A. Adeniran, M. Elshafei, and G. Hamada, Artificial Intelligence Techniques in Reservoir Characterization: Functional networks softsensor for formation porosity and water saturation in oil Wells, 2010.

M. A. Ahmadi and M. Ebadi, Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: Application of artificial intelligence, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.08.026

M. A. Ahmadi, M. Ebadi, and A. Yazdanpanah, Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: Application of particle swarm optimization, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.05.023

M. A. Ahmadi, S. Zendehboudi, L. James, A. Elkamel, M. Dusseault et al., New tools to determine bubble point pressure of crude oils: Experimental and modeling study, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.08.018

T. Aïfa, A. Zerrouki, A. Baddari, K. Géraud, and Y. , Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oilfield, Journal of Petroleum Science and Engineering, 2014.

T. Aïfa, R. Baouche, and K. Baddari, Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R??Mel gas field, Algeria, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.09.019

F. Aminzadeh and P. De-groot, Neural Networks and Other Soft Computing Techniques with Applications in the Oil Industry, p.161, 2006.

A. Acton and Q. , Advances in Machine Learning Research and Application, 2013.

S. Chaki, A. K. Verma, A. Routray, W. K. Mohanty, and M. Jenamani, Well tops guided prediction of reservoir properties using modular neural network concept: A case study from western onshore, India, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.06.019

L. A. Costa, C. Maschio, and D. J. Schiozer, Application of artificial neural networks in a history matching process, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.06.004

K. Enab and T. Ertekin, Artificial neural network based design for dual lateral well applications, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.09.004

T. Foroud, A. Seifi, and B. Aminshahidi, Assisted history matching using artificial neural network based global optimization method ??? Applications to Brugge field and a fractured Iranian reservoir, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.034

N. Ghaffarian, R. Eslamloueyan, and B. Vaferi, Model identification for gas condensate reservoirs by using ANN method based on well test data, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.037

J. Lucia and F. , Carbonate Reservoir Characterization. An integrated Approach. 2 nd Ed, p.336, 2007.

H. Kaydani, A. Mohebbi, and M. Eftekhari, Permeability estimation in heterogeneous oilr eservoirs by multi-gene genetic programming algorithm, Journal of Petroleum Science and Engineering, 2014.

E. Khamehchi, R. I. Kivi, and M. Akbari, A novel approach to sand production prediction using artificial intelligence, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.033

C. Maschio and D. J. Schiozer, Bayesian history matching using artificial neural network and Markov Chain Monte Carlo, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.05.016

P. Masoudi, B. Arbab, and H. Mohammadrezaei, Net pay determination by artificial neural network: Case study on Iranian offshore oil fields, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.007
URL : https://hal.archives-ouvertes.fr/insu-01382434

P. Masoudi, B. Arbab, and H. Mohammadrezaei, Net pay determination by Dempster rule of combination: Case study on Iranian offshore oil fields, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.014
URL : https://hal.archives-ouvertes.fr/insu-01382425

O. O. Mosobalaje, O. D. Orodu, and D. Tiab, New techniques for estimating properties of saturated reservoirs, using readily-available rate decline data, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.06.023

M. Nikravesh, F. Aminzadeh, and L. A. Zadeh, Soft Computing and Intelligent Data Analysis in Oil Exploration, p.723, 2003.

A. Rabbani, S. Jamshidi, and S. Salehi, An automated simple algorithm for realistic pore network extraction from micro-tomography images, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.08.020

W. Sandham, M. Leggett, L. Cosultchi, A. Martínez-muñoz, J. Gonzalez-sánchez et al., ISBN: 1-4020-1729-4 Data-driven forecasting of naturally fractured reservoirs based on nonlinear qutoregressive neural networks with exogenous input, Geophysical Applications of Artificial Neural Networks and Fuzzy Logic. Kluwer Acad. Publ. Journal of Petroleum Science and Engineering, 2003.

G. Shi, Data Mining and Knowledge Discovery for Geoscientists, 2014.

S. Saljooghi, B. Hezarkhani, and A. , Comparison of WAVENET and ANN for predicting the porosity obtained from well log data, Journal of Petroleum Science and Engineering, vol.123, p.14, 2014.
DOI : 10.1016/j.petrol.2014.08.025

W. Yanfang and S. Salehi, Refracture candidate selection using hybrid simulation with neural network and data analysis techniques, Journal of Petroleum Science and Engineering, vol.123, 2014.
DOI : 10.1016/j.petrol.2014.07.036