Petrophysical model generation and uses thereof

Patent metadata

Field	Value
Publication number	US-2026043784-A1
Application number	US-202418799475-A
Country	US
Kind code	A1
Filing date	Aug 9, 2024
Priority date	Aug 9, 2024
Publication date	Feb 12, 2026
Grant date	—

Abstract

Systems and methods are disclosed relating to reservoir characterization. A computed tomography (CT) imaging device is used to generate a CT image of a rock sample from a reservoir and segmented into CT slices. The CT slices are processed to identify textures of the rock sample to provide texture data. The rock sample is scanned using nuclear magnetic resonance (NMR) to provide NMR data. The NMR data is segmented to provide NMR segments. The NMR segments and texture data are analyzed to determine a contribution of each texture in each CT slice to one or more relaxation times in a corresponding NMR segment for each CT slice. A petrophysical property is predicted for each texture of each CT slice based on a contribution of each texture and the corresponding NMR segment for each CT slice. A petrophysical model for the reservoir is generated based on the predicted petrophysical property.

First claim

The invention claimed is:

1 . A method comprising:
imaging, using a computed tomography (CT) imaging device to generate a CT image of a rock sample from a reservoir; segmenting the CT image into CT slices; processing the CT slices using a texture classifier to identify textures of the rock sample to provide texture data for each CT slice; scanning the rock sample using an nuclear magnetic resonance (NMR) device to provide NMR data for the rock sample, the NMR data characterizing relaxation times across different regions of the rock sample reflecting variations in pore sizes within the rock sample; segmenting the NMR data into intervals corresponding to a scanning interval of a portion of the rock sample by the NMR device to provide NMR segments; analyzing the NMR segments and the texture data to determine a contribution of each texture in each CT slice to one or more relaxation times in a corresponding NMR segment of the NMR segments for each CT slice; predicting one or more petrophysical properties for each texture of each CT slice based on a determined contribution of each texture and the corresponding NMR segment for each CT slice; and generating a petrophysical model for the reservoir based on the predicted one or more petrophysical properties for each texture of each CT slice.

2 . The method of claim 1 , wherein the imaging comprises scanning using the CT imaging the device to generate the image of the rock sample according to scanning parameters, the scanning parameters identifying a scanning resolution.

3 . The method of claim 1 , wherein the segmenting the CT images is based on segmentation criteria, the segmentation criteria defines a volume of each segment that is segmented from the CT image to provide a corresponding CT slice of the CT slices.

4 . The method of claim 1 , wherein a thickness of each CT slice of the CT slices is same or similar to a thickness to the corresponding NMR segment of the NMR segments.

5 . The method of claim 1 , wherein the texture classifier is trained based on a training dataset that comprises texture characteristics and a texture of textures for each of the texture characteristics, the texture characteristics including a value or a range of values indicative of one or more of a grain size, shape, orientation, and pore structure for the texture, and the textures including a coarse-grained texture, fine-grained texture, and shale texture.

6 . The method of claim 1 , saturating the rock sample to fill pore spaces of the rock sample with a liquid to simulate conditions of the reservoir to provide a saturated rock sample, the NMR device being used to analyze the saturated rock sample to provide the NMR data.

7 . The method of claim 1 , wherein the analyzing comprises using Exploratory Factor Analysis (EFA) to determine the contributions.

8 . The method of claim 1 , wherein the one or more petrophysical properties is estimated using a Coates or Schlumberger-Doll Research (SDR) model.

9 . The method of claim 1 , wherein the petrophysical model is a continuum model.

10 . The method of claim 1 , using the petrophysical model to predict a fluid flow and/or a behavior of the reservoir.

11 . A system comprising:
one or more computing platforms configured to:
segment a computed tomography (CT) image of a rock sample from a reservoir into CT slices;
process the CT slices using a texture classifier to identify textures of the rock sample to provide texture data for each CT slice;
receive nuclear magnetic resonance (NMR) data generated from an NMR scan of the rock sample, the NMR data characterizing relaxation times across different regions of the rock sample reflecting variations in pore sizes within the rock sample;
segment the NMR data into intervals corresponding to a scanning interval of a portion of the rock sample scanned by an NMR device to provide NMR segments;
analyze the NMR segments and the texture data to determine a contribution of each texture in each CT slice to one or more relaxation times in a corresponding NMR segment of the NMR segments for each CT slice;
determine one or more petrophysical properties for each texture of each CT slice based on a determined contribution of each texture and the corresponding NMR segment for each CT slice; and
generate a petrophysical model for the reservoir based on the predicted one or more petrophysical properties for each texture of each CT slice.

12 . The system of claim 11 , wherein the CT image is segmented based on segmentation criteria, the segmentation criteria defines a volume of each segment that is segmented from the CT image to provide a corresponding CT slice of the CT slices.

13 . The system of claim 11 , wherein the texture classifier is trained based on a training dataset that comprises texture characteristics and a texture of textures for each of the texture characteristics.

14 . The system of claim 11 , wherein the analysis of the NMR segments and the texture data is done using Exploratory Factor Analysis (EFA).

15 . The system of claim 11 , wherein the one or more petrophysical properties is determined using a Coates or Schlumberger-Doll Research (SDR) model.

16 . The system of claim 1 , wherein the CT image of the rock is provided by a micro-CT scanner and the NMR data is provided by an NMR device.

17 . The system of claim 11 , wherein the one or more computing platforms are further configured to predict a fluid flow and/or a behavior of the reservoir using the petrophysical model.

18 . The system of claim 11 , wherein a thickness of each CT slice of the CT slices is same or similar to a thickness to the corresponding NMR segment of the NMR segments.

19 . A system comprising:
memory to store machine-readable instructions; one or more processors to access the memory and execute the machine-readable instructions, the machine-readable instructions comprising:
a computed tomography (CT) image segmentor to segment a CT image of a rock sample from a reservoir into CT slices;
a texture classifier to identify textures of the rock sample to provide texture data for each CT slice;
an nuclear magnetic resonance (NMR) image segmentor to segment NMR data into intervals corresponding to a scanning interval of a portion of the rock sample scanned by an NMR device to provide NMR segments;
a factor analyzer to analyze the NMR segments and the texture data to determine a contribution of each texture in each CT slice to one or more relaxation times in a corresponding NMR segment of the NMR segments for each CT slice; a calculator to determine one or more petrophysical properties for each texture of each CT slice based on a determined contribution of each texture and the corresponding NMR segment for each CT slice; and a model generator to generate a petrophysical model for the reservoir based on the predicted one or more petrophysical properties for each texture of each CT slice.

20 . The system of claim 19 , wherein the petrophysical model is used to predict a fluid flow and/or a behavior of the reservoir.

Assignees

• Saudi Arabian Oil Co

Inventors

• Gao Jun
• He Xupeng
• Kwak Hyung

Classifications

• G01N2223/616

  earth materials · CPC title

• G01N24/081

  Making measurements of geologic samples, e.g. measurements of moisture, pH, porosity, permeability, tortuosity or viscosity · CPC title

• G16C60/00

  Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation · CPC title

• G01N33/24 Primary

  Earth materials (G01N33/42 takes precedence) · CPC title

• G01N2223/649

  porosity · CPC title