Logging Curve Recovery Based on LSTM Multi-scale Symbiotic Relationship Mining

HAN Jian; LI Jing; CAO Zhimin; GAO Pan

doi:10.11999/JEIT210424

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2559-2567

HAN Jian, LI Jing, CAO Zhimin, GAO Pan. Logging Curve Recovery Based on LSTM Multi-scale Symbiotic Relationship Mining[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2559-2567. doi: 10.11999/JEIT210424

Citation:

HAN Jian, LI Jing, CAO Zhimin, GAO Pan. Logging Curve Recovery Based on LSTM Multi-scale Symbiotic Relationship Mining[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2559-2567. doi: 10.11999/JEIT210424

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Logging Curve Recovery Based on LSTM Multi-scale Symbiotic Relationship Mining

doi: 10.11999/JEIT210424

1.
School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing 163318, China
2.
Research and Development Center for Testing and Measurement Technology and Instrumentation, Heilongjiang Province Universities, Northeast Petroleum University, Daqing 163318, China

Funds: The National Natural Science Foundation of China (51574087)

Received Date: 2021-05-18
Accepted Date: 2022-01-12
Rev Recd Date: 2021-12-16

Available Online: 2022-02-02

Publish Date: 2022-07-25

Abstract

Abstract

In the application of reservoir geological description using logging data, some of the logging curves are often distorted or missing, and for this reason, the recovery of logging curves is a research hotspot and difficulty in related research fields. Traditional signal recovery methods and recovery methods based on machine learning such as neural networks do not adequately represent and utilize correlation information between different logging curves of the same well, and have poor adaptability to cross-well models. In response to these problems, a log curve restoration method is proposed in this paper based on Long Short-Term Memory (LSTM) neural network multi-scale symbiosis mining: on the basis of neural network log curve restoration method, by introducing the multi-scale Gray Level Co-occurrence Matrix (GLCM) completes the characterization of the lateral correlation information between different logging curves so as to realize the full utilization of the vertical and horizontal semantic information of the logging curve set, thereby realizing the restoration of missing logging curves. The experimental results show that, compared with the BP neural network, Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Deep Forest (DF) and LSTM network methods, the method proposed in this paper has better signal restoration accuracy, and the constructed model has a certain cross-well adaptability.
- Signal recovery,
- Logging curves,
- Long Short Term Memory (LSTM) neural network,
- Gray Level Co-occurrence Matrix (GLCM)

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References(16)

References

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