Putra, Adhitya Valentino Putra (2025) Optimisasi Distribusi Injeksi Waterflooding Untuk Proses Enhanced Oil Recovery Menggunakan Capacitance Resistance Model (CRM) dan Stochastic Algorithm. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Penurunan produksi minyak akibat deplesi tekanan reservoir memerlukan penerapan strategi peningkatan perolehan minyak seperti waterflooding yang efektif dan adaptif. Penelitian ini bertujuan untuk memodelkan konektivitas antara sumur injeksi dan sumur produksi menggunakan pendekatan Capacitance Resistance Model – Injection-Production (CRM-IP) serta mengoptimalkan distribusi injeksi berdasarkan konektivitas tersebut guna meningkatkan efisiensi sweeping dan menjaga keseimbangan tekanan reservoir. Analisis dilakukan pada lapangan dengan pola injeksi peripheral dan heterogenitas reservoir yang tinggi berdasarkan nilai Dykstra-Parsons. Hasil pemodelan menunjukkan bahwa CRM-IP mampu mengidentifikasi pasangan injektor-produser dominan, dengan nilai kontribusi injeksi signifikan pada beberapa sumur produksi seperti Alphax-10 dan Alphax-12. Optimasi distribusi injeksi menghasilkan peningkatan produksi sebesar 13,44%, dari 10 BOPD menjadi 11,34 BOPD, serta menunjukkan potensi perbaikan efisiensi injeksi pada sistem waterflooding. Evaluasi performa model menggunakan Root Mean Square Error (RMSE) menunjukkan hasil yang masih dalam batas toleransi teknis (<30%), sehingga CRM-IP dapat dianggap layak sebagai alat bantu strategi distribusi injeksi air yang lebih presisi dan berbasis data. Temuan ini memberikan kontribusi terhadap perencanaan injeksi yang adaptif terhadap kondisi reservoir serta mampu meminimalkan risiko early water breakthrough dan ketidakseimbangan tekanan antar sumur.
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Declining oil production due to reservoir pressure depletion necessitates the implementation of enhanced oil recovery strategies such as waterflooding, which must be both effective and adaptive. This study aims to model the connectivity between injection and production wells using the Capacitance Resistance Model – Injection-Production (CRM-IP) and to optimize injection distribution based on that connectivity in order to improve sweeping efficiency and maintain reservoir pressure balance. The analysis was conducted on a field with a peripheral injection pattern and high reservoir heterogeneity, as indicated by the Dykstra-Parsons coefficient. The modeling results show that CRM-IP effectively identifies dominant injector–producer pairs, with significant injection contributions observed in production wells such as Alphax-10 and Alphax-12. Injection optimization led to a 13.44% increase in oil production, from 10 BOPD to 11.34 BOPD, demonstrating the potential for improved injection efficiency under waterflooding schemes. The model's performance, evaluated using the Root Mean Square Error (RMSE), remained within acceptable technical limits (<30%), confirming that CRM-IP is a reliable tool for guiding water injection strategies in a more targeted and data-driven manner. These findings contribute to the development of adaptive injection planning approaches that account for reservoir conditions while minimizing the risks of early water breakthrough and interwell pressure imbalance.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Waterflooding, CRM-IP, Konektivitas Sumur, Optimasi Injeksi Air, Early Water Breakthrough, Waterflooding, CRM-IP, Konektivitas Sumur, Optimasi Injeksi Air, Early Water Breakthrough. |
| Subjects: | Q Science > Q Science (General) > Q180.55.M38 Mathematical models Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA273.5 Stochastic geometry Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QC Physics > QC111 Density and specific gravity Q Science > QC Physics > QC151 Fluid dynamics |
| Divisions: | Faculty of Industrial Technology > Physics Engineering > 30201-(S1) Undergraduate Thesis |
| Depositing User: | Adhitya Valentino Putra |
| Date Deposited: | 31 Jul 2025 01:32 |
| Last Modified: | 31 Jul 2025 01:32 |
| URI: | http://repository.its.ac.id/id/eprint/123493 |
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