Automated well productivity estimation and continuous average well pressure monitoring through integration of real-time surface and downhole pressure and temperature measurements

1. An intelligent estimation system comprising at least two pressure sensors, a neural network model, and a data processor, wherein:

the at least two pressure sensors are configured to generate well parameters comprising at least pressure data respectively associated with two points of a well bore, the two points comprising a surface point of the well bore and a downhole point of the well bore, wherein the well parameters further comprise at least a measured or estimated bottomhole pressure of the well bore;

the neural network model comprises historical flowrate data associated with at least the well bore; and

the data processor is communicatively coupled to the at least two pressure sensors and the neural network model and is operable to:

receive the well parameters comprising (i) the pressure data from the at least two pressure sensors respectively indicative of pressure at each of the two points of the well bore and (ii) the measured or estimated bottomhole pressure of the well bore,

estimate a real-time productivity index value in real-time based on (i) the well parameters comprising the pressure data from the at least two pressure sensors and the measured or estimated bottomhole pressure of the well bore and (ii) 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.

2. The intelligent estimation system of claim 1 , wherein the data processor is operable to estimate the real-time productivity index by solving:

J

⁡

(

Productivity

⁢

Index

)

=

Q

P

r

-

P

wf

,

wherein J is representative of the real-time productivity index value, Q is representative of a surface flowrate at standard conditions for the well bore, P r is representative of a reservoir pressure for the well bore, and P wf is representative of a flowing bottomhole pressure for the well bore.

3. The intelligent estimation system of claim 2 , wherein Q is based on the historical flowrate data, P r is an input associated with a pressure point after a pressure buildup is stabilized and based on the pressure data, and P wf is an input associated with a last flowing pressure point before the pressure buildup is initiated and based on the pressure data.

4. The intelligent estimation system of claim 1 , further comprising a flowmeter configured to provide a flowrate associated with the well bore, wherein the data processor is operable to estimate the real-time productivity index by solving:

J

⁡

(

Productivity

⁢

Index

)

=

Q

P

r

-

P

wf

,

wherein J is representative of the real-time productivity index value, Q is representative of a surface flowrate at standard conditions for the well bore based on the flowrate from the flowmeter, P r is representative of a reservoir pressure for the well bore, and P wf is representative of a flowing bottomhole pressure for the well bore.

5. The intelligent estimation system of claim 1 , wherein the data processor is further operable to:

generate a productivity index graphical representation including the real-time productivity index value for the well bore; and

display the productivity index graphical representation in real-time.

6. The intelligent estimation system of claim 5 , wherein the data processor is further operable to:

display the productivity index graphical representation comprising each real-time productivity index value for a plurality of well bores, the plurality of well bores comprising the well bore.

7. The intelligent estimation system of claim 1 , wherein the data processor is further operable to:

generate a reservoir pressure graphical representation including the reservoir pressure value of the well bore at the flowing condition, the reservoir pressure value of the well bore at the shut-in condition, or both; and

display the reservoir pressure graphical representation.

8. The intelligent estimation system of claim 7 , wherein the data processor is further operable to:

display the reservoir pressure graphical representation comprising each reservoir pressure value of each well bore at the flowing condition, the shut-in condition, or both, for a plurality of well bores, the plurality of well bores comprising the well bore.

9. The intelligent estimation system of claim 1 , wherein the data processor is operable to determine when the well bore is in the flowing condition, the shut-in condition, or both, based on the pressure data and received temperature data to identify exact pressure build-up periods indicative of the shut-in condition.

10. An intelligent estimation system comprising at least two pressure sensors, a neural network model, and a data processor, wherein:

the at least two pressure sensors are configured to generate well parameters comprising at least pressure data respectively associated with two points of a well bore, the two points comprising a surface point of the well bore and a downhole point of the well bore of a plurality of well bores, the well parameters further comprising at least a measured or estimated bottomhole pressure of the well bore of the plurality of well bores;

the neural network model comprises historical flowrate data associated with at least the well bore of the plurality of well bores; and

the data processor is communicatively coupled to the at least two pressure sensors and the neural network model and is operable to:

receive the well parameters comprising (i) the pressure data from the at least two pressure sensors respectively indicative of pressure at each of the two points of the well bore and (ii) the measured or estimated bottomhole pressure of the well bore,

estimate a real-time productivity index value of the well bore in real-time based on (i) the well parameters comprising the pressure data from the at least two pressure sensors and the measured or estimated bottomhole pressure of the well bore (ii) and the historical flowrate data of the neural network model,

