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Application of PCA and Machine Learning for Predicting Oil Measurement Discrepancies in Custody Transfer Systems: Understanding from an Indonesian Mature Onshore Facility

Scientific Contributions Oil and Gas · Vol. 48 No. 4 (2025) · hlm. 191–202

DOI
10.29017/scog.v48i4.404
ISSN
2541-0520, 2089-3361
Terbit
2025-12-16
Penerbit
Testing Center for Oil and Gas LEMIGAS
Bahasa
eng
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Oil measured volume discrepancies in custody transfer systems is becoming a persistent challenge, which is often caused by complex thermal, hydraulic, and compositional interactions. Therefore, this study aimed to introduce a data-driven framework incorporating Principal Component Analysis (PCA) and machine learning (ML) to identify as well as predict discrepancies at a representative onshore gathering station (GS) in Indonesia (Field-X). Major operational parameters, including gross volume, unallocated net oil, pressure, temperature, and Basic Sediment & Water (BS&W), were analyzed to assess the impact on volumetric imbalance. During the analysis, PCA reduced 64 correlated variables to five principal components, explaining 95% of the total variance and showing gross volume, pressure, and temperature as dominant factors. Four ML models, namely XGBoost, Random Forest, Support Vector Regression, and ElasticNet, were trained as well as validated with three-fold time series cross-validation for temporal robustness. Incorporating PCA significantly improved predictive performance, with Support Vector Regression showing the largest R² increase (from –0.0082 to 0.82). Results signified that discrepancies were primarily governed by thermodynamic shrinkage, temperature changes, and BS&W-related metering errors. In addition, the proposed PCA–ML framework offered an interpretable, reliable method for early detection and mitigation of oil volume discrepancies in complex production environments.

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33 penulis · 109 tautan kepenulisan bersama · 43 lintas organisasi

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  • Islamic University of Riau
  • University of Technology Malaysia
  • Universiti Teknologi MARA
  • Universiti Malaysia Pahang Al-Sultan Abdullah
  • Pertamina (Indonesia)
  • University of Calgary
  • Riau University
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Rekan penulisOrganisasiArtikel bersama
Tomi ErfandoIslamic University of Riau6
Dita Putri PurnamaIslamic University of Riau4
Rika Lala SaputriIslamic University of Riau4
Mohd Zaidi JaafarUniversity of Technology Malaysia3
Ichsan Al Sabah LukmanIslamic University of Riau3
Anas HidayatIslamic University of Riau2
Razif Muhammed NordinUniversiti Teknologi MARA2
MursyidahIslamic University of Riau2
Ari PrasetyoIslamic University of Riau2
Adiella Zakky JuneidRiau University2
M Haidar PutraAfiliasi tidak diketahui2
Muhammad AriyonIslamic University of Riau2
Ku Muhd Na’im KhalifAfiliasi tidak diketahui1
Ichsan LukmanAfiliasi tidak diketahui1
Ahmad KurniawanIslamic University of Riau1
Apri SiswantoIslamic University of Riau1
Muhammad FadhilIslamic University of Riau1
Hasan Ali Ma’sumAfiliasi tidak diketahui1
Ayyi HusbaniIslamic University of Riau1
Muhammad Zaidi JaafarUniversity of Technology Malaysia1
M. Haidar T. PutraUniversity of Calgary1
Novia RitaIslamic University of Riau1
Tengku Amran Tengku MohdUniversiti Teknologi MARA1
Agus DahliaIslamic University of Riau1
Novi Lestari YulianiIslamic University of Riau1
Neneng PurnamawatiIslamic University of Riau1
Novrianti NovriantiIslamic University of Riau1

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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">Application of PCA and Machine Learning for Predicting Oil Measurement Discrepancies in Custody Transfer Systems: Understanding from an Indonesian Mature Onshore Facility</dc:title>
	<dc:creator xml:lang="en">Fadly, Wan</dc:creator>
	<dc:creator xml:lang="en">Hidayat, Fiki</dc:creator>
	<dc:creator xml:lang="en">Abu, Noratikah</dc:creator>
	<dc:creator xml:lang="en">Afdhol, Muhammad Khairul</dc:creator>
	<dc:creator xml:lang="en">Putra, Dike</dc:creator>
	<dc:creator xml:lang="en">Mulyandri</dc:creator>
	<dc:subject xml:lang="en">Oil measured volume discrepancies</dc:subject>
	<dc:subject xml:lang="en">Time-Series Forecasing</dc:subject>
	<dc:subject xml:lang="en">Principal Component Analysis</dc:subject>
	<dc:subject xml:lang="en">Time Series Cross-Validation</dc:subject>
	<dc:description xml:lang="en">Oil measured volume discrepancies in custody transfer systems is becoming a persistent challenge, which is often caused by complex thermal, hydraulic, and compositional interactions. Therefore, this study aimed to introduce a data-driven framework incorporating Principal Component Analysis (PCA) and machine learning (ML) to identify as well as predict discrepancies at a representative onshore gathering station (GS) in Indonesia (Field-X). Major operational parameters, including gross volume, unallocated net oil, pressure, temperature, and Basic Sediment &amp;amp; Water (BS&amp;amp;W), were analyzed to assess the impact on volumetric imbalance. During the analysis, PCA reduced 64 correlated variables to five principal components, explaining 95% of the total variance and showing gross volume, pressure, and temperature as dominant factors. Four ML models, namely XGBoost, Random Forest, Support Vector Regression, and ElasticNet, were trained as well as validated with three-fold time series cross-validation for temporal robustness. Incorporating PCA significantly improved predictive performance, with Support Vector Regression showing the largest R² increase (from –0.0082 to 0.82). Results signified that discrepancies were primarily governed by thermodynamic shrinkage, temperature changes, and BS&amp;amp;W-related metering errors. In addition, the proposed PCA–ML framework offered an interpretable, reliable method for early detection and mitigation of oil volume discrepancies in complex production environments.</dc:description>
	<dc:publisher xml:lang="en">Testing Center for Oil and Gas LEMIGAS</dc:publisher>
	<dc:date>2025-12-16</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/404</dc:identifier>
	<dc:identifier>10.29017/scog.v48i4.404</dc:identifier>
	<dc:source xml:lang="en">Scientific Contributions Oil and Gas; Vol. 48 No. 4 (2025); 191-202</dc:source>
	<dc:source xml:lang="id">Scientific Contributions Oil and Gas; Vol 48 No 4 (2025); 191-202</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/404/1620</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>