Predicting part deformation based on deformation force data using Physics-informed Latent Variable Model

Production forecasting is the basis for decision making in the oil and gas industry, and can be quite challenging, especially in terms of complex geological modeling of the subsurface. To help solve this problem, assisted history matching built on ensemble-based analysis such as the ensemble smoother and ensemble Kalman filter is useful in estimating models that preserve geological realism and have predictive capabilities. These methods tend, however, to be computationally demanding, as they require a large ensemble size for stable convergence. In this paper, we propose a novel method of uncertainty quantification and reservoir model calibration with much-reduced computation time. This approach is based on a sequential combination of nonlinear dimensionality reduction techniques: t-distributed stochastic neighbor embedding or the Gaussian process latent variable model and clustering K-means, along with the data assimilation method ensemble smoother with multiple data assimilation. The cluster analysis with t-distributed stochastic neighbor embedding and Gaussian process latent variable model is used to reduce the number of initial geostatistical realizations and select a set of optimal reservoir models that have similar production performance to the reference model. We then apply ensemble smoother with multiple data assimilation for providing reliable assimilation results. Experimental results based on the Brugge field case data verify the efficiency of the proposed approach.

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Chinese Character Image Completion Using a Generative Latent Variable Model

Applied Sciences ◽

10.3390/app11020624 ◽

2021 ◽

Vol 11 (2) ◽

pp. 624

Author(s):

In-su Jo ◽

Dong-bin Choi ◽

Young B. Park

Keyword(s):

Latent Variable ◽

Chinese Character ◽

Image Inpainting ◽

Latent Variable Model ◽

Chinese Characters ◽

Image Completion ◽

Variable Model ◽

Variational Autoencoder ◽

Convolution Model ◽

Detection Technologies

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.

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