# US 2014/0360259 A1 · System And Method For Quantifying Uncertainty Of Predicted Petroleum Fluid Properties

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- Number: US 2014/0360259 A1
- Document ID: US20140360259A1
- Status: Published application
- Kind: A1
- Application number: 13/912,920
- Filed: 2013-06-07
- Published: 2014-12-11
- Earliest claimed: 2013-06-07
- Assignees: Schlumberger Technology Corporation
- Applicants: Schlumberger Technology Corporation
- Inventors: Kentaro Indo; Kai Hsu; Julian Pop
- Web page: https://patentgrep.com/patent/US20140360259A1

## CPC

- E21B 49/088: {combined with sampling}
- E21B 47/113: using electrical indications; using light radiations
- 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)}
- G06F 1/3203: Power management, i.e. event-based initiation of a power-saving mode
- E21B: EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A system includes a downhole formation fluid sampling tool. The system also includes an optical spectrometer of the downhole formation fluid sampling tool and a processor. The optical spectrometer is able to measure an optical characteristic of a formation fluid flowing through the downhole formation fluid sampling tool over a plurality of wavelengths. The optical spectrometer is designed to generate optical spectra data indicative of the optical characteristic. The processor is able to receive the optical spectra data generated by the optical spectrometer, to predict a parameter corresponding to one component of multiple components of the formation fluid based on the optical spectra data, and to calculate an uncertainty in the predicted parameter based on the optical spectra data.

## Claims

### Claim 1

A system, comprising:
a downhole formation fluid sampling tool;
an optical spectrometer of the downhole formation fluid sampling tool, wherein the optical spectrometer is configured to measure an optical characteristic of a formation fluid flowing through the downhole formation fluid sampling tool over a plurality of wavelengths and configured to generate optical spectra data indicative of the optical characteristic; and
a processor configured to receive the optical spectra data generated by the optical spectrometer, to predict a parameter corresponding to one component of multiple components of the formation fluid based on the optical spectra data, and to calculate an uncertainty in the predicted parameter based on the optical spectra data.

### Claim 2 (depends on 1)

The system of claim 1, wherein the processor is configured to:
predict the parameter corresponding to one or more components of the formation fluid by fitting the optical spectra data to a model, the model being derived from a calibration dataset stored in a database;
determine a measurement uncertainty in the received optical spectra data;
identify a model uncertainty in the model derived from the calibration dataset; and
determine the uncertainty in the predicted parameter based on the measurement uncertainty and the model uncertainty.

### Claim 3 (depends on 1)

The system of claim 1, wherein the parameter corresponding to the one component of multiple components of the formation fluid comprises a concentration of the one component in the formation fluid.

### Claim 4 (depends on 1)

The system of claim 1, wherein the processor is configured to:
estimate a property of the formation fluid based on respective predicted parameters for the multiple components of the formation fluid;
calculate uncertainties in predicting the respective parameters for the multiple components; and
calculate an uncertainty of the estimated property of the formation fluid based on the calculated uncertainties in predicting the respective parameters.

### Claim 5 (depends on 3)

The system of claim 3, wherein the estimated property of the formation fluid comprises a gas to oil ratio (GOR) of the formation fluid.

### Claim 6 (depends on 1)

The system of claim 1, wherein the processor is configured to diagnose an operational issue in the downhole formation fluid sampling tool based on the calculated uncertainty.

### Claim 7 (depends on 1)

The system of claim 1, wherein the processor is configured to determine a confidence interval of the predicted parameter based on the calculated uncertainty.

### Claim 8 (depends on 1)

The system of claim 1, wherein the optical characteristic comprises an optical density of the formation fluid at the plurality of wavelengths.

### Claim 9 (depends on 1)

The system of claim 1, wherein the downhole formation fluid sampling tool is disposed within a wellbore extending into a formation, via a wireline or a string of tubulars.

### Claim 10

A method, comprising:
receiving optical spectra data into a processor, the optical spectra data being representative of optical characteristics of a formation fluid;
predicting, via the processor, a parameter corresponding to one component of multiple components of the formation fluid based on the optical spectra data; and
determining, via the processor, an uncertainty in the predicted parameter corresponding to the one component of the multiple components of the formation fluid based on the optical spectra data.

### Claim 11 (depends on 10)

The method of claim 10, comprising predicting the parameter by fitting the optical spectra data to a model, the model being derived from a calibration dataset stored in a database.

### Claim 12 (depends on 11)

The method of claim 11, comprising:
identifying a model uncertainty corresponding to the model, wherein the model uncertainty is derived from the calibration dataset;
determining a measurement uncertainty derived from a measurement of the optical spectra data; and
determining the uncertainty in the predicted parameter based on the model uncertainty and the measurement uncertainty.

### Claim 13 (depends on 12)

The method of claim 12, wherein the model comprises a mapping matrix, wherein predicting the parameter comprises performing a linear transformation of the optical spectra data according to the mapping matrix, and wherein the model uncertainty comprises a covariance matrix of the mapping matrix.

### Claim 14 (depends on 10)

The method of claim 10, comprising determining an uncertainty in predicting an aggregate parameter corresponding to the formation fluid, based on the uncertainty of the parameter corresponding to the one component.

### Claim 15 (depends on 10)

The method of claim 10, comprising predicting the parameter and determining the uncertainty in the predicted parameter, while the formation fluid is flowing through a downhole formation fluid sampling tool.

### Claim 16

A method, comprising:
receiving optical spectra data into a processor, the optical spectra data being representative of optical characteristics of a formation fluid flowing through a downhole formation fluid sampling tool;
predicting, via the processor, a parameter corresponding to one or more components of the formation fluid based on the optical spectra data and a model derived from a calibration dataset;
determining, via the processor, a measurement uncertainty in the received optical spectra data;
identifying, via the processor, a model uncertainty in the model derived from the calibration dataset; and
determining, via the processor, an uncertainty in the predicted parameter based on the measurement uncertainty and the model uncertainty.

### Claim 17 (depends on 16)

The method of claim 16, wherein predicting the parameter corresponding to the one or more components of the formation fluid comprises determining a formation fluid type based on the optical spectra data and selecting the model corresponding to the determined formation fluid type.

### Claim 18 (depends on 16)

The method of claim 16, comprising deriving the model uncertainty in the model by applying a bootstrapping method to the calibration dataset.

### Claim 19 (depends on 16)

The method of claim 16, comprising updating the model with additional calibration data, wherein the additional calibration data is determined based in part on the optical spectra data.

### Claim 20 (depends on 16)

The method of claim 16, wherein determining the measurement uncertainty comprises filtering the optical spectra data to generate filtered data, and subtracting the filtered data from the optical spectra data to obtain a measurement noise estimate.