Построение связи сейсмических параметров и ФЕС

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Построение связи сейсмических параметров и ФЕС

bne3: Одна из проблем - разномасштабность и роль мезонеоднородностей Вторая - малое число экспериментальных данных по распространенным и полноценным объектам (обычно цитируют старые данные по смеси песков с глинами или аналогичные) для которых можно проводить сопоставление

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bne3: Copyright © 2009 Elsevier Ltd All rights reserved. Petrophysical data prediction from seismic attributes using committee fuzzy inference system References and further reading may be available for this article. To view references and further reading you must purchase this article. Ali Kadkhodaie-Ilkhchia, , M. Reza Rezaeea, b, , , Hossain Rahimpour-Bonaba, and Ali Chehrazia, c, aDepartment of Geology, College of Science, University of Tehran, Tehran, Iran Department of Petroleum Engineering, Curtin University of Technology, ARRC Building, 26 Dick Perry Avenue, Kensington, Perth, WA 6151, Australia cGeology Division, Iranian Offshore Oilfields Company, No. 38, Tooraj St., Vali-Asr Ave., NIOC, Tehran 19395, Iran Received 27 August 2008; revised 16 April 2009; accepted 20 April 2009. Available online 4 September 2009. Abstract This study presents an intelligent model based on fuzzy systems for making a quantitative formulation between seismic attributes and petrophysical data. The proposed methodology comprises two major steps. Firstly, the petrophysical data, including water saturation (Sw) and porosity, are predicted from seismic attributes using various fuzzy inference systems (FISs), including Sugeno (SFIS), Mamdani (MFIS) and Larsen (LFIS). Secondly, a committee fuzzy inference system (CFIS) is constructed using a hybrid genetic algorithms-pattern search (GA-PS) technique. The inputs of the CFIS model are the outputs and averages of the FIS petrophysical data. The methodology is illustrated using 3D seismic and petrophysical data of 11 wells of an Iranian offshore oil field in the Persian Gulf. The performance of the CFIS model is compared with a probabilistic neural network (PNN). The results show that the CFIS method performed better than neural network, the best individual fuzzy model and a simple averaging method. Keywords: Committee fuzzy inference system; Sugeno; Larsen; Mamdani; Hybrid genetic algorithm-pattern search; Probabilistic neural network; Petrophysical data; Seismic attributes Nomenclature A, B input fuzzy sets (membership functions) αi firing strength of ith fuzzy rule β1, β2, β3, β4 constants in constructing output membership functions of the Sugeno model C output fuzzy set (membership function) C′i truncated output fuzzy set for the ith rule C′ aggregated output fuzzy set CC correlation coefficient D(x,xi) distance between the input point x and each of the training points FCM Fuzzy c-means clustering FIS fuzzy inference system GA-PS genetic algorithm-pattern search k number of training samples in constructing CFIS and neural network Li target petrophysical data LFIS Larsen fuzzy inference system LOM large of maximum m number of clusters MF membership function MFIS Mamdani fuzzy inference system MIMO multiple inputs and multiple outputs MISO multiple inputs and single output MOM mean of maximum MSE mean squared error n number of fuzzy rules Oi outputs of fuzzy models ON output of the probabilistic neural network ONs validation result of the probabilistic neural network PNN probabilistic neural networks p, q coefficients of output membership functions in the Sugeno fuzzy model ρ distance scale factor porosity Ri ith fuzzy rule r constant of output membership functions in the Sugeno fuzzy model RB fuzzy rule base γ weight coefficients of MFIS, LFIS, SFIS and average of them, respectively Sw water saturation SFIS Sugeno fuzzy inference system SOM small of maximum t number of fuzzy rule base with MISO τi Degree of fulfillment of rule i σij2 variance of cluster i in jth rule sum operator in fuzzy sets ui ith cluster center μCi(z) grade of membership of element z in output fuzzy set C for ith rule firing strength of ith fuzzy rule μAi(x0) grade of membership of element x0 in ith input fuzzy set A μAi(x0) grade of membership of element y0 in ith input fuzzy set B μRi grade of membership for ith fuzzy rule vij ith cluster centers of jth rule in input space wij ith cluster centers of jth rule in output space x, y input data for fuzzy sets and neural network λ constant z output of fuzzy set Article Outline