic algorithm dapat mengoptimalkan kondisi operasi steam flood hingga 219.6%. Pada studi kasus Hamaca Field, profit dapat dioptimasi dari 7904.89 USD/hari menjadi 17360.34 USD/hari. Penambahakan sucker rod pump yang kemudian dioptimasi dengan genetic algorithm, mampu menurunkan kebutuhan laju aliran injeksi (3.66%), menurunkan biaya water treatment (7.8%), menurunkan tekanan injeksi (34%), dan meningkatkan net profit (18.69%). ======================================================================================================================== Heavy oil has a large amount accumulation. This oil type has a high viscosity, making it difficult to be lifted to the surface. Steam flood is one of Enhanced Oil Recovery which able to lift heavy oil. Steam flood needs 2 wells, injection well and production well. Injection well is used to inject steam. The steam then release the heat into reservoir so as reduce oil viscosity. Then crude oil will be pushed and lifted to a production well. Steam flood requires a large number of energy in order to produce steam, and produces a large ammount of hazardous waste water. Therefore oil production at steam flood operation needs to be optimized by minimize energy requirement and waste water. Beggs-Brill method is used to model the pressure gradient of vertical two-phase flow in the injection well and production well. The mean error of Beggs-Brill method against PIPESIM simulation result is 2.369%. The steam flow in reservoir can be modelled as fluid flow in porous media with Darcy equation. The mean error of Darcy equation model against COMSOL simulation result is 0.39%. These models are joined and used to calculate the profit of steam flood operation. Genetic algorithm optimize the operating conditions of steam flood up to 219.6%. In the case study at Hamaca Field, profit can be optimized from 7904.89 USD/day to 17360.34 USD/day. Adding sucker rod pump then optimized with genetic algorithm, is able to reduce the requirement of injection flow rate (3.66%), lower the cost of water treatment (7.8%), lower the injection pressures (34%), and increases net profit (18.69%).

Item Type: Thesis (Undergraduate)
Additional Information: RSF 005.12 And o
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Subjects: Q Science > QA Mathematics > QA76.758 Software engineering
Divisions: Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User: ansi aflacha
Date Deposited: 03 Mar 2020 07:34
Last Modified: 03 Mar 2020 07:34
URI: https://repository.its.ac.id/id/eprint/75274

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