Method for computing complexity, confidence and technical maturity indices for reservoir evaluations

• US 6,810,332 B2
• Filed: 01/31/2003
• Issued: 10/26/2004
• Est. Priority Date: 01/31/2003
• Status: Expired due to Fees

• Current Assignee: Chevron USA Incorporated (Chevron Corp.)
• Original Assignee: Chevron USA Incorporated (Chevron Corp.)
• Inventors: Harrison, Christopher J.
• Primary Examiner(s): Barlow, John
• Assistant Examiner(s): Taylor, Victor J.
• Application Number:
  US10/356,266
• Publication Number:
  US 20040158406A1
• Time in Patent Office: 634 Days
• Field of Search: 703/10, 700/28, 702/9, 702/13, 702/16, 702/14, 702/12, 702/6, 705/8, 166/250.15
• US Class Current: 702/13
• CPC Class Codes: E21B 49/00 Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
• CPC Class Codes: G01V 1/40 specially adapted for well-logging
• 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

Abstract

A method for computing complexity, confidence and technical maturity indices for the evaluation of a reservoir is disclosed. The method comprising the steps of (a) making estimates of reservoir components which are associated with a subsurface reservoir evaluation; (b) making estimates of confidence values for the estimates of the reservoir components; and (c) computing an evaluation index for the evaluation of the reservoir based upon at least one of the estimates of the reservoir components and the estimates of the confidence values. Complexity and confidence scores are assigned to the respective estimates of the reservoir components and estimates of confidence values. These scores are combined to produce complexity and confidence indices. This evaluation tool is intended to help improve the assessment of the reliability of probabilistic reservoir production forecasts. The tool provides an indicator of the reliability of the forecast production profiles and reserves estimations. Moreover, a list of the uncertainties in the subsurface evaluation is ranked by likely impact on forecasts.

Claims

1. A method for computing an evaluation index for an evaluation of a reservoir, the method comprising the steps of:

(a) making estimates of reservoir components which are associated with a subsurface reservoir evaluation;

(b) making estimates of confidence values for the estimates of the reservoir components; and

(c) computing an evaluation index for the evaluation of the reservoir based upon at least one of the estimates of the reservoir components and the estimates of the confidence values.

2. The method of claim 1 further comprising:

(a) determining component complexity scores associated with the estimate of the reservoir components; and

(b) computing a complexity index as the evaluation index based upon the complexity scores.

3. The method of claim 1 further comprising:

(a) determining confidence scores associated with the estimate of the confidence values; and

(b) computing a confidence index as the evaluation index based upon the confidence scores.

4. The method of claim 1 wherein:

the reservoir components include at least one subsurface characteristic component.

5. The method of claim 1 wherein:

the reservoir components include at least one developmental scheme component of the reservoir.

6. The method of claim 1 wherein:

the reservoir components include at least one of a subsurface characteristic component and at least one developmental scheme characteristic.

7. The method of claim 1 wherein:

estimating confidence values is made in cooperation with a graphic feedback display which depicts at least one probability curve dependent upon the relative level of an estimated confidence value.

8. The method of claim 1 wherein:

at least one of the estimates of the reservoir components and the confidence values is made in response to literal and numerical descriptions.

9. The method of claim 1 wherein:

a complexity look-up table is used to establish the relationship between the estimate of reservoir components and their associated complexity scores.

10. The method of claim 9 wherein:

the complexity look-up table is updated using an experience based knowledge updating scheme.

11. The method of claim 1 wherein:

a non-linear weighting scheme is used in computing the complexity index.

12. The method of claim 11 wherein:

the weighting scheme is one an exponentiated power law average scheme and an arithmetic power law scheme and a logistic sigmoid exponential weighting scheme.

13. The method of claim 2 further comprising:

determining which of the estimates of the reservoir components is most outcome determinative in computing the complexity index.

