[1] Rahimi, M., Riahi, M.A. Static reservoir modeling using geostatistics method: a case study of the Sarvak Formation in an offshore oilfield, 2020, Carbonates Evaporites, 35, 62. https: /doi.org/10.1007/s13146020005981.
[2] Singha DK, Chatterjee R (2014) Detection of overpressure zones and a statistical model for pore pressure estimation from well logs in the Krishna–Godavari basin, India. Geochem Geophys Geosyst15(4):1009–1020.
[3] Ruth D, Lindsay C and Allen M 2013 Combining electrical measurements and mercury porosimetry to predict permeability Petrophysics 54 531–7.

[4] Al-Bulushi, N., King, P., Blunt, M. and Kraaijveld, M., 2012, Artificial neural networks workflow and its application in the petroleum industry. Neural Computing and Applications, 21, (3): 409-421.

[5] Aminzadeh, F., Barhen, J., Glover, C.W., Toomarian, N.B., 2000. Reservoir parameter estimation using a hybrid neural network. Computers & Geosciences (26), 869-875. 3) Battiti, R., 1992. First and second order methods for learning: Between steepest descent and Newton’s method. Neural Computation 4 (2), 141–166.

[6] Lim, J.S., 2005, “Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea”, Journal of Petroleum Science and Engineering (49), 182– 192.

[7] Lashin, A., Din, S., 2013, Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt: Arabian Journal of Geosciences, 6(8): 27892806.

[8] Deutsch, C. V., and Journel, A. G., 1998, GSLIB, geostatistical software library and user’s guide (2nd ed.). Oxford, England: Oxford University Press.

[9] Hohn, M. E., 1999, Geostatistics and petroleum geology (2nd ed.). Dordrecht, the Netherlands: Kluwer Academic. Journel, A. G., and Huijbregts, Ch. J. (1978). Mining Geostatistics. London: Academic Press.

[10] Murris, R.J. (1980) Middle East: Stratigraphic Evolution and Oil Habitat. AAPG Bulletin,64,597-618.

[11] Al-Husseini, M.I., 1997, Jurassic sequence stratigraphy of the western and southern Arabian Gulf: GeoArabia, v. 2, no. 4, p. 361-382.

[12] James, G.A., & Wynd, J.G., 1965. Stratigraphic Nomenclature of Iranian Oil Consortium Agreement Area. AAPG Bulletin, 49: 2182-2245.

[13] Powers, R.W., 1962, Arabian Upper Jurassic Carbonate Reservoir Rocks, In: W.E. Ham (Eds.), Classification of Carbonate Rocks: American Association of Petroleum Geologists Memoir, 1, p.122192.
[14] Mohaghegh, S., Virtual-intelligence Applications in Petroleum Engineering: Part I., Artificial Neural Networks J. Pet. Technol., Vol. 52, p. 64-73, 2000.

[15] Wong. P.M, Henderson D.J, Brooks. L.J, 1997, Reservoir permeability determination from well log data using artificial neural networks: an example from the Ravva field, offshore India, Proc. SPE Asia Pacific Oil and Gas Conference.

[16] Lim, Jong-Se, 2003. Reservoir permeability determination using artificial neural network. J. Korean Soc. Geosyst. Eng. 40, 232–238.
[17] Kelkar, M., Perez, G., Chopra. A, 2002, Applied geostatistics for reservoir characterization., Texas, Society of Petroleum Engineers (SPE).

[18] Mata Lima. H, 2005, GEOSTATISTIC IN RESERVOIR CHARACTERIZATION: FROM ESTIMATION TO SIMULATION METHODS, Estudios Geol., 61: 135_145.
[19] Viste, I., 2008. 3D Modelling and Simulation of Multi-Scale Heterogeneities in Fluvial Reservoir Analogues, Lourinh~a Fm, Portugal: from Virtual Outcrops to Process-oriented Models. M. Sc., Thesis. Bergen Univ., Norway, 184 pp.

[20] Hasani Pak AA, 2007, Geostatistics, 2nd edn. Univ. of Tehran Press,Tehran.


[21] Shabani FGH, Bashiri M, Izadkhah KM, 2011, Simulation of petrophysical parameters using SGS method in one of Southwest Iranian hydrocarbon reservoirs. J Petrol Res 21(66):53–66.

[22] Dean L (2007) Reservoir engineering for geologists. Part 3- Volumetric Estimation. Reservoir11:20.