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1. WO2020117228 - APPLICATION OF THE ENSEMBLE KALMAN FILTER TO DYNAMIC HISTORY MATCHING IN WELLBORE PRODUCTION

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

[ EN ]

CLAIMS

What is claimed is:

1. A method for identifying a flow parameter in a wellbore comprising:

identifying a state vector at a moment t;

performing a flow simulation using a flow model;

predicting the state vector and a covariance matrix at the moment t; updating the state vector with an EnKF algorithm;

correcting the state vector at the moment t; and

updating the flow simulation model.

2. The method of claim 1, further comprising adding measured data to the EnKF algorithm.

3. The method of claim 2, wherein the EnKF algorithm comprises a forecast step of x = M(XfL i) + wM, wherein x is predicted augmented state vector, M is a flow model function, ^Liis a state vector at moment t-l, and wM is a randomly distributed model error of the flow model function.

4. The method of claim 3, wherein the EnKF algorithm comprises an update step of Xf = x + Gt (Dt— Ht · x[), wherein ^“is the updated state vector, Gt is a Kalman gain matrix, Dt is a matrix of measured data, Ht is an observation matrix, and x( is a predicted matrix.

5. The method of claim 1, further comprising identifying a location for at least one measurement device on a production tubing.

6. The method of claim 5, wherein the measurement device comprises a geophone, a hydrophone, an accelerometer, a transducer, or an optical fiber.

7. The method of claim 5, wherein the measurement device is used to record at least one data set.

8. The method of claim 7, wherein the data set comprises at least one data type selected from the group consisting of temperature, pressure, electromagnetic fields, refraction, borehole properties, and fluid properties.

9. The method of claim 1, wherein the correcting the state vector at moment t is performed

by Xt = wherein Ki is an absolute permeability and Qi is a measured production

rate.

10. A method for identifying a flow parameter in a wellbore comprising:

identifying a state vector at a moment t;

performing a flow simulation using a flow model;

predicting the state vector and a covariance matrix at the moment t; updating the state vector with an EnKF algorithm, wherein the EnKF algorithm comprises a forecast step of x[ = M C^) + wM, wherein x[ is predicted augmented state vector, M is a flow model function, -X^is a state vector at moment t-\, and wM is a randomly distributed model error of the flow model function, and wherein the EnKF algorithm comprises an update step of Xf = x + Gt (Dt— Ht · x[), wherein X^is the updated state vector, Gt is a

Kalman gain matrix, Dt is a matrix of measured data, Ht is an observation matrix, and x is a predicted matrix;

correcting the state vector at the moment t, wherein the correcting the state vector

at moment t is performed by X? = wherein Ki is an absolute permeability and Qi is

a measured production rate; and

updating the flow simulation model, wherein the flow simulation model is a three dimensional model or a productivity index-based model.

11. A system for identifying a flow parameter in a wellbore comprising:

a distributed acoustic system into a wellbore, wherein the distributed acoustic system comprises:

a fiber optic cable; and

at least one measurement device; and

an information handling system configured to:

identify a state vector at moment t-1 ;

perform a flow simulation model;

predict the state vector and a covariance matrix at moment t;

update the state vector with an EnKF algorithm;

correct the state vector at moment t; and

update the flow simulation model.

12. The system of claim 11, wherein the information handling system is further configured to add measured data to the EnKF algorithm.

13. The system of claim 11, wherein the EnKF algorithm comprises a forecast step of x[ = M{Xf-1) + M, wherein x[ is predicted augmented state vector of the system, Mis a flow model function, .X^tjis a state vector at moment t-l, and wM is a randomly distributed model error of the flow model function.

14. The system of claim 13, wherein the EnKF algorithm comprises an update step of Xf = x + Gt (Dt— Ht x[), wherein X^is the updated stat vector, Gt is a Kalman gain matrix, Dt is a matrix of measured data, Ht is an observation matrix, and x[ is a predicted matrix.

15. The system of claim 11, wherein the measurement device comprises a geophone, a hydrophone, an accelerometer, a transducer, or an optical fiber.

16. The system of claim 15, wherein the measurement device is operable to record at least one data set.

17. The system of claim 16, wherein the data set comprises at least one data type selected from the group consisting of temperature, pressure, electromagnetic fields, refraction, borehole properties, and fluid properties.

18. The system of claim 11, wherein the correct the state vector at moment t is performed by

Xt = I, wherein Ki is an absolute permeability and Qi is a measured production rate.

19. The system of claim 11, wherein the flow simulation model is a three dimensional model or a productivity index-based model.

20. The system of claim 11, wherein the information handling system is further configured for dynamic history matching with the EnKF algorithm.