تخمین درجه اشباع آب با استفاده از روش سیستم استنتاج عصبی- فازی تطبیقی( ANFIS) در یکی از مخازن هیدروکربوری جنوب غربی ایران (میدان نفتی آزادگان شمالی)

Acharya, C., Mohanty, S., Sukla, L., Misra, V (2006) Prediction of sulphur removal with Acidithiobacillus sp. using artificial neural networks, Ecological modelling, 190 (1): 223-230.

Aghli, Gh., Moussavi-Harami, R., Mortazavi, S., Mohammadian, R (2019) Evaluation of new method for estimation of fracture parameters using conventional petrophysical logs and ANFIS in the carbonate heterogeneous reservoirs, Journal of Petroleum Science and Engineering, Volume 172, January, Pages 1092-1102.

Ahmadi, M. A., Ahmadi, M. R., Hosseini, S. M., Ebadi, M (2014) 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, 123: 183-200.

Ahmadi, M. A., Zhangxing, C (2019) Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs, Petroleum, 5 (3): 271-284.

Al-AbdulJabbar, A., Al-Azani, Kh., Elkatatny, S (2020) Estimation of Reservoir Porosity from Drilling Parameters Using Artificial Neural Networks, Petrophysics, 61 (3): 318–330.

Anemangely, M., Ramezanzadeh, A., Amiri, H., Hoseinpour, S. A (2019) Machine learning technique for the prediction of shear wave velocity using petrophysical logs, Journal of Petroleum Science and Engineering, 174: 306-327.

Anifowose, F., Abdulraheem, A (2011) Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization, Journal of Natural Gas Science and Engineering, 3: 505-517.

Anifowose, F., Labadin, J., Abdulraheem, A (2015) Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines, Applied Soft Computing, 26: 483-496.

Ansari, H. R., Gholami, A (2015) an improved support vector regression model for estimation of saturation pressure of crudoils, Fluid Phase Equilibria, 402: 124-132.

Asoodeh, M., Bagheripour, P (2012) Prediction of compressional, shear, and stoneley wave velocities from conventional well log data using a committee machine with intelligent systems, Rock Mechanics and Rock Engineering45: 45-63.

Bagheripour, P., Gholami, A., Asoodeh, M., Vaezzadeh-Asadi, M (2015) Support vector regression based determination of shear wave velocity, Journal of Petroleum Science and Engineering, 125: 95-99.

Baziar, S., Gafoori, M. M., Pour, M., Mehdi, S., Bidhendi, M. N., Hajiani, R (2015) Toward a Thorough Approach to Predicting Klinkenberg Permeability in a Tight Gas Reservoir: A Comparative Study, Iranian Journal of Oil & Gas Science and Technology, 4: 18-36.

Bezdek, J. C (1973) Fuzzy mathematics in pattern classification. Cornell university, Ithaca.

Demuth, H., Beale, M (1993) Neural network toolbox for use with MATLAB.

Doulati Ardejanii, F., Rooki, R., Jodieri Shokri, B., Eslam Kish, T., Aryafar, A., Tourani, P (2012) Prediction of rare earth elements in neutral alkaline mine drainage from Razi Coal Mine, Golestan Province,  northeast Iran, using general regression neural network, Journal of Environmental Engineering, 139 (6): 896-907.

Eskandari, H., Rezaee, M., Mohammadnia, M (2004) Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South-West Iran, CSEG recorder, 42: 48.

Eskandari, H., Rezaee, M., Mohammadnia, M (2004) Application of multiple regression and artificial neural network techniques to predict shear wave velocity from wireline log data for a carbonate reservoir South-West Iran, CSEG recorder, 42 (48).

FitzGerald, E. M., Bean, C. J., Reilly, R (1999) Fracture-frequency prediction from borehole wireline logs using artificial neural networks, Geophys Prospect, 47: 1031–44.

Flavio, S. A., Gregor, P. E (1999) The velocity-deviation log a tool to predict pore type and permeability trends in carbonate drill holes from sonic and porosity or density logs, Am. Assoc. Pet. Geol. Bull, 83: 450–66.

 Gholami, R., Moradzadeh, A (2012) Support vector regression for prediction of gas reservoirs permeability, Journal of Mining and Environment, 2: 41-52.

Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S., Hanachi, J (2014) Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs, J Pet Sci Eng, 122: 643-56.

Hagan, M. T., Demuth, H. B., Beale, M. H (1996) Neural network design, Pws Pub. Boston.

Ja’fari, A., Kadkhodaie-Ilkhchi, A., Sharghi, Y., Ghanavati, K (2012) Fracture density estimation from petrophysical log data using the adaptive neuro-fuzzy inference system, J. Geophys, Eng. 9:105–114.

Jang, J. S. R., Sun, C. T., Mizutani, E (1997) Neuro-Fuzzy and Soft Computing A Computional Approach to Learning and Machine Intelligence, Prentice Hall, 640p.

