IP Library › Granted Patent US 12,379,515
Granted Patent B2
US 12,379,515 · App. 18/487,769 · Granted Aug 5, 2025

Systems and methods for updating hydrocarbon reservoir parameters

Inventors: Babatope Kayode (Dhahran, SA); Santiago Ariel Ganis (Dhahran, SA)
Assignee: Saudi Arabian Oil Company
G01V1/282G01V2210/6244G01V2210/6246G01V2210/66
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Quick Facts
Patent No.
US 12,379,515
App. No.
18/487,769
Granted
Aug 5, 2025
Kind
B2
Abstract

Techniques for updating hydrocarbon parameters include identifying well data associated with wells formed in subterranean formations of a hydrocarbon reservoir; determining a data density value for each well; assigning each well into a pressure grouping based on a wellbore pressure similarity of the well relative to an initial pattern well; generating a two-dimensional (2D) model of the hydrocarbon reservoir; converting the 2D model into a three-dimensional (3D) model of the hydrocarbon reservoir; and updating a permeability or a porosity associated with a grid cell of the 3D model.

Claims (113)

1. A computer-implemented method of updating one or more hydrocarbon parameters, comprising:

identifying, with a computing system, well data associated with a plurality of wells formed in one or more subterranean formations of a hydrocarbon reservoir, the well data comprising at least one recorded wellbore pressure associated with each well in the plurality of wells;

determining, with the computing system, a data density value for each well of the plurality of wells, where the data density value comprises a number of time windows in which the well has a recorded wellbore pressure;

determining, with the computing system, at least one well of the plurality of wells that has a data density value less than a threshold value;

removing, with the computing system, the determined at least one well from the plurality of wells;

assigning, with the computing system, each well of the plurality of wells into a pressure grouping of a plurality of pressure groupings based on a wellbore pressure similarity of the well relative to an initial pattern well that is defined by a maximum data density value, wherein the assigning comprises:

determining, with the computing system, for each removed well, a differential between the recorded pressure data of the removed well and a pressure of each of the plurality of pressure groupings; and

assigning, with the computing system, each removed well to a particular pressure grouping based on the differential between the recorded pressure data of the removed well and the pressure the particular pressure grouping being minimal;

generating, with the computing system, a two-dimensional (2D) model of the hydrocarbon reservoir that comprises a plurality of grid cells, each grid cell assigned to one of the plurality of pressure groupings based on an assigned pressure grouping of a well of the plurality of wells that is located within or nearest the grid cell;

converting, with the computing system, the 2D model into a three-dimensional (3D) model of the hydrocarbon reservoir by duplicating the assigned one of the plurality of pressure groupings of each grid cell along a column of vertical grid cells; and

updating, with the computing system, at least one of a permeability or a porosity associated with a grid cell of the 3D model that is assigned to each of the plurality of pressure groupings based on a history match of the one or more wells production data or pressure data.

2. The computer-implemented method of claim 1 , further comprising:

generating, with the computing system, one or more well field management operations based on the history match of the one or more wells production data or pressure data.

3. The computer-implemented method of claim 1 , wherein the assigning comprises:

comparing, with the computing system, the at least one recorded wellbore pressure associated with each well in the plurality of wells against the recorded wellbore pressure associated with the initial pattern well to determine a similarity coefficient for each well of the plurality of wells exclusive of the initial pattern well;

assigning, with the computing system, each well with the similarity coefficient greater than a particular value to the pressure grouping of the initial pattern well;

determining, with the computing system, another initial pattern well that is defined by a next-most maximum data density value;

comparing, with the computing system, the at least one recorded wellbore pressure associated with each unassigned well in the plurality of wells against the recorded wellbore pressure associated with the another initial pattern well to determine a similarity coefficient for each unassigned well of the plurality of wells exclusive of the another initial pattern well; and

assigning, with the computing system, each unassigned well with the similarity coefficient greater than the particular value to the pressure grouping of the another initial pattern well.

4. The computer-implemented method of claim 3 , further comprising:

updating, with the computing system, recorded pressure data in a time window associated with the initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the initial pattern well; and

updating, with the computing system, recorded pressure data in a time window associated with the another initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the another initial pattern well.

5. The computer-implemented method of claim 1 , wherein the differential comprises a root mean square differential.

6. The computer-implemented method of claim 1 , further comprising:

determining, with the computing system, for each cell in the 3D model associated with a particular pressure grouping, a depth level of the cell relative to a free water level; and

assigning, with the computing system, each cell with the depth level below the free water level as an aquifer cell.

7. The computer-implemented method of claim 6 , wherein the depth level of the cell is based on a perforation depth of a perforation in the well.

8. The computer-implemented method of claim 6 , further comprising:

associating, with the computing system, each aquifer cell to the particular pressure grouping.

