Abstract: The determination of subsurface three-phase saturation (that is, oil, water, and gas saturation) across perforations and open completions of production wells using production rates and pressure measurements. A process may use the surface production rates of oil, water, and gas and measured pressures to determine well fractional flows. The subsurface three-phase saturation may be determined using the cell fractional flows calculated from the well fractional flows. Computer-readable media and systems for determining subsurface three-phase saturation are also provided.

Abstract: Embodiments described herein include a system for dynamic real-time water-cut monitoring that includes a plurality of sensors for sensing pressure across a pipe that includes logic that causes the system to determine whether to use a direct approach for determining water-cut. In some embodiments, in response to determining not to use the direct approach, the logic causes the system to determine an initial guess for water-cut, estimate at least one dynamic pressure loss across the distance, estimate at least one potential energy pressure loss across the distance, estimate a value for water-cut from the at least one dynamic pressure loss and the at least one potential energy pressure loss, determine whether the initial guess is within a predetermined threshold of the value for water-cut, and in response to determining that the initial guess is within the predetermined threshold of the value for water-cut, output the value for water-cut.

Abstract: The determination of subsurface three-phase saturation (that is, oil, water, and gas saturation) across perforations and open completions of production wells using production rates and pressure measurements. A process may use the surface production rates of oil, water, and gas and measured pressures to determine well fractional flows. The subsurface three-phase saturation may be determined using the cell fractional flows calculated from the well fractional flows. Computer-readable media and systems for determining subsurface three-phase saturation are also provided.

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: An aggregate MRC well includes a plurality of maximum reservoir contact (MRC) wells, a plurality of independently operated flow control or completion units installed in each of the plurality of MRC wells, a plurality of pressure regimes corresponding to the plurality of MRC wells, and a single production string connecting each of the plurality of MRC wells. The method includes providing a plurality of maximum reservoir contact (MRC) wells forming an aggregate MRC well, providing a plurality of independently operated flow control valves in each of the plurality of MRC wells, providing a plurality of pressure regimes corresponding to the plurality of MRC wells, and providing a single production string connecting each of the plurality of MRC wells.

Abstract: Embodiments described herein include a system for dynamic real-time water-cut monitoring that includes a plurality of sensors for sensing pressure across a pipe that includes logic that causes the system to determine whether to use a direct approach for determining water-cut. In some embodiments, in response to determining not to use the direct approach, the logic causes the system to determine an initial guess for water-cut, estimate at least one dynamic pressure loss across the distance, estimate at least one potential energy pressure loss across the distance, estimate a value for water-cut from the at least one dynamic pressure loss and the at least one potential energy pressure loss, determine whether the initial guess is within a predetermined threshold of the value for water-cut, and in response to determining that the initial guess is within the predetermined threshold of the value for water-cut, output the value for water-cut.

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.

Abstract: An aggregate MRC well and method for extracting hydrocarbons from one or multiple subsurface formations are disclosed. The aggregate MRC well includes a plurality of maximum reservoir contact (MRC) wells, a plurality of independently operated flow control or completion units installed in each of the plurality of MRC wells or laterals, a plurality of pressure regimes corresponding to the plurality of MRC wells or laterals, and a single production string connecting each of the plurality of MRC wells or laterals. The method includes providing a plurality of maximum reservoir contact (MRC) wells forming an aggregate MRC well, providing a plurality of independently operated flow control valves in each of the plurality of MRC wells or laterals, providing a plurality of pressure regimes corresponding to the plurality of MRC wells or laterals, and providing a single production string connecting each of the plurality of MRC wells or laterals.