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Enhancing model parameterization with linearly constrained deep generative network for ensemble-based history matching
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AI
DOI：10.1016/j.aiig.2026.100201.png)
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Abstract
En 中文
Ensemble-based data assimilation methods have been widely used for history matching in subsurface reservoir modeling, but struggle to handle the complex nonlinear and non-Gaussian behaviors prevalent in real field applications. To address these limitations, this paper introduces a deep generative model-based parameterization that effectively reduces dimensionality, preserves non-Gaussian patterns, and enhances linearity with observational data. We propose a regularized variational autoencoder (RVAE) comprising three integrated components: (1) an encoder that projects high-dimensional reservoir model parameters into a low-dimensional latent space, capturing complex non-Gaussian distributions; (2) a decoder that maps latent variables back to the original model space, ensuring accurate and geologically consistent reconstruction; and (3) a lightweight linear subnetwork that imposes additional regularization on the latent space, enforcing a linear relationship with observational data. This RVAE framework strengthens ensemble-based methods by aligning the parameterization more closely with their linear-Gaussian assumptions, thereby enhancing compatibility and improving history matching accuracy. Given the high computational cost and time typically required for forward reservoir simulations, we adopt a semi-supervised learning approach, utilizing a dataset where only a small subset of the generated model realizations is paired with production data derived from these simulations. For the network design, the core architecture of the RVAE is based on a convolutional DenseNet, integrated with an attention mechanism to optimize feature representation. Experimental results demonstrate that the proposed approach effectively captures non-Gaussian patterns in permeability fields and significantly improves the assimilation of highly nonlinear production data.
Keywords:
Variational autoencoder
Ensemble methods
Data assimilation
Semi-supervised learning
Attention mechanism
Journal
A
IF:
4.2
Papers:
46
Citations:
0