Abstract: Estimating a production prediction of a target well includes computing, based on production time series from training wells, a smoothed production history curves. Each smoothed production history curve corresponds to a training well. Based on the smoothed production history curves, a fitting function defined by a set of fitting coefficients is selected. A machine learning process determines, based on a set of well parameters for each training well, a set of predicted fitting coefficients as a function of a set of well parameters of the target well. Estimating the production prediction further includes applying the predicted fitting coefficients to the fitting function to compute a production prediction curve for the target well, and presenting the production prediction curve.

Abstract: Estimating a production prediction of a target well includes computing, based on production time series from training wells, a smoothed production history curves. Each smoothed production history curve corresponds to a training well. Based on the smoothed production history curves, a fitting function defined by a set of fitting coefficients is selected. A machine learning process determines, based on a set of well parameters for each training well, a set of predicted fitting coefficients as a function of a set of well parameters of the target well. Estimating the production prediction further includes applying the predicted fitting coefficients to the fitting function to compute a production prediction curve for the target well, and presenting the production prediction curve.