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 appl...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.展开更多
文摘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.
