شماره ركورد كنفرانس :
4731
عنوان مقاله :
Application of Geophysical Strata Rating (GSR) in carbonate reservoir characterization
پديدآورندگان :
Faraji Mohammad Ali , School of Geology, College of Science, University of Tehran , Kadkhodaie Ali Department of Geology, Faculty of Natural Science, University of Tabriz , Rahimpour-Bonab Hossain School of Geology, College of Science, University of Tehran , Aghazade Kamal Institute of Geophysics, University of Tehran
كليدواژه :
GSR , South Pars , well logs , Probabilistic Neural Network , geomechanical mode
عنوان كنفرانس :
هجدهمين كنگره ملي ژئوفيزيك ايران
چكيده فارسي :
The Geophysical Strata Rating (GSR), which is introduced in this study for carbonate reservoirs, is an empirical strength rating of rocks. It provides ratings from 10 to 100 where the lower values correspond to rocks such as shales which are weak from a borehole stability point of view and also the porous, permeable reservoir rocks. In comparison, the higher values of GSR are associated with intact rocks with few defects in the form of fractures and discontinuities and low porosity. In this study, GSR is calculated from petrophysical data using the equations developed in clastic rocks. The region investigated is the South Pars gas field where the Permo-Triassic Dalan and Kangan reservoirs host the largest accumulations of gas in the world. A 3D GSR model is then estimated from 3D post-stack seismic data of the South Pars gas field by using a probabilistic neural network model. Strong correlations between neural network predictions and actual GSR data at unseen borehole locations proved the validity of the intelligent model in GSR estimation. This 3D GSR cube can be utilized for construction of geomechanical models over the South Pars gas field.