1887
Volume 49, Issue 6
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

[

In this study, the geophysical strata rating (GSR) is calculated from petrophysical data using the equations developed for clastic rocks. The region being investigated is the South Pars gas field in the Persian Gulf Basin, where the Permian–Triassic Dalan and Kangan reservoirs host the largest accumulations of gas in the world. A 3D GSR model is estimated from 3D poststack seismic data by using a probabilistic neural network model. In this study, two methodologies are used to obtain GSR values at the different scales of wireline logs and 3D seismic data.

Strong correlations between neural network predictions and actual GSR data at a blind well prove the validity of the intelligent model for estimating GSR. The GSR results are also in good agreement with porosity and elastic moduli of these carbonate rocks. Discrimination between the reservoir and non-reservoir shaly units can easily be obtained by comparing GSR and well logs. Very low GSR values with high gamma ray log responses indicate shaly intervals. These can cause washouts, casing collapse and other related drilling problems. Intervals with low GSR values and low gamma ray log responses indicate the presence of good reservoir units.

,

In this study, the geophysical strata rating (GSR), which is an empirical measure of rock competency, is calculated from petrophysical data. The GSR is then extended to the whole South Pars gas field in the framework of 3D seismic data through an acoustic impedance poststack seismic inversion.

]
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2018-11-01
2026-01-21

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/content/journals/10.1071/EG17068
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Geophysical strata rating (GSR) as an aid in carbonate reservoir characterisation: an example from the South Pars gas field, Persian Gulf Basin
Exploration Geophysics 49, 812 (2018); https://doi.org/10.1071/EG17068
/content/journals/10.1071/EG17068
/content/journals/10.1071/EG17068
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  • Article Type: Research Article
Keyword(s): geophysical strata rating; multi-regression analysis; probabilistic neural network; South Pars gas field; well logs; wellbore stability

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