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- Volume 18, Issue 9, 2000
First Break - Volume 18, Issue 9, 2000
Volume 18, Issue 9, 2000
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Fibre optic sensor technology could offer way forward for reservoir monitoring
BP is investing in fiber optic sensor technology. Could this be the way forward for reservoir monitoring.
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GeoNet takes the road ASP from Kazakhstan
Interview with GeoNet services.com founder Pat Herbert as the provider of multi-vendor E&P software via the Internet and private networks opens for serious business.
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Potential power in the application of seismic volume attributes
German company TEEC explains the virtues of applying seismic volume attributes.
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The effect of acquisition direction on preSDM imaging
Authors I.F. Jones, H.W.N. Baud, B. Henry, A. Strachan, J. H. Kommedal and M. GainskiA production 3D preSDM project from the southern North Sea using several vintages of input data was run using a velocity-depth model common to the whole area, with good final results. The expected progressive improvement from postSTM to postSDM to pre SDM was demonstrated for the target horizons. As a seperate study to the production project, we undertook to investigate the effect of acquisition direction on final image quality. To achieve this, we selected two vintages of data which were shot orthogonally to one another, but which otherwise had the same acquisition parameters. These data had sufficient overlap to permit full imaging in the area under investigation. Ray trace studies were performed to assess the effects of target illumination from the dip-shot and the strike-shot surveys. Using the actual recorded navigation positions from the two surveys, 3D two-point finite offset ray tracing was performed using a common model.
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Using neural networks to predict porosity thickness from 3D seismic data
Authors H. Trappe and C. HellmichThis article describes the use of an artificial neural network to predict area-wide reservoir parameters by classifying multi-attributes derived from 3D seismic data. Special emphasis is placed on the comparison between the artificial neural network and geostatistical appoaches.
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Volumes & issues
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Volume 42 (2024)
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Volume 41 (2023)
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Volume 40 (2022)
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Volume 39 (2021)
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Volume 38 (2020)
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Volume 37 (2019)
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Volume 36 (2018)
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Volume 35 (2017)
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Volume 34 (2016)
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Volume 33 (2015)
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Volume 32 (2014)
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Volume 31 (2013)
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Volume 30 (2012)
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Volume 29 (2011)
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Volume 28 (2010)
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Volume 27 (2009)
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Volume 26 (2008)
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Volume 25 (2007)
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Volume 24 (2006)
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Volume 23 (2005)
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Volume 22 (2004)
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Volume 21 (2003)
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Volume 20 (2002)
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Volume 19 (2001)
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Volume 18 (2000)
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Volume 17 (1999)
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Volume 16 (1998)
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Volume 15 (1997)
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Volume 14 (1996)
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Volume 13 (1995)
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Volume 12 (1994)
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Volume 11 (1993)
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Volume 10 (1992)
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Volume 9 (1991)
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Volume 8 (1990)
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Volume 7 (1989)
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Volume 6 (1988)
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Volume 5 (1987)
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Volume 4 (1986)
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Volume 3 (1985)
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Volume 2 (1984)
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Volume 1 (1983)