|تعداد مشاهده مقاله||103,460,652|
|تعداد دریافت فایل اصل مقاله||81,433,134|
Rock physical modeling enhancement in hydrocarbon reservoirs using Choquet fuzzy integral fusion approach
|International Journal of Mining and Geo-Engineering|
|مقاله 2، دوره 54، شماره 2، اسفند 2020، صفحه 101-108 اصل مقاله (1.38 M)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/ijmge.2019.277343.594789|
|Hamid Seifi 1؛ Behzad Tokhmechi2؛ Ali Moradzadeh 3|
|1Faculty of Mining Engineering., Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran|
|2Associate professor, Faculty of Mining Engineering., Petroleum and Geophysics, Shahrood University of Technology, Shahrood, Iran|
|3Professor, School of Mining, College of Engineering, University of Tehran, Tehran,Iran|
|Rock physics models are widely used in hydrocarbon reservoir studies. These models make it possible to simulate a reservoir more accurately and reduce the economic risk of oil and gas exploration. In the current study, two models of Self-Consistent Approximation followed by Gassmann (SCA-G) and Xu-Payne (X-P) were implemented on three wells of a carbonate reservoir in the southwest of Iran. Then, in order to increase the accuracy and improve the efficiency of the models, a fusion model of Choquet Fuzzy Integral (CFI) was applied as a new approach. The compressionalwave velocities were estimated using two models, i.e., SCA-G and X-P, and were then integrated using the CFI fusion model. Finally, by comparing the model results and the real well log data, the Choquet model was confirmed as a compatible model with proper results. The correlation coefficient (CC) and Root Mean Squared Error (RMSE) for the estimated velocities versus the actual values showed the reliability of the constructed models. For example, in one of the studied wells, the CC and RMSE values were 99.2 and 44 m/s, respectively, in support of the fusion model. This could be related to the optimization algorithms in the heart of the Choquet model that led to the optimization of the model parameters and also better results in the studied carbonate reservoir.|
|Carbonate reservoirs؛ Data fusion؛ Self-Consistent Approximation model؛ Rock physics؛ Xu-Payne model|
 Avseth, P., Mukerji, T., & Mavko, G. (2010). Quantitative seismic interpretation: Applying rock physics tools to reduce interpretation risk. Sixth Edition, Cambridge university press, 356p.
 Lumley, D. E. (2001). Time-lapse seismic reservoir monitoring. Geophysics, 66 (1), 50-53.
 Hu, X., Hu, S., Jin, F., & Huang, S. (Eds.). (2017). Physics of petroleum reservoirs. Springer.
 Wang, Z., Wang, R., Schmitt D.R., Zhou, Y., Wang, F., (2017). Carbonate rock physics modelling at ultrasonic and seismic frequencies. 4th International Workshop on Rock Physics, Trondheim, Norway.
 Zhao, L., Nasser, M., & Han, D. H. (2013). Quantitative geophysical pore-type characterization and its geological implication in carbonate reservoirs. Geophysical Prospecting, 61 (4), 827-841.
 Ghon, G., Rankey, E. C., Baechle, G. T., Schlaich, M., Ali, S. H., Mokhtar, S., & Poppelreiter, M. C. (2018, June). Carbonate Reservoir Characterisation of an Isolated Platform Integrating Sequence Stratigraphy and Rock Physics in Centr. In 80th EAGE Conference and Exhibition 2018.
 Li, H., & Zhang, J. (2018). Well log and seismic data analysis for complex pore-structure carbonate reservoir using 3D rock physics templates. Journal of Applied Geophysics, 151, 175-183.
 Mavko, G., Mukerji, T., & Dvorkin, J. (2009). The rock physics handbook: Tools for seismic analysis of porous media. Second Edition, Cambridge university press,511p.
 Xu, S., & White, R. E. (1995). A new velocity model for clay‐sand mixtures 1. Geophysical prospecting, 43 (1), 91-118.
 Xu, S., & Payne, M. A. (2009). Modeling elastic properties in carbonate rocks. The Leading Edge, 28 (1), 66-74.
 Nishizawa, O. (1982). Seismic velocity anisotropy in a medium containing oriented cracks. Journal of Physics of the Earth, 30 (4), 331-347.
 Berryman, J. G. (1980). Long‐wavelength propagation in composite elastic media II. Ellipsoidal inclusions. The Journal of the Acoustical Society of America, 68 (6), 1820-1831.
