Method to quantify discrete pore shapes, volumes, and surface areas using confocal profilometry

• US 8,311,788 B2
• Filed: 07/01/2009
• Issued: 11/13/2012
• Est. Priority Date: 07/01/2009
• Status: Active Grant

• Current Assignee: Schlumberger Technology Corporation (SLB)
• Original Assignee: Schlumberger Technology Corporation (SLB)
• Inventors: Hurley, Neil Francis, Zhang, Tuanfeng, Xu, Guangping, Xu, Lili, Slim, Mirna
• Primary Examiner(s): ALHIJA, SAIF A
• Application Number:
  US12/459,454
• Publication Number:
  US 20110004448A1
• Time in Patent Office: 1,231 Days
• Field of Search: 703/9
• US Class Current: 703/9
• CPC Class Codes: G02B 21/0024 Confocal scanning microscopes (CSOMs) or confocal "macroscopes"; Accessories which are not restricted to use with CSOMs, e.g. sample holders
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects

Abstract

Methods for characterizing a sample of porous media using a measuring device along with a multipoint statistical (MPS) model. Retrieving a set of reflected measured data provided by the measuring device of a surface of the sample in order to produce a sample imaging log, wherein the retrieved set of measured data is communicated to a processor. Selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model. Determining pattern based simulations from the training image using one of a pixel-based template which is applied to the training image. Constructing from the pattern based simulations a complete-sampling image log of surface portions of the sample. Repeat the above steps in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations so as to construct at least one 3D model of the sample.

Claims

1. A method for characterizing a sample of porous media using at least one measuring device and a multipoint statistical (MPS) model, the method comprising:

a) retrieving one or more sets of reflected measured data provided by the at least one measuring device of at least one portion of a surface of the sample to produce a sample imaging log, wherein the retrieved one or more sets of measured data is communicated to a processor;

b) using at least one noise reduction algorithm to identify noise data in the retrieved one or more sets of measured data;

the identified noise data is one of removed from the retrieved one or more sets of measured data, replaced with null values or both;

c) selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model;

d) determining pattern based simulations from the training image using at least one of a pixel-based template which is applied to the training image;

e) constructing from the pattern based simulations one or more sampling image logs of surface portions of the sample; and

f) repeating steps (b) to (e) through the one or more logged surface portions in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations to construct at least one 3D model of the sample.

2. The method according to claim 1, further comprising step (g), using the at least one 3D model of the sample to compute one of a 3D pore surface area, a 3D pore volume, one or more 3D flow simulation models to assist in determining flow properties of the sample, the 3D flow simulation model computed after the conversion of at least one pore-network model or some combination thereof.

3. The method according to claim 1, wherein the one or more sets of reflected measured data includes data gathered from a confocal profilometry measuring device of the at least one measuring device;

and wherein the confocal profilometry measuring device includes a white light.

4. The method according to claim 3, wherein the white-light confocal profilometry measuring device reflects light from the surface of the sample positioned on a movable stage, thereby producing an x-y grid of z-values or depths that measures a microtopography of the surface of the sample.

5. The method according to claim 3, wherein the retrieved one or more sets of reflected measured data is gathered from the confocal profilometry measuring device which provides for computing one of a pore-size distribution, a pore shape factor, at least one pore network model, a pore diameter or a surface area and surface volume of one or more pores on the sample.

6. The method according to claim 1, wherein the retrieved one or more sets of measured data includes data relating to the at least one portion of the surface of the sample in terms of one of a pore, a pore depth, a pore volume, a pore shape, a surface area for each discrete pore or some combination thereof.

7. The method according to claim 1, wherein the at least one noise reduction algorithm includes a spike spatial filter algorithm that identifies spike noise data, whereby the identified spike noise data is removed from the retrieved one or more sets of reflected measured data and replaced with null values to complete the training image.

8. The method according to claim 7, wherein the spike noise data is data that is recorded at depth values larger than a maximum cutoff value or less than a minimum cutoff value.

9. The method according to claim 7, wherein the null values are derived from adjacent values of each respective removed spike location, the null values completing the training image.

10. The method according to claim 1, wherein the at least one noise reduction algorithm includes a leveling algorithm with a least-squares plane-leveling algorithm.

11. The method according to claim 7, wherein the spike spatial filter algorithm is a truncation plane algorithm that identifies spike noise data, wherein the identified spike noise data is removed from the retrieved one or more sets of measured data to reduce the noise.

12. The method according to claim 1, wherein the sample imaging log includes one of unidentified sample image data, data gaps or some combination thereof.

13. The method according to claim 1, wherein the at least one measuring device is from the group consisting of one of transmitted confocal microscopy, white-light confocal profilometry, laser confocal profilometry or some combination thereof.

14. The method according to claim 1, wherein the sample is shaped as one of a uniform geometric shape, a non-uniform geometric shape or some combination thereof.

15. The method according to claim 1, wherein step (f) further comprises using each logged surface portion of the one or more logged surface portions to generate a mirror image logged surface portion, then juxtaposing the generated mirror image logged surface portion with the logged surface portion that was mirror after, wherein both logged surface portions are stacked with a spacing that equals a deepest pore modeled in each logged surface portion, resulting in a stacked series of pores that match the retrieved one or more sets of reflected measured data.

16. A method for characterizing a sample of porous media to identify flow properties of the sample whereby flow simulation models are generated from one or more sets of reflected measured data provided by at least one measuring tool in combination with a multipoint statistical (MPS) model, the method comprising:

a) retrieving the one or more sets of reflected measured data which includes data of at least one portion of a surface of the sample to produce a sample imaging log, wherein the retrieved one or more sets of measured data is communicated to a processor;

b) using at least one noise reduction algorithm to identify noise data in the retrieved one or more sets of measured data so that the identified noise data is one of removed from the retrieved one or more sets of measured data, replaced with null values or both;

c) selecting depth-defined surface portions of the sample from the sample imaging log as a training image for inputting in the MPS model;

d) determining pattern based simulations from the training image using one of a pixel-based template which is applied to the training image;

e) constructing from the pattern based simulations one or more sampling image logs of surface portions of the sample; and

f) repeating steps (b) to (e) through the one or more logged surface portions in order to construct three dimensional (3D) sample images from stacked successive pattern based simulations which provides for constructing at least one 3D model of the sample,

wherein the at least one 3D model of the sample provides for further constructing one or more flow simulation models to assist in determining flow properties of the sample.

17. The method according to claim 16, wherein the sample imaging log includes processed raw data that consists of measured values and non-measured values.

18. The method according to claim 17, wherein the processed raw data includes processing the raw data by one of data leveling of the sample, data filtering for identifying noise so the noise data is removed, data filtering to identify spike data so the spike data is removed and replaced with the null values, multiplying shift depth values or x-y-z values by a constant to make a maximum frequency occur at a depth of z=0, subtracting values from a horizontal truncation plane or some combination thereof, wherein the processed raw data is loaded into the MPS model to assist in determining flow properties of the sample.

19. The method according to claim 16, wherein the constructed sample image logs of surface portions of the sample includes plotting a digital file of the constructed sample images onto one of a digital media or hard copy media.

20. The method according to claim 16, wherein the sample is from a geological formation and shaped as one of a rectangle shape, a cylindrical shape, a shape having at least one planar surface or some combination thereof.

21. The method according to claim 16, wherein a set of depth-defined data from the retrieved one or more sets of measured data in combination with the MPS model provides for computing a surface training image of the at least one portion of the sample, wherein the spike noise data is identified and replaced with null values to generate a complete surface training image.