Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

Deng, Yaping; Kang, Xueyuan; Ma, Haichun; Qian, Jiazhong; Ma, Lei; Luo, Qiankun

doi:10.1016/j.jhydrol.2024.130819

Journal of Hydrology

2024

DOI: 10.1016/j.jhydrol.2024.130819

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Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

Yaping Deng,

Xueyuan Kang,

Haichun Ma

et al.

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Cited by 3 publications

References 48 publications

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Source localization in subsurface aquifers based on conservation data by learning a Gaussian kernel

Feng,

Temani,

Dudun

2024

Comput Geosci

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Source localization in subsurface aquifers based on conservation data by learning a Gaussian kernel

Feng,

Temani,

Dudun

2024

Comput Geosci

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Enhancing hydraulic conductivity field characterization through integration of hydraulic head and tracer data using multi-modal neural network models

Wang,

Dou,

Zhu

et al. 2025

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Machine-Learned Inference of Fracture Flowrate From Temperature Logs

Yang,

Horne,

Tartakovsky

2024

J Mach Learn Model Comput

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Reliable identification of a fracture flowrate is essential to successful reservoir exploration and optimization. We developed a machine learning approach to identify fracture flowrate from spatiotemporal wellbore temperature measurements. A long short-term memory fully convolutional network was employed to jointly detect fractures intersecting the wellbore and quantify their contribution to the overall flow during fluid injection. Training data for the algorithm were generated by a wellbore and fractured-reservoir thermal model. The machine learning algorithm trained on single injectionstage temperature shows high fracture flowrate estimation accuracy on synthetic validation cases. It outperforms an optimization-based particle swarm optimization method on a real field case. For the machine learning method, the fusion of various-stage temperature as the input feature set improves the robustness of fracture detection and flowrate estimation to noise interference.

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Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion

Cited by 3 publications

References 48 publications

Source localization in subsurface aquifers based on conservation data by learning a Gaussian kernel

Source localization in subsurface aquifers based on conservation data by learning a Gaussian kernel

Enhancing hydraulic conductivity field characterization through integration of hydraulic head and tracer data using multi-modal neural network models

Machine-Learned Inference of Fracture Flowrate From Temperature Logs

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