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The Application of Machine Learning (DT-Chan-Performance) in Determining Idle Well Reactivation Candidates at PT. Pertamina EP Regional 4 Zone 11 Cepu Field

Scientific Contributions Oil and Gas · Vol. 48 No. 2 (2025) · hlm. 69–94

DOI
10.29017/scog.v48i2.1657
ISSN
2541-0520, 2089-3361
Terbit
2025-08-07
Penerbit
Testing Center for Oil and Gas LEMIGAS
Bahasa
eng
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Indonesia faces a significant challenge in achieving its goal of oil production 1 million barrels of oil per day by 2030, particularly as it relies on old fields or mature fields (brownfields) to extract remaining hydrocarbons. One of the strategies involves reactivating of idle wells in Cepu field, managed by PT. Pertamina EP Regional 4 zone 11. This study focuses on identifying suitable candidates for reactivation through combination of research, innovation and production-focus analysis. The process begins with problem definition, aiming to understand the factors influencing idle wells and review recent advancements in reactivation prediction. Data were collected from both primary and secondary sources covering period 2018-2023. The next stage is implementing Machine Learning (ML), specifically Decision Tree (DT) model, to overcome problems related to data accuracy and complexity. A web application was developed to support decision-makers in selecting wells with high reactivation potential which can provide the best solution of increasing oil recovery. The research results show a high success rate on Accuracy Under Curve and Receiver Operating Curve score of 0.99, indication strong predictive capability. Using entropy-based analysis, two potential wells were identified for reactivation for improvement. These wells were further evaluated using Chan Diagnostic and Production Performance analysis.

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40 penulis · 159 tautan kepenulisan bersama · 41 lintas organisasi