 Eberli, G. P., Baechle, G. T., Anselmetti, F. S., & Incze, M. L. (2003). Factors controlling elastic properties in carbonate sediments and rocks. The Leading Edge, 22 (7), 654-660.
 Bashah, N. S. I., & Pierson, B. J. (2011, January). Quantification of pore structure in a miocene carbonate build-up of Central Luconia, sarawak and its relationship to sonic velocity. In International Petroleum Technology Conference. International Petroleum Technology Conference, Thailand.
 Lubis, L. A., & Harith, Z. Z. T. (2014). Pore type classification on carbonate reservoir in offshore Sarawak using rock physics model and rock digital images. In IOP Conference Series: Earth and Environmental Science (Vol. 19, No. 1, p. 012003). IOP Publishing.
 Hall, D., & Llinas, J. (2001). Multisensor data fusion. CRC press LLC.
 Abdulaheem, A., Sabakhy, E., Ahmed, M., Vantala, A., Raharja, P. D., & Korvin, G., (2007). Estimation of permeability from wireline logs in a middle eastern carbonate reservoir using fuzzy logic. In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers
 Cuddy, S. J., (2000). Litho-facies and permeability prediction from 108 H. Seifi et al. / Int. J. Min. & Geo-Eng. (IJMGE), 54-2 (2020) 101-108 electrical logs using fuzzy logic. SPE Reservoir Evaluation & Engineering, 3 (04), 319-324.
 Valet, L., Mauris, G., Bolon, P., & Keskes, N., (2001). Seismic image segmentation by fuzzy fusion of attributes. IEEE Transactions on Instrumentation and Measurement, 50 (4), 1014-1018.
 Guo, H. X., Zhu, K. J., Gao, S. W., Li, Y., & Zhou, J. J., (2009). Extracting fuzzy rules based on fusion of soft computing in oil exploration management. Expert Systems with Applications, 36 (2), 2081-2087.
 Ziyong, Z., Hangyu, Y., & Xiaodan, G., (2017). Fuzzy fusion of geological and geophysical data for mapping hydrocarbon potential based on GIS. Petroleum Geoscience, petgeo2016-100.
 Hajian, A., & Styles, P., (2018). Applications of Fuzzy Logic in Geophysics. In Application of Soft Computing and Intelligent Methods in Geophysics (pp. 301-371). Springer, Cham.
 Zimmerman, R. W. (1990). Compressibility of sandstones (Vol. 29). Elsevier,183p.
 Hill, R. (1965). A self-consistent mechanics of composite materials. Journal of the Mechanics and Physics of Solids, 13 (4), 213-222.
 Berryman, J. G. (1995). Mixture theories for rock properties. Rock physics and phase relations: A handbook of physical constants, American Geophysical Union, 3, 205-228.
 Misaghi, A., Negahban, S., Landrø, M., & Javaherian, A. (2010). A comparison of rock physics models for fluid substitution in carbonate rocks. Exploration Geophysics, 41 (2), 146-154.
 Berryman, J. G. (2007). Exact seismic velocities for transversely isotropic media and extended Thomsen formulas for stronger anisotropies. Geophysics, 73 (1), D1-D10.
 Budiansky, B. (1965). On the elastic moduli of some heterogeneous materials. Journal of the Mechanics and Physics of Solids, 13 (4), 223-227.
 Gassmann, F., Maggiorini, M., Städler, E., & Winkler, W. (1951). Verteljahrsschrift der Naturforschenden Gesellschaft in Zurich. Uber die elastizitat poroser medien, 96, 1-23.
 Schön, J. H. (2015). Physical properties of rocks: Fundamentals and principles of petrophysics (Vol. 65). Elsevier.
 Kuncheva, L. I. (2004). Combining pattern classifiers: methods and algorithms. John Wiley & Sons,350p.
 Torra, V., & Narukawa, Y. (2006). The interpretation of fuzzy integrals and their application to fuzzy systems. International Journal of Approximate Reasoning, 41 (1), 43-58.
 Yager, R. R. (2002). On the cardinality index and attitudinal character of fuzzy measures. International Journal of General Systems, 31 (3), 303-329.
 Ayub, M., (2009). Choquet and Sugeno Integrals. MSc. Thesis, Blekinge Institute of Technology, Sweden, 80p.
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