Abstract
Formation Micro Imager (FMI) can directly reflect changes of wall stratum and rock structures. It is also an important method to divide stratum and identify lithology. However, people usually deal with FMI images manually, which is extremely inefficient and may incur heavy burdens in practice. In this paper, with characteristics of rock structures from FMI images, we develop an efficient and intelligent rock structure recognition system by engaging image processing and pattern recognition technologies. First, we choose the most effective color and shape features for rock images. Then, the corresponding single classifier is designed to recognize the FMI images. Finally, all these classifiers are combined to construct the recognition system. Experimental results show that our system is able to achieve promising performance and significantly reduce the complexity and difficulty of the rock structure recognition task.
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References
Singh, U., Van Der Baan, D.: FMS/FMI borehole imaging of carbonate gas reservoirs, Central Luconia Province, offshore Sarawak, Malaysia. In: 1994 American Association of Petroleum Geologists (AAPG) international conference and exhibition, Kuala Lumpur, Malaysia, pp. 1162–1163. AAPG Bulletin (2001)
Laubach, S.E., Gale, J.F.W.: Obtaining Fracture Information for Low-Permeability (Tight) Gas Sandstones from Sidewall Cores. Journal of Petroleum Geology 29(2), 147–158 (2006)
Endres, H., Lohr, T., Trappe, H.: Quantitative fracture prediction from seismic data. Petroleum Geoscience 14, 369–377 (2008)
Payenberg, T.H.D., Lang, S.C., Koch, R.: A Simple Method for Orienting Conventional Core Using Microresistivity (FMS) Images and a Mechanical Goniometer to Measure Directional Structures on Cores. Journal of Sedimentary Research 70, 419–422 (2000)
Russell, S.D., Akbar, M., Vissapragada, B., Walkden, G.M.: Rock Types and Permeability Prediction from Dipmeter and Image Logs: Shuaiba Reservoir (Aptian), Abu Dhabi. AAPG Bulletin 86, 1709–1732 (2002)
Roli, F., Giacinto, G., Vernazza, G.: Methods for Designing Multiple Classifiers Systems. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 78–87. Springer, Heidelberg (2001)
Chen Lei, K.: A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems. Pattern Recognition 42(5), 629–644 (2009)
Kang, H.J., David, D.: Selection of classifiers for the construction of multiple classifier systems. In: 8th International Conference on Document Analysis and Recognition, Seoul, Korea, pp. 1194–1198 (2005)
Ruta, D., Gabrys, B.: Classifier selection for majority voting. Information Fusion 6(1), 63–81 (2005)
Ruta, D., Gabrys, B.: Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 399–408. Springer, Heidelberg (2001)
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Yin, XC., Liu, Q., Hao, HW., Wang, ZB., Huang, K. (2009). A Rock Structure Recognition System Using FMI Images. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_95
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DOI: https://doi.org/10.1007/978-3-642-10677-4_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
Online ISBN: 978-3-642-10677-4
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