The present disclosure describes the use of growth models and data driven models that are combined for quickly and efficiently modeling SAGD reservoir oil production. Growth function surrogate models are used for efficient and reliable reservoir modeling and production forecasting as opposed to CPU intensive simulations based on finite difference models. A data-driven technique can then compare the growth function surrogate model with real field data to find discrepancies and inconsistencies between the two, allowing for an updates and improvements of the growth function model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of predicting a hydrocarbon production from a reservoir, said method comprising i) fitting temporal hydrocarbon production data from an enhanced oil recovery process with a growth function model to achieve the smallest residual error, as determined by a data-driven model, and ii) forecasting hydrocarbon production from said an enhanced oil recovery process. 2. An improved method of predicting a hydrocarbon production from a reservoir by i) inputted reservoir parameters and historical field data into a computer and ii) modeling hydrocarbon production from a reservoir based on said parameters and data, the improvement comprising using a growth function model and data driven model that are combined to form a surrogate model for said modeling step. 3. The method of claim 1 or 2, wherein said growth function model is a modified Gompertz model using Equation 1: y(t)=c·e−ae−bt−c·e−a 4. The method of claim 0 or 2, wherein said growth function model is a modified Gompertz growth function model and said data driven model is an ARMAX model. 5. A method for predicting a hydrocarbon production from a reservoir comprising: a) inputting production data for one or more production parameters into a computer; b) applying a growth function model to said production data, wherein said growth function uses said one or more production parameters; c) applying a data driven model to a residual of said growth function model; d) combining said growth function model and said data driven model to form a surrogate model; and e) predicting a future production of hydrocarbon with said surrogate model. 6. The method of claim 5, wherein said growth function model is a tumor growth model. 7. The method of claim 5, wherein said growth function model is selected from logistic, Gompertz, Schnute and Stannard, Logistic, Richards or a variation or combination thereof. 8. The method of claim 5, wherein said growth function model is selected from a modified Gompertz model using Equation 1: y(t)=c·e−ae−bt−c·e−a. 9. The method of claim 5, wherein said data driven model is selected from neural networks, polynomial regression, clustering, principal components analysis, partial least squares, auto-regressive integrated moving average (ARIMA), ARMAX radial basis function (RBF), fuzzy rule-based systems, and support vector machines. 10. The method of claim 5, wherein said growth function model is a modified Gompertz growth function model and said data driven model is an ARMAX model. 11. A method for predicting a future hydrocarbon production from a reservoir comprising: a) determining a field production level in a hydrocarbon production process by placement of sensors at various stages within the hydrocarbon production process and hydrocarbon-containing reservoir; b) overlaying a growth function representation that simulates the production level within the hydrocarbon production process based on one or more parameters of said hydrocarbon-containing reservoir on a field production level representation; c) predicting a future production level within the hydrocarbon production process based on observation of the production growth of the growth function representation overlaid on the field production level representation; d) determining the discrepancies in said growth function representation and said production level using a data driven representation; and e) selectively modifying at least one parameter in the hydrocarbon production process based on said data driven representation. 12. The method of claim 11, wherein said parameters are geological, geometrical, operational or a combination thereof. 13. The method of claim 11, wherein said growth function model is a tumor growth model. 14. The method of claim 11, wherein said growth function model is selected from logistic, Gompertz, Schnute and Stannard, Logistic, Richards or a variation or combination thereof. 15. The method of claim 11, wherein said data driven representation is selected from neural networks, polynomial regression, clustering, principal components analysis, partial least squares, auto-regressive integrated moving average (ARIMA), radial basis function (RBF), fuzzy rule-based systems, and support vector machines. 16. A method for predicting performance in a hydrocarbon production comprising: a) inputting one or more reservoir physical parameters into a computer; b) applying a growth function model to said one or more reservoir physical parameters; c) applying a data driven model to a residual of said growth function model; d) combining said growth function model and said data driven model to form a surrogate model; and, e) predicting a future production of hydrocarbon with said surrogate model. 17. The method of claim 16, wherein said growth function model is a tumor growth model. 18. The method of claim 16, wherein said growth function model is selected from logistic, Gompertz, Schnute and Stannard, Logistic, Richards or a variation or combination thereof. 19. The method of claim 16, wherein said data driven model is selected from neural networks, polynomial regression, clustering, principal components analysis, partial least squares, auto-regressive integrated moving average (ARIMA), ARMAX radial basis function (RBF), fuzzy rule-based systems, and support vector machines. 20. The method of claim 16, wherein said growth function model is a modified Gompertz growth function model and said data driven model is an ARMAX model.