AUTOMATED DOWNHOLE FLUID CLASSIFICATION USING PRINCIPAL SPECTROSCOPY COMPONENT DATA

• US 20180371905A1
• Filed: 11/04/2016
• Published: 12/27/2018
• Est. Priority Date: 11/04/2016
• Status: Active Grant

• Current Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Original Assignee: Halliburton Energy Services Incorporated (Halliburton Company)
• Inventors: Chen, Dingding, Dai, Bin, Jones, Christopher Michael, Quirein, John Andrew
• Granted Patent: US 10,641,091 B2
• Time in Patent Office: Days
• CPC Class Codes: E21B 2200/22 Fuzzy logic, artificial intelligence, neural networks or the like
• CPC Class Codes: E21B 49/08 Obtaining fluid samples or testing fluids, in boreholes or wells
• CPC Class Codes: E21B 49/0875 determining specific fluid parameters
• CPC Class Codes: G01N 21/31 Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry G01N21/72 takes precedence
• CPC Class Codes: G01N 2201/1293 resolving multicomponent spectra
• CPC Class Codes: G01N 2201/1296 using neural networks
• CPC Class Codes: G01V 11/002 Details, e.g. power supply systems for logging instruments, transmitting or recording data, specially adapted for well logging, also if the prospecting method is irrelevant means for transmitting well survey signals E21B47/12; signal transmission systems in general G08C; transmission in general H04B

Abstract

System and methods for downhole fluid classification are provided. Measurements are obtained from one or more downhole sensors located along a current section of wellbore within a subsurface formation. The measurements obtained from the one or more downhole sensors are transformed into principal spectroscopy component (PSC) data. One or more fluid types are identified for the current section of the wellbore within the subsurface formation, based on the PSC data and a fluid classification model. The fluid classification model is refined for one or more subsequent sections of the wellbore within the subsurface formation, based at least partly on the one or more fluid types identified for the current section of the wellbore.

Claims

1. A method of downhole fluid classification, the method comprising:

obtaining measurements from one or more downhole sensors located along a current section of wellbore within a subsurface formation;

transforming the measurements obtained from the one or more downhole sensors into principal spectroscopy component (PSC) data;

identifying one or more fluid types for the current section of the wellbore within the subsurface formation, based on the PSC data and a fluid classification model; and

refining the fluid classification model for one or more subsequent sections of the wellbore within the subsurface formation, based at least partly on the one or more fluid types identified for the current section of the wellbore.

2. The method of claim 1, wherein the one or more downhole sensors are components of a downhole fluid analysis tool disposed within the wellbore.

3. The method of claim 1, wherein the one or more downhole sensors include at least one optical sensor having an integrated computational element (ICE) for measuring one or more fluid properties.

4. The method of claim 1, wherein the PSC data is applied as an input to the fluid classification model, and the one or more fluid types are identified based on the applied PSC data.

5. The method of claim 4, wherein the fluid classification model is an adaptive neuro-fuzzy inference system (ANFIS) model used to identify the one or more fluid types from the applied PSC data.

6. The method of claim 4, wherein the fluid classification model uses cluster mean supporting vectors to identify the one or more fluid types from the applied PSC data.

7. The method of claim 6, wherein the cluster mean supporting vectors for the one or more fluid types are representative fluid patterns generated by clustering the PSC data based on measured distances between features of corresponding fluid types.

8. The method of claim 7, wherein the clustering is based on a hierarchical tree of fluid type clusters.

9. The method of claim 7, wherein the clustering is based on a self-organized-feature-map (SOFM) of fluid type clusters.

10. The method of claim 1, wherein the one or more fluid types are selected from the group consisting of:

heavy and medium oil;

medium and light oil;

light oil and condensates;

condensates and gas;

water;

synthetic drilling fluid; and

mud filtrate.

11. The method of claim 1, wherein transforming the measurements obtained from the one or more downhole sensors is performed using a neural network converter including one or more neural networks.

12. A system comprising:

at least one processor; and

a memory coupled to the processor having instructions stored therein, which when executed by the processor, cause the processor to perform functions including functions to;

obtain measurements from one or more downhole sensors located along a current section of wellbore within a subsurface formation;

transform the measurements obtained from the one or more downhole sensors into principal spectroscopy component (PSC) data;

identify one or more fluid types for the current section of the wellbore within the subsurface formation, based on the PSC data and a fluid classification model; and

refine the fluid classification model for one or more subsequent sections of the wellbore within the subsurface formation, based at least partly on the one or more fluid types identified for the current section of the wellbore.

13. The system of claim 12, wherein tire one or more downhole sensors include one or more optical sensors and one or more non-optical sensors disposed within the wellbore.

14. The system of claim 13, wherein the one or more optical sensors include at least one optical sensor having an integrated computational element (ICE) for measuring one or more fluid properties.

15. The system of claim 12, wherein the functions performed by the processor further include functions to:

apply the PSC data to an adaptive neuro-fuzzy inference system (ANFIS) model; and

identify the one or more fluid types based on the applied PSC data.

16. The system of claim 12, wherein the functions performed by the processor further include functions to:

generate cluster mean supporting vectors based on the PSC data, the cluster mean supporting vectors representing fluid patterns generated by clustering the PSC data based on measured distances between features of corresponding fluids types; and

identify the one or more fluid types based on the generated cluster mean supporting vectors.

17. The system of claim 16, wherein the cluster mean supporting vectors are generated based on a hierarchical free of fluid type clusters.

18. The system of claim 12, wherein the one or more fluid types are selected from the group consisting of;

heavy and medium oil;

medium and light oil;

light oil and condensates;

condensates and gas;

water;

synthetic drilling fluid; and

mud filtrate.

19. The system of claim 12, wherein the transformation of the measurements obtained from the one or more downhole sensors is performed using one or more neural networks.

20. A computer-readable storage medium having instructions stored therein, which when executed by a computer cause the computer to perform a plurality of functions, including functions to:

obtain measurements from one or more downhole sensors located along a current section of wellbore within a subsurface formation;

transform the measurements obtained from the one or more downhole sensor'"'"'s into principal spectroscopy component (PSC) data;

identify one or more fluid types for the current, section of the wellbore within the subsurface formation, based on the PSC data and a fluid classification model; and

refine the fluid classification model for one or more subsequent sections of the wellbore within the subsurface formation, based at least partly on the one or more fluid types identified for the current section of the wellbore.