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Application of Artificial Neural Network for Assisting Seismic-Based Reservoir Characterization

Scientific Contributions Oil and Gas · Vol. 25 No. 1 (2022) · hlm. 2–11

DOI
10.29017/SCOG.25.1.879
ISSN
2541-0520, 2089-3361
Terbit
2022-03-31
Penerbit
Testing Center for Oil and Gas LEMIGAS
Bahasa
eng
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

 Reservoir rock physical properties, such as porosity and water saturation, always play prominent roles in the development of oil and gas fields. Accurate information regarding their distribution is always desired. For this new approach that uses a purpose, a combination of intelligent computing (artificial neural network or ANN) and rock physics, with a full utilization of core data, well logs and seismic-derived attributes, is proposed. The method is basically an effort to link the required rock physical properties to seismic- derived attributes through the use of rock physics theories. The ANN itself is used to fill the gaps of data array required by the proposed method through its capacity for pattern recognition. The proposed method is applied to a limestone reservoir in East Java. Validation is carried out by comparing the results to the observed data at well locations as well as by geological justification. The application has shown a new potential for supporting reservoir modeling and field development.

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  • National Research and Innovation Agency
  • Hasanuddin University
  • Universitas Merdeka Madiun
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Junita Trivianty MusuAfiliasi tidak diketahui7
Heru AtmokoAfiliasi tidak diketahui6
Suliantara SuliantaraNational Research and Innovation Agency4
Herru Lastiadi SetiawanNational Research and Innovation Agency4
Humbang PurbaAfiliasi tidak diketahui4
Tri Muji SusantoroNational Research and Innovation Agency3
Djoko SunarjantoNational Research and Innovation Agency3
Ridwan RidwanHasanuddin University2
Kosasih KosasihAfiliasi tidak diketahui2
Merkurius. F. MendrofaAfiliasi tidak diketahui2
Mohamad RomliNational Research and Innovation Agency2
Diana DwiyanartiNational Research and Innovation Agency2
Aziz Masykur LubadAfiliasi tidak diketahui2
Herizal HerizalNational Research and Innovation Agency2
Nurkamelia NurkameliaNational Research and Innovation Agency2
Sunting KepiesNational Research and Innovation Agency2
Yusep K CaryanaAfiliasi tidak diketahui2
Maizar RahmanAfiliasi tidak diketahui2
Tunggal TunggalAfiliasi tidak diketahui2
Ridwan RidwanUniversitas Merdeka Madiun2
Jonathan Setyoko HadimuljonoAfiliasi tidak diketahui1
Julikah JulikahAfiliasi tidak diketahui1
Desi YensusminarAfiliasi tidak diketahui1
Ongki Ari PrayogaAfiliasi tidak diketahui1
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Humbang PurbaAfiliasi tidak diketahui1
Hadi PrasetyoAfiliasi tidak diketahui1
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Bambang A WidjayantoAfiliasi tidak diketahui1
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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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en">Application of Artificial Neural Network for Assisting Seismic-Based Reservoir Characterization</dc:title>
	<dc:creator xml:lang="en">Widarsono, Bambang</dc:creator>
	<dc:creator xml:lang="en">Saptono, Fakhriyadi</dc:creator>
	<dc:creator xml:lang="en">Wong, Patrick M</dc:creator>
	<dc:creator xml:lang="en">Munadi, Suprajitno</dc:creator>
	<dc:subject xml:lang="en">Artificial Neural</dc:subject>
	<dc:subject xml:lang="en">Seismic-Based</dc:subject>
	<dc:description xml:lang="en"> Reservoir rock physical properties, such as porosity and water saturation, always play prominent roles in the development of oil and gas fields. Accurate information regarding their distribution is always desired. For this new approach that uses a purpose, a combination of intelligent computing (artificial neural network or ANN) and rock physics, with a full utilization of core data, well logs and seismic-derived attributes, is proposed. The method is basically an effort to link the required rock physical properties to seismic- derived attributes through the use of rock physics theories. The ANN itself is used to fill the gaps of data array required by the proposed method through its capacity for pattern recognition. The proposed method is applied to a limestone reservoir in East Java. Validation is carried out by comparing the results to the observed data at well locations as well as by geological justification. The application has shown a new potential for supporting reservoir modeling and field development.</dc:description>
	<dc:publisher xml:lang="en">Testing Center for Oil and Gas LEMIGAS</dc:publisher>
	<dc:date>2022-03-31</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/879</dc:identifier>
	<dc:identifier>10.29017/SCOG.25.1.879</dc:identifier>
	<dc:source xml:lang="en">Scientific Contributions Oil and Gas; Vol. 25 No. 1 (2002); 2-11</dc:source>
	<dc:source xml:lang="id">Scientific Contributions Oil and Gas; Vol 25 No 1 (2002); 2-11</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/879/pdf</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 1970 SCIENTIFIC CONTRIBUTIONS OIL AND GAS (SCOG)</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>