Method and system for predicting real plant dynamics performance in green energy generation utilizing physics and artificial neural network models

A well system, comprising:

• a well site;
• a physics-based model 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, first dynamics behavior data of the well site, where the first dynamics behavior data includes a plurality of measurements comprising:
• an incomplete measurement that is missing well mass flow data for a time interval;
• and
• a complete measurement that includes well pressure and well temperature performance data for the time interval;
• obtains modeled dynamics behavior data for the well site using a physics-based model that models dynamics behavior including well mass flow, well pressure, and well temperature performance, where the physics-based model includes:
• an initial stage that models dynamics behavior from a model reservoir including a reservoir flow restriction module;
• a second stage that models dynamics behavior from a constant volume midstream chamber, including a midstream flow restriction module, connected to the model reservoir;
• a connection branch stage that models dynamics behavior from the constant volume midstream chamber connected to a model well head;
• a terminal stage that models dynamics behavior from a constant volume chamber including a terminal flow restriction model in the model well head;
• and
• a valve module that corresponds to and emulates a control mechanism of the well system;
• trains a physics constrained machine learning model using one or more machine learning algorithms based on the first dynamics behavior data and the modeled dynamics behavior data corresponding to the first dynamics behavior data as inputs;
• obtains, by the digital twin manager, second dynamics behavior data of the well system;
• updates the second dynamics behavior data of the well site to complete the missing well mass flow data of the incomplete measurement for the time interval based on the physics-based model and the physics constrained machine learning model;
• outputs updated dynamics behavior data for the well site;
• and
• adjusts the control mechanism of the well system to maintain well mass flow performance of the well system based on the updated dynamics behavior data.

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

• obtaining first dynamics behavior data of a well system based on a predetermined monitoring criterion, where the first dynamics behavior data includes a plurality of measurements comprising:
• an incomplete measurement that is missing well mass flow data for a time interval;
• and
• a complete measurement that includes well pressure and well temperature performance data for the time interval;
• obtaining modeled dynamics behavior data for the well system using a physics-based model using a physics-based model that models dynamics behavior including well mass flow, well pressure, and well temperature performance, where the physics-based model includes:
• an initial stage that models dynamics behavior from a model reservoir including a reservoir flow restriction module;
• a second stage that models dynamics behavior from a constant volume midstream chamber, including a midstream flow restriction module, connected to the model reservoir;
• a connection branch stage that models dynamics behavior from the constant volume midstream chamber connected to a model well head;
• a terminal stage that models dynamics behavior from a constant volume chamber including a terminal flow restriction model in the model well head;
• and
• a valve module that corresponds to and emulates a control mechanism of the well system;
• training a physics constrained machine learning model using one or more machine learning algorithms based on the dynamics behavior data and the modeled dynamics behavior data corresponding to the first dynamics behavior data as inputs;
• obtaining second dynamics behavior data of the well system;
• updating the second dynamics behavior data to complete the missing well mass flow data of the incomplete measurement for the time interval based on the physics-based model and the physics constrained machine learning model;
• outputting updated dynamics behavior data for the well system;
• and
• adjusting the control mechanism of the well system to maintain well mass flow performance of the well system based on the updated dynamics behavior data.