User interface for proxy modeling of reactive transport modeling

A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

• 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);
• receiving user selections of training parameters for generating training sample data, and generating the training sample data in response to receiving a parameter files generation indication from the user;
• receiving a training cases generation indication from the user, and executing training sample cases using the training sample data;
• receiving user selections of proxy models training parameters, and in response to receiving training selection indications, training a set of parameter-specific proxy models represented by a neural network, wherein each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters;
• receiving user selections of blind tests to be run, and performing blind tests using the set of parameter-specific proxy models, wherein each blind test tests a specific one of the parameter-specific proxy models;
• and
• receiving user selections for generating predictions, and generating predictions using the set of parameter-specific proxy models.

A computer-implemented system, comprising:

• one or more processors;
• and
• a non-transitory computer-readable storage medium coupled to the one or more processors and storing programming instructions for execution by the one or more processors, the programming instructions instructing the one or more processors to perform operations comprising:
• 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);
• receiving user selections of training parameters for generating training sample data, and generating the training sample data in response to receiving a parameter files generation indication from the user;
• receiving a training cases generation indication from the user, and executing training sample cases using the training sample data;
• receiving user selections of proxy models training parameters, and in response to receiving training selection indications, training a set of parameter-specific proxy models represented by a neural network, wherein each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters;
• receiving user selections of blind tests to be run, and performing blind tests using the set of parameter-specific proxy models, wherein each blind test tests a specific one of the parameter-specific proxy models;
• and
• receiving user selections for generating predictions, and generating predictions using the set of parameter-specific proxy models.