The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 31, 2016

Filed:

Nov. 15, 2012
Applicant:

Julian Thorne, Orange, CA (US);

Inventor:

Julian Thorne, Orange, CA (US);

Assignee:

Chevron U.S.A. Inc., San Ramon, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/18 (2006.01); G01V 11/00 (2006.01); G01V 99/00 (2009.01);
U.S. Cl.
CPC ...
G06F 17/18 (2013.01); G01V 11/00 (2013.01); G01V 99/005 (2013.01); G01V 2210/6244 (2013.01); G01V 2210/665 (2013.01); G01V 2210/667 (2013.01);
Abstract

A system and method, implemented on a computer, for determining the uncertainty of soft-data debiasing of property distributions of spatially correlated reservoir data are provided. The method includes inputting, into the computer, soft-data at a plurality of spatial locations on a grid and hard-data at a plurality of location along well paths that intersect the grid. Each location of the hard-data is collocated with soft-data values of one or more types. The method further includes calculating, using the computer, a variogram of the hard-data and creating subsets of independent hard-data using the variogram and the soft-data values that are collocated with each of the hard-data location; applying, using the computer, a bootstrap process on each of the plurality of subsets of independent hard-data to obtain a plurality of bootstrap data sets from each of the plurality of subsets of independent hard-data; calculating soft-data debiased histograms for each of the obtained plurality of bootstrap data sets using a binary decision tree and the collocated soft-data; and characterizing the uncertainty of the soft-data debiased histograms.


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