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Volumn 38, Issue 6, 2013, Pages 1379-1395

Non-linear Heterogeneous Ensemble Model for Permeability Prediction of Oil Reservoirs

Author keywords

Adaptive neuro fuzzy system; Artificial neural network; Ensemble network; Support vector machine

Indexed keywords


EID: 84878088038     PISSN: 2193567X     EISSN: 21914281     Source Type: Journal    
DOI: 10.1007/s13369-013-0588-z     Document Type: Article
Times cited : (29)

References (75)
  • 1
    • 67349152094 scopus 로고    scopus 로고
    • Comparison between neuro-fuzzy and fractal models for permeability prediction
    • Hurtado N., Aldana M., Torres J.: Comparison between neuro-fuzzy and fractal models for permeability prediction. Comput. Geosci. 13, 181-186 (2009).
    • (2009) Comput. Geosci. , vol.13 , pp. 181-186
    • Hurtado, N.1    Aldana, M.2    Torres, J.3
  • 2
    • 29544449323 scopus 로고    scopus 로고
    • Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea
    • Lim J.: Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J. Petrol. Sci. Eng. 49, 182-192 (2005).
    • (2005) J. Petrol. Sci. Eng. , vol.49 , pp. 182-192
    • Lim, J.1
  • 3
    • 0000222410 scopus 로고    scopus 로고
    • Permeability prediction based on fractal pore-space geometry
    • Pape H., Clauser C., Iffland J.: Permeability prediction based on fractal pore-space geometry. Geophysics 5(64), 1447-1460 (1999).
    • (1999) Geophysics , vol.5 , Issue.64 , pp. 1447-1460
    • Pape, H.1    Clauser, C.2    Iffland, J.3
  • 4
    • 67651043292 scopus 로고    scopus 로고
    • A hybrid neuro-genetic approach to short-term traffic volume prediction
    • Afandizadeh S., Kianfar J.: A hybrid neuro-genetic approach to short-term traffic volume prediction. Int. J. Civil Eng. 1(7), 41-48 (2009).
    • (2009) Int. J. Civil Eng. , vol.1 , Issue.7 , pp. 41-48
    • Afandizadeh, S.1    Kianfar, J.2
  • 5
    • 0033844049 scopus 로고    scopus 로고
    • Using artificial intelligence to predict permeability from petrographic data
    • Ali M., Chawathe A.: Using artificial intelligence to predict permeability from petrographic data. Comput. Geosci. 26, 915-925 (2000).
    • (2000) Comput. Geosci. , vol.26 , pp. 915-925
    • Ali, M.1    Chawathe, A.2
  • 6
    • 78349266079 scopus 로고    scopus 로고
    • Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs
    • Helmy T., Fatai A.: Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs. Int. J. Comput. Intell. Appl. 4(9), 313-337 (2010).
    • (2010) Int. J. Comput. Intell. Appl. , vol.4 , Issue.9 , pp. 313-337
    • Helmy, T.1    Fatai, A.2
  • 7
    • 0031127821 scopus 로고    scopus 로고
    • Fuzzy kmodeling and the prediction of porosity and permeability from the compositional and textural attributes of sandstone
    • Fang J. H., Chen H. C.: Fuzzy kmodeling and the prediction of porosity and permeability from the compositional and textural attributes of sandstone. J. Petrol. Geol. 2(20), 185-204 (1997).
    • (1997) J. Petrol. Geol. , vol.2 , Issue.20 , pp. 185-204
    • Fang, J.H.1    Chen, H.C.2
  • 8
    • 29544446578 scopus 로고    scopus 로고
    • Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria
    • El Ouahed A. K., Tiab D., Mazouzi A.: Application of artificial intelligence to characterize naturally fractured zones in Hassi Messaoud Oil Field, Algeria. J. Pet. Sci. Eng. 49, 122-141 (2005).
    • (2005) J. Pet. Sci. Eng. , vol.49 , pp. 122-141
    • El Ouahed, A.K.1    Tiab, D.2    Mazouzi, A.3
  • 10
    • 77957900643 scopus 로고    scopus 로고
    • Optimizing reservoir features in oil exploration management based on fusion of soft computing
    • Haixiang G., Xiuwu L., Kejun Z., Chang D., Yanhui G.: Optimizing reservoir features in oil exploration management based on fusion of soft computing. Appl. Soft Comput. 1(11), 1144-1155 (2011).
