Building large-scale density model via a deep-learning-based data-driven method
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Published:2021-01-01
Issue:1
Volume:86
Page:M1-M15
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ISSN:0016-8033
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Container-title:GEOPHYSICS
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language:en
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Short-container-title:GEOPHYSICS
Author:
Gao Zhaoqi1ORCID, Li Chuang1ORCID, Zhang Bing1, Jiang Xiudi2, Pan Zhibin3, Gao Jinghuai1ORCID, Xu Zongben4
Affiliation:
1. Xi’an Jiaotong University, School of Information and Communications Engineering, Xi’an, Shaanxi 710049, China and Xi’an Jiaotong University, National Engineering Laboratory for Offshore Oil Exploration, Xi’an, Shaanxi 710049, China.(corresponding author); . 2. CNOOC Research Institute, Beijing 100028, China.. 3. Xi’an Jiaotong University, School of Information and Communications Engineering, Xi’an, Shaanxi 710049, China.. 4. Xi’an Jiaotong University, School of Mathematics and Statistics, Xi’an, Shaanxi 710049, China..
Abstract
As a rock-physics parameter, density plays a crucial role in lithology interpretation, reservoir evaluation, and description. However, density can hardly be directly inverted from seismic data, especially for large-scale structures; thus, additional information is needed to build such a large-scale model. Usually, well-log data can be used to build a large-scale density model through extrapolation; however, this approach can only work well for simple cases and it loses effectiveness when the medium is laterally heterogeneous. We have adopted a deep-learning-based method to build a large-scale density model based on seismic and well-log data. The long short-term memory network is used to learn the relation between seismic data and large-scale density. Except for the data pairs directly obtained from well logs, many velocity and density models randomly generated based on the statistical distributions of well logs are also used to generate several pairs of seismic data and the corresponding large-scale density. This can greatly enlarge the size and diversity of the training data set and consequently leads to a significant improvement of the proposed method in dealing with a heterogeneous medium even though only a few well logs are available. Our method is applied to synthetic and field data examples to verify its performance and compare it with the well extrapolation method, and the results clearly display that the proposed method can work well even though only a few well logs are available. Especially in the field data example, the built large-scale density model of the proposed method is improved by 11.9666 dB and 0.6740, respectively, in peak signal-to-noise ratio and structural similarity compared with that of the well extrapolation method.
Funder
National Science and Technology Major Project National Postdoctoral Program for Innovative Talents National Natural Science Foundation of China National Key RD Program of the Ministry of Science and Technology of China
Publisher
Society of Exploration Geophysicists
Subject
Geochemistry and Petrology,Geophysics
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