Uncertainty constrained subsurface modeling

• US 6,549,854 B1
• Filed: 02/11/2000
• Issued: 04/15/2003
• Est. Priority Date: 02/12/1999
• Status: Expired due to Term

• Current Assignee: Schlumberger Technology Corporation (SLB)
• Original Assignee: Schlumberger Technology Corporation (SLB)
• Inventors: Prange, Michael, Malinverno, Alberto
• Primary Examiner(s): Picard, Leo
• Assistant Examiner(s): KOSOWSKI, ALEXANDER J
• Application Number:
  US09/502,920
• Time in Patent Office: 1,159 Days
• Field of Search: 700/98, 700/97, 367/73, 175/45, 175/50, 702/5, 702/6, 702/16
• US Class Current: 702/16
• CPC Class Codes: G01V 1/282 Application of seismic models, synthetic seismograms
• CPC Class Codes: G01V 11/00 Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
• CPC Class Codes: G01V 2210/66 Subsurface modeling
• CPC Class Codes: G01V 2210/667 Determining confidence or uncertainty in parameters

Abstract

A method, apparatus, and article of manufacture are provided that use measurement data to create a model of a subsurface area. The method includes creating an initial parameterized model having an initial estimate of model parameter uncertainties; considering measurement data from the subsurface area; updating the model and its associated uncertainty estimate; and repeating the considering and updating steps with additional measurement data. A computer-based apparatus and article of manufacture for implementing the method are also disclosed. The method, apparatus, and article of manufacture are particularly useful in assisting oil companies in making hydrocarbon reservoir data acquisition, drilling and field development decisions.

Claims

1. A method of using measurement data to create a model of a subsurface area comprising the steps of:

creating a parameterized model of the subsurface area having an initial estimate of model parameter uncertainties;

considering measurement data from the subsurface area;

updating said parameterized model to fit said measurement data, said updated parameterized model having an updated estimate of model parameter uncertainties; and

repeating said considering and updating steps with additional measurement data to produce a further updated parameterized model having a further updated estimate of model parameter uncertainties.

2. A method according to claim 1, wherein said model of the subsurface area has geometrical model parameters representing geological boundaries.

3. A method according to claim 1, wherein said model of the subsurface area has material parameters representing properties of distributed subsurface materials.

4. A method according to claim 1, wherein said measurement data comprise seismic data, drilling data, well logging data, well test data, production history data, or permanent monitoring data.

5. A method according to claim 1, wherein said measurement data and said additional measurement data comprise different types of data.

6. A method according to claim 5, wherein said measurement data comprises seismic data and said additional measurement data comprises well logging data.

7. A method according to claim 1, wherein said parameterized model of the subsurface area comprises a layered model having material properties associated with each layer.

8. A method according to claim 1, wherein said parameterized model of the subsurface area comprises a geocellular model having material property values defined on a regular or irregular three-dimensional grid.

9. A method according to claim 1, wherein said parameterized model of the subsurface area comprises a geometry-based model having material property values defined on a plurality of discrete geometrical sub-regions within the subsurface area.

10. A method according to claim 1, wherein said initial and updated estimates of model parameter uncertainties comprise probability density functions computed using deterministic optimization or Monte Carlo sampling.

11. A method according to claim 10, wherein said probability density functions are multivariate normal probability density functions definable by mean vectors and covariance matrices.

12. A method according to claim 11, wherein a mean and covariance matrix of said updated estimate of model parameter uncertainties is computed using deterministic optimization or Monte Carlo sampling.

13. A method according to claim 1, wherein said considering step comprises comparing said measurement data to a model parameter, said updating step comprises updating said parameterized model to fit said measurement data, and said updating step further comprising updating said initial estimate of model parameter uncertainties to be consistent with uncertainties associated with said measurement data.

14. A method according to claim 1, wherein said considering step comprises comparing said additional measurement data to predicted data produced by a prediction algorithm operating on said initial parameterized model.

15. A method according to claim 1, wherein said considering step produces a likelihood function that is combined with said initial estimate of model parameter uncertainties to produce said updated estimate of model parameter uncertainties.

16. A method according to claim 1, wherein said updating step comprises directly updating a sample from a posterior PDF obtained by a Monte Carlo method using said measurement data.

17. A method according to claim 1, wherein said updating step comprises directly updating said sample using travel time-depth measurement data obtained while drilling.

18. A method according to claim 1, wherein said parameterized model parameters may be changed during said updating step only when said change is consistent with said initial estimate of model parameter uncertainties.

19. A method according to claim 1, wherein any changes to said parameterized model parameters that are inconsistent with said initial estimate of model parameter uncertainties are identified during said updating step.

20. An apparatus for creating a model of a subsurface area comprising:

means for creating a parameterized model of the subsurface area having an initial estimate of model parameter uncertainties;

means for considering measurement data from the subsurface area;

means for updating said parameterized model to fit said measurement data, said updated parameterized model having an updated estimate of model parameter uncertainties; and

means for repeating said considering and updating steps with additional measurement data to produce a further updated parameterized model having a further updated estimate of model parameter uncertainties.

21. An article of manufacture, comprising:

a computer usable medium having a computer readable program code means embodied therein for creating a model of a subsurface area, the computer readable program code means in said article of manufacture comprising;

computer-readable program means for creating a parameterized model of the subsurface area having an initial estimate of model parameter uncertainties;

computer-readable program means for considering measurement data from the subsurface area;

computer-readable program means for updating said parameterized model to fit said measurement data, said updated parameterized model having an updated estimate of model parameter uncertainties; and

computer-readable program means for repeating said considering and updating steps with additional measurement data to produce a further updated parameterized model having a further updated estimate of model parameter uncertainties.