Nomenclature 1. Introduction 2. Methodology 2.1. Fuzzy inference system 2.2. Committee fuzzy inference system 3. Application to the Iranian offshore oilfield 3.1. Correlation of well logs to seismic data 3.2. Selection of optimal seismic attributes 3.3. Fuzzy clustering 3.4. Construction of fuzzy rule base 3.5. Construction of a committee fuzzy interference system—CFIS 3.6. Design of a probabilistic neural network 4. Results and discussion 5. Conclusion Acknowledgements References Fig. 1. Main parts of an FIS (Lee, 2004). View Within Article -------------------------------------------------------------------------------- Fig. 2. Graphical illustrations of MFIS (a), LFIS (b) and SFIS (c). View Within Article -------------------------------------------------------------------------------- Fig. 3. A schematic diagram of CFIS designed in this research. View Within Article -------------------------------------------------------------------------------- Fig. 4. Map showing location of wells in Iranian Offshore Oilfield. View Within Article -------------------------------------------------------------------------------- Fig. 5. A 3D crossline showing general quality of seismic data across study field (Crossline: 1976). View Within Article -------------------------------------------------------------------------------- Fig. 6. A sample of well to seismic tie at well A9. View Within Article -------------------------------------------------------------------------------- Fig. 7. Crossplots showing relationships between seismic attributes and water saturation. View Within Article -------------------------------------------------------------------------------- Fig. 8. Crossplots showing relationships between seismic attributes and porosity. View Within Article -------------------------------------------------------------------------------- Fig. 9. Results of running GA-PS including best and mean fitness values (top left), average distance between individuals (top right), fitness scaling (down left) and calculated scores (down right) for optimizing porosity estimation problem. View Within Article -------------------------------------------------------------------------------- Fig. 10. Correlation coefficient between measured and predicted water saturation for test samples using SFIS (a), MFIS (b), LFIS (c) and CFIS (d). View Within Article -------------------------------------------------------------------------------- Fig. 11. Graphical comparison between measured and predicted water saturation for test samples using SFIS (a), MFIS (b), LFIS (c) and CFIS (d). View Within Article -------------------------------------------------------------------------------- Fig. 12. Correlation coefficient between measured and predicted porosity for test samples using SFIS (a), MFIS (b), LFIS (c) and CFIS (d). View Within Article -------------------------------------------------------------------------------- Fig. 13. Graphical comparison between measured and predicted porosity for test samples using SFIS (a), MFIS (b), LFIS (c) and CFIS (d). View Within Article -------------------------------------------------------------------------------- Fig. 14. Map showing distribution of CFIS estimated water saturation for Top Ghar reservoir. View Within Article -------------------------------------------------------------------------------- Fig. 15. Map showing distribution of CFIS estimated porosity for Top Ghar reservoir. View Within Article -------------------------------------------------------------------------------- Table 1. Multi-attribute list for predicting water saturation (a) and porosity (b). View Within Article -------------------------------------------------------------------------------- Table 2. Gaussian membership function parameters derived by FCM for predicting Sw using MFIS and LFIS. View Within Article -------------------------------------------------------------------------------- Table 3. Gaussian and linear membership function parameters derived by subtractive clustering and gradient descent methods for predicting Sw using SFIS. View Within Article -------------------------------------------------------------------------------- Table 4. Performance of different fuzzy models for estimating water saturation and porosity. View Within Article Corresponding author at: Department of Petroleum Engineering, Curtin University of Technology, ARRC Building, 26 Dick Perry Avenue, Kensington, Perth, WA 6151, Australia. Tel.: +61 8 9266 7980; fax: +61 8 9266 7063.

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