Method and system for identifying real plant broadband dynamics performance in green energy generation utilizing artificial intelligence technology

A well system, comprising:

• a well site;
• a physics-based modeling server that outputs modeled dynamics behavior 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 dynamics behavior data of the well site;
• obtains modeled well dynamics behavior data for the well system using a physics-based model;
• decomposes each of the well dynamics behavior data and the modeled well dynamics behavior data into a plurality of frequency band components based on a plurality of predetermined frequency partitions;
• trains a physics constrained machine learning model using one or more machine learning algorithms based on the plurality of frequency band components of the decomposed well dynamics behavior data and the decomposed modeled well dynamics behavior data as input data;
• obtains new well dynamics behavior data of the well site;
• outputs predicted well dynamics behavior data based on the new well dynamics behavior data using the physics-based model and the trained physics constrained machine learning model;
• and
• transmits a command to the well system that adjusts a well operation based on the predicted well dynamics behavior data.

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

• obtaining well dynamics behavior data of a well system based on a predetermined monitoring criterion;
• obtaining modeled well dynamics behavior data for the well system using a physic s-based model;
• decomposing each of the well dynamics behavior data and the modeled well dynamics behavior data into a plurality of frequency band components based on a plurality of predetermined frequency partitions;
• training a physics constrained machine learning model using one or more machine learning algorithms based on the plurality of frequency band components of the decomposed well dynamics behavior data and the decomposed modeled well dynamics behavior data as input data;
• obtaining new well dynamics behavior data of the well system;
• outputting predicted well dynamics behavior data for the well system based on the new well dynamics behavior data using the physics-based model 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 dynamics behavior data.