A Performance Analysis and Comparison of Different Fuzzy Inference Models for Advanced Prediction of Reservoir Properties

References

• Alavi , N. , Nozari , V. , Mazloumzadeh , S. M. and Nezamabadi-por , H. 2010 . Irrigation water quality evaluation using adaptive network-based fuzzy inference system . Paddy Water Environ. , 8 : 259 – 266 .
   Google Scholar
• Al-Bulushi , N. , King , P. R. , Blunt , M. J. and Kraaijveld , M. 2009 . Development of artificial neural network models for predicting water saturation and fluid distribution . J. Petrol. Sci. & Engineer. , 68 : 197 – 208 .
   Google Scholar
• Ali , M. and Chawathe , A. 2000 . Using artificial intelligence to predict permeability from petrographic data . Comput. & Geosci. , 26 : 915 – 925 .
   Google Scholar
• Bezdek , J. C. 1981 . Pattern Recognition with Fuzzy Objective Function Algorithms , New York : Plenum Press .
   Google Scholar
• Chikhi , S. 2006 . A fuzzy ART versus hybrid NN-HMM methods for lithology identification in the Triassic Province . IEEE Trans , 1 : 1884 – 1887 . 7803-9521-2/06
   Google Scholar
• Chikhi , S. and Batouche , M. 2004 . Probabilistic neural method combined with radial-bias functions applied to reservoir characterization in the Algerian Triassic Province . J. Geophys. & Engineer. , 1 : 134 – 142 .
   Google Scholar
• El Ouahed , A. K. , Tiab , D. and Mazouzi , A. 2005 . Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria . J. Petrol. Sci. & Engineer. , 49 : 122 – 141 .
   Google Scholar
• Engelbrecht , A. P. 2007 . Computational Intelligence: An Introduction, , Second ed. , West Sussex : Wiley .
   Google Scholar
• Fang , J. H. and Chen , H. C. 1997 . Fuzzy modeling and the prediction of porosity and permeability from the compositional and textural attributes of sandstone . J. Petrol. Geol. , 20 : 185 – 204 .
   Google Scholar
• Helle , H. B. and Bhatt , A. 2002 . Fluid saturation from well logs using committee neural networks . Petrol. Geosci. , 8 : 109 – 118 .
   Google Scholar
• Herrera , F. and Lozano , M. 2003 . Fuzzy adaptive genetic algorithm: Design, taxonomy, and future directions . Soft Comput. , 7 : 545 – 562 .
   Google Scholar
• Heseldin , G. M. A method of averaging capillary pressure curves . Proceedings of SPWLA Fifteenth Annual Logging Symposium . June 2–5 , McAllen , TX .
   Google Scholar
• Jang , J. S. R. 1993 . ANFIS: Adaptive-network-based fuzzy inference system . IEEE Trans. Syst., Man & Cyber. , 23 : 665 – 685 .
   Google Scholar
• Leverett , M. C. 1941 . Capillary behavior inporous solids . Trans. AIME , 142 : 152 – 169 .
   Google Scholar
• Lim , J. 2005 . Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea . J. Petrol. Sci. & Engineer. , 49 : 182 – 192 .
   Google Scholar
• Mamdani , E. and Assilian , S. 1975 . An experiment in linguistic synthesis with a fuzzy logic controller . Int. J. Man. Mach. Stud. , 7 : 1 – 13 .
   Google Scholar
• Olson , T. M. Porosity and permeability prediction in low-permeability gas reservoirs from well logs using neural networks . SPE Rocky Mountain Regional Symposium and Exhibition . April 5–8 , Denver , Colorado . pp. 10 SPE Paper (39964)
   Google Scholar
• Sivanandam , S. N. , Sumathi , S. and Deepa , S. N. 2007 . Introduction to Fuzzy Logic Using Matlab , New York : Springer .
   Google Scholar
• Takagi , T. and Sugeno , M. 1985 . Fuzzy identification of systems and its application to modeling and control . IEEE Trans. Syst., Man & Cyber. , 15 : 116 – 132 .
   Google Scholar
• Witten , H. I. and Frank , E. 2005 . Data Mining Practical Machine Learning Tools and Techniques, , Second ed. , San Francisco , CA : Elsevier Inc .
   Google Scholar
• Xie , D. , Wilkinson , D. and Yu , T. Permeability estimation using a hybrid genetic programming and fuzzy/neural inference approach . SPE Annual Technical Conference and Exhibition . October 9–12 , Dallas , Texas . SPE Paper (95167)
   Google Scholar