Method and system for forecasting real-time well mass flow in green energy generation utilizing digital twin technology

A well system, comprising:

• a well site;
• a physics-based modeling server that outputs modeled well mass flow data for the well site based on a physics-based model;
• and
• a digital twin manager, coupled to the physics-based modeling server and the well site, that includes a processor,
• wherein the digital twin manager:
• obtains, based on a predetermined monitoring criterion, well mass flow data of the well site;
• obtains modeled well mass flow data for the well site using the physics-based model;
• trains a physics constrained machine learning model using one or more machine learning algorithms based on the well mass flow data and the modeled well mass flow data as inputs;
• obtains real-time well mass flow data of the well site;
• outputs predicted well mass flow data using the real-time well mass flow data and the trained physics constrained machine learning model;
• and
• transmits a command to the well site that adjusts a well operation based on the predicted well mass flow data.

A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

• obtaining well mass flow data of a well system based on a predetermined monitoring criterion;
• obtaining modeled well mass flow data for the well system using a physics-based model;
• training a physics constrained machine learning model using one or more machine learning algorithms based on the well mass flow data and the modeled well mass flow data as inputs;
• obtaining real-time well mass flow data of the well system;
• outputting predicted well mass flow data using the real-time well mass flow data and the trained physics constrained machine learning model;
• and
• transmitting a command to the well system that adjusts a well operation based on the predicted well mass flow data.