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- Volume 38, Issue 9, 2020
First Break - Volume 38, Issue 9, 2020
Volume 38, Issue 9, 2020
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Bayesian lithology analysis with seismic-driven likelihood to anisotropic medium: a case study from Northwest Australia
More LessAbstractAn isotropic medium is generally assumed for reservoir characterization although it has been reported that velocity anisotropy often has a non-negligible impact. One of the difficulties in incorporating anisotropy is that the exact anisotropy parameters are generally unknown. To overcome the problem, a seismic-driven approach to Bayesian probability analysis is proposed, which defines the likelihood from seismic inversion data. Comparing this approach for lithology prediction to one based on well data, it is found that an isotropic well-data approach does not work well but is improved by taking anisotropy into account. A larger improvement is achieved by the seismic-driven approach which provides a robust result which is insensitive to the anisotropy assumption.
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Predicting variability in well performance using the concept of shale capacity: An application of machine learning techniques
Authors Ritesh Kumar Sharma and Satinder ChopraAbstractThe production from a shale well depends on how accurately that well has been placed through zones with superior reservoir quality and completion quality. To be able to locate such pockets, an integration of different types of reservoir properties such as organic richness, frackability, fracture density and porosity is essential. One way of achieving this is by using cut off values for the different reservoir properties and generating a shale capacity volume. However, in this regard, a couple of things need to be borne in mind. Not only seismic data provide indirect measurements of the individual reservoir properties, but different seismically derived attributes may contribute to the computation of the individual components of the shale capacity volume. Therefore, the definition of the different reservoir property cut off values is subjective and difficult to finalize. What may be desirable is that the different seismically derived attributes are interpreted simultaneously for predicting the performance of a well, which may not be a straightforward task. The application of machine learning techniques seems like an attractive alternative here, which we explore in this article. For doing so, data examples from the Delaware Basin as well as the Western Canadian Sedimentary Basin (WCSB) have been considered. The dataset from the Delaware Basin is used to gain confidence in applying unsupervised machine learning techniques as one of them is used thereafter on the dataset from WCSB, where production data associated with different wells were available.
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Why the resistivity log should not be used to calculate or predict pore pressure in the North Sea
More LessAbstractModels to calculate pore pressure from the resistivity log have been developed in the Gulf of Mexico since the 1960s. The same approach has been difficult in the North Sea. Eight hundred released Norwegian exploration wells have been studied in detail. The statistical relationship between the following five logs; the sonic, density, neutron, gamma ray and resistivity log evaluated on 98 selected exploration wells. The statistical method chosen is the Principal Component Analysis. It suggests that the resistivity response is more or less random compared to the typical porosity logs; density, neutron and sonic. This suggests that no porosity or compaction information can be extracted from the resistivity log in the North Sea, Norwegian Sea or the Barents Sea. The resistivity log should therefore not be used to calculate porosity or predict or calculate pore pressure offshore Norway, UK, Denmark or Holland.
It has been suggested that the main case for the random resitivity (salinity) could be fresh water input from the glacial ice cover during the Quaternary period. But there are also studies that suggest there must be a meteoric water influx that is much older than Quaternary. Influx is not from the surface and down, but either lateral or from deeper down in the strata.
This suggests that detailed resistivity analysis must be done prior to interpreting CSEM data. So, far CSEM data acquired in the North Sea have given questionable results.
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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)