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Identifikasi Batuan Berdasarkan Data Well Log Menggunakan K-Means Clustering Susanty, Meredita; Ebelaristra, Prinsislamsheeny Brilliantdianty; Rahman, Ahmad Fauzan; Irawan, Ade; Madrinovella, Ikri; Astuti, Weny
Jurnal Migasian Vol. 4 No. 1 (2020): Jurnal Migasian
Publisher : LPPM Akademi Minyak dan Gas Balongan Indramayu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.96

One of the stages in oil and gas exploration is a Petrophysical analysis, which aims to determine the structure of rock layers below the earth's surface. The petrophysical analysis uses physical properties in a well-log to determine the rock type below the surface. Nowadays, the software for conducting petrophysical analysis has utilized a machine-learning approach to predict rock types. Most of the software uses the supervised learning method to classify rock types. This research uses a different approach, unsupervised learning, to group rock types based on various features in a well-log. Using a publicly available well-log in Stafford, United States, and the k-means clustering algorithm, this study groups the data into 3 clusters. The result is compared with manual analysis interpretation and shows an alignment between them. From the result, it shows that the unsupervised learning method effectively predicts limestone, shale, and evaporites in the well. It classifies the dataset into useful clusters, generates useful lithologies, provides useful rock characterization, and less time-consuming.

The Result Discrepancies between Histological and PCR Method for Detecting Helicobacter pylori in Patients with Dyspepsia due to Inappropriate Preparation before Endoscopy Maruni Wiwin Diarti; Haris Widita; Soewignjo Soemohardjo; Weny Astuti; Troef Sumarno; Yunan Jiwintarum; Zainul Mutaqin; Retno Handayani
The Indonesian Journal of Gastroenterology, Hepatology, and Digestive Endoscopy VOLUME 10, ISSUE 2, August 2009
Publisher : The Indonesian Society for Digestive Endoscopy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24871/102200946-50

Background: False negative result of Helicobacter pylori (H. pylori) detection in gastric tissue can be due to inappropriate preparation before endoscopy. The objectives of this study is to compare the result of H. pylori detection in gastric biopsy by histological method and ure C polymerase chain reaction (PCR) in patients with dyspepsia who underwent upper gastrointestinal (GI) endoscopy without preparations other than six hours fasting before endoscopy. Method: We obtained 156 paraffin blocks of gastric endoscopic biopsy samples, taken from antrum and corpus of patients with dyspepsia who underwent endoscopic examination at the Endoscopy Unit of Biomedika hospital, Mataram. All biopsy samples were stained with Hematoxylin and Eosin for tissue diagnosis and Giemsa stain for detecting H. pylori Ure C PCR were done on all blocks. Cag PCR were performed on all Ure C PCR positive samples. Results: Of 156 paraffin blocks, only 17 blocks (10.9%) were positive for H. pylori by histological examination. All of the 17 samples showed positive results on PCR method. Of 156 paraffin blocks, positive results were found in 73 patients (45.9%) by ure C PCR method. The PCR method has increased the positivity rates of H. pylori more than four times compared to histological method. This study showed that the rate of cag a was 63.0%. Conclusion: Ure C PCR is superior to histological examination in patients who did not stop consuming acid supressor drug and antibiotic two weeks prior to endoscopy. This phenomenon can be explained by the change of spiral form into coccoid form of H. pylori, which is hardly detected using Giemsa stain. Keywords: Helicobacter pylori, histology, ureC, Cag a, PCR

Identifikasi Batuan Berdasarkan Data Well Log Menggunakan K-Means Clustering Meredita Susanty; Prinsislamsheeny Brilliantdianty Ebelaristra; Ahmad Fauzan Rahman; Ade Irawan; Ikri Madrinovella; Weny Astuti
Jurnal Migasian Vol 4 No 1 (2020): Jurnal Migasian
Publisher : LPPM Institut Teknologi Petroleum Balongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36601/jurnal-migasian.v4i1.96

One of the stages in oil and gas exploration is a Petrophysical analysis, which aims to determine the structure of rock layers below the earth's surface. The petrophysical analysis uses physical properties in a well-log to determine the rock type below the surface. Nowadays, the software for conducting petrophysical analysis has utilized a machine-learning approach to predict rock types. Most of the software uses the supervised learning method to classify rock types. This research uses a different approach, unsupervised learning, to group rock types based on various features in a well-log. Using a publicly available well-log in Stafford, United States, and the k-means clustering algorithm, this study groups the data into 3 clusters. The result is compared with manual analysis interpretation and shows an alignment between them. From the result, it shows that the unsupervised learning method effectively predicts limestone, shale, and evaporites in the well. It classifies the dataset into useful clusters, generates useful lithologies, provides useful rock characterization, and less time-consuming.

EVALUASI DAN OPTIMASI SUCKER ROD PUMP (SRP) SUMUR-Z, SUMUR-F, DAN SUMUR-M LAPANGAN NAWASENA PT. PERTAMINA HULU ROKAN. Astuti, Weny; Mardiani, Zhilda Fiqi
Petro : Jurnal Ilmiah Teknik Perminyakan Vol. 14 No. 2 (2025): Juni 2025
Publisher : Jurusan Teknik Perminyakan Fakultas Teknologi Kebumian dan Energi Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/petro.v14i2.20668

Penelitian ini mendiskusikan tentang optimasi sumur menggunakan artificial lift sucker rod pump. Penelitian ini bertujuan untuk menilai kinerja sucker rod pump (SRP) pada sumur-Z, sumur-F, dan sumur-M dan melakukan optimasi pada sumur-Z, sumur-F, dan sumur-M melalui cara melakukan design ulang dengan mengubah parameter sederhana dalam sucker rod pump (SRP) seperti kecepatan pompa, panjang langkah, dan ukuran plunger. Metodologi yang digunakan yaitu dengan melakukan pengumpulan data – data penelitian dari perusahaan dan dilakukan perhitungan manual menggunakan software excel, mengevaluasi berdasarkan efesiensi volumetris, optimasi pada pompa terpasang, dan menganalisa nilai keekonomian pada ketiga sumur. Hasil penelitian ini adalah berupa evaluasi kinerja sucker rod pump existing masih kurang dari 70% yang artinya sumur belum efesien sehingga perlu dilakukan perhitungan ulang untuk mengoptimasikan kinerja sumur tersebut. Target laju produksi yang diinginkan yaitu 70% - 80% dari laju maksimum yang dihasilkan dari kurva Inflow Performance Relantionship (IPR) ketiga sumur yang telah ditentukan. Kemudian menghitung nilai keekonomian pada sumur-Z, sumur-F, dan sumur-M berdasarkan kenaikkan laju produksi yang didapatkan setelah dilakukan optimasi dengan cost yang dikeluarkan. Sehingga mendapatkan nilai profit dari setiap sumur serta menentukan nilai production cost/barrel yang dibutuhkan pada Sumur – Z, Sumur – F, dan Sumur – M.

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