Mr. Jun Wang Profile

Jun Wang

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Proceedings Article | 19 November 2021 Paper

Layered low-frequency extrapolation with deep learning in full-waveform inversion

Yao Liu, Baodi Liu, Jianping Huang, Jun Wang, Honglong Chen, Weifeng Liu

Proceedings Volume 12059, 1205919 (2021) https://doi.org/10.1117/12.2612130

KEYWORDS: Data modeling, Fusion energy, Data fusion, Wavelets, Convolution, Machine learning, Statistical modeling, Statistical analysis, Signal attenuation, Neural networks

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The low-frequency information of seismic records can enhance the recognition ability of lithological bodies and make the inversion results clear and reliable. Affected by conventional acquisition technology, low-frequency information is usually missing in the imaging profile. Therefore, extrapolating low-frequency seismic data from band-limited seismic data is an important research topic in full-waveform inversion (FWI). Most of the existing methods directly use machine learning to extrapolate the low-frequency, but the amplitude of the seismic records will be greatly attenuated with the increase of offset and time. The energy gap of seismic records is wide, and the contribution of high-energy seismic records to the network weight is far greater than that of low-energy data. Therefore, it is difficult to extrapolate deep low-energy data. To solve this problem, we propose a method of layered low-frequency extrapolation with deep learning. The seismic records are divided into several layers according to the change of depth and the similarity of energy, and the convolutional neural network is used for training. Experimental results show that this method can accurately extrapolate low-frequency data, and the extrapolation data in the deep layer are close to true data in both the time domain and the frequency domain. In addition, this method occupies lesser computing resources and has the potential for field data application. We verify the effectiveness of the method through two datasets obtained from the Marmousi model and the overthrust model.

Proceedings Article | 19 November 2021 Paper

3D-pix2pix generative adversarial network for history matching with complex geologies

Yunxue Lü, Weifeng Liu, Kai Zhang, Baodi Liu, Yanjiang Wang, Jun Wang

Proceedings Volume 12059, 120591C (2021) https://doi.org/10.1117/12.2612149

KEYWORDS: 3D modeling, Data modeling, Visual process modeling, Process modeling, Gallium nitride, Convolution, Mathematical modeling, Geology, Visualization, Computer simulations

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Automatic history matching is a process of using an optimization algorithm to adjust the parameters of the reservoir model. The reservoir model can reproduce the historical performance of the reservoir and realize the prediction for future production. Accurate prediction of oil well performance guarantees to establish a reliable reservoir model, which is traditionally realized by ESMDA and ensemble Kalman filter. We design and implement history matching using a 3D-pix2pix generative adversarial network(3D-pix2pix GAN) structure for the first time, which can correct the parameters of the complex heterogeneous reservoir based on dynamic response. The adversarial generative network includes generator and discriminator. The generator attempts to use the fast feedforward operation of historical production data (input) to reconstruct the calibrated model, while the discriminator attempts to distinguish the pseudo output and the prior (real data) so that 3D-pix2pixGAN finally learns an infinitely close to the real reservoir model. The most significant contribution of this work is to train a 3D-pix2pixGAN model to correct reservoir model parameters. Compared with traditional work ow, 3D-pix2pixGAN has several advantages. First, the reservoir parameters estimated from history matching help to improve 3D reservoir characterization. Second, the reservoir obtained by history matching can accurately predict the future production of water and oil. Third, 3D-pix2pixGAN is used as a proxy model instead of using a numerical simulator in the training process to reduce the amount of computation and achieve end-to-end offline processing.

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