Abstract: Systems and methods for generating pressure data from at least two pressure sensors, storing one or more parameters indicative of water cut in a neural network model, receiving pressure data from at least the two pressure sensors respectively indicative of the pressure at two points of a well bore, determining a pressure drop between the two points, generating an input water cut estimate, estimating a dynamic pressure loss to initiate an iterative process, estimating a potential energy loss, inverse modeling a water cut estimate, comparing the water cut estimate to the input water cut estimate to generate a water cut ?, utilizing the water cut estimate as the input water cut estimate for the iterative process when the water cut ? exceeds a threshold, and continuing the iterative process until the water cut ? is below the threshold.

Abstract: Systems and methods for generating pressure data from at least two pressure sensors, storing one or more parameters indicative of water cut in a neural network model, receiving pressure data from at least the two pressure sensors respectively indicative of the pressure at two points of a well bore, determining a pressure drop between the two points, generating an input water cut estimate, estimating a dynamic pressure loss to initiate an iterative process, estimating a potential energy loss, inverse modeling a water cut estimate, comparing the water cut estimate to the input water cut estimate to generate a water cut ?, utilizing the water cut estimate as the input water cut estimate for the iterative process when the water cut ? exceeds a threshold, and continuing the iterative process until the water cut ? is below the threshold.

Abstract: Systems and methods for intelligent estimation of productivity index and reservoir pressure values using pressure sensors, a neural network model comprising historical flow rate data of at least a well bore, and a data processor. The pressure sensors generate pressure data associated with a well bore's surface point and a downhole point. The data processor, communicatively coupled to the two pressure sensors and the neural network model, is operable to receive the pressure data from the sensors respectively indicative of pressure at each of the two points, estimate a real-time productivity index value in real-time based on the pressure data from the pressure sensors and the historical flowrate data of the neural network model, and estimate a reservoir pressure value of the well bore at a flowing condition, a reservoir pressure value of the well bore at a shut-in condition, or both, based on the real-time productivity index.