Wenlong Liao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Wenlong Liao has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Bin Zhao
College of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China
2 shared publications Chuqiao Gao
1 shared publication- Chuqiao Gao
Laboratory of Exploration Technologies for Oil and Gas Resources, Ministry of Education, Yangtze University, Wuhan 430113, China
1 shared publication Hangyu Zhu
1 shared publicationHuanhuan Wang
1 shared publication- Jiadi Fang
Hangqing Branch, China Petroleum Logging Co., Ltd., Xi’an 710005, China
1 shared publication
Latest Publications
- Open Access
- 30 March 2025
- Published by Elsevier in Geoenergy Science and Engineering
- Vol. 251
Application of domain-adaptive network with data augmentation in lithology identification of buried hill igneous rocks
- 30 March 2025
- Published by Elsevier in Geoenergy Science and Engineering
- Vol. 251
Open AccessApplication of domain-adaptive network with data augmentation in lithology identification of buried hill igneous rocks
- Open Access
A TCN-BiGRU Density Logging Curve Reconstruction Method Based on Multi-Head Self-Attention Mechanism
In the process of oil and natural gas exploration and development, density logging curves play a crucial role, providing essential evidence for identifying lithology, calculating reservoir parameters, and analyzing fluid properties. Due to factors such as instrument failure and wellbore enlargement, logging data for some well segments may become distorted or missing during the actual logging process. To address this issue, this paper proposes a density logging curve reconstruction model that integrates the multi-head self-attention mechanism (MSA) with temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU). This model uses the distance correlation coefficient to determine curves with a strong correlation to density as a model input parameter and incorporates stratigraphic lithology indicators as physical constraints to enhance the model’s reconstruction accuracy and stability. This method was applied to reconstruct density logging curves in the X depression area, compared with several traditional reconstruction methods, and verified through core calibration experiments. The results show that the reconstruction method proposed in this paper exhibits high accuracy and generalizability.
Open AccessA TCN-BiGRU Density Logging Curve Reconstruction Method Based on Multi-Head Self-Attention Mechanism
In the process of oil and natural gas exploration and development, density logging curves play a crucial role, providing essential evidence for identifying lithology, calculating reservoir parameters, and analyzing fluid properties. Due to factors such as instrument failure and wellbore enlargement, logging data for some well segments may become distorted or missing during the actual logging process. To address this issue, this paper proposes a density logging curve reconstruction model that integrates the multi-head self-attention mechanism (MSA) with temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU). This model uses the distance correlation coefficient to determine curves with a strong correlation to density as a model input parameter and incorporates stratigraphic lithology indicators as physical constraints to enhance the model’s reconstruction accuracy and stability. This method was applied to reconstruct density logging curves in the X depression area, compared with several traditional reconstruction methods, and verified through core calibration experiments. The results show that the reconstruction method proposed in this paper exhibits high accuracy and generalizability.