9. The computer-implemented method of claim 2 , further comprising:

determining, with the computing system, for each cell in the 3D model associated with a particular pressure grouping, a depth level of the cell relative to a free water level;

assigning, with the computing system, each cell with the depth level below the free water level as an aquifer cell; and

associating, with the computing system, each aquifer cell to the particular pressure grouping.

10. The computer-implemented method of claim 9 , wherein the depth level of the cell is based on a perforation depth of a perforation in the well.

11. A computing system, comprising:

one or more tangible, non-transitory memory; and

one or more hardware processors communicably coupled to the one or more tangible, non-transitory memory and configured to execute instructions stored on the memory to perform operations comprising:

identifying well data associated with a plurality of wells formed in one or more subterranean formations of a hydrocarbon reservoir, the well data comprising at least one recorded wellbore pressure associated with each well in the plurality of wells;

determining a data density value for each well of the plurality of wells, where the data density value comprises a number of time windows in which the well has a recorded wellbore pressure;

determining at least one well of the plurality of wells that has a data density value less than a threshold value;

removing the determined at least one well from the plurality of wells;

assigning each well of the plurality of wells into a pressure grouping of a plurality of pressure groupings based on a wellbore pressure similarity of the well relative to an initial pattern well that is defined by a maximum data density value, wherein the operation of assigning comprises:

determining for each removed well, a differential between the recorded pressure data of the removed well and a pressure of each of the plurality of pressure groupings; and

assigning each removed well to a particular pressure grouping based on the differential between the recorded pressure data of the removed well and the pressure the particular pressure grouping being minimal;

generating a two-dimensional (2D) model of the hydrocarbon reservoir that comprises a plurality of grid cells, each grid cell assigned one of the plurality of pressure groupings based on an assigned pressure grouping of a well of the plurality of wells that is located within or nearest the grid cell;

converting the 2D model into a three-dimensional (3D) model of the hydrocarbon reservoir by duplicating the assigned one of the plurality of pressure groupings of each grid cell along a column of vertical grid cells; and

updating at least one of a permeability or a porosity associated with a grid block of the 3D model that is assigned to each of the plurality of pressure groupings based on a history match of the one or more wells production data or pressure data.

12. The computing system of claim 11 , wherein the operations further comprise:

generating one or more well field management operations based on the history match of the one or more wells production data or pressure data.

13. The computing system of claim 11 , wherein the operation of assigning comprises:

comparing the at least one recorded wellbore pressure associated with each well in the plurality of wells against the recorded wellbore pressure associated with the initial pattern well to determine a similarity coefficient for each well of the plurality of wells exclusive of the initial pattern well;

assigning each well with the similarity coefficient greater than a particular value to the pressure grouping of the initial pattern well;

determining another initial pattern well that is defined by a next-most maximum data density value;

comparing the at least one recorded wellbore pressure associated with each unassigned well in the plurality of wells against the recorded wellbore pressure associated with the another initial pattern well to determine a similarity coefficient for each unassigned well of the plurality of wells exclusive of the another initial pattern well; and

assigning each unassigned well with the similarity coefficient greater than the particular value to the pressure grouping of the another initial pattern well.

14. The computing system of claim 13 , wherein the operations further comprise:

updating recorded pressure data in a time window associated with the initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the initial pattern well; and

updating recorded pressure data in a time window associated with the another initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the another initial pattern well.

15. The computing system of claim 11 , wherein the differential comprises a root mean square differential.

16. The computing system of claim 11 , wherein the operations further comprise:

determining for each cell in the 3D model associated with a particular pressure grouping, a depth level of the cell relative to a free water level; and

assigning each cell with the depth level below the free water level as an aquifer cell.

17. The computing system of claim 16 , wherein the depth level of the cell is based on a perforation depth of a perforation in the well.

18. The computing system of claim 16 , wherein the operations further comprise:

associating each aquifer cell to the particular pressure grouping.

19. The computing system of claim 12 , wherein the operation of assigning comprises:

comparing the at least one recorded wellbore pressure associated with each well in the plurality of wells against the recorded wellbore pressure associated with the initial pattern well to determine a similarity coefficient for each well of the plurality of wells exclusive of the initial pattern well;

assigning each well with the similarity coefficient greater than a particular value to the pressure grouping of the initial pattern well;

determining another initial pattern well that is defined by a next-most maximum data density value;

comparing the at least one recorded wellbore pressure associated with each unassigned well in the plurality of wells against the recorded wellbore pressure associated with the another initial pattern well to determine a similarity coefficient for each unassigned well of the plurality of wells exclusive of the another initial pattern well; and

assigning each unassigned well with the similarity coefficient greater than the particular value to the pressure grouping of the another initial pattern well.

20. The computing system of claim 19 , wherein the operations further comprise:

updating recorded pressure data in a time window associated with the initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the initial pattern well; and

updating recorded pressure data in a time window associated with the another initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the another initial pattern well.

