Prameswari, Windia Cinde (2016) Pemodelan Produksi Minyak Dan Gas Bumi Pada Platform "MK" Di PT. X Menggunakan Metode ARIMA, Neural Network, Dan Hibrida ARIMA-Neural Network. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
Preview |
Text
1314105010-Undergraduate_Thesis.pdf - Accepted Version Download (1MB) | Preview |
Preview |
Text
1314105010-Paper.pdf - Accepted Version Download (240kB) | Preview |
Preview |
Text
1314105010-Presentation.pdf - Presentation Download (915kB) | Preview |
Abstract
Minyak dan gas bumi dapat diambil secara langsung melalui sumursumur yang dibuat, namun sumur-sumur tersebut tidak akan
menghasilkan jumlah minyak dan gas bumi yang konstan setiap hari.
Ketika kandungan minyak dan gas mulai turun maka yang harus
dilakukan adalah memberikan treatment terhadap sumur tersebut,
sehingga minyak dan gas yang masih terkandung di dasar bumi bisa naik
dengan jumlah yang lebih banyak. Tujuan dilakukannya penelitian ini
adalah untuk membantu perusahaan dalam menganalisis jumlah produksi
minyak dan gas bumi selama periode 14 hari selanjutnya, sehingga dapat
diketahui apakah selama periode 14 hari selanjutnya diperlukan treatment
terhadap sumur. Data yang digunakan adalah jumlah produksi minyak
dan gas bumi pada platform “MK” pada tahun 2015. Pemodelan jumlah
produksi minyak dan gas bumi dilakukan menggunakan tiga metode,
yaitu ARIMA, neural network, dan Hibrida ARIMA-neural network.
Hasil yang diperoleh berdasarkan analisis ketiga metode tersebut adalah
pada jumlah produksi minyak bumi model terbaik diperoleh dari metode
hibrida ARIMA-neural network, dengan hasil ramalan yang cenderung
sama selama 14 hari yaitu 1961 barel. Sedangkan jumlah produksi gas
bumi model terbaik diperoleh dari metode neural network, dengan
ramalan produksi untuk 14 hari selanjutnya cenderung meningkat.
====================================================================================================
Petroleum and natural gas can be taken directly through the wells that
were made, but the wells will not produce the amount of oil and gas is a
constant every day. When petroleum and natural gas begin to fall then
that should be done is to provide treatment to those wells, so that oil and
gas are still contained in the bottom of the earth could rise with higher
numbers. The purpose of this study is to assist companies in analyzing the
amount of petroleum and natural gas production over the next 14 day
period, so it can be known whether during the period of 14 d ays is
required subsequent treatment of the wells. The data used is the number
of petroleum and natural gas production on the platform "MK" in 2015.
Modelling the number of oil and gas production is done using three
methods, namely ARIMA, Neural Network, and Hybrid ARIMA-NN. The
results obtained by the analysis of these three methods is the best model
of petroleum production is obtained from a hybrid method ARIMA-NN,
with production that tends same forecast for 14 days, about 1961 barel.
Meanwhile, natural gas production is obtained from the best models
Neural Network methods, with production forecast production for the
next 14 days is likely to increase.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Additional Information: | RSSt 519.535 Pra p 3100016065898 |
| Uncontrolled Keywords: | ARIMA, Hibrida ARIMA-Neural Network, Minyak dan Gas Bumi, Neural Network |
| Subjects: | H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49201-(S1) Undergraduate Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 25 Feb 2020 07:42 |
| Last Modified: | 25 Feb 2020 07:42 |
| URI: | http://repository.its.ac.id/id/eprint/75122 |
Actions (login required)
 |
View Item |