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,

generate (i) a productivity index graphical representation comprising each real-time productivity index value for each well bore of the plurality of well bores, (ii) a reservoir pressure graphical representation comprising each reservoir pressure value of each well bore, (iii) or both; and

display the productivity index graphical representation, the reservoir pressure graphical representation, or both.

11. The intelligent estimation system of claim 10 , further comprising a flowmeter configured to provide a flowrate associated with the well bore, wherein the data processor is operable to estimate the real-time productivity index by solving:

J

⁡

(

Productivity

⁢

Index

)

=

Q

P

r

-

P

wf

,

wherein J is representative of the real-time productivity index value, Q is representative of a surface flowrate at standard conditions for the well bore based on the flowrate from the flowmeter, P r is representative of a reservoir pressure for the well bore, and P wf is representative of a flowing bottomhole pressure for the well bore.

12. The intelligent estimation system of claim 11 , wherein Q is further based on the historical flowrate data, P r is an input associated with a pressure point after a pressure buildup is stabilized and based on the pressure data, and P wf is an input associated with a last flowing pressure point before the pressure buildup is initiated and based on the pressure data.

13. The intelligent estimation system of claim 10 , wherein the data processor is further operable to:

generate the reservoir pressure graphical representation including the reservoir pressure value of the well bore at the flowing condition, the reservoir pressure value of the well bore at the shut-in condition, or both.

14. The intelligent estimation system of claim 13 , wherein the data processor is further operable to:

display the reservoir pressure graphical representation, the reservoir pressure graphical representation comprising each reservoir pressure value of each well bore at the flowing condition, the shut-in condition, or both, for the plurality of well bores.

15. The intelligent estimation system of claim 10 , wherein the data processor is operable to determine when the well bore is in the flowing condition, the shut-in condition, or both, based on the pressure data and received temperature data to identify exact pressure build-up periods indicative of the shut-in condition.

16. A method comprising:

generating well parameters comprising at least pressure data respectively associated with two points of a well bore from at least two pressure sensors, the two points comprising a surface point of the well bore and a downhole point of the well bore, the well parameters further comprising at least a measured or estimated bottomhole pressure of the well bore;

receiving historical flowrate data associated with at least the well bore from a neural network model;

receiving the well parameters comprising (i) the pressure data from the at least two pressure sensors respectively indicative of pressure at each of the two points of the well bore and (ii) the measured or estimated bottomhole pressure of the well bore;

estimating a real-time productivity index value of the well bore in real-time based on (i) the well parameters comprising the pressure data from the at least two pressure sensors and the measured or estimated bottomhole pressure of the well bore and (ii) the historical flowrate data of the neural network model; and

estimating 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.

17. The method of claim 16 , further comprising receiving a flowrate associated with the well bore from a flowmeter, and wherein the estimating of the real-time productivity index based on solving:

J

⁡

(

Productivity

⁢

Index

)

=

Q

P

r

-

P

wf

,

wherein J is representative of the real-time productivity index value, Q is representative of a surface flowrate at standard conditions for the well bore based on the flowrate from the flowmeter, P r is representative of a reservoir pressure for the well bore, and P wf is representative of a flowing bottomhole pressure for the well bore.

18. The method of claim 16 , further comprising:

generating a productivity index graphical representation including the real-time productivity index value for the well bore; and

displaying the productivity index graphical representation in real-time.

19. The method of claim 18 , further comprising:

displaying the productivity index graphical representation comprising each real-time productivity index value for a plurality of well bores, the plurality of well bores comprising the well bore.

20. The method of claim 16 , further comprising determining when the well bore is in the flowing condition, the shut-in condition, or both, based on the pressure data and received temperature data to identify exact pressure build-up periods indicative of the shut-in condition.