Analysis of multiple assets in view of functionally-related uncertainties

• US 8,458,000 B2
• Filed: 05/17/2012
• Issued: 06/04/2013
• Est. Priority Date: 04/29/2005
• Status: Active Grant

• Current Assignee: Landmark Graphics Corporation (Halliburton Company)
• Original Assignee: Landmark Graphics Corporation (Halliburton Company)
• Inventors: Narayanan, Keshav, Heath, David E., Cullick, Alvin Stanley
• Primary Examiner(s): Boyce, Andre
• Assistant Examiner(s): DICKERSON, TIPHANY B
• Application Number:
  US13/473,720
• Publication Number:
  US 20120226512A1
• Time in Patent Office: 383 Days
• Field of Search: 705/7.11, 705/7.28, 703/2, 703/9, 703/10
• US Class Current: 705/7.11
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities

Abstract

A client-server based system for building and executing flows (i.e., interconnected systems of algorithms). The client allows a user to build a flow specification and send the flow specification to the server. The server assembles the flow from the flow spec and executes the flow. A flow may be configured to analyze the impact (e.g., the financial impact) of a number of uncertainties associated with a plurality of assets. Uncertainty variables are used to characterize the uncertainties associated with the assets. An uncertainty variable associated with one asset may be functionally dependent on an uncertainty variable associated with another asset.

Claims

1. A method comprising:

utilizing a computer to perform;

(a) storing in a memory first information specifying a set of uncertainty variables that characterize uncertainties associated with a plurality of assets, wherein the first information also specifies a functional relationship between a first and a second of the uncertainty variables, wherein the first uncertainty variable is associated with a first of the assets, wherein the second uncertainty variable is associated with a second of the assets, wherein the assets are assets related to the exploration and production of one or more of oil and gas;

(b) generating and storing values for each of the uncertainty variables, wherein said generating includes generating a value for the first uncertainty variable, and computing a value for the second uncertainty variable from the value of the first uncertainty variable based on the functional relationship;

(c) determining for each of the assets a corresponding input data set using at least a corresponding subset of the uncertainty variable values, wherein said determining includes;

determining a first input data set corresponding to the first asset using at least the value of the first uncertainty variable; and

determining a second input data set corresponding to the second asset using at least the value of the second uncertainty variable;

(d) for each of the assets, invoking execution of a corresponding set of one or more algorithms on the computer, wherein each set of one or more algorithms operates on the corresponding input data set to generate a corresponding output data set;

(e) performing (b), (c) and (d) a plurality of times to generate a plurality of output data sets for each asset;

(f) computing one or more statistics for each of the assets based on the corresponding plurality of output data sets;

(g) generating resultant data based at least partially on the statistics of the assets;

(h) displaying an indication of the resultant data on a display device.

2. The method of claim 1, wherein the first asset is a first oil and/or gas field, wherein the second asset is a second oil and/or gas field.

3. The method of claim 1 wherein the one or more algorithms corresponding to each of the assets are selected by a user.

4. The method of claim 1, wherein (d) includes:

distributing the input data sets of the assets to one or more remote computers over a network for remote execution of the corresponding sets of algorithms; and

receiving the output data sets from the one or more remote computers.

5. The method of claim 1, wherein said first information also specifies one or more user-specified correlations among the uncertainty variables, wherein said generation of values for each of the uncertainty variables respects the one or more specified correlations.

6. The method of claim 5, wherein the uncertainty variables include a third uncertainty variable and a fourth uncertainty variable associated with different ones of the assets, wherein the one or more correlations include a correlation between the third and fourth uncertainty variables.

7. The method of claim 1, wherein (c) includes selecting one of two or more models for incorporation into one of the input data sets based on the value of a third of the uncertainty variables.

8. The method of claim 1, wherein (c) includes:

selecting one of two or more models based on the value of a third of the uncertainty variables;

selecting one of two or more submodels of the selected model based on the value of a fourth of the uncertainty variables;

wherein the selected submodel is usable to determine data to be incorporated into one of the input data sets.

9. The method of claim 1 further comprising:

utilizing the computer to;

(i) receive second information specifying a plurality of decision variables;

(j) generate values for the decision variables, wherein the decision variable values are used to determine the input data sets for the assets;

(k) compute a value of a global objective from the one or more statistics of each of the assets;

execute an optimizer in order to determine one or more sets of values for the decision variables, wherein said executing the optimizer includes performing at least (j), (e), (f) and (k) a plurality of times, wherein the optimizer is configured to search for a maximum or minimum of the global objective over at least a portion of a space defined by the decision variables.

10. The method of claim 9, wherein the optimizer is selected by a user from a set of supported optimizers.

11. The method of claim 9, wherein said displaying an indication of the resultant data includes displaying a graphical representation of at least a subset of the one or more sets of values for the decision variables.

