Automated lithology prediction from PGNAA and other geophysical logs

Borşaru, M.; Zhou, Binzhong; Aizawa, Takao; Karashima, Hiroshi; Hashimoto, Takumi

doi:10.1016/j.apradiso.2005.07.012

Cited by 45 publications

(12 citation statements)

References 8 publications

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“…Both conventional statistical analysis and machine learning techniques have been applied to downhole geophysical logs (Borsaru et al, 2006). The statistical techniques include principal component analysis (Davis, 1986), discrimination function (Davis, 2002) and cluster analysis (Davis, 2002).…”

Section: Related Workmentioning

confidence: 99%

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Silversides

Melkumyan

Wyman

et al. 2015

Computers & Geosciences

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Section: Related Workmentioning

confidence: 99%

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Silversides

Melkumyan

Wyman

et al. 2015

Computers & Geosciences

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“…The gamma-ray log is record of a formation's natural radioactivity which comes spontaneously from naturally occurring uranium, thorium, and potassium (Russell 1941). Recently, the prompt gamma neutron activation analysis (PGNAA) for lithology prediction was established for in situ element analysis of rocks in boreholes (Borsaru, Zhou, Aizawa et al 2006). The curve of gamma-ray is clearly characterized by the variation of grain size, indicating the degree of sand and=or shale contents (Serra 1984).…”

Section: Depositional Environments Based On Electrofaciesmentioning

confidence: 99%

Physical Property Interpretation Using Log Data from the Ulleung Basin Sediments, East Sea of Korea

Kim

Bahk

Yoo

et al. 2012

Marine Georesources & Geotechnology

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This research focuses on the physical properties of a drilled site (UBGH1-4) in the Ulleung Basin, East Sea of Korea. Based on seismic profile and core=log data, the upper 50 m sediments of the drilled site are dominated by turbidite=hemipelagic sediments, whereas the sediments below the depth are characterized by thick debris-flow deposits. The physical properties data (velocity, density, porosity, resistivity, natural gamma, etc.) were acquired using Schlumberger's logging tools during Logging-While-Drilling (LWD). In-depth interval between 70 and 120 mbsf (meter below seafloor) corresponding to debris-flow deposits, is characterized by high resistivity, high velocity, high density, and low porosity. Physical properties seem mostly related to consolidation=compaction effects and the type of sediment, as occurs typically in unconsolidated marine sediments. The overall relationships among the physical properties have relatively good correlations, but some scattering and clustering of the data exist on the plot. This can be interpreted as the result of the difference of sediment properties caused by mass flow deposits (e.g., turbidite= hemipelagic and debris-flow deposit). Based on natural gamma response, seven electrofacies were identified throughout the logged depth. This may suggest that seven events were likely to have occurred during deposition and=or after deposition.

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“…Bieng et al (2005) studied the identification of lithology through the fuzzy logic analysis of logging parameters. The identification of lithology by using a PGNAA method combined with the logging data was examined by Borsarua et al (2006). All of these research studies have made some achievements in the lithology identification of shallow carbonate rocks.…”

Section: Introductionmentioning

confidence: 99%

A new method of deep carbonate lithology identification at the Tadong uplift in the eastern section of the Tarim Basin

et al. 2016

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The lithology identification of deep carbonate rocks has always been a difficult field of research. The deep carbonate rock formations in the eastern section of the Tarim Basin have complex tectonism, deposition, and diagenesis. Therefore, it is difficult to use conventional logging information to identify the lithology of deep carbonate rocks. In this study, a natural gamma ray spectrometry log in unconventional logging was used to discover a new method (comprehensive superposition method) for the lithology identification of deep carbonate rocks. The research results showed that the TH, U, K, and KTH information in a natural gamma ray spectrometry log can reflect the lithological characteristics of carbonate rocks extremely well. In addition, this new identification method was able to optimize the typical logging parameters which could best reflect the lithology of the carbonate rocks after analyzing the original record curve and a variety of combined curves of the natural gamma ray spectrometry. This new method could comprehensively superpose the typical logging parameters and was capable of finally quantitatively identifying the lithology of dolomite, micrite, calcarenite, and finely crystalline limestone in deep carbonate rocks, with an identification accuracy of more than 79 %. On the basis of the above research results, a special statistical method was made to analyze the typical logging parameters of natural gamma ray spectrometry and to assist and test the identification of a comprehensive superposition method, in order to improve the lithology identification accuracy. This new method for the lithology identification of deep carbonate rocks, based on a natural gamma ray spectrometry log, achieved good application effects in the eastern section of the Tarim Basin.

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Automated lithology prediction from PGNAA and other geophysical logs

Cited by 45 publications

References 8 publications

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits

Physical Property Interpretation Using Log Data from the Ulleung Basin Sediments, East Sea of Korea

A new method of deep carbonate lithology identification at the Tadong uplift in the eastern section of the Tarim Basin

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