Method to determine relative thermal maturity from porosities measured by quantitative imaging on uncleaned samples and GRI porosity measured on crushed cleaned samples

1. A method of determining a thermal maturity model of a subterranean region of interest, comprising:

obtaining a plurality of rock samples for the subterranean region of interest;

for each of the plurality of rock samples, using a computer processor:

determining a first porosity value,

determining a second porosity value,

determining an amount of organic matter,

determining a thermal maturity index based, at least in part, on the first porosity value, the second porosity value and the amount of organic matter, and

determining, using a computer processor, the thermal maturity model based, at least in part, on the thermal maturity index for each of the plurality of rock samples.

2. The method of claim 1 , further comprising planning a wellbore trajectory using a wellbore planning system based, at least in part, on the thermal maturity model.

3. The method of claim 2 , further comprising drilling a wellbore based, at least in part, on the planned wellbore trajectory using a drilling system.

4. The method of claim 1 , wherein determining the first porosity value comprises:

determining a bulk density;

determining a grain density;

determining a fluid density, and

determining the first porosity based, at least in part on a ratio of the bulk density to the grain density.

5. The method of claim 1 , wherein determining the second porosity value comprises:

determining a scanning electron microscope (SEM) image of a cross-section through the sample;

segmenting the SEM image into a labeled pore space phase, and a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the second porosity value based upon a total area of labeled pore pixels and a total area of the SEM image.

6. The method of claim 5 , wherein segmenting the SEM image comprises applying a trained machine learning network to the SEM image.

7. The method of claim 1 , wherein determining the amount of organic matter comprises:

determining a scanning electron microscope (SEM) image of a cross-section through the sample;

segmenting the SEM image into a labeled pore space phase, and a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the amount of organic matter based upon a total area of labeled organic matter pixels and a total area of the SEM image.

8. The method of claim 1 , wherein a low value of the thermal maturity index indicates a high thermal maturity.

9. A non-transitory computer readable medium storing instructions executable by a computer processor, the instructions comprising functionality for:

receiving, for each of a plurality of rock samples:

a bulk density,

a grain density,

a fluid density, and

a scanning electron microscope (SEM) image of a cross-section;

determining a first porosity value based, at least in part on, the bulk density, grain density, and fluid density;

determining a second porosity value from the SEM image,

determining an amount of organic matter from the SEM image,

determining a thermal maturity index based, at least in part, on the first porosity value, the second porosity value and the amount of organic matter; and

determining, using a computer processor, a thermal maturity model based, at least in part, on the thermal maturity index for each of the plurality of rock samples.

10. The non-transitory computer readable medium of claim 9 , the instructions furthering comprising functionality for planning a wellbore trajectory based, at least in part, on the thermal maturity model.

11. The non-transitory computer readable medium of claim 9 , wherein determining the first porosity is based, at least in part on a ratio of the bulk density to the grain density.

12. The non-transitory computer readable medium of claim 9 , wherein determining the second porosity value comprises:

segmenting the SEM image into a labeled pore space phase, a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the second porosity value based upon a total area of labeled pore pixels and a total area of the SEM image.

13. The non-transitory computer readable medium of claim 12 , wherein segmenting the SEM image comprises applying a trained machine learning network to the SEM image.

14. The non-transitory computer readable medium of claim 9 , wherein determining the amount of organic matter comprises:

segmenting the SEM image into a labeled pore space phase, a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the amount of organic matter based upon a total area of labeled organic matter pixels and a total area of the SEM image.

15. A system comprising:

a scanning electron microscope (SEM) configured to form an SEM image of a cross-section through a plurality of rock samples;

a computer processor, configured to:

for each of the plurality of the rock samples,

receive a bulk density and a grain density,

determine a first porosity value based, at least in part, on the bulk density and the grain density,

from the SEM image:

determine a second porosity value; and

determine an amount of organic matter, and

determine a thermal maturity index based, at least in part, on the first porosity value, the second porosity value and the amount of organic matter; and

determine a thermal maturity model based, at least in part, on the thermal maturity index for each of the plurality of rock samples.

16. The system of claim 15 , further comprising a wellbore planning system configured to plan a planned wellbore trajectory using a wellbore planning system based, at least in part, on the thermal maturity model.

17. The system of claim 16 , further comprising wellbore drilling system configured to drill a wellbore based, at least in part, on the planned wellbore trajectory.

18. The system of claim 16 , wherein determining the second porosity value comprises:

segmenting the SEM image into a labeled pore space phase, a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the second porosity value based upon a total area of labeled pore pixels and a total area of the SEM image.

19. The system of claim 15 , wherein determining the amount of organic matter comprises:

segmenting the SEM image into a labeled pore space phase, a labeled matrix mineral phase, and a labeled organic matter phase; and

determining the amount of organic matter based upon a total area of labeled organic matter pixels and a total area of the SEM image.

20. The system of claim 15 , wherein segmenting the SEM image comprises applying a trained machine learning network to the SEM image.