Abstract
In this paper, we studies on a prediction model of Pressure-Volume-Temperature (PVT) properties of crude oil systems using a hybrid type-2 fuzzy logic system (type-2 FLS) and sensitivity based linear learning method (SBLLM). The PVT properties are very important in the reservoir engineering computations whereby an accurate determination of PVT properties is important in the subsequent development of an oil field. In the formulation used, for the type-2 FLS the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty, while in the case of SBBLM, the sensitivity analysis coupled with a linear training algorithm by human subject selections for each of the two layers is employed which ensures that the learning curve stabilizes soon and behave homogenously throughout the entire process operation based on the collective intelligence algorithms. Results indicated that type-2 FLS had better performance for the case of dataset with large data points (782-dataset) while SBLLM performed better for the small dataset (160-dataset).
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References
Omole, O., Falode, O.A., Deng, A.D.: Prediction of Nigerian Crude Oil Viscosity Using Artificial Neural Network. Petroleum & Coal. International Journal for Petroleum Processing 51(3) (2009)
Osman, E.A., Al-Marhoun, M.A.: Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines. In: 14th SPE Middle East Oil & Gas Show and Conference, Bahrain (2005)
Standing, M.B.: Oil-System Correlation. In: Frick, T.C. (ed.) Petroleum Production Handbook, 2nd edn., McGraw-Hill Book Co., New York City (1962)
Kumoluyi, A.O., Daltaban, T.S.: High-Order Neural Networks in Petroleum Engineering. In: SPE Western Regional Meeting, Longbeach, California, USA (1994)
Goda, H.M., et al.: Prediction of the PVT Data using Neural Network Computing Theory. In: The 27th Annual SPE International Technical Conférence and Exhibition in Abuja, Abuja, Nigeria (2003)
Olatunji, S.O., Selamat, A., Raheem, A.A.A.: Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems. Expert Systems with Applications 38(9), 10911–10922 (2011)
Castillo, E., et al.: A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis. Journal of Machine Learning Research 7, 1159–1182 (2006)
Castillo, A.S., et al.: A general method for local sensitivity analysis with application to regression models and other optimization problems. Technometrics 46(4), 430–445 (2004)
Olatunji, S.O., et al.: Modeling the correlations of crude oil properties based on sensitivity based linear learning method. Engineering Applications of Artificial Intelligence 24(4), 686–696 (2011)
Olatunji, S.O., Selamat, A., Raheem, A.A.A.: Modeling PVT Properties of Crude Oil Systems Using Type-2 Fuzzy Logic Systems. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ICCCI 2010, Part I. LNCS, vol. 6421, pp. 499–508. Springer, Heidelberg (2010)
Hong, T.-P., Wu, C.-H.: An Improved Weighted Clustering Algorithm for Determination of Application Nodes in Heterogeneous Sensor Networks. Journal of Information Hiding and Multimedia Signal Processing 2(2), 173–184 (2011)
Lin, T.C., et al.: An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index. International Journal of Computer Sciences and Engineering Systems 1(4), 253–257 (2007)
Olatunji, S.O., Selamat, A., Abdulraheem, A.: Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems. Computers in Industry 62(2), 147–163 (2011)
Al-Marhoun, M.A.: PVT Correlations for Middle East Crude Oils. Journal of Petroleum Technology, 650–666 (1988)
Osman, E.A., Abdel Wahab, O.A., Al-Marhoun, M.A.: Prediction of Oil PVT Properties Using Neural Networks. In: SPE Middle East Oil Show, Bahrain (2001)
Tarek, H., Anifowose, F., Kanaan, F.: Hybrid computational models for the characterization of oil and gas reservoirs. Expert Systems with Applications 37(7), 5353–5363 (2010)
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Selamat, A., Olatunji, S.O., Abdul Raheem, A.A. (2012). Modeling PVT Properties of Crude Oil Systems Based on Type-2 Fuzzy Logic Approach and Sensitivity Based Linear Learning Method. In: Nguyen, NT., Hoang, K., JÈ©drzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_15
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DOI: https://doi.org/10.1007/978-3-642-34630-9_15
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