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  • Universitas Pembangunan Nasional Veteran Yogyakarta
  • Bandung Institute of Technology
  • Universitas Pattimura
  • IPB University
  • Pertamina (Indonesia)
  • University of Malaya
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
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Rekan penulisOrganisasiArtikel bersama
Dedi KristantoUniversitas Pembangunan Nasional Veteran Yogyakarta7
Harry Budiharjo SulistyarsoUniversitas Pembangunan Nasional Veteran Yogyakarta4
HariyadiUniversitas Pembangunan Nasional Veteran Yogyakarta4
Taufan MarhaendrajanaBandung Institute of Technology3
Eko Widi PramudyohadiAfiliasi tidak diketahui3
Dewi AsmorowatiBandung Institute of Technology3
Aditya Fahrizal KurniawanUniversitas Pembangunan Nasional Veteran Yogyakarta3
Unggul Setiadi NursidikUniversitas Pembangunan Nasional Veteran Yogyakarta3
Muhammad Rizky Rahmadsyah LubisPertamina (Indonesia)3
Mahruri SanmurjanaAfiliasi tidak diketahui3
Mia Ferian HelmyUniversitas Pembangunan Nasional Veteran Yogyakarta3
Bintang PramudithaUniversitas Pembangunan Nasional Veteran Yogyakarta3
Ivan KurniaBandung Institute of Technology2
Yulius Deddy HermawanUniversitas Pembangunan Nasional Veteran Yogyakarta2
Joko PamungkasUniversitas Pembangunan Nasional Veteran Yogyakarta2
Sudirman YahyaIPB University1
Asri Subkhan MahuletteUniversitas Pattimura1
Ade WachjarIPB University1
Ilyas MarzukiUniversitas Pattimura1
Leksono MucharamBandung Institute of Technology1
Hasian P. Septoratno SiregarBandung Institute of Technology1
Tutuka AriadjiBandung Institute of Technology1
Fanata Yudha NugrahaUniversitas Pembangunan Nasional Veteran Yogyakarta1
Karina LarasatiUniversitas Pembangunan Nasional Veteran Yogyakarta1
Azhar Faari FatahillahAfiliasi tidak diketahui1
Dian Indri AstutiAfiliasi tidak diketahui1
Muhammad NurcholisAfiliasi tidak diketahui1
Aris BuntoroUniversitas Pembangunan Nasional Veteran Yogyakarta1
Allen Haryanto LukmanaUniversitas Pembangunan Nasional Veteran Yogyakarta1
Dimas Suryo WicaksonoUniversitas Pembangunan Nasional Veteran Yogyakarta1
Teddy Eka PutraPertamina (Indonesia)1
Zulhemi AmirUniversity of Malaya1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.lemigas.esdm.go.id/index.php/SCOG/oai, pertama di-harvest 10 Nov 2026, terakhir terlihat 10 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">The Application of Machine Learning (DT-Chan-Performance) in Determining Idle Well Reactivation Candidates at  PT. Pertamina EP Regional 4 Zone 11 Cepu Field</dc:title>
	<dc:creator xml:lang="en">Prayitno, Sayoga Heru</dc:creator>
	<dc:creator xml:lang="en">Swadesi, Boni</dc:creator>
	<dc:creator xml:lang="en">Hariyadi, Hariyadi</dc:creator>
	<dc:creator xml:lang="en">Nandi Wardhana, Damar</dc:creator>
	<dc:creator xml:lang="en">Jayadianti, Herlina</dc:creator>
	<dc:creator xml:lang="en">Branchiny Imasuly, Geovanny</dc:creator>
	<dc:creator xml:lang="en">Widiyaningsih, Indah</dc:creator>
	<dc:creator xml:lang="en">Cahyaningtyas, Ndaru</dc:creator>
	<dc:subject xml:lang="en">machine learning</dc:subject>
	<dc:subject xml:lang="en">reactivation</dc:subject>
	<dc:subject xml:lang="en">idle well</dc:subject>
	<dc:subject xml:lang="en">decision tree</dc:subject>
	<dc:subject xml:lang="en">increased oil recovery</dc:subject>
	<dc:description xml:lang="en">Indonesia faces a significant challenge in achieving its goal of oil production 1 million barrels of oil per day by 2030, particularly as it relies on old fields or mature fields (brownfields) to extract remaining hydrocarbons. One of the strategies involves reactivating of idle wells in Cepu field, managed by PT. Pertamina EP Regional 4 zone 11. This study focuses on identifying suitable candidates for reactivation through combination of research, innovation and production-focus analysis. The process begins with problem definition, aiming to understand the factors influencing idle wells and review recent advancements in reactivation prediction. Data were collected from both primary and secondary sources covering period 2018-2023. The next stage is implementing Machine Learning (ML), specifically Decision Tree (DT) model, to overcome problems related to data accuracy and complexity. A web application was developed to support decision-makers in selecting wells with high reactivation potential which can provide the best solution of increasing oil recovery. The research results show a high success rate on Accuracy Under Curve and Receiver Operating Curve score of 0.99, indication strong predictive capability. Using entropy-based analysis, two potential wells were identified for reactivation for improvement. These wells were further evaluated using Chan Diagnostic and Production Performance analysis.</dc:description>
	<dc:publisher xml:lang="en">Testing Center for Oil and Gas LEMIGAS</dc:publisher>
	<dc:date>2025-08-07</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.lemigas.esdm.go.id/index.php/SCOG/article/view/1657</dc:identifier>
	<dc:identifier>10.29017/scog.v48i2.1657</dc:identifier>
	<dc:source xml:lang="en">Scientific Contributions Oil and Gas; Vol. 48 No. 2 (2025); 69-94</dc:source>
	<dc:source xml:lang="id">Scientific Contributions Oil and Gas; Vol 48 No 2 (2025); 69-94</dc:source>
	<dc:source>2541-0520</dc:source>
	<dc:source>2089-3361</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.lemigas.esdm.go.id/index.php/SCOG/article/view/1657/1405</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 © Copyright by Authors. Published by LEMIGAS</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>