User interface for proxy modeling of reactive transport modeling

Abstract

Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.