Method, system and apparatus for formation tester data processing

• US 7,680,600 B2
• Filed: 07/25/2007
• Issued: 03/16/2010
• Est. Priority Date: 07/25/2007
• Status: Active Grant

• Current Assignee: Schlumberger Technology Corporation (SLB)
• Original Assignee: Schlumberger Technology Corporation (SLB)
• Inventors: Hsu, Kai, Carnegie, Andrew, Pop, Julian
• Primary Examiner(s): Cosimano; Edward R
• Application Number:
  US11/828,082
• Publication Number:
  US 20090030614A1
• Time in Patent Office: 965 Days
• Field of Search: 340/538.12, 340/853.1, 700/280, 700/275, 702/11, 702/195, 702/191, 702/1, 702/2, 702/6, 702/7, 702/9, 702/14, 702/17, 702/69, 702/127, 702/187, 702/188, 702/189, 702/190, 731/520.1, 731/520.2, 731/520.3, 731/520.5, 731/520.6, 731/520.8, 731/524.3, 731/525.4, 166/250.01, 166/254.1, 166/254.2, 175/40, 175/50, 324/323, 367/14, 367/37, 367/38, 367/43, 367/44, 367/45, 367/46, 367/81, 367/83, 708/300, 708/319, 708/321, 708/819
• US Class Current: 702/17
• CPC Class Codes: G01V 1/48 Processing data

Abstract

A method for data processing includes transforming measurement data acquired in the time domain during an oilfield operation into a second domain to produce transformed data; identifying distortions in the transformed data; removing the distortions from the transformed data; and transforming back from the second domain to the time domain to produce cleaned-up data. The transforming measurement data may use a Fourier transform or a wavelet transform. The method may further include compressing the cleaned-up data or reconstructing signals from the cleaned-up data. A method for data processing includes decomposing measurement data, which are acquired in an oilfield operation, using a low pass filter to produce a first dataset; decomposing the measurement data using a high pass filter to produce a second dataset; removing distortions from the second dataset to yield a corrected second dataset; and reconstructing a corrected dataset from the first dataset and the corrected second dataset.

Claims

1. A method for processing formation tester data, comprising:

obtaining the formation tester data, wherein the formation tester data are time domain data acquired with a formation tester in a wellbore;

transforming, using a wavelet transform, the formation tester data into a second domain other than the time domain to produce transformed data;

identifying distortions in the transformed data by analyzing a distribution of wavelet coefficients;

removing the distortions from the transformed data to produce de-noised data; and

transforming the de-noised data from the second domain back to the time domain to produce cleaned-up formation tester data.

2. The method of claim 1, wherein the wavelet transform is a discrete wavelet transform.

3. The method of claim 1, further comprising deriving a formation property from the cleaned-up formation tester data.

4. The method of claim 1, further comprising compressing the cleaned-up formation tester data.

5. The method of claim 4, wherein the compressing the cleaned-up formation tester data uses a wavelet transform.

6. A method for processing formation tester data, comprising:

obtaining the formation tester data, wherein the formation tester data are time domain data acquired with a formation tester in a wellbore;

decomposing the formation tester data using a low-pass filter to produce a first dataset;

decomposing the formation tester data using a high-pass filter to produce a second dataset;

identifying and removing distortions from the second dataset to yield a corrected second dataset; and

reconstructing a corrected formation tester data from the first dataset and the corrected second dataset.

7. The method of claim 6, wherein the low-pass filter and the high-pass filter are derived from Fourier transform or wavelet transform.

8. The method of claim 6, further comprising deriving a formation property from the corrected formation tester data.

9. The method of claim 6, wherein low-pass filter and the high-pass filter are derived from discrete wavelet transform.

10. The method of claim 9, wherein the removing the distortions comprises analyzing a distribution of wavelet coefficients.

11. The method of claim 10, wherein the analyzing comprising analysis of at least one selected from the group consisting of a mean and a standard deviation of the distribution of wavelet coefficients as a function of a decomposition level.

12. The method of claim 6, further comprising compressing the corrected formation tester data to produce a compressed dataset.

13. The method of claim 12, wherein the compressing uses a wavelet lifting technique.

14. The method of claim 12, further comprising reconstructing signals from the compressed dataset.

15. The method of claim 14, further comprising identifying a trend or occurrence of an event from the reconstructed signals.

16. A system for data processing, comprising a processor and a memory, wherein the memory stores a program having instructions for:

transforming, using a wavelet transform, formation tester data, which are in time domain, into a second domain other than the time domain to produce transformed data;

identifying distortions in the transformed data by analyzing a distribution of wavelet coefficients;

removing the distortions from the transformed data to produce de-noised data; and

transforming the de-noised data from the second domain back to the time domain to produce cleaned-up formation tester data.

17. The system of claim 16, wherein the program further comprises instructions for compressing the cleaned-up formation tester data.

18. The system of claim 16, wherein the wavelet transform is a discrete wavelet transform.

19. The system of claim 16, wherein the system is disposed in a formation tester.