    • (2011) Appl. Soft Comput. , vol.1 , Issue.11 , pp. 1144-1155
    • Haixiang, G.1    Xiuwu, L.2    Kejun, Z.3    Chang, D.4    Yanhui, G.5
  • 11
    • 77950300963 scopus 로고    scopus 로고
    • Prediction of porosity and permeability of oil and gas reservoirs using hybrid computational intelligence models
    • Helmy T., Fatai A., Faisal A. K.: Prediction of porosity and permeability of oil and gas reservoirs using hybrid computational intelligence models. Expert Syst. Appl. 7(37), 5353-5363 (2010).
    • (2010) Expert Syst. Appl. , vol.7 , Issue.37 , pp. 5353-5363
    • Helmy, T.1    Fatai, A.2    Faisal, A.K.3
  • 12
    • 77049121009 scopus 로고    scopus 로고
    • 3D fracture modeling in Parsi oil field using artificial intelligence tools
    • Darabi H., Kavousi A., Moraveji M., Masihi M.: 3D fracture modeling in Parsi oil field using artificial intelligence tools. J. Petrol. Sci. Eng. 71(1-2), 67-76 (2010).
    • (2010) J. Petrol. Sci. Eng. , vol.71 , Issue.1-2 , pp. 67-76
    • Darabi, H.1    Kavousi, A.2    Moraveji, M.3    Masihi, M.4
  • 13
    • 34548671418 scopus 로고    scopus 로고
    • Design of neural networks using genetic algorithm for the permeability estimation of the reservoir
    • Saemi, M.; Ahmadi, M.; Varjani, A. Y.: Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J. Petrol. Sci. Eng. 97-105 (2007).
    • (2007) J. Petrol. Sci. Eng , pp. 97-105
    • Saemi, M.1    Ahmadi, M.2    Varjani, A.Y.3
  • 14
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • Jang J. S. R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 3(23), 665-684 (1993).
    • (1993) IEEE Trans. Syst. Man Cybern. , vol.3 , Issue.23 , pp. 665-684
    • Jang, J.S.R.1
  • 15
    • 0036565376 scopus 로고    scopus 로고
    • Permeability prediction in shaly formations: The fuzzy modeling approach
    • Finol, J. J.; Guo, Y. K.; Jing, X. Permeability prediction in shaly formations: the fuzzy modeling approach. Geophysics 3(67), 817-829 (2001).
    • (2001) Geophysics , vol.3 , Issue.67 , pp. 817-829
    • Finol, J.J.1    Guo, Y.K.2    Jing, X.3
  • 16
    • 60549088670 scopus 로고    scopus 로고
    • A committee neural network for prediction of normalized oil content from well log data: An example from South Pars gas field, Persian Gulf
    • Ilkhchi, A. K.; Rezaee, M. R.; Bonab, H. R.: A committee neural network for prediction of normalized oil content from well log data: an example from South Pars gas field, Persian Gulf. J. Petrol. Sci. Eng. 1-2(64), 23-32 (2009).
    • (2009) J. Petrol. Sci. Eng , vol.1-2 , Issue.64 , pp. 23-32
    • Ilkhchi, A.K.1    Rezaee, M.R.2    Bonab, H.R.3
  • 17
    • 77955173749 scopus 로고    scopus 로고
    • A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs
    • Al-Anazi; Gates I. D.: A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng. Geol. 114(3-4), 267-277 (2010).
    • (2010) Eng. Geol , vol.114 , Issue.3-4 , pp. 267-277
    • Al-Anazi1    Gates, I.D.2
  • 19
    • 0000262562 scopus 로고
    • Hierarchical mixtures of experts and the EM algorithm
    • Jordan M. I., Jacobs R. A.: Hierarchical mixtures of experts and the EM algorithm. Neural Comput. 6, 181-214 (1994).
    • (1994) Neural Comput. , vol.6 , pp. 181-214
    • Jordan, M.I.1    Jacobs, R.A.2
  • 20
    • 33748611921 scopus 로고    scopus 로고
    • Ensemble based systems in decision making
    • Polikar R.: Ensemble based systems in decision making. IEEE Circuit Syst. Mag. 6, 21-45 (2006).
    • (2006) IEEE Circuit Syst. Mag. , vol.6 , pp. 21-45
    • Polikar, R.1
  • 25
    • 33644883318 scopus 로고    scopus 로고
    • A committee machine with empirical formulas for permeability prediction
    • Chen C. H., Lin Z. S.: A committee machine with empirical formulas for permeability prediction. Comput. Geosci. 32, 485-496 (2006).