14. The method of claim 13 further comprising:

(a) visually displaying an data input tree which includes input nodes representative of reservoir components and connecting branches leading to an output node representative of the overall complexity index; and

(b) visually displaying which branch of the data input tree is most outcome determinative of the complexity index.

15. The method of claim 3 further comprising:

(a) visually displaying an data input tree which includes input nodes representative of reservoir components and connecting branches leading to an output node representative of the overall confidence index; and

(b) visually displaying which branch of the data input tree is most outcome determinative of the confidence index.

16. The method of claim 1 wherein:

the evaluation index is a technical maturity index (TMI) for the reservoir evaluation which is a function of the confidence values for the reservoir components and a function of maturity index weights which relate the impact of uncertainty that a reservoir component will have on the predicted outcome of a reservoir evaluation.

17. The method of claim 16 wherein:

a maturity lookup table is used to determine the estimated maturity index weights.

18. The method of claim 16 further comprising:

determining which of the confidence scores associated with a reservoir component is most outcome determinative of the TMI score.

19. The method of claim 1 wherein a technical maturity index is calculated using the following formula:

$\mathrm{TMI}=\left(\frac{\sum <br><br>\left(\mathrm{MI\_Wts}*5\right)-\sum <br><br>\left(\mathrm{MI\_Wts}*\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{conf}\right)}{(\sum <br><br>\left(\mathrm{MI\_Wts}*5\right)}\right)$

where TMI=technical maturity index;

MI_Wts=maturity index weight; and

Δ

conf=confidence difference between perfect knowledge and the confidence score for an estimate of a reservoir component.

20. A computer readable media containing executable code for carrying out the operational steps of:

(a) making estimates of reservoir components which are associated with a subsurface reservoir evaluation;

(b) making estimates of confidence values for the estimates of the reservoir components; and

(c) computing an evaluation index for the evaluation of the reservoir based upon at least one of the estimates of the reservoir components and the estimates of the confidence values.

21. The media of claim 20 further carrying out operational steps comprising:

(a) determining component complexity scores associated with the estimate of the reservoir components; and

(b) computing a complexity index as the evaluation index based upon the complexity scores.

22. The media of claim 20 further carrying out operational steps comprising:

(a) determining confidence scores associated with the estimate of the confidence values; and

(b) computing a confidence index as the evaluation index based upon the confidence scores.

23. The media of claim 20 wherein:

the reservoir components include at least one of a subsurface characteristic component and at least one developmental scheme characteristic.

24. The media of claim 20 wherein:

estimating confidence values is made in cooperation with a graphic feedback display which depicts at least one probability curve dependent upon the relative level of an estimated confidence value.

25. The media of claim 22 further carrying out the operational steps of:

(a) visually displaying an data input tree which includes input nodes representative of reservoir components and connecting branches leading to an output node representative of the overall complexity index; and

(b) visually displaying which branch of the data input tree is most outcome determinative of the complexity index.

26. The media of claim 22 further carrying out the operational steps of:

(a) visually displaying an data input tree which includes input nodes representative of reservoir components and connecting branches leading to an output node representative of the overall confidence index; and

(b) visually displaying which branch of the data input tree is most outcome determinative of the confidence index.

27. The media of claim 22 wherein:

the evaluation index is a technical maturity index (TMI) for the reservoir evaluation which is a function of the confidence values for the reservoir components and a function of maturity index weights which relate the impact of uncertainty that a reservoir component will have on the predicted outcome of a reservoir evaluation.

28. The media of claim 27 wherein:

the technical maturity index is calculated using the following formula;

$\mathrm{TMI}=\left(\frac{\sum <br><br>\left(\mathrm{MI\_Wts}*5\right)-\sum <br><br>\left(\mathrm{MI\_Wts}*\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{conf}\right)}{(\sum <br><br>\left(\mathrm{MI\_Wts}*5\right)}\right)$

 

where TMI=technical maturity index;

MI_Wts=maturity index weight; and

Δ

conf=confidence difference between perfect knowledge and the confidence score for an estimate of a reservoir component.