Optimisasi Kondisi Operasi pada Alkaline Surfactant Polymer (ASP) Enhanced Oil Recovery Menggunakan Stochastic Algorithms

Rahman, Ahmad Hasinur (2017) Optimisasi Kondisi Operasi pada Alkaline Surfactant Polymer (ASP) Enhanced Oil Recovery Menggunakan Stochastic Algorithms. Other thesis, Institut Teknologi Sepuluh Nopember.

Text 2413100059-Undergraduate_Theses.pdf Restricted to Repository staff only Download (3MB) | Request a copy

Abstract

Produksi minyak seiring waktu mengalami penurunan sedangkan konsumsi minyak mengalami kenaikan yang berdampak terjadinya krisis minyak. Penurunan produksi minyak disebabkan oleh kondisi sumur minyak yang sudah tua sehingga kegiatan eksploitasi tidak optimal dan cenderung menurun. Enhanced Oil Recovery (EOR) adalah metode yang digunakan untuk mengeksplorasi minyak dari reservoir setelah dilakukan metode primer dan sekunder. EOR secara umum terbagi menjadi tiga jenis yaitu chemical flooding, miscible flooding dan thermal recovery. Alkaline Surfactant Polymer (ASP) EOR merupakan salah satu jenis dari chemical flooding dengan efisiensi peningkatan oil recovery dari 11 uji lapangan ASP EOR berkisar 19% sampai 34% original oil in place (OOIP). Dalam penerapan ASP EOR agar optimal perlu mempertimbangkan beberapa parameter yaitu konsentrasi ASP, biaya pengadaan bahan ASP, tekanan injeksi dan laju aliran massa injeksi. Metode Beggs-Brill digunakan untuk memodelkan pressure drop pada injection well dan production well dengan rata-rata perbedaan pemodelan terhadap hasil simulasi PIPESIM adalah 0.5076%. Pemodelan pressure drop pada reservoir dimodelkan dengan persamaan Darcy dengan rata-rata perbedaan terhadap hasil simulasi COMSOL Multiphysics sebesar 3.378×10-5%. Berdasarkan hasil optimisasi menggunakan Stochastic Algorithms yang terdiri dari 3 jenis teknik optimisasi yaitu Killer Whale Algorithm (KWA), Duelist Algorithm (DA) dan Genetic Algorithm (GA) diperoleh hasil terbaik dari GA dengan mengoptimalkan kondisi operasi ASP EOR hingga 88.13% dimana profit dapat dioptimisasi dari 9586.40 USD/hari menjadi 18034.59 USD/hari.
==============================================================================================================================
Oil production decreased meanwhile oil demand increased over the time, hence lead to oil crisis. The decrease oil production is caused by the condition of the oil wells are old, hence the exploitation isn’t optimal and tends to decline. Enhanced Oil Recovery (EOR) is a method used to explore oil from the reservoir after primary and secondary methods. EOR is divided into three main catagories: chemical flooding, miscible flooding, and thermal recovery. Alkaline Surfactant Polymer (ASP) EOR is one method of chemical flooding in which the increase of oil recovery is ranging from 19% to 34% of original oil in place (OOIP), obtained from 11 ASP EOR field tests. In order to obtain optimal results of ASP EOR needs to consider several parameters: the concentration of ASP, ASP material procurement costs, the injection pressure and mass flow rate of injection. Beggs-Brill method is used to model the pressure drop in the injection well and production well. The mean error of Beggs-Brill method to PIPESIM simulation result is 0.5076%. Meanwhile the modelling of reservoir pressure conducted using Darcy equation shows that the mean error of Darcy equation model to COMSOL Multiphysics simulation result is 3.378×10-5%. Based on the optimization results using Stochastic Algorithms, which consists of 3 types of optimization techniques i.e. Killer Whale Algorithm (KWA), Duelist Algorithm (DA) and Genetic Algorithm (GA), the best result obtained from the GA, by optimizing operating condition ASP EOR up to 88.13% where profit can be optimized from 9586.40 USD/day to 18034.59 USD/day.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	ASP EOR, Beggs-Brill, Darcy, Stochastic Algorithms, Genetic Algorithm
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis
Depositing User:	Ahmad Hasinur Rahman
Date Deposited:	14 Nov 2024 01:52
Last Modified:	14 Nov 2024 01:52
URI:	http://repository.its.ac.id/id/eprint/42364

Actions (login required)

View Item