IP Library › Granted Patent US 12,560,742
Granted Patent B2
US 12,560,742 · App. 18/089,255 · Granted Feb 24, 2026

Characterization of subsurface geological formations

Inventors: Xingquan Zhang (Dhahran, SA); Neelesh R. Tripathi (Dhahran, SA)
Assignee: Saudi Arabian Oil Company
G01V20/00G01V11/002
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Quick Facts
Patent No.
US 12,560,742
App. No.
18/089,255
Granted
Feb 24, 2026
Kind
B2
Abstract

Systems and methods for characterizing a subsurface formation include: measuring a property of the subsurface formation at a plurality of locations; storing values of the property of the subsurface formation at the plurality of locations; defining a distance of influence for the property in the subsurface formation based on the property and on a degree of heterogeneity of the subsurface formation; identifying clusters of locations that are located within the distance of influence of each other; for each cluster identified, calculating an average value of the property of the subsurface formation in the cluster; for each cluster identified, replacing multiple values of the property and locations of the identified cluster with the average value of the property of the subsurface formation in the cluster at a single location; and calculating summary statistics of the reduced data set.

Claims (40)

1 . A method for characterizing a subsurface formation, the method comprising:

measuring a property of the subsurface formation at a plurality of locations;

storing values of the property of the subsurface formation at the plurality of locations in a database;

defining a distance of influence for the property in the subsurface formation based on the property and on a degree of heterogeneity of the subsurface formation;

identifying clusters of locations that are located within the distance of influence of each other;

for each cluster identified, calculating an average value of the property of the subsurface formation in the cluster;

defining a reduced data set by, for each cluster identified, replacing multiple values of the property and locations of the identified cluster with the average value of the property of the subsurface formation in the cluster at a single location; and

calculating summary statistics of the reduced data set,

wherein measuring the property of the subsurface formation comprises drilling a test well in the subsurface formation.

2 . The method of claim 1 , further comprising logging the test well in the subsurface formation.

3 . The method of claim 1 , wherein the property is a continuous property of the formation.

4 . The method of claim 1 , wherein the distance of influence varies with direction.

5 . The method of claim 1 , wherein defining a distance of influence comprises assessing a reservoir heterogeneity and a main depositional direction.

6 . The method of claim 1 , wherein the average value for a cluster of locations is based on an equal weighting of the values of the property at the locations of the cluster of locations.

7 . The method of claim 1 , wherein the distance of influence of the property is different in different directions.

8 . The method of claim 1 , further comprising, modelling an oil reservoir within the subsurface formation.

9 . A system for characterizing a subsurface formation, the system comprising:

one or more processing devices and one or more non-transitory machine-readable storage devices storing instructions that are executable by the one or more processing devices to cause performance of operations comprising:

defining a distance of influence for a property in the subsurface formation based on the property and on a degree of heterogeneity of the subsurface formation;

identifying clusters of locations within a plurality of locations where the property has been measured that are located within the distance of influence of each other;

for each cluster identified, calculating an average value of the property of the subsurface formation in the cluster;

defining a reduced data set by, for each cluster identified, replacing multiple values of the property and locations of the identified cluster with the average value of the property of the subsurface formation in the cluster at a single location; and

calculating summary statistics of the reduced data set,

wherein measuring the property of the subsurface formation comprises drilling a test well in the subsurface formation.

10 . The system of claim 9 , wherein the distance of influence varies with direction.

11 . The system of claim 9 , wherein defining a distance of influence comprises comparing a spacing of the plurality of locations and the degree of heterogeneity of the subsurface formation.

12 . The system of claim 9 , wherein the average value for a cluster of locations is based on an equal weighting of the values of the property at the locations of the cluster of locations.

13 . The system of claim 9 , wherein the distance of influence of the property is different in different directions.

14 . The system of claim 9 , further comprising, modelling an oil reservoir within the subsurface formation.

15 . One or more non-transitory machine-readable storage devices storing instructions for characterizing a subsurface formation, the instructions being executable by one or more processing devices to cause performance of operations comprising:

defining a distance of influence for a property in the subsurface formation based on the property and on a degree of heterogeneity of the subsurface formation;

identifying clusters of locations within a plurality of locations where the property has been measured that are located within the distance of influence of each other;

for each cluster identified, calculating an average value of the property of the subsurface formation in the cluster;

defining a reduced data set by, for each cluster identified, replacing multiple values of the property and locations of the identified cluster with the average value of the property of the subsurface formation in the cluster at a single location; and

calculating summary statistics of the reduced data set,

wherein measuring the property of the subsurface formation comprises drilling a test well in the subsurface formation.