    • (2006) Comput. Geosci. , vol.32 , pp. 485-496
    • Chen, C.H.1    Lin, Z.S.2
  • 26
    • 51749101521 scopus 로고    scopus 로고
    • Bootstrap methods for foreign currency exchange rates prediction
    • IJCNN
    • He, H.; Shen, X.: Bootstrap methods for foreign currency exchange rates prediction. Neural Netw. IJCNN, 1272-1277 (2007).
    • (2007) Neural Netw , pp. 1272-1277
    • He, H.1    Shen, X.2
  • 27
    • 69249202487 scopus 로고    scopus 로고
    • Estimating VaR in crude oil market: a novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network
    • He K., Xie C., Chen S., Lai K. K.: Estimating VaR in crude oil market: a novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing 72(16-18), 3428-3438 (2009).
    • (2009) Neurocomputing , vol.72 , Issue.16-18 , pp. 3428-3438
    • He, K.1    Xie, C.2    Chen, S.3    Lai, K.K.4
  • 28
    • 42149176689 scopus 로고    scopus 로고
    • Decision support system for predicting benefits of left-turn lanes at un-signalized intersections, transportation research record
    • Ranade S., Sadek A. W., Ivan J. N.: Decision support system for predicting benefits of left-turn lanes at un-signalized intersections, transportation research record. J. Transport. Res. Board 2023, 28-36 (2007).
    • (2007) J. Transport. Res. Board , vol.2023 , pp. 28-36
    • Ranade, S.1    Sadek, A.W.2    Ivan, J.N.3
  • 29
    • 0025627940 scopus 로고
    • Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks
    • Hornik K., Stinchcombe M., White H.: Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Netw. 5(3), 551-560 (1990).
    • (1990) Neural Netw. , vol.5 , Issue.3 , pp. 551-560
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 30
    • 0032634129 scopus 로고    scopus 로고
    • Pasting small votes for classification in large databases and on-line
    • Breiman L.: Pasting small votes for classification in large databases and on-line. Mach. Learn. 36, 85-103 (1999).
    • (1999) Mach. Learn. , vol.36 , pp. 85-103
    • Breiman, L.1
  • 31
    • 84877831255 scopus 로고
    • Inductive principles of statistics and learning theory
    • Vapnik, V.: Inductive principles of statistics and learning theory. Math. Perspect. Neural Netw. (1995).
    • (1995) Math. Perspect. Neural Netw
    • Vapnik, V.1
  • 33
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • Burges C. J. C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2, 121-167 (1998).
    • (1998) Data Min. Knowl. Discov. , vol.2 , pp. 121-167
    • Burges, C.J.C.1
  • 40
    • 0028423359 scopus 로고
    • Fuzzy Kohonen clustering network
    • Tsao E. C., Bezdek J. C., Pal N. R.: Fuzzy Kohonen clustering network. Pattern Recognit. 5(27), 757-764 (1994).
    • (1994) Pattern Recognit. , vol.5 , Issue.27 , pp. 757-764
    • Tsao, E.C.1    Bezdek, J.C.2    Pal, N.R.3
  • 41
    • 4444347719 scopus 로고    scopus 로고
    • A novel kernelized fuzzy c-means algorithm with application in medical image segmentation
    • Zhang D. Q., Chen S. C.: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif. Intell. Med. 32, 37-50 (2004).
    • (2004) Artif. Intell. Med. , vol.32 , pp. 37-50
    • Zhang, D.Q.1    Chen, S.C.2
  • 42
    • 0004063090 scopus 로고
    • Macmillan College Publishing Company, Inc, New York
    • Haykin, S.: Neural networks. Macmillan College Publishing Company, Inc, New York (1994).
    • (1994) Neural Networks
    • Haykin, S.1
  • 44
    • 0032623401 scopus 로고    scopus 로고
    • Forecasting freeway link travel times with a feedforward multilayer neural network
    • Park D., Rilett L. R.: Forecasting freeway link travel times with a feedforward multilayer neural network. Comput. Aided Civil Infrastruct. Eng. 14, 357-367 (1999).
    • (1999) Comput. Aided Civil Infrastruct. Eng. , vol.14 , pp. 357-367
    • Park, D.1    Rilett, L.R.2
  • 45
    • 33746610646 scopus 로고    scopus 로고
    • a novel fuzzy neural approach to road traffic analysis and prediction
    • Quek C., Pasquier M., Lim B. B. S.: a novel fuzzy neural approach to road traffic analysis and prediction. IEEE Trans. Intell. Transport. Syst. 7, 133-146 (2006).
