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Multi-Attribute Analysis and Probabilistic Neural Network (PNN) Method for Predicting Carbonate Volume and Water Saturation in Biogenic Gas Reservoir: A Case Study of the Nias Basin, North Sumatra

JURNAL GEOCELEBES · Vol. 10 No. 2 (2026) · pp. 343–358

DOI
10.70561/geocelebes.v10i2.52563
ISSN
2579-5546, 2579-5821
Published
2026-10-02
Publisher
Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar
Language
en
License
creativecommons.org/licenses/by-nc-sa/4.0

Abstract

Carbonate reservoirs frequently exhibit high heterogeneity, complicating the lateral prediction of petrophysical properties from sparse well log data. This study evaluates the hydrocarbon potential of a biogenic gas-bearing carbonate reservoir in the ‘RC’ Field, Nias Basin, by integrating well log and 2D seismic data. A multi-attribute analysis was conducted to identify seismic attributes sensitive to reservoir properties, which were subsequently used as input to a Probabilistic Neural Network (PNN) algorithm to predict the lateral distribution of Carbonate Volume (Vcr) and Water Saturation (Sw). Results identify a prospective gas-bearing carbonate zone in Well S-1 at depths of 4990 – 5693 ft, characterized by favorable reservoir quality and water saturation values predicted by the PNN model, with an average 0.4 (v/v). The PNN model significantly improved prediction accuracy, achieving correlation coefficients of 0.88 for Carbonate Volume and 0.83 for Water Saturation. These findings demonstrate that the PNN method effectively captures non-linear relationships between seismic attributes and petrophysical parameters, providing a robust framework for mapping prospective biogenic gas zones and optimizing future exploration planning in the Nias Basin.

Keywords

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9 authors · 16 co-authorship links

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Syamsurijal RasimengAffiliation unknown1
Emir Dzakwan Kamal ZeinAffiliation unknown1
Nur Ayu AnasAffiliation unknown1
Erlangga Ibrahim FattahAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/geocelebes/oai, first harvested 3 Nov 2026, last seen 3 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Multi-Attribute Analysis and Probabilistic Neural Network (PNN) Method for Predicting Carbonate Volume and Water Saturation in Biogenic Gas Reservoir: A Case Study of the Nias Basin, North Sumatra</dc:title>
	<dc:creator xml:lang="en">Yuliani, Chikita Mery</dc:creator>
	<dc:creator xml:lang="en">Vadila, Renita Dwi</dc:creator>
	<dc:creator xml:lang="en">Erlangga, Mokhammad Puput</dc:creator>
	<dc:creator xml:lang="en">Hutami, Harnanti Yogaputri</dc:creator>
	<dc:creator xml:lang="en">Wijaksono, Egie</dc:creator>
	<dc:subject xml:lang="en">Biogenic Gas</dc:subject>
	<dc:subject xml:lang="en">Carbonate Reservoir</dc:subject>
	<dc:subject xml:lang="en">Carbonate Volume</dc:subject>
	<dc:subject xml:lang="en">Probabilistic Neural Network</dc:subject>
	<dc:subject xml:lang="en">Water Saturation</dc:subject>
	<dc:description xml:lang="en">Carbonate reservoirs frequently exhibit high heterogeneity, complicating the lateral prediction of petrophysical properties from sparse well log data. This study evaluates the hydrocarbon potential of a biogenic gas-bearing carbonate reservoir in the ‘RC’ Field, Nias Basin, by integrating well log and 2D seismic data. A multi-attribute analysis was conducted to identify seismic attributes sensitive to reservoir properties, which were subsequently used as input to a Probabilistic Neural Network (PNN) algorithm to predict the lateral distribution of Carbonate Volume (Vcr) and Water Saturation (Sw). Results identify a prospective gas-bearing carbonate zone in Well S-1 at depths of 4990 – 5693 ft, characterized by favorable reservoir quality and water saturation values predicted by the PNN model, with an average 0.4 (v/v). The PNN model significantly improved prediction accuracy, achieving correlation coefficients of 0.88 for Carbonate Volume and 0.83 for Water Saturation. These findings demonstrate that the PNN method effectively captures non-linear relationships between seismic attributes and petrophysical parameters, providing a robust framework for mapping prospective biogenic gas zones and optimizing future exploration planning in the Nias Basin.</dc:description>
	<dc:publisher xml:lang="en">Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar</dc:publisher>
	<dc:date>2026-10-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/geocelebes/article/view/52563</dc:identifier>
	<dc:identifier>10.70561/geocelebes.v10i2.52563</dc:identifier>
	<dc:source xml:lang="en">JURNAL GEOCELEBES; Vol. 10 No. 2: October 2026; 343–358</dc:source>
	<dc:source>2579-5546</dc:source>
	<dc:source>2579-5821</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/geocelebes/article/view/52563/15086</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 JURNAL GEOCELEBES</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by-nc-sa/4.0</dc:rights>
</oai_dc:dc>