Hello.

Any idea to choose a suitable journal for a manuscript, whose abstract is given below? More specifically, a high-quality journal that makes a final decision within three months or so.

Thank you so much.

In multiscale modeling of subsurface fluid flow in heterogeneous porous media, standard polynomial basis functions are replaced by multiscale basis functions. For instance, to produce such functions in the mixed Generalized Multiscale Finite Element Method (mixed GMsFEM), a number of Partial Differential Equations (PDEs) must be solved. Thus, it makes sense to replace PDE solvers with data-driven methods given their great capabilities and general acceptance in recent decades. The main purpose of the present study was to develop four distinct Convolutional Neural Network (CNN) models to predict four different multiscale basis functions for the mixed GMsFEM. These models were applied to 249,375 samples, with the permeability field as the only input. The statistical results indicate that the AMSGrad optimization algorithm with a coefficient of determination (R2) of 0.8434 - 0.9165 and Mean Squared Error (MSE) of 0.0078 - 0.0206 performs slightly better than Adam with an R2 of 0.8328 - 0.9049 and MSE of 0.0109 - 0.0261. Graphically, all models precisely follow the observed trend in each coarse block. This work could contribute to many domains, especially the determination of pressure, velocity, and saturation in the development of oil/gas fields. Looking at this work as an image (matrix)-to-image (matrix) regression problem, the constructed data-driven-based models may have applications beyond reservoir engineering, such as hydrogeology and rock mechanics.