Geophysical strata rating (GSR) as an aid in carbonate reservoir characterisation: an example from the South Pars gas field, Persian Gulf Basin
Mohammad Ali Faraji 1 Ali Kadkhodaie 2 4 Hossain Rahimpour-Bonab 1 Peter Hatherly 3
+ Author Affiliations
- Author Affiliations
1 Department of Geology, College of Science, University of Tehran, Tehran 1417466191, Iran.
2 Earth Science Department, Faculty of Natural Science, University of Tabriz, Tabriz 5166616471, Iran.
3 Lavender Bay, NSW 2060, Australia.
4 Corresponding author. Email: kadkhodaie_ali@tabrizu.ac.ir
Exploration Geophysics 49(6) 812-824 https://doi.org/10.1071/EG17068
Submitted: 17 May 2017 Accepted: 14 November 2017 Published: 21 December 2017
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.
Key words: geophysical strata rating, multi-regression analysis, probabilistic neural network, South Pars gas field, well logs, wellbore stability.
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