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Hafwandi, Babas samudera
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Energy Engineering

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ANALISIS KEBERHASILAN KERJA ULANG PINDAH LAPISAN PADA SUMUR SKW-33 LAPANGAN SUKOWATI Desyana Nindya Prastiwi; Abror, Hadziqul; Hafwandi, Babas samudera
Journal of Sustainable Energy Development Vol. 2 No. 2 (2024): Journal of Sustainable Energy Development (JSED)
Publisher : Petroleum Engineering, Faculty of Engineering, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jsed.v2i2.1603

Abstract

This study explores the effectiveness of re-perforation operations at Well SKW-33 in the Sukowati Field to enhance oil production. The background highlights the decline in production rates despite the well's initial success with natural flow since June 2015. The primary objective is to evaluate the impact of re-perforation on oil production and water cut. The research employs methods such as squeeze cementing, Cement Bond Log (CBL) analysis, well logging and subsequent re-perforation. The results demonstrate a significant increase in oil production from 100-150 barrels per day (bopd) to a peak of 305 bopd post-re-perforation. Additionally, the water cut reduced from 95-96% to 80%, indicating a successful reduction in water production. The conclusion asserts that re-perforation significantly boosts short-term oil production and reduces water production, though ongoing management is essential for maintaining long-term efficiency.
Studi Prediksi Porositas Dengan Menggunakan Metode Deterministik dan Machine Learning Pada Lapangan “X” Hafwandi, Babas Samudera
Journal of Sustainable Energy Development Vol. 3 No. 1 (2025): Journal of Sustainable Energy Development (JSED)
Publisher : Petroleum Engineering, Faculty of Engineering, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Porosity is one of the most critical parameters in reservoir characterization, as it directly influences hydrocarbon storage capacity. Accurate porosity prediction becomes even more essential in fields with limited core data, such as Field “X”, located in the South Sumatra Basin. This study compares two different porosity prediction approaches: a deterministic method based on well log interpretation using NPHI and RHOB logs, and various Machine Learning (ML) algorithms, including Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GBR), AdaBoost (ADA), Support Vector Machine (SVM), and Decision Tree (DT). Data preprocessing involved feature selection using Pearson, Spearman, and Kendall correlation coefficients to identify the most influential log parameters. The dataset was then divided into training (70%) and testing (30%) subsets. Model performance was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The deterministic method yielded an MAE of 0.0658 and RMSE of 0.0906, while the best ML model, Random Forest, achieved an MAE of 0.0329 and RMSE of 0.0434 on the testing dataset. In conclusion, Machine Learning, especially the Random Forest model, proves to be a more reliable and accurate tool for porosity prediction in geologically complex fields, offering significant potential for enhancing reservoir modeling and field development planning.
Co-Authors Desyana Nindya Prastiwi Hadziqul Abror