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An Intelligent Approach For Obtaining True Resistivity (𝑅𝑇) From Rock Acoustic Data: A Laboratory Verification

Scientific Contributions Oil and Gas · Vol. 26 No. 1 (2022) · hlm. 2–7

DOI
10.29017/SCOG.26.1.875
ISSN
2541-0520, 2089-3361
Terbit
2022-03-30
Penerbit
Testing Center for Oil and Gas LEMIGAS
Bahasa
eng
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Rock true resistivity (Rt) is known as more sensitive than compressional-wave velocity (Vp), the principal output of a seismic survey, to variation in water saturation. Therefore, it would be of a great value if there were a way to predict resistivity distribution from seismic signals. This study is essentially an effort to see the possibility of predicting Rt from Vp through a pattern recognition approach. For the purpose, a series of laboratory tests were performed on some Central Sumatran clay-free sandstone samples of various porosity values and at various water saturation levels. For studying the pattern of relationship, artificial neural networks (ANNs) were applied. From the ‘training’ (i.e.pattern recognition) activity performed using the ANNs, it has been show between Vp and Rt in the following ‘blind test’, it has also been shown that the trained relationship can be used to estimate Rt reliably using other data as input. Comparisons between estimated and observed Rt data have indicated good agreement implying the success of the approach taken in the study. This has laid the foundation and justification for further application of the approach on seismic and well-log data.

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  • National Research and Innovation Agency
  • Hasanuddin University
  • Universitas Merdeka Madiun
  • University of Indonesia
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Junita Trivianty MusuAfiliasi tidak diketahui7
Suliantara SuliantaraNational Research and Innovation Agency4
Herru Lastiadi SetiawanNational Research and Innovation Agency4
Suprajitno MunadiAfiliasi tidak diketahui4
Ridwan RidwanHasanuddin University4
Kosasih KosasihAfiliasi tidak diketahui4
Tri Muji SusantoroNational Research and Innovation Agency3
Tunggal TunggalAfiliasi tidak diketahui3
Ridwan RidwanUniversitas Merdeka Madiun3
Merkurius. F. MendrofaAfiliasi tidak diketahui2
Djoko SunarjantoNational Research and Innovation Agency2
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
Patrick M. WongAfiliasi tidak diketahui2
Jonathan Setyoko HadimuljonoAfiliasi tidak diketahui1
Julikah JulikahAfiliasi tidak diketahui1
Desi YensusminarAfiliasi tidak diketahui1
Ongki Ari PrayogaAfiliasi tidak diketahui1
Yusep K CaryanaAfiliasi tidak diketahui1
Maizar RahmanAfiliasi tidak diketahui1
Hadi PrasetyoAfiliasi tidak diketahui1
Kosasih SartadiredjaAfiliasi tidak diketahui1
Bambang A WidjayantoAfiliasi tidak diketahui1
Himawan SutantoAfiliasi tidak diketahui1
Humbang PurbaAfiliasi tidak diketahui1
Andi RuswandiAfiliasi tidak diketahui1
Septi AnggraeniAfiliasi tidak diketahui1
Sugihardjo SugihardjoNational Research and Innovation Agency1
Panca WahyudiNational Research and Innovation Agency1
Danang SismartonoUniversity of Indonesia1
Wanda Ali AkbarAfiliasi tidak diketahui1
Usman UsmanNational Research and Innovation Agency1
Arie RahmadiNational Research and Innovation Agency1

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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">An Intelligent Approach For Obtaining True Resistivity (𝑅𝑇) From Rock Acoustic Data : A Laboratory Verification</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">Atmoko, Heru</dc:creator>
	<dc:subject xml:lang="en">Rock Acoustic</dc:subject>
	<dc:subject xml:lang="en">Laboratory</dc:subject>
	<dc:description xml:lang="en">Rock true resistivity (Rt) is known as more sensitive than compressional-wave velocity (Vp), the principal output of a seismic survey, to variation in water saturation. Therefore, it would be of a great value if there were a way to predict resistivity distribution from seismic signals. This study is essentially an effort to see the possibility of predicting Rt from Vp through a pattern recognition approach. For the purpose, a series of laboratory tests were performed on some Central Sumatran clay-free sandstone samples of various porosity values and at various water saturation levels. For studying the pattern of relationship, artificial neural networks (ANNs) were applied. From the ‘training’ (i.e.pattern recognition) activity performed using the ANNs, it has been show between Vp and Rt in the following ‘blind test’, it has also been shown that the trained relationship can be used to estimate Rt reliably using other data as input. Comparisons between estimated and observed Rt data have indicated good agreement implying the success of the approach taken in the study. This has laid the foundation and justification for further application of the approach on seismic and well-log data.</dc:description>
	<dc:publisher xml:lang="en">Testing Center for Oil and Gas LEMIGAS</dc:publisher>
	<dc:date>2022-03-30</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/875</dc:identifier>
	<dc:identifier>10.29017/SCOG.26.1.875</dc:identifier>
	<dc:source xml:lang="en">Scientific Contributions Oil and Gas; Vol. 26 No. 1 (2003); 2-7</dc:source>
	<dc:source xml:lang="id">Scientific Contributions Oil and Gas; Vol 26 No 1 (2003); 2-7</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/875/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>