Abstract

In this paper we present an integrated approach to derive reservoir parameters from core and well-log data in clay–sand mixtures. This method is based on matching core and log data, and the linear and non-linear regressions are then used to build respective relationships between core and log data to determine formation parameters such as porosity, shale volume, clay content, permeability and fluid saturation. This information is then fed into a velocity prediction model to estimate seismic parameters such as elastic moduli, shear wave velocity and anisotropy coefficients. Finally, we test the method on real data from the North Sea and show that reservoir parameters can be accurately predicted.

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Published In

cover image Petroleum Geoscience
Petroleum Geoscience
Volume 8Number 1March 2002
Pages: 63 - 69

History

Received: 18 September 2000
Accepted: 30 July 2001
Published: March 2002

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Keywords

  1. log calibration
  2. reservoir characteristic
  3. velocity logging
  4. sand
  5. shale ratio
  6. North Sea

Authors

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Jun Yan
British Geological Survey, West Mains Road, Edinburgh EH9 3LA, Scotland, UK and Department of Geology and Geophysics, University of Edinburgh, UK (e-mail: [email protected]) (Present address: Robertson Research International Ltd, Llandudno LL30 15A, UK)

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  • Statistical distribution of geomechanical properties and ‘Sweet Spots’ identification in part of the upper Bakken, Petroleum Research, 10.1016/j.ptlrs.2022.10.005, (2022).
  • Anisotropy analysis in shallow marine gas hydrate bearing sediments: a case study from the offshore Mahanadi basin, India, Marine Geophysical Research, 10.1007/s11001-021-09465-7, 43, 1, (2022).
  • Reservoir quality estimation using a new ternary diagram approach applied to carbonate formations in north-western Iraq, Journal of Petroleum Science and Engineering, 10.1016/j.petrol.2020.108024, 196, (108024), (2021).
  • Best Practices for Shale Core Handling: Transportation, Sampling and Storage for Conduction of Analyses, Journal of Marine Science and Engineering, 10.3390/jmse8020136, 8, 2, (136), (2020).

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