Process of determining an injection performance of a well when injecting a fluid into a geological formation

Patent metadata

Field	Value
Publication number	US-2026092512-A1
Application number	US-202219111033-A
Country	US
Kind code	A1
Filing date	Sep 16, 2022
Priority date	Sep 16, 2022
Publication date	Apr 2, 2026
Grant date	—

Abstract

A process of determining a future injection performance of a well, a fluid being intended to be injected from a surface network into a geological formation defining a reservoir, the injection performance providing a relationship between the flowrate and a bottom hole flowing pressure, the process comprising obtaining a first set of data (VLP and bottom hole enthalpy), obtaining a surface network simulator, and a reservoir simulator, providing the reservoir simulator with a current working point of the well, running the reservoir simulator and obtaining updated pressure conditions in the reservoir, calculating a second set of data (IPR) for a next time step, providing the surface network simulator with the second set of data, and obtaining the bottom hole flowing pressure for the next time step and an updated working point. The injection performance is obtained from the second set of data.

First claim

1 . A process of determining a future injection performance of a well, a fluid being intended to be injected from a surface network via the well into a geological formation defining a reservoir at a reservoir pressure intended to increase over time, the fluid being injected at a wellhead at a wellhead flowing pressure and a wellhead flowing temperature at which the fluid is in liquid or dense phase, the fluid flowing at a flowrate from the wellhead to a bottom hole where the fluid is at a bottom hole flowing pressure, a bottom hole flowing temperature and a bottom hole flowing enthalpy, and the fluid flowing at the flow rate from the bottom hole into the reservoir, the injection performance providing a relationship between the flowrate and the bottom hole flowing pressure, the process comprising the following steps:
a) obtaining a first set of data providing the bottom hole flowing pressure, the bottom hole flowing temperature and the bottom hole flowing enthalpy as functions of the wellhead flowing pressure, of the wellhead flowing temperature and of the flowrate, b) obtaining a surface network simulator adapted for performing a nodal analysis of the surface network and the well, and obtaining a reservoir simulator adapted for modeling flows in the reservoir, c) providing the reservoir simulator with a current working point of the well comprising the wellhead flowing pressure, the wellhead flowing temperature and the flowrate for a current time step, d) running the reservoir simulator over the current time step, and obtaining updated pressure conditions in the reservoir, e) using the updated pressure conditions in the reservoir the wellhead flowing pressure and the wellhead flowing temperature, calculating a second set of data, the second set of data providing the bottom hole flowing pressure as a function of the flowrate for a next time step, f) providing the surface network simulator with the second set of data, and g) using the second set of data and at least part of the first set of data, obtaining the bottom hole flowing pressure for the next time step and, using the surface network simulator, obtaining an updated working point of the well comprising the wellhead flowing pressure, the wellhead flowing temperature, and the flowrate is for the next time step, wherein steps c) to g) are iterated over a plurality of time steps, the process further comprising obtaining the injection performance from the second set of data obtained at one of the time steps.

2 . The process according to claim 1 , wherein the fluid has a composition such that the fluid is at least partly liquid at the bottom hole for at least a value of the wellhead flowing pressure comprised between 70 and 150 bars absolute, assuming a reservoir pressure of 20 bars absolute and a wellhead flowing temperature of 20° C.

3 . The process according to claim 1 , wherein the fluid, has a pressure of its critical point greater than 60 bar absolute.

4 . The process according to claim 1 , wherein the fluid comprises at least 50 vol % of CO2.

5 . The process according to claim 4 , wherein the fluid comprises at least 80 vol % of CO2.

6 . The process according to claim 1 , wherein the wellhead flowing pressure is greater than 74 bar absolute.

7 . The process according to claim 1 , wherein the first set of data comprises:
a VLP table providing the bottom hole flowing pressure and the bottom hole flowing temperature as functions of the wellhead flowing pressure, of the wellhead flowing temperature and of the flowrate, and an enthalpy table providing the bottom hole flowing enthalpy as a function of the wellhead flowing pressure, of the wellhead flowing temperature and of the flowrate.

8 . The process according to claim 7 , wherein the VLP table is stored in the surface network simulator and the enthalpy table is stored in the reservoir simulator.

9 . The process according to claim 1 , wherein, in step e), calculating the second set of data includes the following substeps:
e1) selecting a plurality of potential values of the flowrate for the next time step, e2) using the wellhead flowing pressure and the wellhead flowing temperature of the current working point, the flowrate potential values and part of the first set of data, obtaining values of the bottom hole flowing enthalpy corresponding respectively to the flowrate potential values, and e3) using the values of the bottom hole flowing enthalpy and the updated pressure conditions of the reservoir, calculating values of the bottom hole flowing pressure corresponding respectively to the flowrate potential values.

10 . The process according to claim 9 , wherein, in substep e3), the values of the bottom hole flowing pressure are obtained by iterative convergence until the differences between the updated pressure conditions and each of the values of the bottom hole flowing pressure match with pressure drops calculated by the reservoir simulator between the bottom hole and the reservoir.

11 . The process according to claim 1 , wherein:
in step c), the working point further comprises the bottom hole flowing pressure for the current time step and, in step g), the updated working point further comprises the bottom hole flowing pressure for the next time step, or in step d), the reservoir simulator is adapted for using the first set of data in order to obtain the bottom hole flowing pressure, knowing the wellhead flowing pressure, the wellhead flowing temperature, and the flowrate.

12 . The process according to claim 1 , comprising determining future injection performances of a plurality of wells, a fluid or several fluids being intended to be respectively injected from the surface network via the wells into at least one reservoir.

13 . A process of controlling at least a well, a fluid being intended to be injected from a surface network via the well into a geological formation defining a reservoir at a reservoir pressure intended to increase over time, the fluid being injected at a wellhead at a wellhead flowing pressure and a wellhead flowing temperature at which the fluid is in liquid or dense phase, the fluid flowing at a flowrate from the wellhead to a bottom hole where the fluid is at a bottom hole flowing pressure, a bottom hole flowing temperature and a bottom hole flowing enthalpy, and the fluid flowing at the flow rate from the bottom hole into the reservoir, the injection performance providing a relationship between the flowrate and the bottom hole flowing pressure, the process comprising the following steps:
obtaining a target flowrate, and using a process according to claim 1 , determining a needed wellhead flowing pressure in order to achieve the target flowrate.

14 . The process according to claim 13 , wherein the target flowrate is provided by the surface network simulator.

15 . The process according to claim 13 , further comprising injecting the fluid at the wellhead at the needed wellhead flowing pressure.

Assignees

• Totalenergies Onetech

Inventors

• Rodriguez Martinez Alejandro

Classifications

• E21B2200/22

  Fuzzy logic, artificial intelligence, neural networks or the like · CPC title

• E21B47/07

  Temperature · CPC title

• E21B2200/20

  Computer models or simulations, e.g. for reservoirs under production, drill bits · CPC title

• E21B43/164 Primary

  Injecting CO2 or carbonated water (in combination with organic material C09K8/594) · CPC title

• E21B41/0064 Primary

  Carbon dioxide sequestration (storing fluids in porous layers B65G5/005) · CPC title