Methods of calibration transfer for a testing instrument

1. A method of calibration transfer for a testing reservoir formation instrument, the method comprising:

collecting a plurality of first samples;

generating a plurality of standard responses of a first instrument based, at least in part, on the plurality of first samples;

performing instrument standardization of a second reservoir formation instrument based, at least in part, on the plurality of standard responses of the first instrument;

obtaining data corresponding to a second sample using the second reservoir formation instrument;

building a calibration model based on a first subset of the plurality of first samples;

identifying a component of the second sample based, at least in part, on the calibration model;

performing a formation testing operation using the second reservoir formation instrument;

acquiring a signal for at least one of the plurality of first samples at the second reservoir formation instrument;

determining a response of the second reservoir formation instrument based on the acquired signal;

transforming the response of the second reservoir formation instrument based, at least in part, on at least one of the plurality of standard responses of the first instrument, wherein the response of the second reservoir formation instrument is transformed to at least one of the plurality of standard responses of the first instrument based, at least in part, on one or more of temperature, vibration, and pressure; and

adjusting the calibration model based, at least in part, on the transformed response of the second reservoir formation instrument.

2. The method of claim 1 , further comprising:

determining a parameter of the second sample based, at least in part, on the component of the second sample.

3. The method of claim 1 , further comprising:

determining a parameter of the second sample based, at least in part, on a General Standard Addition Method.

4. The method of claim 1 , further comprising:

applying a transformation algorithm to data corresponding to the second sample prior to identifying the component of the second sample.

5. The method of claim 1 , wherein the calibration model is a single-output model.

6. The method of claim 1 , wherein the calibration transfer is used to adjust one or more components of one or more of saturates, aromatics, resins, asphaltenes, methane, ethane, propane, butane, pentane, carbon dioxide, hydrogen sulfide, water, synthetic drilling fluid components, phase composition, density, API gravity, gas/oil ratio (GOR), and contamination.

7. The method of claim 1 , wherein a signal from the first instrument is translated to a response function of the second reservoir formation instrument.

8. The method of claim 1 , wherein the first instrument is a spectrometer.

9. The method of claim 1 wherein at least one of a first signal from the first instrument and the signal for the at least one of the plurality of first samples at the second reservoir formation instrument is an optical signal.

10. The method of claim 1 , wherein the step of generating the plurality of standard responses of the first instrument comprises determining a property of interest, further comprising:

developing a transformation algorithm to adjust data corresponding to the property of interest based, at least in part, on the response of the second reservoir formation instrument; and

adjusting data corresponding to the property of interest, based on at least one of the response of the second reservoir formation instrument and the transformation algorithm.

11. The method of claim 1 , wherein the step of generating the standard response of the first instrument comprises determining a property of interest, and wherein the step of transforming the response of the second reservoir formation instrument comprises:

selecting a transformation algorithm to adjust data corresponding to the property of interest based, at least in part, on the response of the second reservoir formation instrument; and

adjusting data corresponding to the property of interest based, at least in part, on the transformation algorithm.

12. The method of claim 11 , wherein the transformation algorithm is a neural network transformation algorithm, a support vector machine (SVM), or a radial basis function with optical inputs.

13. The method of claim 1 , wherein identifying the component of the second sample based, at least in part, on the calibration model comprises:

selecting a calibration training sample; and

developing the calibration model, at least in part, with the calibration training sample.

14. The method of claim 13 , wherein developing the calibration model comprises:

one or more of training, validating, and testing based, at least in part, on the response of the second reservoir formation instrument and one or more of:

a neural network;

a partial least squares regression; and

a principal component regression.

15. The method of claim 1 , wherein the step of generating the plurality of standard responses of the first instrument comprises determining a property of interest.

16. The method of claim 15 , wherein the property of interest is a classification property.

17. The method of claim 1 , further comprising:

analyzing the plurality of the first samples at the first instrument to determine a deterministic response.

18. The method of claim 17 , wherein the deterministic response corresponds to optical density at a characteristic wavelength.

19. The method of claim 1 , wherein the first instrument comprises an optical tool, wherein the optical tool comprises a detector, and wherein generating the standard response of the first instrument comprises:

checking at least one of a lamp detector function and an ambient light intensity of the optical tool;

identifying at least one of a dark reading and a maximum intensity of the detector;

placing the optical tool in a temperature-controllable environment;

directing a fluid through the optical tool; and

verifying an output of the optical tool against a known fluid spectral signature.

20. The method of claim 19 , wherein the optical tool is selected from a group consisting of an array-filter spectrometer and an ICE (Integrated Computational Element) based application.