US 12,085,687 B2
Model-constrained multi-phase virtual flow metering and forecasting with machine learning
Tao Lin, Katy, TX (US); Weichang Li, Katy, TX (US); Muhammad Arsalan, Dhahran (SA); and Abdulla Al Sarraf, Dhahran (SA)
Assigned to Saudi Arabian Oil Company, Dhahran (SA)
Filed by Saudi Arabian Oil Company, Dhahran (SA)
Filed on Jan. 10, 2022, as Appl. No. 17/571,907.
Prior Publication US 2023/0221460 A1, Jul. 13, 2023
Int. Cl. G01V 20/00 (2024.01); G06F 30/27 (2020.01); G06F 30/28 (2020.01); G06N 20/00 (2019.01); G06F 113/08 (2020.01)
CPC G01V 20/00 (2024.01) [G06F 30/27 (2020.01); G06F 30/28 (2020.01); G06N 20/00 (2019.01); G06F 2113/08 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for model constrained multi-phase virtual flow metering and forecasting, the method comprising:
forecasting, with one or more hardware processors, target flow rates for real-time monitoring using unconstrained flow models trained using current and historic multi-phase flow rates;
forecasting, with the one or more hardware processors, auxiliary sensor data for real-time monitoring using trained well dynamics models;
constructing, with the one or more hardware processors, at least one virtual sensing model trained using the forecast target flow rates and the forecast auxiliary sensor data, wherein the at least one virtual sensing model predicts real-time constrained multi-phase flow rates; and
monitoring, with the one or more hardware processors, the predicted real-time constrained multi-phase flow rates output by the at least one virtual sensing model of a virtual flow metering system that enables production planning based on the real-time constrained multi-phase flow rates.