21. An apparatus comprising one or more tangible, non-transitory memory configured to store instructions operable, when executed by one or more hardware processors, to cause the one or more hardware processors to perform operations comprising:

identifying well data associated with a plurality of wells formed in one or more subterranean formations of a hydrocarbon reservoir, the well data comprising at least one recorded wellbore pressure associated with each well in the plurality of wells;

determining a data density value for each well of the plurality of wells, where the data density value comprises a number of time windows in which the well has a recorded wellbore pressure;

determining at least one well of the plurality of wells that has a data density value less than a threshold value;

removing the determined at least one well from the plurality of wells;

assigning each well of the plurality of wells into a pressure grouping of a plurality of pressure groupings based on a wellbore pressure similarity of the well relative to an initial pattern well that is defined by a maximum data density value, wherein the operation of assigning comprises:

determining for each removed well, a differential between the recorded pressure data of the removed well and a pressure of each of the plurality of pressure groupings; and

assigning each removed well to a particular pressure grouping based on the differential between the recorded pressure data of the removed well and the pressure the particular pressure grouping being minimal;

generating a two-dimensional (2D) model of the hydrocarbon reservoir that comprises a plurality of grid cells, each grid cell assigned one of the plurality of pressure groupings based on an assigned pressure grouping of a well of the plurality of wells that is located within or nearest the grid cell;

converting the 2D model into a three-dimensional (3D) model of the hydrocarbon reservoir by duplicating the assigned one of the plurality of pressure groupings of each grid cell along a column of vertical grid cells; and

updating at least one of a permeability or a porosity associated with a grid block of the 3D model that is assigned to each of the plurality of pressure groupings based on a history match of the one or more wells production data or pressure data.

22. The apparatus of claim 21 , wherein the operations further comprise:

generating one or more well field management operations based on the history match of the one or more wells production data or pressure data.

23. The apparatus of claim 21 , wherein the operation of assigning comprises:

comparing the at least one recorded wellbore pressure associated with each well in the plurality of wells against the recorded wellbore pressure associated with the initial pattern well to determine a similarity coefficient for each well of the plurality of wells exclusive of the initial pattern well;

assigning each well with the similarity coefficient greater than a particular value to the pressure grouping of the initial pattern well;

determining another initial pattern well that is defined by a next-most maximum data density value;

comparing the at least one recorded wellbore pressure associated with each unassigned well in the plurality of wells against the recorded wellbore pressure associated with the another initial pattern well to determine a similarity coefficient for each unassigned well of the plurality of wells exclusive of the another initial pattern well; and

assigning each unassigned well with the similarity coefficient greater than the particular value to the pressure grouping of the another initial pattern well.

24. The apparatus of claim 23 , wherein the operations further comprise:

updating recorded pressure data in a time window associated with the initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the initial pattern well; and

updating recorded pressure data in a time window associated with the another initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the another initial pattern well.

25. The apparatus of claim 21 , wherein the differential comprises a root mean square differential.

26. The apparatus of claim 21 , wherein the operations further comprise:

determining for each cell in the 3D model associated with a particular pressure grouping, a depth level of the cell relative to a free water level; and

assigning each cell with the depth level below the free water level as an aquifer cell.

27. The apparatus of claim 26 , wherein the depth level of the cell is based on a perforation depth of a perforation in the well.

28. The apparatus of claim 26 , wherein the operations further comprise:

associating each aquifer cell to the particular pressure grouping.

29. The apparatus of claim 21 , wherein the operation of assigning comprises:

comparing the at least one recorded wellbore pressure associated with each well in the plurality of wells against the recorded wellbore pressure associated with the initial pattern well to determine a similarity coefficient for each well of the plurality of wells exclusive of the initial pattern well;

assigning each well with the similarity coefficient greater than a particular value to the pressure grouping of the initial pattern well;

determining another initial pattern well that is defined by a next-most maximum data density value;

comparing the at least one recorded wellbore pressure associated with each unassigned well in the plurality of wells against the recorded wellbore pressure associated with the another initial pattern well to determine a similarity coefficient for each unassigned well of the plurality of wells exclusive of the another initial pattern well; and

assigning each unassigned well with the similarity coefficient greater than the particular value to the pressure grouping of the another initial pattern well, and

wherein the operations further comprise:

updating recorded pressure data in a time window associated with the initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the initial pattern well; and

updating recorded pressure data in a time window associated with the another initial pattern well with recorded pressure data of the each well assigned to the pressure grouping of the another initial pattern well.

30. The apparatus of claim 29 , wherein the differential comprises a root mean square differential.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 8, 2024
From: KAYODE, BABATOPE; GANIS, SANTIAGO ARIEL
To: SAUDI ARABIAN OIL COMPANY
Reel/Frame 066419/0981 →
Continuity (1)
Related Publication 20250123416A1 · Apr 17, 2025
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