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UI - Skripsi Membership :: Kembali

Karakterisasi reservoir hidrokarbon menggunakan inversi AI, multiatribut seismik dan probabilistic neural network pada lapangan ‘B’ cekungan Kutai, Kalimantan Timur = Characterization of hydrocarbon reservoir using AI inversion seismic multiattribute and probabilistic neural network of ‘B’ Field, Kutai Basin, East Kalimantan

Destya Andriyana; Supriyanto, supervisor; Raju Eka Candra, supervisor; Iskandarsyah, examiner; Agus Riyanto, examiner (Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020)

 Abstrak

Lapangan ‘B’ merupakan lapangan prospek hidrokarbon yang berlokasi di offshore
cekungan Kutai, Kalimantan Timur. Untuk mengetahui karakterisasi reservoir lapangan
‘B’, dilakukan pemodelan porositas dan saturasi air menggunakan inversi AI, multiatribut
seismik dan probabilistic neural network. Penelitian ini menggunakan data seismik 3D
PSTM dan data sumur (AND-1, AND-2, AND-3 dan AND-4). Pada data seismik dan data
sumur dilakukan inversi AI untuk mengetahui sifat litologi area penelitian. Kemudian,
hasil AI ditransformasikan untuk mendapatkan model porositas. Metode multiatribut
seismik menggunakan beberapa atribut untuk memprediksi model porositas dan saturasi
air. Setelah itu, diaplikasikan sifat non-linear dari probabilistic neural network sehingga
menghasilkan model porositas dan saturasi air hasil probabilistic neural network (PNN).
Model porositas dan saturasi air transformasi AI, multiatribut seismik dan PNN divalidasi
dengan nilai porositas dan saturasi air data sumur untuk mengetahui apakah model
porositas dan saturasi air tersebut merepresentatifkan nilai data sumur. Validasi dilakukan
pada sumur AND-1 dan AND-2. Nilai porositas dan saturasi air data sumur untuk AND-
1 adalah 25.3 – 35.9% dan 45 – 60%, dan nilai porositas dan saturasi air AND-2 adalah
11 – 35% dan 15 – 82%. Nilai porositas AND-1 hasil transformasi AI sekitar 16 – 67%,
multiatribut seismik sekitar 11.5 – 27% dan PNN sekitar 11.5 – 27%. Nilai saturasi air
AND-1 hasil multiatribut seismik sekitar 4 – 63% dan PNN sekitar 18 – 63%. Nilai
porositas AND-2 hasil transformasi AI sekitar 52 – 72%, multiatribut seismik sekitar 11
– 21.5% dan PNN sekitar 11 – 21.5%. Nilai saturasi air AND-2 hasil multiatribut seismik
sekitar 63 – 85% dan PNN sekitar 63 – 85%. Kemudian, metode multiatribut seismik dan
PNN didapatkan nilai korelasi antara parameter target dengan parameter prediksi. Model
porositas multiatribut seismik memiliki korelasi 0.840836 dan PNN memiliki korelasi
0.936868. Model saturasi air multiatribut seismik memiliki korelasi 0.915254 dan PNN
memiliki korelasi 0.994566. Model porositas transformasi AI memiliki rentang yang
lebih tinggi dibandingkan dengan data sumur. Model porositas dan saturasi air metode
PNN memiliki rentang nilai yang cukup dekat dengan data sumur dan memiliki korelasi
yang lebih tinggi dibandingkan dengan metode multiatribut seismik. Oleh sebab itu,
model porositas dan saturasi air metode PNN merupakan model prediksi terbaik.
Berdasarkan model PNN, reservoir zona target lapangan ‘B’ memiliki nilai impedansi
akustik 25384 – 26133 ((ft/s)*(g/cc)), porositas sekitar 15 – 27% dan nilai saturasi air
sekitar 11 – 63%.

The 'B' field is a hydrocarbon prospect field located in the offshore Kutai Basin, East
Kalimantan. To determine the characterization of the ‘B’ field reservoir, porosity and
water saturation modeling was carried out using AI inversion, seismic multiattribute and
probabilistic neural network. This study uses 3D PSTM seismic data and wells data
(AND-1, AND-2, AND-3 and AND-4). In seismic data and wells data, AI inversion was
carried out to determine the lithological characteristics of the research area. Then, the AI
results were transformed to obtain a porosity model. The seismic multiattribute method
uses several attributes to predict the porosity and water saturation model. After that, the
non-linear properties of the probabilistic neural network were applied to produce the
porosity and water saturation model of the probabilistic neural network (PNN). The
porosity and water saturation model of AI transformation, seismic multiattribute and PNN
were validated with the porosity and water saturation values of the wells data to determine
whether the porosity and water saturation models represent the wells data values.
Validation was carried out on AND-1 and AND-2 wells. The porosity and water
saturation value of the well data for AND-1 around 25.3 - 35.9% and 45 - 60%, and the
porosity and water saturation value of AND-2 around 11 - 35% and 15 - 82%. The
porosity value of AND-1 as a result of AI transformation is around 16 - 67%, the seismic
multiattribute about 11.5 - 27% and the PNN about 11.5 - 27%. The water saturation value
of AND-1 resulted from seismic multiattribute around 4 - 63% and PNN around 18 - 63%.
The porosity value of AND-2 transformed by AI around 52 - 72%, the seismic
multiattribute around 11 - 21.5% and the PNN around 11 - 21.5%. The water saturation
value of AND-2 result from the seismic multiattribute around 63 - 85% and PNN around
63 - 85%. Then, the multiattribute seismic and PNN methods obtained the correlation
value between the target parameter and the predicted parameter. The seismic
multiattribute porosity model has a correlation of 0.840836 and PNN has a correlation of
0.936868. The multiattribute seismic water saturation model has a correlation of 0.915254
and PNN has a correlation of 0.994566. The AI transformation porosity model has a
higher range than the wells data. The PNN method of porosity and water saturation model
has a fairly close range of values to wells data and has a higher correlation than the
multiattribute seismic method. Therefore, the porosity and water saturation model of the
PNN method is the best prediction model. Based on the PNN model, the field target zone
reservoir 'B' has an acoustic impedance value about 25384 – 26133 ((ft/s) * (g/cc)), a
porosity of 15 - 27% and a water saturation of 11 - 63%.

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Jenis Koleksi : UI - Skripsi Membership
No. Panggil : S-pdf
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Program Studi :
Subjek :
Penerbitan : Depok: Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020
Bahasa : ind
Sumber Pengatalogan : LibUI ind rda
Tipe Konten : text
Tipe Media : computer
Tipe Carrier : online resource
Deskripsi Fisik : xv, 77 pages : illustration ; appendix
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI
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