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A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction

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Abstract

Various computational intelligence techniques have been used in the prediction of petroleum reservoir properties. However, each of them has its limitations depending on different conditions such as data size and dimensionality. Hybrid computational intelligence has been introduced as a new paradigm to complement the weaknesses of one technique with the strengths of another or others. This paper presents a computational intelligence hybrid model to overcome some of the limitations of the standalone type-2 fuzzy logic system (T2FLS) model by using a least-square-fitting-based model selection algorithm to reduce the dimensionality of the input data while selecting the best variables. This novel feature selection procedure resulted in the improvement of the performance of T2FLS whose complexity is usually increased and performance degraded with increased dimensionality of input data. The iterative least-square-fitting algorithm part of functional networks (FN) and T2FLS techniques were combined in a hybrid manner to predict the porosity and permeability of North American and Middle Eastern oil and gas reservoirs. Training and testing the T2FLS block of the hybrid model with the best and dimensionally reduced input variables caused the hybrid model to perform better with higher correlation coefficients, lower root mean square errors, and less execution times than the standalone T2FLS model. This work has demonstrated the promising capability of hybrid modelling and has given more insight into the possibility of more robust hybrid models with better functionality and capability indices.

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References

  1. Jong-Se L (2005) Reservoir properties determination using fuzzy logic and neural networks from well data in offshore Korea. J Pet Sci Eng 49:182–192

    Article  Google Scholar 

  2. Helmy T, Anifowose F, Faisal K (2010) Hybrid computational models for the characterization of oil and gas reservoirs. Int J Exp Syst Appl 37:5353–5363

    Article  Google Scholar 

  3. Ferraz IN and Garcia CB (2005) Lithofacies recognition hybrid bench. In: Proceedings of the 5th international conference on hybrid intelligent systems, IEEEXplore/ACM digital library

  4. Salim C (2006) A fuzzy art versus hybrid NN-HMM methods for lithology identification in the Triasic province. In: Proceedings of the 2nd international conference on information and communication technologies. IEEEXplore 1:1884–1887

  5. Xie D, Wilkinson D, Yu T (2005) Permeability estimation using a hybrid genetic programming and fuzzy/neural inference approach. In: Proceedings of the SPE annual technical conference and exhibition, Dallas, Texas, USA

  6. Anifowose F and Abdulraheem A (2010) Prediction of porosity and permeability of oil and gas reservoirs using hybrid computational intelligence models. Paper SPE-126649. In: Proceedings of the society of petroleum engineers North Africa technical conference and exhibition, Cairo, Egypt, February

  7. Chang FM (2008) Characteristics analysis for small data set learning and the comparison of classification methods. In: Proceedings of the 7th WSEAS international conference on artificial intelligence, knowledge engineering and data bases, Cambridge. Kazovsky L, Borne P, Mastorakis N, Kuri-Morales A and Sakellaris I (eds) Artificial Intelligence Series. World Scientific and Engineering Academy and Society (WSEAS), Stevens Point, Wisconsin, pp 122–127

  8. Van BV, Pelckmans K, Van HS, Suykens JA (2011) Improved performance on high-dimensional survival data by application of Survival-SVM. Bioinformatics 27(1):87–94

    Article  Google Scholar 

  9. Mendel JM (2003) Type-2 fuzzy sets: some questions and answers. IEEE Connect Newsl IEEE Neural Netw Soc 1:10–13

    Google Scholar 

  10. Mendel JM, Robert IJ, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821

    Article  Google Scholar 

  11. Peddabachigari S, Abraham A, Grosan C, Thomas J (2011) Modelling intrusion detection system using hybrid intelligent systems. J Netw Comput Appl 30:114–132

    Article  Google Scholar 

  12. Mendoza O, Licea G, Melin P (2007) Modular neural networks and type-2 fuzzy logic for face recognition. In: Proceedings of the annual meeting of the north american fuzzy information processing society, pp 622–627

  13. Jin B, Tang YC, Yan-Qing Z (2007) Support vector machines with genetic fuzzy feature transformation for biomedical data classification. Info Sci 177:476–489

    Article  Google Scholar 

  14. Helmy T, Al-Harthi MM, Faheem MT (2012) Adaptive ensemble and hybrid models for classification of bioinformatics datasets. Trans Fuzzy Neural Netw Bioinform Glob J Technol Optim 3:20–29

    Google Scholar 

  15. Lean Y, Kin KL, Shouyang W (2006) Credit risk assessment with least squares fuzzy support vector machines. In: Proceedings of the 6th IEEE international conference on data mining workshops, IEEEXplore, pp 823–827

  16. Evaggelos S, Giorgos S, Yannis A, Stefanos K (2006) Fuzzy support vector machines for image classification fusing Mpeg-7 visual descriptors. Technical Report. Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Greece

  17. Bullinaria JA, Li X (2007) An introduction to computational intelligence techniques for robot control. Ind Robot 34(4):295–302

    Article  Google Scholar 

  18. Helmy T, Anifowose F (2010) Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs. Int J Comput Intell Appl 9(4):313–337

    Article  Google Scholar 

  19. Accessscience Encyclopaedia Article (2011) Well Logging. Available online: http://www.accessscience.com/abstract.aspx?id=744100&referURL=http%3a%2f%2fwww.Accessscience.com%2fcontent.aspx%3fid%3d744100. Accessed on 28th March