16 . The machine-readable storage devices of claim 15 , wherein the operations further comprise: defining a distance of influence that varies with direction.

17 . The machine-readable storage devices of claim 15 , wherein the operations further comprise: defining a distance of influence comprises comparing a spacing of the plurality of locations and the degree of heterogeneity of the subsurface formation.

18 . The machine-readable storage devices of claim 15 , wherein the operations further comprise: defining the distance of influence of the property to be different in different directions.

19 . The machine-readable storage devices of claim 15 , wherein the operations further comprise: modelling an oil reservoir within the subsurface formation.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 10, 2023
From: ZHANG, XINGQUAN; TRIPATHI, NEELESH R.
To: SAUDI ARABIAN OIL COMPANY
Reel/Frame 062327/0414 →
Continuity (1)
Related Publication 20240210591A1 · Jun 27, 2024
References Cited (29)
US 5850560A · Kang · 1998 [cited by applicant]
US 5995906A · Doyen et al. · 1999 [cited by applicant]
US 6490526B2 · Matteucci et al. · 2002 [cited by applicant]
US 9355070B2 · Thorne · 2016 [cited by applicant]
US 9448313B2 · Hofland et al. · 2016 [cited by applicant]
US 9817143B2 · Groenestijn · 2017 [cited by applicant]
US 9959144B2 · Callegari et al. · 2018 [cited by applicant]
US 10061046B2 · Hofland et al. · 2018 [cited by applicant]
US 11248448B2 · Zhang · 2022 [cited by applicant]
US 11693150B2 · Zhang · 2023 [cited by applicant]
US 20060241920A1 · Le Ravalec-Dupin · 2006 [cited by applicant]
US 20090260415A1 · Suarez-Rivera · 2009 [cited by examiner]
US 20160146973A1 · Johnson · 2016 [cited by applicant]
US 20180275301A1 · Ma et al. · 2018 [cited by applicant]
US 20220179883A1 · Biernacki · 2022 [cited by examiner]
EP 3004947A1 · 2016 [cited by applicant]
EP 3756110A1 · 2020 [cited by applicant]
WO WO2012108917A1 · 2012 [cited by examiner]
WO WO2020142257 · 2020 [cited by applicant]
Basarir et al., “Geostatistical modeling of spatial variability of SPT data for a borax stockpile site,” Engineering Geology, Apr. 2010, 114:154-163, 10 pages. [cited by applicant]
Bourgault, “Using Non-Gaussian Distributions in Geostatistical Simulations,” Mathematical Geology, 1997, 29(3):315-334, 20 pages. [cited by applicant]
Deutsch et al., “GSLIB: Geostatistical Software Library and User's Guide,” 2nd Edition, Oxford University Press, 1997, New York, New York, 375 pages. [cited by applicant]
Emery et al., “Histogram and variogram inference in the multigaussian model,” Stochastic environmental research and risk assessment, Feb. 2005, 19(1):48-58, 17 pages. [cited by applicant]
Kerry et al., “Determining the effect of asymmetric data on the variogram. I. Underlying asymmetry,” Computers & Geosciences, Oct. 2007, 33(10):1212-1232, 21 pages. [cited by applicant]
Prades et al., “Geostatistics and clustering for geochemical data analysis,” Thesis for the degree of Master of Science in Mining Engineering, University of Alberta, 2017, 96 pages. [cited by applicant]
Pyrcz et al., “Geostatistical Reservoir Modeling, ” 2nd Edition, 2014, Oxford University Press, New York, New York, pp. 53-58, 6 pages. [cited by applicant]
Qu et al., “Geostatistical simulation with a trend using gaussian mixture models,” Natural Resources Research, Aug. 2017, 27(3):347-363, 17 pages. [cited by applicant]
Zhang et al., “Geostatistics for Spatial Uncertainty Characterization,” Geo-Spatial Information Science, Mar. 2009, 12(1):7-12, 6 pages. [cited by applicant]
SAIP Examination Report in Saudi Arabian Appln. No. 123451089, dated Mar. 18, 2025, 9 pages (with English translation). [cited by applicant]