US 12,435,908 B2
Method and system for predicting real plant dynamics performance in green energy generation utilizing physics and artificial neural network models
Othman Elkhomri, Wilmington, DE (US)
Assigned to Banpu Innovation & Ventures LLC, Wilmington, DE (US)
Filed by Banpu Innovation & Ventures LLC, Wilmington, DE (US)
Filed on Nov. 10, 2021, as Appl. No. 17/454,360.
Prior Publication US 2023/0144359 A1, May 11, 2023
Int. Cl. F24T 10/20 (2018.01); E21B 41/00 (2006.01)
CPC F24T 10/20 (2018.05) [E21B 2200/22 (2020.05); F24T 2201/00 (2018.05)] 18 Claims
OG exemplary drawing
 
1. A method of managing a well system, comprising:
obtaining, by a digital twin manager and based on a predetermined monitoring criterion, first dynamics behavior data of the well system, 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, by the digital twin manager, modeled dynamics behavior data for the well system 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, by the digital twin manager, 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;
obtaining, by the digital twin manager, second dynamics behavior data of the well system;
updating, by the digital twin manager, 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, by the digital twin manager, 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.