Abstract
Well log interpretation is an important task in the process of petroleum logging. It is able to help the researchers to determine the residual oil volume and to improve the petroleum productivity efficiency. Well log interpretation requires the synthesis of a large amount of data, and it is difficult to manually browse the data from a global perspective. It is urgent to introduce big data analysis methods to deal with the complex oil well logs data. The accuracy of logging interpretation greatly depends on the logging features selection and representation. However, the conventional methods using expert experiences easily lead to feature incomplete problem and affects the interpretation results. In this paper, we propose a deep neural network based feature learning method for well log interpretation. Firstly, we select original features of the well log data according to the physical characteristics of well logging sensors. And then, we formulate a deep neural network based autoencoder model to explore the intrinsic representation of original features. At last, we utilize linear SVM classifier on well log interpretation problem to evaluate the proposed feature learning method. The experimental results demonstrate that the classification accuracy by using learned feature representation increase to \(99.8\%\) compared with that of \(74.6\%\) by using original feature representation.
This work was supported in part by the National Key R&D Program of China (No. 2018AAA0102801 and No. 2018AAA0102803), in part of the National Natural Science Foundation of China (No. 61772424, No. 61702418, and No. 61602383), and in part by 13th Five-Year Plan of China National Petroleum Corporation Limited (2019A-3610).
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Bao, L., Cao, X., Yu, C., Zhang, G., Zhou, W. (2021). A Deep Neural Network Based Feature Learning Method for Well Log Interpretation. In: Li, B., Li, C., Yang, M., Yan, Z., Zheng, J. (eds) IoT as a Service. IoTaaS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-67514-1_43
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