An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media
TLDR
The results show that TNNA‐AUS successfully reduces the inversion bias and improves the computational efficiency and inversion accuracy, compared with the global improvement strategy of adding training samples according to the prior distribution of model parameters.About:
This article is published in Water Resources Research.The article was published on 2021-11-10 and is currently open access. It has received 25 citations till now. The article focuses on the topics: Deep learning.read more
Citations
More filters
Journal ArticleDOI
An Integrated Inversion Framework for Heterogeneous Aquifer Structure Identification with Single-Sample Generative Adversarial Network
Chuanjun Zhan,Zhenxue Dai,Javier Samper,Shangxian Yin,Reza Ershadnia,Xiaoying Zhang,Yanwei Wang,Zhijie Yang,Xiaoyan Luan,Mohamad Reza Soltanian +9 more
TL;DR: In this paper , the authors developed an integrated inversion framework, which combines the geological single-sample generative adversarial network (GeoSinGAN), the deep octave convolution dense residual network (DOCRN), and the iterative local updating ensemble smoother (ILUES), named GeoSinGAN-DOCRN-ILUES, for more efficiently generating heterogeneous aquifer structures.
Journal ArticleDOI
An upscaling approach to predict mine water inflow from roof sandstone aquifers
Lulu Xu,Meifeng Cai,Shun Dong,Shangxian Yin,Ting Xiao,Zhenxue Dai,Yanwei Wang,Mohamad Reza Soltanian +7 more
TL;DR: Wang et al. as discussed by the authors developed a novel upscaling framework to predict roof water inflow by integrating the multiscale hydrogeological properties of roof aquifers, which can guide the development of methods for considering micropores and fractures simultaneously.
Journal ArticleDOI
An integrated experimental design framework for optimizing solute transport monitoring locations in heterogeneous sedimentary media
Zhenxue Dai,Ziqi Ma,Xiaoying Zhang,Junjun Chen,Reza Ershadnia,Xiaoyan Luan,Mohamad Reza Soltanian +6 more
TL;DR: In this article , an integrated framework is developed to guide the monitoring network optimization and duration selection for solute transport in heterogeneous sand tank experiments, which is designed based on entropy and data worth analysis.
Data‐Worth Analysis for Heterogeneous Subsurface Structure Identification With a Stochastic Deep Learning Framework
TL;DR: In this paper , the authors extended a recently developed stage-wise stochastic deep learning inversion framework by coupling it with nonisothermal flow and transport simulations to estimate subsurface structures.
References
More filters
Journal ArticleDOI
An adaptive sparse-grid high-order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling
TL;DR: A new approach is developed to improve the computational efficiency of Bayesian inference by constructing a surrogate of the PPDF, using an adaptive sparse‐grid high‐order stochastic collocation (aSG‐hSC) method, resulting in a significant reduction in the number of required model executions.
Journal ArticleDOI
Efficient Bayesian experimental design for contaminant source identification
TL;DR: In this paper, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants, where the sampling locations that give the maximum expected relative entropy are selected as the optimal design.
Journal ArticleDOI
Co‐optimization of CO2‐EOR and storage processes in mature oil reservoirs
William Ampomah,Robert Balch,Reid B. Grigg,Brian McPherson,Robert Will,Si Yong Lee,Zhenxue Dai,Feng Pan +7 more
TL;DR: In this article, a field-scale compositional reservoir flow model was developed for assessing the performance history of an active CO2 flood and for optimizing both oil production and CO2 storage in the Farnsworth Unit (FWU), Ochiltree County, Texas.
Journal ArticleDOI
A deep convolutional neural network model for rapid prediction of fluvial flood inundation
TL;DR: An innovative modelling approach based on a deep convolutional neural network (CNN) method for rapid prediction of fluvial flood inundation and shows that the CNN model outperforms SVR by a large margin.
Journal ArticleDOI
An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems
TL;DR: In this paper, the authors integrate Gaussian process and MCMC to adaptively construct locally accurate surrogates for Bayesian experimental design in groundwater contaminant source identification problems, and the uncertainty estimate of GP approximation error is incorporated in the Bayesian formula to avoid over-confident estimation of the posterior distribution.