Methods for performing formation evaluation and related systems

Patent metadata

Field	Value
Publication number	US-11624853-B2
Application number	US-202117158318-A
Country	US
Kind code	B2
Filing date	Jan 26, 2021
Priority date	Jan 31, 2020
Publication date	Apr 11, 2023
Grant date	Apr 11, 2023

Abstract

Embodiments of an invention disclosed herein relate to methods for performing formation evaluation of a formation or formation's surrounding to identify and characterize the abundance and morphology of non-ionic conductor grains, “c-grains”, within the formations that are evaluated by formation evaluation (FE) tools. The methods and related systems as disclosed herein are directed to correcting any existing FE logs that can be adversely affected by the presence of c-grains in the detection volume of FE tools, and/or obtaining new FE information that is unavailable by the application of existing FE methods.

First claim

What is claimed:

1. A method comprising:
producing a complex conductivity spectrum for a formation sample over a frequency range of interest, wherein the complex conductivity spectrum is based on a frequency-dependent resistivity for the formation sample; and
identifying a physical property of non-ionic conductor grains (c-grains) in the formation sample based on the complex conductivity spectrum and a spectral signature function between a c-grain property and corresponding complex conductivity spectra; wherein the c-grains are non-ionically conducting sulfides and/or pyrite.

2. The method of claim 1 further comprising:
collecting the complex conductivity spectrum by performing steps comprising:
injecting a sinusoidal wave current into the formation sample;
measuring a voltage signal response; and
identifying phase shifts between the sinusoidal wave current and the voltage signal response as the frequency-dependent resistivity.

3. The method of claim 1 , wherein the physical property is selected from the group consisting of: a concentration of the c-grains, a morphology of the c-grains, a size distribution of the c-grains, an average size of the c-grains, a differential capacitance of the c-grains, a specific surface area of the c-grains and any combination thereof.

4. The method of claim 1 further comprising:
logging a borehole of a formation to measure the frequency-dependent resistivity for at least a portion of the formation.

5. The method of claim 1 , wherein the formation sample is a core sample.

6. The method of claim 1 , wherein the frequency-dependent resistivity comprises amplitude frequency-dependent resistivity information and phase frequency-dependent resistivity information.

7. The method of claim 1 , wherein the frequency-dependent resistivity is measure over a broadband.

8. The method of claim 1 , wherein the frequency-dependent resistivity is assessed for at least 100 discrete frequencies.

9. The method of claim 1 , wherein the frequency-dependent resistivity comprises a frequency content over frequencies within 0.01 Hz to 1.1 GHz.

10. The method of claim 1 , wherein the frequency-dependent resistivity comprises a frequency content over frequencies within 0.01 Hz to 1.1 GHz with at least 4 different frequency bands.

11. The method of claim 1 further comprising:
adjusting the apparent density of the formation sample based on the physical property.

12. The method of claim 1 , wherein the spectral signature function applies an inversion algorithm to the complex conductivity spectrum.

13. The method of claim 12 , wherein the inversion algorithm is calibrated with data from core samples.

14. The method of claim 12 , wherein the inversion algorithm is calibrated with a physics-based model.

15. The method of claim 12 , wherein identifying a physical property of the c-grains uses a machine-learning algorithm.

16. The method of claim 15 , wherein data from core samples is used to train the machine-learning algorithm.

17. The method of claim 15 , wherein a physics-based model is used to train the machine-learning algorithm.

18. A system comprising:
a processor;
a memory coupled to the processor; and
instructions provided to the memory, wherein the instructions are executable by the processor to perform the method of claim 1 .

19. A method comprising:
injecting a sinusoidal wave current into a formation sample;
measuring a voltage signal response;
identifying phase shifts between the sinusoidal wave current and the voltage signal response as the frequency-dependent resistivity;
deriving a complex conductivity spectrum from the frequency-dependent resistivity; and
physically measuring a physical property of non-ionic conductor grains (c-grains) in the formation sample; wherein the c-grains are non-ionically conducting sulfides and/or pyrite; and
correlating the complex conductivity spectrum to the physical property of the c-grains.

20. A method for performing formation evaluation of a formation and/or formation's surrounding, the method comprising:
(a) providing at least one borehole;
(b) measuring a conductivity for the formation's surrounding using a borehole device in a borehole over a plurality of frequencies to produce a complex conductivity spectrum;
(c) collecting the complex conductivity spectrum over a detection volume;
(d) applying at least one inversion technique to the complex conductivity spectrum to produce an inversion result; and
(e) identifying a physical property of non-ionic conductor grains (c-grains) in the detection volume based on the inversion result; wherein the c-grains are non-ionically conducting sulfides and/or pyrite.

21. The method of claim 20 further comprising:
combining the inversion result with other formation evaluation information and/or geologic information.

22. The method of claim 20 further comprising one or more of:
(f) improving the estimation of the resistivity of fluids in the formation;
(g) resource characterization;
(h) detecting exploration targets elsewhere within the same basin or in another analog basin; and
(i) using the inversion result to make well placement and/or geosteering decisions.

Assignees

• Exxonmobil Technology & Engineering Company

Inventors

• Wheelock Brent D
• Feng Lang
• Li Qiuzi
• Deckman Harry W
• Ertas Mehmet D

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N20/00

  Machine learning · CPC title

• G01N33/24 Primary

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