System and method for optimizing hydrocarbon production from subsurface reservoirs

1. A computer-implemented method of reservoir characterization, comprising:

a. receiving, at a computer processor, a time-lapse electromagnetic (EM) dataset and a flow dataset;

b. inverting, via the computer processor, the time-lapse EM dataset using a parametric inversion that models steel well casings to determine a volume of fluid-changed reservoir;

c. inverting, via the computer processor, the time-lapse EM dataset and the flow dataset using a joint inversion using the volume of the fluid-changed reservoir to estimate permeability and porosity;

d. generating, via the computer processor, relative permeability and capillary pressure based on the permeability and the porosity;

e. characterizing, via the computer processor, flow characteristics in the volume of the fluid-changed reservoir using the relative permeability and the capillary pressure; and

f. using the flow characteristics to select one or more of new locations for production wells, locations for injection wells, depths for perforations in the well bores, or type of enhanced hydrocarbon recovery method.

2. The method of claim 1 wherein the volume of the fluid-changed reservoir is affected by hydrocarbon production or injection of other fluids.

3. The method of claim 1 wherein the parametric inversion procedure iteratively adjusts reservoir geometry parameters until values in the time-lapse EM dataset are matched by their corresponding trial predictions and wherein data noise is taken into account.

4. The method of claim 1 wherein the joint inversion uses parametric functions representing the relative-permeability (RP) and the capillary pressure (CP) and minimizes

ϕ

RP

,

CP

⁡

(

m

)

=

∑

i

=

1

N

⁢

(

d

i

obs

-

d

i

pred

⁡

(

RP

,

CP

)

ɛ

i

)

.

wherein ϕ RP,CP refers to the joint inversion, m refers to a model parameter vector, wherein d i obs refers to EM data observations, wherein d i pred (RP,CP) refers to a function of the N RP relative permeability parameters and N CP capillary pressure parameters, and wherein ε i refers to measurement errors.

5. The method of claim 1 , further comprising generating a 3D model of the flow characteristics based on the flow characteristics.

6. The method of claim 1 , wherein the flow dataset comprises one of injection pressure, injection rate, and production rate.

7. The method of claim 1 wherein a fluid in the fluid-changed reservoir comprises one of water, CO2, gas, and produced oil.

8. A computer system, comprising:

one or more processors;

memory; and

one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions that when executed by the one or more processors cause the system to:

a. receive, at the one or more processors, a time-lapse electromagnetic (EM) dataset and a flow dataset;

b. invert the time-lapse EM dataset using a parametric inversion that models steel well casings to determine a volume of fluid-changed reservoir;

c. invert the time-lapse EM dataset and the flow dataset using a joint inversion using the volume of the fluid-changed reservoir to estimate permeability and porosity;

d. generate relative permeability and capillary pressure based on the permeability and the porosity; and

e. characterize flow characteristics in the volume of the fluid-changed reservoir using the relative permeability and the capillary pressure; and

f. use the flow characteristics to select one or more of new locations for production wells, locations for injection wells, depths for perforations in the well bores, or type of enhanced hydrocarbon recovery method.

9. The system of claim 8 , wherein the volume of the fluid-changed reservoir is affected by hydrocarbon production or injection of other fluids.

10. The system of claim 8 , wherein the parametric inversion procedure iteratively adjusts reservoir geometry parameters until values in the time-lapse EM dataset are matched by their corresponding trial predictions and wherein data noise is taken into account.

11. The system of claim 8 , wherein the joint inversion uses parametric functions representing the relative-permeability (RP) and the capillary pressure (CP) and minimizes

ϕ

RP

,

CP

⁡

(

m

)

=

∑

i

=

1

N

⁢

(

d

i

obs

-

d

i

pred

⁡

(

RP

,

CP

)

ɛ

i

)

,

wherein ϕ RP,CP refers to the joint inversion, m refers to a model parameter vector, wherein d i obs refers to EM data observations, wherein −d i pred (RP,CP) refers to a function of the N RP relative permeability parameters and N CP capillary pressure parameters, and wherein ε i refers to measurement errors.

12. The system of claim 8 , wherein the flow dataset comprises one of injection pressure, injection rate, and production rate.

13. The system of claim 8 , wherein a fluid in the fluid-changed reservoir comprises one of water, CO2, gas, and produced oil.

14. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more processors and memory, cause the device to:

a. receive, at the one or more processors, a time-lapse electromagnetic (EM) dataset and a flow dataset;

b. invert the time-lapse EM dataset using a parametric inversion that models steel well casings to determine a volume of fluid-changed reservoir;

c. invert the time-lapse EM dataset and the flow dataset using a joint inversion that uses the volume of the fluid-changed reservoir to estimate permeability and porosity;

d. generate relative permeability and capillary pressure based on the permeability and the porosity;

e. characterize flow characteristics in the volume of the fluid-changed reservoir using the relative permeability and the capillary pressure; and

f. use the flow characteristics to select one or more of new locations for production wells, locations for injection wells, depths for perforations in the well bores, or type of enhanced hydrocarbon recovery method.

15. The non-transitory computer readable storage medium of claim 14 , wherein the volume of the fluid-changed reservoir is affected by hydrocarbon production or injection of other fluids.

16. The non-transitory computer readable storage medium of claim 14 , wherein the parametric inversion procedure iteratively adjusts reservoir geometry parameters until values in the time-lapse EM dataset are matched by their corresponding trial predictions and wherein data noise is taken into account.

17. The non-transitory computer readable storage medium of claim 14 , wherein the joint inversion uses parametric functions representing the relative-permeability (RP) and the capillary pressure (CP) and minimizes

ϕ

RP

,

CP

⁡

(

m

)

=

∑

i

=

1

N

⁢

(

d

i

obs

-

d

i

pred

⁡

(

RP

,

CP

)

ɛ

i

)

,

wherein ϕ RP,CP refers to the joint inversion, m refers to a model parameter vector, wherein d i obs refers to EM data observations, wherein −d i pred (RP,CP) refers to a function of the N RP relative permeability parameters and N CP capillary pressure parameters, and wherein ε i refers to measurement errors.

18. The non-transitory computer readable storage medium of claim 14 , wherein the flow dataset comprises one of injection pressure, injection rate, and production rate.

19. The non-transitory computer readable storage medium of claim 14 , wherein a fluid in the fluid-changed reservoir comprises one of water, CO2, gas, and produced oil.