Jayalakshmi, T., Santhakumaran, A (2011) Statistical normalization and back propagation for classification, Int J Comput Theory Eng, 3(1): 1793-8201.

Jorjani, E., Chelgani, S. C., Mesroghli, S (2008) Application of artificial neural networks to predict  chemical desulfurization of Tabas coal, Fuel, 87 (12).

Kadkhodaie Ilkhchi, A., Rezaee, M., Moallemi, S. A (2006) a fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field, J. Geophys. Eng, 3: 356–369.

Kadkhodaie-Ilkhchi, A., Rezaee, M. R., Rahimpour-Bonab, H., Chehrazi, A (2009) Petrophysical data prediction from seismic attributes using committee fuzzy inference system, Computers & Geosciences, 35: 2314-2330.

Kosko, B (1992) Neural Networks and Fuzzy Systems. A Dynamical Approach to Machine Intelligence, Prentice Hall, Englewood Cliffs, NJ, 449p.

MATLAB user’s guide (2006) Fuzzy logic Toolbox, by the math works Inc.

Mehrabian, A. R., Lucas, C (2006) A novel numerical optimization algorithm inspired from weed colonization, Ecological informatics, 1 (4): 355-366.

Moatazedian, I., Rahimpour-Bonab, H., Kadkhodaie-Ilkhchi, A., Rajoli, M (2011) Prediction of shear and Compressional Wave Velocities from petrophysical data utilizing genetic algorithms technique: A case study in Hendijan and Abuzar fields located in Persian Gulf, Geopersia, 1: 1-17.

Monjezi, M., Dehghani, H (2008) Evaluation of effect of blasting pattern parameters on back break using neural networks, International Journal of Rock Mechanics and Mining Sciences, 45 (8): 1446-1453.

Nava, P., Taylor, J (1996) the Optimization of Neural Network Performance through Incorporation of Fuzzy Theory, In: 11th Conference on Systems Engineering, 897-901.

Nazari, S., Kuzma, H. A., Rector III, J. W (2011) Predicting permeability from well log data and core measurements using support vector machines, SEG Annual Meeting, Society of Exploration Geophysicists.

Nouri Taleghani, M., Saffarzadeh, S., Karimi Khaledi, M., Zargar, Gh (2013) Development of an Intelligent System to Synthesize Petrophysical Well Logs, Iranian Journal of Oil & Gas Science and Technology, 2 (3): 11-24.

Oden, C. O., LoCoco, J. J (2000) Variable Frequency Monopole-Dipole Sonic Logging for Mechanical and Hydrogeologic parameters, Conference Proceedings of the Annual Meeting of Environmental and Engineering Geophysical Society, Denver, CO.

Ohen, H. A (2003) Calibrated wire-line Mechanical Rock Properties Model for Predicting and preventing wellbore Collapse and Sanding, SPE, European Formation Damage Conference.

Okon, A. N., Adewole S. E., Uguma, E. M (2020) artificial neural network model for reservoir petrophysical properties: porosity, permeability and water aturation prediction, Modeling Earth Systems and Environment.

Rajabi, M., Bohloli, B., Ahangar, E. G (2010) Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran), Computers & Geosciences, 36: 647-664.

Rezaee, M. R., Kadkhodaie-Ilkhchi, A., Alizadeh, P. M (2007) Intelligent approaches for the synthesis of petrophysical logs, Journal of Geophysics and Engineering, 5 (12).

Saemi, M., Ahmadi, M., Yazdian Varjani, A (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir, Journal of Petroleum Science and Engineering, 59: 97–105.

Schlumberger (1989) Schlumberger Log Interpretation Principles / Applications, July, Hoston, Texas.

Sebtosheikh, M. A., Motafakkerfard, R., Riahi, M. A., Moradi, S (2015) Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir, Iranian Journal of Oil & Gas Science and Technology, 4: 1-14.

Specht, D. F (1991) A general regression neural network, IEEE Transactions on Neural Networks, 2 (6): 568-576.

Srinivasan, K., Fisher, D (1995) Machine Learning Approaches to Estimating Software Development Effort, IEEE Transactions on Software Engineering, 21(2): 126–137.

Tariq, Z., Mahmoud, M., Abdulraheem, A (2019) An Artificial Intelligence Approach to Predict the Water Saturation in Carbonate Reservoir Rocks, Paper presented at the SPE Annual Technical Conference and Exhibition, Calgary, Alberta, Canada.

Yue, Y., Wang, J (2007) SVM method for predicting the thickness of sandstone, Applied Geophysics, 4: 276-281.

Zoveidavianpoor, M., Samsuri, A., Shadizadeh, S.R (2013) Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir, Journal of Applied Geophysics, 89: 96-107.