Deep learning assisted well log inversion for fracture identification
| العنوان: | Deep learning assisted well log inversion for fracture identification |
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| المؤلفون: | Xiaozheng Lang, Miao Tian, Huaimin Xu, Bingtao Li, Dezhi Yan, Yining Gao |
| المصدر: | Geophysical Prospecting. 69:419-433 |
| بيانات النشر: | Wiley, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Discrete wavelet transform, 010504 meteorology & atmospheric sciences, Artificial neural network, business.industry, Computer science, Deep learning, Pattern recognition, 010502 geochemistry & geophysics, 01 natural sciences, Autoencoder, Convolutional neural network, Random forest, Support vector machine, Geophysics, Geochemistry and Petrology, Artificial intelligence, AdaBoost, business, 0105 earth and related environmental sciences |
| الوصف: | Manual fracture identification methods based on cores and image logging pseudo‐pictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end‐to‐end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non‐overlapping subsections by estimating boundaries. The deep neural network based auto‐encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non‐fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one‐dimensional well profile predicted by the proposed changing point detection‐deep learning classifier is more consistent with the manual identification result. |
| تدمد: | 1365-2478 0016-8025 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f99f34b383a30947d28743bf2fc09753 https://doi.org/10.1111/1365-2478.13054 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........f99f34b383a30947d28743bf2fc09753 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 13652478 00168025 |
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