Model-constrained multi-phase virtual flow metering and forecasting with machine learning
A computer-implemented method for constrained multi-phase virtual flow metering and forecasting is described. The method includes predicting instantaneous flow rates and forecasting future target flow rates and well dynamics. The method includes constructing a virtual sensing model trained using forecasted target flow rates and well dynamics. The method includes building a constrained forecasting model by combining unconstrained flow forecasting models, well dynamics models, and virtual sensing models, wherein the constrained forecasting model forecasts multi-phase flow rates.
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.
2. The computer-implemented method of claim 1 , comprising combining the trained unconstrained flow models, the trained well dynamics models, and the at least one trained virtual sensing model by providing outputs of the trained unconstrained flow forecasting models and the trained well dynamics models as input features to the at least one trained virtual sensing model.
3. The computer-implemented method of claim 1 , wherein the unconstrained flow models are trained from historical flow rates of each individual target flow, or a combination of selected or all flows.
4. The computer-implemented method of claim 1 , wherein the unconstrained flow models are based on physics models, machine learning models, or any combinations thereof.
5. The computer-implemented method of claim 1 , wherein the well dynamics models are trained using historical sensor data.
6. The computer-implemented method of claim 1 , wherein the at least one trained virtual sensing model predicts instantaneous multi-phase flow rates based on historical flow rates and sensor data.
7. The computer-implemented method of claim 1 , wherein the virtual flow metering system enables virtual flow metering that predicts constraint flow rates as a function of time.
8. An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
predicting target flow rates for real time monitoring using unconstrained flow models trained using current and historic multi-phase flow rates;
predicting future auxiliary sensor data for real-time monitoring using trained well dynamics models;
constructing at least one virtual sensing model trained using predicted target flow rates and predicted auxiliary sensor data, wherein the at least one virtual sensing model predicts real-time constrained multi-phase flow rates; and
monitoring 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.
9. The apparatus of claim 8 , comprising combining the trained unconstrained flow models, the trained well dynamics models, and the at least one trained virtual sensing model by providing outputs of the trained unconstrained flow forecasting models and the trained well dynamics models as input features to the at least one trained virtual sensing model.
10. The apparatus of claim 8 , wherein the unconstrained flow is models are trained from historical flow rates of each individual target flow, or a combination of selected or all flows.
11. The apparatus of claim 8 , wherein the unconstrained flow is models are based on physics models, machine learning models, or any combinations thereof.
12. The apparatus of claim 8 , wherein the well dynamics models are trained using historical sensor data.
13. The apparatus of claim 8 , wherein the at least one trained virtual sensing model predicts instantaneous multi-phase flow rates based on historical flow rates and sensor data.
14. The apparatus of claim 8 , wherein the virtual flow metering system enables virtual flow metering that predicts constraint flow rates as a function of time.
15. A system, comprising:
one or more memory modules;
one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory models to perform operations comprising:
predicting target flow rates for real-time monitoring using unconstrained flow models trained using current and historic multi-phase flow rates;
predicting auxiliary sensor data for real-time monitoring using trained well dynamics models;
constructing at least one virtual sensing model trained using the predicted target flow rates and the predicted auxiliary sensor data, wherein the at least one virtual sensing model predicts real-time constrained multi-phase flow rates; and
monitoring 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.
16. The system of claim 15 , comprising the operations comprise combining the trained unconstrained flow models, the trained well dynamics models, and the at least one trained virtual sensing model by providing outputs of the trained unconstrained flow forecasting models and the trained well dynamics models as input features to the at least one trained virtual sensing model.
17. The system of claim 15 , wherein the operations comprise training the unconstrained flow models from historical flow rates of each individual target flow, or a combination of selected or all flows.
18. The system of claim 15 , wherein the operations comprise generating the unconstrained flow models based on physics models, machine learning models, or any combinations thereof.
19. The system of claim 15 , wherein the well dynamics models are trained using historical sensor data.
20. The system of claim 15 , wherein the at least one trained virtual sensing model predicts instantaneous multi-phase flow rates based on historical flow rates and sensor data.