Ramadhani, Nanda (2025) Penerapan Metode Self Organizing Maps (SOM) Untuk Identifikasi Zona Low Quality reservoir (LQR) Pada Lapangan "NR",Cekungan Sumatera Selatan. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Reservoir berkualitas rendah atau disebut Low Quality Reservoir (LQR) sering kali diabaikan dalam pengembangan lapangan karena memiliki porositas dan permeabilitas yang rendah. Namun, studi-studi terbaru menunjukkan bahwa zona LQR tetap memiliki potensi hidrokarbon yang signifikan jika diidentifikasi dan dianalisis dengan metode yang tepat. Penelitian ini menerapkan metode Self Organizing Maps (SOM), sebuah teknik pengelompokan data berbasis pembelajaran tidak terawasi (unsupervised learning), untuk mengidentifikasi zona Low Quality Reservoir (LQR) pada Lapangan “NR”, Cekungan Sumatera Selatan. Data input yang digunakan berupa volume shale, porositas, dan permeabilitas, yang kemudian dikalibrasi menggunakan pendekatan Hydraulic Flow Unit (HFU) berbasis data batuan inti. Validasi dilakukan melalui analisis cutoff dengan acuan data perforasi sumur untuk membedakan zona produktif dan tidak produktif secara objektif. Hasilnya menunjukkan bahwa Self Organizing Maps (SOM) mampu mengidentifikasi 38 zona LQR dengan karakteristik porositas antara 5 - 11% dan permeabilitas antara 0,30 - 6,48 mD. Nilai cutoff yang ditetapkan untuk porositas efektif adalah 0,11 (dec) dan untuk permeabilitas adalah 6,48 mD, yang menjadi batas kunci dalam membedakan zona LQR dari zona berkualitas baik. Temuan ini menegaskan bahwa zona-zona LQR di Lapangan “NR” berpotensi untuk dikembangkan lebih lanjut, terutama dengan strategi kerja ulang sumur (workover), pembukaan zona baru, atau pengembangan sumur tambahan. Penelitian ini menjadi rujukan penting dalam optimalisasi reservoir non-konvensional melalui pendekatan berbasis data dan pemodelan petrofisika yang terintegrasi.
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Low Quality Reservoirs (LQR) are often overlooked in field development due to their low porosity and permeability. However, recent studies have demonstrated that LQR zones can still hold significant hydrocarbon potential when identified and analyzed using the right approach. This study applies the Self Organizing Maps (SOM) method, an unsupervised learning-based clustering technique, to identify LQR zones in the "NR" Field, South Sumatra Basin. Input data include shale volume, porosity, and permeability, which are calibrated using the Hydraulic Flow Unit (HFU) approach based on core data. Validation is conducted through a cutoff analysis derived from well perforation data, allowing for a more objective distinction between productive and non-productive zones. The results show that SOM effectively identified 38 LQR zones with porosity ranging from 5–11% and permeability from 0.30 to 6.48 mD. The established cutoff values 0.11 for effective porosity (decimal) and 6.48 mD for permeability serve as critical thresholds in classifying LQR from higher-quality reservoir zones. These findings confirm the significant development potential of LQR zones in the "NR" Field, especially through strategies such as workover, zone reactivation, and new development wells. This research serves as a valuable reference for optimizing unconventional reservoirs using data-driven and integrated petrophysical modelling approaches.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Self Organizing Maps, Low Quality Reservoir, porositas, permeabilitas, cutoff. Self Organizing Maps, Low Quality Reservoir, porosity, permeability, cutoff. |
| Subjects: | Q Science > QE Geology > QE601 Geology, Structural |
| Divisions: | Faculty of Civil, Planning, and Geo Engineering (CIVPLAN) > Geophysics Engineering > 33201-(S1) Undergraduate Thesis |
| Depositing User: | Ramadhani Nanda |
| Date Deposited: | 28 Jul 2025 06:44 |
| Last Modified: | 28 Jul 2025 06:44 |
| URI: | http://repository.its.ac.id/id/eprint/122084 |
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