    • (2006) IEEE Trans. Intell. Transport. Syst. , vol.7 , pp. 133-146
    • Quek, C.1    Pasquier, M.2    Lim, B.B.S.3
  • 46
    • 40449104106 scopus 로고    scopus 로고
    • Forecasting of short-term freeway volume with v-support vector machines
    • TRB, National Research Council, Washington, DC
    • Zhang, Y.; Xie, Y.: Forecasting of short-term freeway volume with v-support vector machines. In: Transportation Research Record: Journal of the Transportation Research Board, No. 2024, TRB, National Research Council, Washington, DC, pp. 92-99 (2007).
    • (2007) In: Transportation Research Record: Journal of the Transportation Research Board , Issue.2024 , pp. 92-99
    • Zhang, Y.1    Xie, Y.2
  • 47
    • 84878022979 scopus 로고    scopus 로고
    • Introduction to support vector machines
    • Department of Computer Science, Rutgers University, The State University of New Jersey, (2003)
    • Littman, W. T. A.: Introduction to support vector machines. Machine learning course 536, Department of Computer Science, Rutgers University, The State University of New Jersey, (2003). http://www. cs. rutgers. edu/~mlittman/courses/ml03/ (2010).
    • (2010) Machine Learning Course , vol.536
    • Littman, W.T.A.1
  • 49
    • 0001920992 scopus 로고    scopus 로고
    • Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks
    • AAAI Press, Portland
    • Cherkauer, K.: Human expert-level performance on a scientific image analysis task by a system using combined artificial neural networks. In: Proceedings of Thirteenth National Conference on Artificial Intelligence, AAAI Press, Portland (1996).
    • (1996) In: Proceedings of Thirteenth National Conference On Artificial Intelligence
    • Cherkauer, K.1
  • 51
    • 84868595337 scopus 로고    scopus 로고
    • Genetic algorithms for optimizing ensemble of models in software reliability prediction
    • Aljahdali S. H., El-Telbany M. E.: Genetic algorithms for optimizing ensemble of models in software reliability prediction. Int. J. Artif. Intell. Mach. Learn. 1(8), 5-13 (2008).
    • (2008) Int. J. Artif. Intell. Mach. Learn. , vol.1 , Issue.8 , pp. 5-13
    • Aljahdali, S.H.1    El-Telbany, M.E.2
  • 52
    • 33748577636 scopus 로고    scopus 로고
    • Tapping the power of text mining
    • Fan W., Wallace L., Rich S., Zhang Z.: Tapping the power of text mining. Commun. ACM 49(9), 76-82 (2006).
    • (2006) Commun. ACM , vol.49 , Issue.9 , pp. 76-82
    • Fan, W.1    Wallace, L.2    Rich, S.3    Zhang, Z.4
  • 53
    • 85052849114 scopus 로고    scopus 로고
    • A fuzzy logic approach for the estimation of permeability and rock types from conventional well log data: an example from the Kangan reservoir in Iran Offshore Gas Field
    • Ilkhchi K.: A fuzzy logic approach for the estimation of permeability and rock types from conventional well log data: an example from the Kangan reservoir in Iran Offshore Gas Field. Iran J. Geophys. Eng. 3, 356-369 (2006).
    • (2006) Iran J. Geophys. Eng. , vol.3 , pp. 356-369
    • Ilkhchi, K.1
  • 54
    • 48749090493 scopus 로고    scopus 로고
    • Greedy regression ensemble selection: theory and an application to water quality
    • Partalas I., Tsoumakas G., Hatzikos V., Vlahavas I.: Greedy regression ensemble selection: theory and an application to water quality. Inf. Sci. 20(178), 3867-3879 (2008).
    • (2008) Inf. Sci. , vol.20 , Issue.178 , pp. 3867-3879
    • Partalas, I.1    Tsoumakas, G.2    Hatzikos, V.3    Vlahavas, I.4
  • 57
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman L.: Bagging predictors. Mach. Learn. 2(24), 123-140 (1996).
    • (1996) Mach. Learn. , vol.2 , Issue.24 , pp. 123-140
    • Breiman, L.1
  • 58
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman L.: Random forests. Mach. Learn. 1(45), 5-32 (2001).
    • (2001) Mach. Learn. , vol.1 , Issue.45 , pp. 5-32
    • Breiman, L.1
  • 59
    • 0031211090 scopus 로고    scopus 로고
    • Decision-theoretic generalization of on-line learning and an application to boosting
    • Freund Y., Schapire R. E.: Decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1(55), 119-139 (1997).
    • (1997) J. Comput. Syst. Sci. , vol.1 , Issue.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 60
    • 0032139235 scopus 로고    scopus 로고
    • Random subspace method for constructing decision forests
    • Ho T. K.: Random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 8(20), 832-844 (1998).
