Abstract: Methods for predicting hydrocarbon production rates for a hydrocarbon reservoir include receiving data from a hydrocarbon reservoir. The data includes reservoir characterization data, well log data, and hydraulic fracturing data. A physics-constrained machine learning model predicts a hydrocarbon production rate for the hydrocarbon reservoir as a function of time. The physics-constrained machine learning model includes an artificial neural network and a hydrocarbon fluid flow model. Predicting the hydrocarbon production rate includes generating, by the artificial neural network, multiple parameters of the hydrocarbon fluid flow model based on the data from the hydrocarbon reservoir. The hydrocarbon fluid flow model provides the predicted hydrocarbon production rate as a function of time based on the parameters. A display device of the computer system presents the predicted hydrocarbon production rate for the hydrocarbon reservoir as a function of time.

Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: identifying historical production data related to a plurality of previously-drilled wells; determining, based on the historical production data, a correlation between productivity index and estimated ultimate recovery (EUR); calculating a first productivity index for a current well; and determining, based on the productivity index of the current well and the correlation, a first EUR of the current well.

Abstract: A method for determining sweet spots in a subterranean formation includes drilling a plurality of wellbores in the subterranean formation using a drill tool; lowering a logging tool in each of the plurality of wellbores to collect measurements; calculating a reservoir quality index parameter for each wellbore of the plurality of wellbores based on petrophysical logs; creating a reservoir quality index map using the petrophysical logs; calculating a linear flow index parameter for each wellbore of the plurality of wellbores based on production data provided by the petrophysical logs; correlating the reservoir quality index parameter and the linear flow index parameter for each wellbore of the plurality of wellbores to locate sweet spots; and ranking a basin based on the located sweet spots and the correlated parameters.

Abstract: A method for determining sweet spots in a subterranean formation includes drilling a plurality of wellbores in the subterranean formation using a drill tool; lowering a logging tool in each of the plurality of wellbores to collect measurements; calculating a reservoir quality index parameter for each wellbore of the plurality of wellbores based on petrophysical logs; creating a reservoir quality index map using the petrophysical logs; calculating a linear flow index parameter for each wellbore of the plurality of wellbores based on production data provided by the petrophysical logs; correlating the reservoir quality index parameter and the linear flow index parameter for each wellbore of the plurality of wellbores to locate sweet spots; and ranking a basin based on the located sweet spots and the correlated parameters.

Abstract: Embodiments herein relate to a technique that may include identifying historical data related to at least one remote well. The technique may further include identifying, based on the historical data, a correlation between gas flow capacity and estimated ultimate recovery (EUR) of the at least one other well. The technique may further include identifying gas flow capacity of a well. The technique may further include predicting, based on the gas flow capacity of the well and the identified correlation between gas flow capacity and EUR of the at least one other well, EUR of the well. The technique may further include operating the well based on the predicted EUR. Other embodiments may be described or claimed.

Abstract: Methods for predicting hydrocarbon production rates for a hydrocarbon reservoir include receiving data from a hydrocarbon reservoir. The data includes reservoir characterization data, well log data, and hydraulic fracturing data. A physics-constrained machine learning model predicts a hydrocarbon production rate for the hydrocarbon reservoir as a function of time. The physics-constrained machine learning model includes an artificial neural network and a hydrocarbon fluid flow model. Predicting the hydrocarbon production rate includes generating, by the artificial neural network, multiple parameters of the hydrocarbon fluid flow model based on the data from the hydrocarbon reservoir. The hydrocarbon fluid flow model provides the predicted hydrocarbon production rate as a function of time based on the parameters. A display device of the computer system presents the predicted hydrocarbon production rate for the hydrocarbon reservoir as a function of time.