System, method and computer program product for photometric system design and environmental ruggedization

Patent metadata

Field	Value
Publication number	US-10509223-B2
Application number	US-201314763408-A
Country	US
Kind code	B2
Filing date	Mar 5, 2013
Priority date	Mar 5, 2013
Publication date	Dec 17, 2019
Grant date	Dec 17, 2019

Abstract

A photometric system design methodology employs genetic algorithms to optimize the selection of optical elements for inclusion in the photometric system in order to improve system performance with respect to environmental conditions (i.e., to “ruggedize” the photometric system). The genetic algorithms utilize a multi-objective fitness function to evolve simulated optical element selection, which may be a combination of optical filters and integrated computational elements. The system may also output a size reduced database that serve as simulated candidate optical elements through global optimization, or may output a fixed number of simulated optical elements through conditional optimization for actual tool implementation and calibration analysis.

First claim

What is claimed is:

1. A method to design a photometric system, the method comprising:
selecting an initial population of simulated candidate optical elements, wherein the optical elements are optical filters and/or Integrated Computational Elements (ICE) of the photometric system configured to be disposed within a downhole environment;
determining one or more base environmental factors associated with the downhole environment;
evolving the one or more base environmental factors associated with the downhole environment to one or more evolved environmental factors associated with the downhole environment;
applying an evolutionary algorithm comprising the one or more evolved environmental factors to evaluate the one or more of the simulated candidate optical elements, where applying the evolutionary algorithm further comprises utilizing a multi-objective fitness function,
wherein utilizing the multi-objective fitness function comprises determining central wavelengths of the simulated candidate optical elements and penalizing central wavelength overlap among the simulated candidate optical elements; and
selecting at least one of the simulated candidate optical elements in response to the applying the evolutionary algorithm, the selected simulated candidate optical elements being environmentally rugged optical elements to be utilized within the photometric system.

2. A method as defined in claim 1 , further comprising:
applying the evolutionary algorithm comprising the one or more base environmental factors to one or more of the simulated candidate optical elements, wherein the one or more evolved environmental factors are evolved in response to reaction of the simulated candidate optical elements to at least one of a temperature, pressure or vibration variation.

3. A method as defined in claim 1 , wherein the applying the evolutionary algorithm further comprises ranking the simulated candidate optical elements based upon a fitness function performance.

4. A method as defined in claim 1 , wherein the applying the evolutionary algorithm further comprises continuing to apply the evolutionary algorithm until a maximum number of generations have been simulated or a performance criteria has been met.

5. A method as defined in claim 1 , further comprising:
evolving at least one of the simulated candidate optical elements in response to the applying the evolutionary algorithm.

6. A method as defined in claim 1 , wherein the environmental factors of the simulated candidate optical elements comprise at least one of:
light source stability;
detector noise tolerance; or
filter transmittance change as a function of temperature.

7. A method as defined in claim 1 , wherein the applying the evolutionary algorithm further comprises performing global selection optimization using a gradually size-reduced database of simulated candidate optical elements.

8. A method as defined in claim 1 , wherein the applying the evolutionary algorithm further comprises performing conditional selection optimization using a fixed number of simulated candidate optimal elements in each output.

9. A method as defined in claim 1 , further comprising:
fabricating the selected simulated candidate optical elements; or
fabricating the photometric system using the fabricated simulated selected candidate optical elements.

10. A system comprising a controller configured to perform any of the methods in claims 1 - 8 .

11. A non-transitory machine-readable medium comprising instructions which, when executed by at least one processor, causes the processor to perform any of the methods in claims 1 - 8 .

12. A method as defined in claim 1 , further comprising:
evolving at least one of the initial population of simulated candidate optical elements during the applying the evolutionary algorithm.

13. A method as defined in claim 1 , wherein the evolving the one or more environmental factors comprises:
randomly introducing variations within at least one of the base environmental factors.

14. A method as defined in claim 1 , wherein the evolutionary algorithm is a genetic algorithm, and wherein the method further comprises:
evolving the one or more evolved environmental factors associated with the downhole environment to one or more further evolved environmental factors associated with the downhole environment after the selecting the simulated candidate optical elements;
applying a second evolutionary algorithm comprising the one or more further evolved environmental factors to evaluate the one or more selected simulated candidate optical elements; and
further selecting at least one of the selected simulated candidate optical elements in response to the applying the second evolutionary algorithm.

Assignees

• Halliburton Energy Services Inc

Inventors

• Shen Jing
• Jones Christopher Michael
• Chen Dingding
• Zhang Wei
• Perkins David L

Classifications

• G02B27/0012 Primary

  Optical design, e.g. procedures, algorithms, optimisation routines · CPC title

• G06V10/94 Primary

  Hardware or software architectures specially adapted for image or video understanding · CPC title

• G06F18/2111

  by using evolutionary computational techniques, e.g. genetic algorithms · CPC title

• G06N3/126

  Evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06K9/00973

  Physics · mapped topic