    • (1998) IEEE Trans. Pattern Anal. Mach. Intell. , vol.8 , Issue.20 , pp. 832-844
    • Ho, T.K.1
  • 62
    • 84898990837 scopus 로고    scopus 로고
    • Constructing heterogeneous committees via input feature grouping
    • MIT Press, Massachusetts
    • Liao, Y.; Moody, J.: Constructing heterogeneous committees via input feature grouping. Advances in Neural Information Processing Systems, vol. 12. MIT Press, Massachusetts (2000).
    • (2000) Advances In Neural Information Processing Systems , vol.12
    • Liao, Y.1    Moody, J.2
  • 63
    • 0000749354 scopus 로고
    • Neural networks ensembles, cross validation, and active learning
    • Krogh, A.; Vedelsby, J.: Neural networks ensembles, cross validation, and active learning. In: Proceedings of NIPS-7 (1995).
    • (1995) In: Proceedings of NIPS-7
    • Krogh, A.1    Vedelsby, J.2
  • 64
    • 0001066312 scopus 로고    scopus 로고
    • Optimal ensemble averaging of neural networks
    • Naftaly U., Intrator N., Horn D.: Optimal ensemble averaging of neural networks. Comput. Neural Syst. 8, 283-296 (1997).
    • (1997) Comput. Neural Syst. , vol.8 , pp. 283-296
    • Naftaly, U.1    Intrator, N.2    Horn, D.3
  • 65
    • 26944501740 scopus 로고    scopus 로고
    • Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods
    • Valentini G., Dietterich T. G.: Bias-variance analysis of support vector machines for the development of SVM-based ensemble methods. J. Mach. Learn. Res. 5, 725-775 (2004).
    • (2004) J. Mach. Learn. Res. , vol.5 , pp. 725-775
    • Valentini, G.1    Dietterich, T.G.2
  • 66
    • 80054024627 scopus 로고    scopus 로고
    • An ensemble model of multiple classifiers for time series prediction
    • Chitra A., Uma S.: An ensemble model of multiple classifiers for time series prediction. Int. J. Comput. Theory Eng. 2(3), 1793-8201 (2010).
    • (2010) Int. J. Comput. Theory Eng. , vol.2 , Issue.3 , pp. 1793-8201
    • Chitra, A.1    Uma, S.2
  • 67
    • 33846516926 scopus 로고    scopus 로고
    • Stock market prediction with multiple classifiers
    • Qian B., Rasheed K.: Stock market prediction with multiple classifiers. Appl. Intell. 1(26), 25-33 (2007).
    • (2007) Appl. Intell. , vol.1 , Issue.26 , pp. 25-33
    • Qian, B.1    Rasheed, K.2
  • 69
    • 0000926506 scopus 로고
    • When networks disagree: Ensemble methods for hybrid neural networks
    • In: Mammone, R. J. (ed.), Chapman-Hall, London
    • Perrone, M. P.; Cooper, L. N.: When networks disagree: ensemble methods for hybrid neural networks. In: Mammone, R. J. (ed.): Neural Networks for Speech and Image Processing. Chapman-Hall, London, pp. 126-142 (1993).
    • (1993) Neural Networks For Speech and Image Processing , pp. 126-142
    • Perrone, M.P.1    Cooper, L.N.2
  • 70
    • 33749846894 scopus 로고    scopus 로고
    • Credit risk analysis using a reliability-based neural network ensemble model
    • Lai K. K., Yu L., Wang S. Y., Zhou L. G.: Credit risk analysis using a reliability-based neural network ensemble model. Lect. Notes Comput. Sci. 4132, 682-690 (2006).
    • (2006) Lect. Notes Comput. Sci. , vol.4132 , pp. 682-690
    • Lai, K.K.1    Yu, L.2    Wang, S.Y.3    Zhou, L.G.4
  • 71
    • 0000873069 scopus 로고
    • A method for the solution of certain nonlinear problems in least squares
    • Levenberg K.: A method for the solution of certain nonlinear problems in least squares. Quart. Appl. Math. 2, 164-168 (1944).
    • (1944) Quart. Appl. Math. , vol.2 , pp. 164-168
    • Levenberg, K.1
  • 72
    • 0000169232 scopus 로고
    • An algorithm for least squares estimation of nonlinear parameters
    • Marquardt D. W.: An algorithm for least squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431-441 (1963).
    • (1963) SIAM J. Appl. Math. , vol.11 , pp. 431-441
    • Marquardt, D.W.1


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