Abstract
The technology for reservoir structure identification has become a challenging problem in the field of imaging logging technology. Because of the huge amount of information and a wide variety, it causes experts with low efficiency on the interpretation of reservoir evaluation and the performance depends highly on the individual experience (including cognitive level, visual decision, etc.). We proposed a new method for texture feature extraction based on macro and micro features. About 3320 imaging logging datasets are fed to support vector machine (SVM) to validate the gains of new method. As a result, the new proposed method achieved an Area Under roc Curve (AUC) value of 0.94.
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The experimental databases are provided by the China Petroleum Exploration and Development Institute. Here to thank the data support.
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Gao, H. et al. (2016). A Novel Texture Extraction Method for the Sedimentary Structures’ Classification of Petroleum Imaging Logging. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_14
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DOI: https://doi.org/10.1007/978-981-10-3005-5_14
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