12. The method of claim 9, wherein said second information also specifies one or more constraints on the decision variables, wherein said generation of values for the decision variables respects the one or more constraints.

13. The method of claim 9, wherein said second information also specifies one or more functional dependencies between the decision variables, wherein said generation of values for the decision variables respects said one or more functional dependencies between the decision variables.

14. The method of claim 9, wherein each of the decision variables has an associated set of attainable values representing possible outcomes of a corresponding decision, wherein each of the uncertainty variables has an associated set of attainable values.

15. The method of claim 9 further comprising:

utilizing the computer to;

(1) compute a value of an auxiliary function from the one or more statistics of each of the assets;

wherein said performing the optimization includes performing (j), (e), (f), (k) and (1) said plurality of times.

16. The method of claim 15, wherein the optimizer is configured to search for the maximum or minimum of the global objective subject to a constraint of the form J =H/S <

C, where H is the auxiliary function and S is the global objective, wherein C is a constant.

17. The method of claim 15, wherein the optimizer is configured to search for said maximum or minimum of the global objective subject at least to a constraint on a functional combination of the global objective and the auxiliary function.

18. The method of claim 15, wherein said displaying an indication of the resultant data on a display device includes:

displaying a plot of the global objective value versus auxiliary function value.

19. The method of claim 1, wherein the set of algorithms corresponding to at least one of the assets includes a reservoir flow simulator.

20. The method of claim 1, wherein the decision variables include a number of wells to drill in the first asset.

21. A non-transitory computer-readable memory medium storing program instructions that are executable by a computer, wherein the program instructions, when executed by the computer, cause the computer to:

(a) receive first information specifying a set of uncertainty variables that characterize uncertainties associated with a plurality of assets, wherein the first information also specifies a functional relationship between a first and a second of the uncertainty variables, wherein the first uncertainty variable is associated with a first of the assets, wherein the second uncertainty variable is associated with a second of the assets, wherein the assets are assets related to the exploration and production of one or more of oil and gas;

(b) generate values for each of the uncertainty variables, wherein said generating includes generating a value for the first uncertainty variable, and computing a value for the second uncertainty variable from the value of the first uncertainty variable based on the functional relationship;

(c) determine for each of the assets a corresponding input data set using at least a corresponding subset of the uncertainty variable values, wherein said determining includes;

determining a first input data set corresponding to the first asset using at least the value of the first uncertainty variable; and

determining a second input data set corresponding to the second asset using at least the value of the second uncertainty variable;

(d) for each of the assets, invoke execution of a corresponding set of one or more algorithms, wherein each set of one or more algorithms operates on the corresponding input data set to generate a corresponding output data set;

(e) perform (b), (c) and (d) a plurality of times to generate a plurality of output data sets for each asset;

(f) compute one or more statistics for each of the assets based on the corresponding plurality of output data sets;

(g) generate resultant data based at least partially on the statistics of the assets;

(h) display an indication of the resultant data on a display device.

22. The memory medium of claim 21, wherein the program instructions, when executed by the computer, further cause the computer to:

display a graphical representation of progress of the optimization.

23. A computer system comprising:

a memory configured to store program instructions;

at least one processor configured to read and execute the program instructions, wherein, said program instructions are executable to implement;

(a) receiving first information specifying a set of uncertainty variables that characterize uncertainties associated with a plurality of assets, wherein the first information also specifies a functional relationship between a first and a second of the uncertainty variables, wherein the first uncertainty variable is associated with a first of the assets, wherein the second uncertainty variable is associated with a second of the assets, wherein the assets are assets related to the exploration and production of one or more of oil and gas;

(b) generating values for each of the uncertainty variables, wherein said generating includes generating a value for the first uncertainty variable, and computing a value for the second uncertainty variable from the value of the first uncertainty variable based on the functional relationship;

(c) determining for each of the assets a corresponding input data set using at least a corresponding subset of the uncertainty variable values, wherein said determining includes;

determining a first input data set corresponding to the first asset using at least the value of the first uncertainty variable; and

determining a second input data set corresponding to the second asset using at least the value of the second uncertainty variable;

(d) for each of the assets, invoking execution of a corresponding set of one or more algorithms, wherein each set of one or more algorithms operates on the corresponding input data set to generate a corresponding output data set;

(e) performing (b), (c) and (d) a plurality of times to generate a plurality of output data sets for each asset;

(f) computing one or more statistics for each of the assets based on the corresponding plurality of output data sets;

(g) generating resultant data based at least partially on the statistics of the assets;

(h) displaying an indication of the resultant data on a display device.

24. The computer system of claim 23, wherein (d) includes:

distributing the input data sets of the assets to one or more remote computers for remote execution of the corresponding sets of algorithms; and

receiving the output data sets from the one or more remote computers.