Core-level high resolution petrophysical characterization method

Patent metadata

Field	Value
Publication number	US-11727583-B2
Application number	US-202017010372-A
Country	US
Kind code	B2
Filing date	Sep 2, 2020
Priority date	Sep 2, 2020
Publication date	Aug 15, 2023
Grant date	Aug 15, 2023

Abstract

A method for characterizing a subsurface formation includes receiving image data of the subsurface formation obtained by a sensor tool and receiving a plurality of non-image data logs, each non-image data log being obtained by a different type of sensor tool. The method also includes performing an electrofacies analysis on the plurality of non-image data logs where the electrofacies analysis includes defining clusters wherein each cluster has a similar property to provide a plurality of electrofacies blocks with each electrofacies block representing a depth interval. The method further includes partitioning the image data into multiple high-resolution depth segments that share a similar property, feature, and/or pattern for each electrofacies block and assigning data from the plurality of non-image data logs into a corresponding high-resolution depth segment to provide a high-resolution data log that characterizes the subsurface formation.

First claim

What is claimed is:

1. A method for characterizing a subsurface formation, the method comprising:
receiving multi-dimensional high-resolution image data of the subsurface formation, the image data obtained using an imaging sensor tool;
receiving a plurality of non-image data logs, each non-image data log being obtained by a different type of non-imaging sensor tool, wherein received data excludes core data and each non-image data log has a resolution that is less than a resolution of the multi-dimensional high-resolution image data;
performing an electrofacies analysis on the plurality of non-image data logs and the multi-dimensional high-resolution image data, the electrofacies analysis comprising defining clusters based on combinations of responses of the non-image data logs that have specific physical values and specific compositional characteristics of a rock interval of the subsurface formation and extracted statistical parameters from the multi-dimensional high-resolution image data, wherein each cluster has a similar property to provide a plurality of electrofacies blocks, each electrofacies block representing a depth interval;
partitioning the multi-dimensional high-resolution image data into multiple high-resolution segments that share a similar property, feature, and/or pattern for each electrofacies block; and
assigning data from the plurality of non-image data logs into a corresponding high-resolution segment to provide a multi-dimensional high-resolution data log that characterizes the subsurface formation, the multi-dimensional high-resolution data log comprising mineral volumetric values and porosity volumetric values and having a resolution greater than the resolution of each of the non-image data logs.

2. The method according to claim 1 , further comprising:
performing a quality check on the multi-dimensional high-resolution data log;
iterating the performing of the electrofacies analysis, the partitioning, and the assigning in response to the multi-dimensional high-resolution data log not passing the quality check; and
accepting the multi-dimensional high-resolution data log in response to the multi-dimensional high-resolution data log passing the quality check.

3. The method according to claim 2 , wherein passing the quality check comprises a difference between theoretical and input curves be less that a difference threshold value, a confidence level in clustering should be larger than a confidence threshold value, and/or a number of isolated points should be less than isolated point threshold value.

4. The method according to claim 2 , further comprising:
generating a digital rock model of the subsurface formation using the accepted multi-dimensional high-resolution data log;
performing a flow simulation using the digital rock model to provide flow simulation output; and
determining a formation property from the flow simulation output.

5. The method according to claim 4 , wherein the formation property comprises at least one of a permeability, a capillary pressure, and a rock mechanical property.

6. The method according to claim 4 , further comprising performing a borehole-related action based on the formation property using borehole-related equipment.

7. The method according to claim 6 , wherein the borehole-related action comprises at least one of:
drilling a new borehole at a location determined by the formation property;
extending an existing borehole with a trajectory determined by the formation property; or
performing a completion task in the borehole as determined by the formation property.

8. The method according to claim 7 , wherein the completion task comprises perforating a casing at a depth that leads directly to a reservoir of hydrocarbons.

9. The method according to claim 1 , further comprising performing data preparation on the multi-dimensional high-resolution image data and the non-image data logs, the data preparation comprising at least one of:
removing non-formation effects from the multi-dimensional high-resolution image data and/or the non-image data logs;
calculating uncertainties associated with the multi-dimensional high-resolution image data and/or the non-image data logs and normalizing the multi-dimensional high-resolution image data and/or the non-image data logs using the uncertainties;
aligning the multi-dimensional high-resolution image data and the non-image data logs to a reference depth system;
filtering the non-image data logs into a lowest resolution of all the non-image data logs;
extracting a statistical parameter from the multi-dimensional high-resolution image data and calculating a rolling average of that parameter to match the lowest resolution.

10. The method according to claim 1 , further comprising generating a volumetric model comprising quantitative mineral volumetric information, porosity, and fluid saturation by optimizing simultaneous equations describing one or more interpretation models that relate tool responses to the mineral volumetric information, porosity, and/or fluid saturation.

11. The method according to claim 1 , wherein performing the electrofacies analysis comprises reducing a number of components of a feature vector used for characterizing the subsurface formation from a first number to a second number, wherein the second number of components explains at least a threshold value of a variance of the feature vector.

12. The method according to claim 11 , wherein the reducing comprises performing a principal component analysis having an orthogonal linear transformation.

13. The method according to claim 1 , wherein defining clusters comprises using a mathematical technique comprising K-mean, agglomerative hierarchical clustering, Gaussian mixture model (GMM), spectral, and/or fuzzy C-mean clustering.

14. The method according to claim 1 , wherein defining clusters comprises selecting a clustering algorithm and a number of clusters using a quality metric.

15. The method according to claim 14 , wherein the quality metric comprises Silhouette score (SS), Calinski-Harabasz score (CH), Davies-Bouldin score (DB), Bayesian Information Criterion (BIC), minimum/maximum number of elements in a cluster, and/or number of isolated elements in a cluster.

16. The method according to claim 1 , wherein the partitioning comprises normalization, smoothing, and quantization of the multi-dimensional high-resolution image data.

17. The method according to claim 1 , wherein partitioning comprises extracting geological information from the multi-dimensional high-resolution image data using texture analysis.

18. The method according to claim 17 , wherein using texture analysis comprises using a Gray Level Co-occurrence Matrix (GLCM) to convert raw images into attribute images considering different textural aspects, the textural aspects comprising contrast, homogeneity, correlation, and/or entropy within the multi-dimensional high-resolution image data.

19. The method according to claim 1 , wherein assigning comprises imposing volumetric rules, petrophysical constraints, and/or geological constraints on the data from the plurality of non-image data logs with respect to the high-resolution depth segments.

20. An apparatus for characterizing a subsurface formation, the apparatus comprising:
a processor configured to:
receive multi-dimensional high-resolution image data of the subsurface formation penetrated by a borehole, the multi-dimensional high-resolution image data obtained using a downhole imaging sensor tool disposed in the borehole;
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Assignees

• Zhang Hao
• Dymmock Stephen
• Alarcon Nora Patricia
• Le Fei
• Baker Hughes Oilfield Operations Llc

Inventors

• Zhang Hao
• Dymmock Stephen
• Alarcon Nora Patricia
• Le Fei

Classifications

• G01V1/50 Primary

  Analysing data · CPC title

• G01V3/38

  Processing data, e.g. for analysis, for interpretation, for correction · CPC title

• G01V11/00

  Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00 · CPC title

• E21B47/0025

  generating an image of the borehole wall using down-hole measurements, e.g. acoustic or electric · CPC title

• G06T7/50 Primary

  Depth or shape recovery · CPC title