Determination of rock typing in one of carbonate reservoirs in South of Iran by using of MRGC method and hydraulic flow

Paper Details

Research Paper 01/04/2015
Views (169) Download (3)

Determination of rock typing in one of carbonate reservoirs in South of Iran by using of MRGC method and hydraulic flow

Atefe Bastani Karizak, Majid Nabi Bidhendi
J. Bio. Env. Sci.6( 4), 571-575, April 2015.
Certificate: JBES 2015 [Generate Certificate]


Find the productive zones in carbonate reservoirs are challenging issues in the petroleum industry. In this study, Determination of Rock Typing by help of petrophysical rock typing (determine electrofacieses type) described by Multi-Resolution Graph-based Clustering method and classification of hydraulic flow. Mechanism of this study is that using of Neural Network, permeability determined in studied reservoir at desired intervals withdata obtained by core simulation and FZI. Then in the next step, by using Matlab software, FZI Clustering was done. According to the core data, seven clusters has been selected for the reservoir. Clusters obtained in this way, as the diagram along MRGC method was also assessed. In continuation of the study of MRGC method, the cluster with 7 Electrofaciese was selected. The results of clustering by MRGC and HFU methods, has shown very satisfactory compliance with the interpretation of the results of petrophysical logs and core analysis. Using these two categories, reservoir zones measured and productive zones are separated from non-productive, and comparing together. The results show that the overlap rate of determining facieses of the reservoir and non-reservoir zones, relative to each other in both ways is very convincing and good.


Abbaszadeh M, Fujii H, Fujimoto F. 1996. Permeability Prediction by Hydraulic Flow Units-Theory and Applications, SPE Formation Evaluation Journal 11 (4), 263-271.

Kharrat R, Mahdavi R, Bagherpur M, Hejri S. 2009. Rock Typing Permeability Perdiction of a Heterogenouse Carbonate Reservior Using Artifical Neural Network Based On Flow Zone Index Approach. SPE.

Lim JS. 2005. Reservoir properties determination using Fuzzy Logic and neural networks from well data in offshore Korea, Journal of Petroleum Science and Engineering 49 (3-4), 182-192.

Serra O, Abbotte H. 1980. the contribution of Loading data to sedimentololgy and Stratigraphy, 55th Ann. Fall techn. Spe of Aime, Paper spe 9270, and in speJ.In: Serra, o. 1986, Fundamental of well log interpretation 2.