  20. Schlumberger Oil Field Glossary (2011) Porosity, Available online: http://www.glossary.oilfield.slb.com/Display.cfm?Term=porosity. Accessed on April 20

  21. Schlumberger Oil Field Glossary (2011) Permeability, Available online: http://www.glossary.oilfield.slb.com/Display.cfm?Term=permeability. Accessed on April 20

  22. Anifowose F (2009) Hybrid artificial intelligence models for the characterization of oil and gas reservoirs: concept, design and implementation. VDM Verlag, Germany

    Google Scholar 

  23. Singh V, Singh TN (2006) A neuro-fuzzy approach for prediction of Poisson’s ratio and young’s modulus of shale and sandstone. In: Proceedings of the 41st United States symposium on rock mechanics (USRMS), Golden Rocks

  24. Petrus JB, Thuijsman F, Weijters AJ (1995) Artificial neural networks: an introduction to ann theory and practice. Springer, Netherlands

    Google Scholar 

  25. Anifowose F and Abdulraheem A (2010) A functional networks-type-2 fuzzy logic hybrid model for the prediction of porosity and permeability of oil and gas reservoirs. Paper #1569334237. In: Proceedings of the 2nd international conference on computational intelligence, modelling and simulation (CIMSim 2010), IEEEXplore, pp 193–198

  26. Park HJ, Lim JS, Roh U, Kang JM, Min BH (2006) Production-system optimization of gas fields using hybrid fuzzy-genetic approach. In: Proceedings of the SPE Europec/EAGE annual conference and exhibition, Vienna, Austria

  27. Lim J, Park H, Kim J (2006) A new neural network approach to reservoir permeability estimation from well logs. In: Proceedings of the SPE Asia pacific oil and gas conference and exhibition, Adelaide, Australia

  28. Mohsen S, Morteza A, Ali YV (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Pet Sci Eng 59:97–105

    Article  Google Scholar 

  29. Zarei F, Daliri A, Alizadeh N (2008) The use of neuro-fuzzy proxy in well placement optimization. In: Proceedings of the SPE intelligent energy conference and exhibition, Amsterdam, The Netherlands

  30. Al-Anazi A, Gates I, Azaiez J (2009) Innovative data-driven permeability prediction in a heterogeneous reservoir. In: Proceedings of the SPE EUROPEC/EAGE annual conference and exhibition, Amsterdam, The Netherlands

  31. Shahvar MB, Kharrat R, Mahdavi R (2009) Incorporating fuzzy logic and artificial neural networks for building hydraulic unit-based model for permeability prediction of a heterogenous carbonate reservoir. In: Proceedings of the international petroleum technology conference, Doha, Qatar

  32. Gao W (2012) Study on new improved hybrid genetic algorithm. In: Zeng D (ed) Advances in information technology and industry applications. Lecture Notes in Electrical Engineering 136, pp 505–512

  33. Bies RR, Muldoon MF, Pollock BG, Manuck S, Smith G, Sale ME (2006) A genetic algorithm-based, hybrid machine learning approach to model selection. J Pharmacokinet Pharmacodyn 33(2):195–221

    Article  Google Scholar 

  34. Ali L, Bordoloi S, Wardinsky SH (2008) Modelling permeability in tight gas sands using intelligence and innovative data mining techniques. In: Proceedings of the SPE annual technical conference and exhibition, Denver, Colorado

  35. Bruen M, Yang J (2005) Functional networks in real-time flood forecasting: a novel application. Adv Water Res 28:899–909

    Article  Google Scholar 

  36. El-Sebakhy E (2009) Software reliability identification using functional networks: a comparative study. Exp Syst Appl 36(2):4013–4020

    Article  Google Scholar 

  37. El-Sebakhy E, Hadi AS, Kanaan FA (2007) Iterative least squares functional networks classifier. IEEE Trans Neural Netw 18(2):1–7

    Article  Google Scholar 

  38. Castillo E, Gutiérrez JM, Hadi AS, Lacruz B (2001) Some applications of functional networks in statistics and engineering. Technometrics 43:10–24

    Article  MathSciNet  MATH  Google Scholar 

  39. Castillo E (2011) Software repository. Artificial Intelligence Research Group. Available online: http://ccaix3.unican.es/~AIGroup, Accessed March 25

  40. Mendel J (2011) Matlab codes for type-2 fuzzy logic system. Accessed March 25. sipi.usc.edu/~mendel/software/

  41. Grünwald P (2005) A tutorial introduction to the minimum description length principle. In: Grünwald P, Myung IJ, Pitt M (eds) Advances in minimum description length: theory and applications. MIT Press, Cambridge

    Google Scholar 

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Acknowledgments

The authors would like to thank the Universiti Malaysia Sarawak and King Fahd University of Petroleum and Minerals for providing the resources used in the conduct of this study. The authors would also like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) for funding this work under Project No. 08-OIL82-4 as part of the National Science, Technology and Innovation Plan.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia

    Fatai Anifowose & Jane Labadin

  2. Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Abdulazeez Abdulraheem

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  1. Fatai Anifowose
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  2. Jane Labadin
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  3. Abdulazeez Abdulraheem
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Correspondence to Fatai Anifowose.

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Anifowose, F., Labadin, J. & Abdulraheem, A. A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction. Neural Comput & Applic 23 (Suppl 1), 179–190 (2013). https://doi.org/10.1007/s00521-012-1298-2

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  • DOI: https://doi.org/10.1007/s00521-012-1298-2

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