Methods for predicting corrosion rate of crude oil derived samples using x-ray absorption spectroscopy

Patent metadata

Field	Value
Publication number	US-2016349198-A1
Application number	US-201514727395-A
Country	US
Kind code	A1
Filing date	Jun 1, 2015
Priority date	Jun 1, 2015
Publication date	Dec 1, 2016
Grant date	—

Abstract

A method for predicting the corrosion rate of crude oil or other related process streams is provided. The method includes using x-ray absorption spectroscopy to characterize heteroatom species present by functional group and quantify the relative amount of each species in a plurality of samples. The corrosion rate of each sample is measured. A correlation between the relative amount of each species and the corrosion rate is determined and used to create a corrosion prediction model. The corrosion prediction model can be used so that corrosion rate can be predicted for a sample solely from the spectroscopy measurement of the relative amounts of each relevant species.

First claim

1 . A method for predicting a corrosion rate of a crude oil derived sample, comprising:
a. creating a corrosion prediction model, including the steps of:
i. subjecting a plurality of samples to a range of x-ray energies;
ii. collecting transmitted x-ray intensity or fluorescence signals from the plurality of samples;
iii. transforming the collected transmitted x-ray intensity or fluorescence signals to x-ray absorption fine structure data for each sample in the form of absorbance versus x-ray energy;
iv. identifying functional groups present in each of the plurality of samples from the x-ray absorption fine structure data;
v. quantifying relative amounts of the functional groups identified in each of the plurality of samples;
vi. determining a corrosion rate for each of the plurality of samples; and
vii. correlating the corrosion rate for each of the plurality of samples with the relative amounts of each functional group identified in each of the plurality of samples to create the corrosion prediction model in the form of an equation of corrosion rate as a function of relative amounts of functional groups;
b. subjecting a crude oil derived sample for which a corrosion rate prediction is desired to a range of x-ray energies to obtain x-ray absorption fine structure data in the form of absorbance versus x-ray energy; c. identifying functional groups present in the crude oil derived sample from the x-ray absorption fine structure data; d. quantifying relative amounts of the functional groups identified in the crude oil derived sample; and e. solving the corrosion prediction model for corrosion rate as a function of the relative amounts of the functional groups identified in the crude oil derived sample to obtain a prediction of the corrosion rate of the crude oil derived sample.

2 . The method of claim 1 , wherein the x-ray absorption fine structure data for each sample has an edge associated with a rise in absorbance as x-ray energy increases.

3 . The method of claim 2 , wherein the x-ray absorption fine structure data for each sample has an edge jump and the x-ray absorption fine structure data for each sample is normalized such that the edge jump for each sample has an ordinate magnitude of 1.0.

4 . The method of claim 1 , wherein the plurality of samples comprise samples selected from the group consisting of crude oil samples, distilled fractions of crude oil, residual oil, samples produced from the processing or extraction of crude oil, water samples produced with crude oil and combinations thereof.

5 . The method of claim 1 , wherein the functional groups present in each of the plurality of samples and the functional groups present in the crude oil derived sample are identified by peak deconvolution wherein the x-ray absorption fine structure data for each sample is compared to a set of reference patterns.

6 . The method of claim 1 , wherein relative amounts of the functional groups present in each of the plurality of samples and the functional groups present in the crude oil derived sample are quantified using linear combination.

7 . The method of claim 1 , wherein the corrosion rate for each of the plurality of samples is determined by a method selected from the group consisting of use of a corrosion probe including an anode and use of at least one metal alloy weight loss coupon.

8 . The method of claim 1 , wherein the functional groups present in each of the plurality of samples and the functional groups present in the crude oil derived sample include an element selected from the group consisting of sulfur, oxygen, nitrogen, chlorine and combinations thereof.

9 . The method of claim 1 , wherein the plurality of samples includes at least 5 samples.

10 . A system for predicting a corrosion rate of a crude oil derived sample, comprising:
a. a source of a range of x-ray energies; b. a sample holder adapted to subject a sample to the range of x-ray energies; c. a detector for collecting a transmitted x-ray intensity or a fluorescence signal from the sample; d. at least one processor for:
i. receiving the transmitted x-ray intensity or fluorescence signal from the detector;
ii. transforming the transmitted x-ray intensity or fluorescence signal into x-ray absorption fine structure data for the sample in the form of absorbance versus x-ray energy;
iii. comparing the x-ray absorption fine structure data for the sample to a set of reference patterns for identifying and quantifying relative amounts of the functional groups present in the sample;
iv. receiving and correlating corrosion rates and relative amounts of each functional group identified in a plurality of samples to create a corrosion prediction model in the form of an equation of corrosion rate as a function of relative amounts of functional groups; and
v. solving the corrosion prediction model for corrosion rate as a function of the relative amounts of the functional groups identified in the crude oil derived sample to obtain a prediction of the corrosion rate of the crude oil derived sample.

11 . The system of claim 10 , further comprising a memory connected to the processor for storing the corrosion prediction model.

12 . The system of claim 10 , further comprising a corrosion rate determination means for determining a corrosion rate for a sample using a method selected from the group consisting of use of a corrosion probe including an anode and use of at least one metal alloy weight loss coupon.

13 . The system of claim 10 , wherein the x-ray absorption fine structure data for each sample has an edge associated with a rise in absorbance as x-ray energy increases.

14 . The system of claim 11 , wherein the x-ray absorption fine structure data for each sample has an edge jump and the x-ray absorption fine structure data for each sample is normalized such that the edge jump for each sample has an ordinate magnitude of 1.0.

15 . A method for creating a corrosion prediction model, comprising:
a. subjecting a plurality of samples to a range of x-ray energies, to obtain x-ray absorption fine structure data for each sample in the form of absorbance versus x-ray energy; b. identifying functional groups present in each of the plurality of samples from the x-ray absorption fine structure data; c. quantifying relative amounts of the functional groups identified in each of the plurality of samples; d. determining a corrosion rate for each of the plurality of samples; and e. correlating the corrosion rate for each of the plurality of samples with the relative amounts of each functional group identified in each of the plurality of samples to create the corrosion prediction model in the form of an equation of corrosion rate as a function of relative amounts of functional groups.

16 . The method of claim 15 , wherein the x-ray absorption fine structure data for each sample has an edge associated with a rise in absorbance as x-ray energy increases.

17 . The method of claim 16 , wherein the x-ray absorption fine structure data for each sample has an edge jump and the x-ray absorption fine structure data for each sample is normalized such that the edge jump for each sample has an ordinate magnitude of 1.0.

Assignees

• Barney Monica Michele
• Nissan Andrew B
• George Graham N
• Chevron Usa Inc

Inventors

• Barney Monica Michele
• Nissan Andrew B
• George Graham N

Classifications

• G01N23/223 Primary

  by irradiating the sample with X-rays or gamma-rays and by measuring X-ray fluorescence · CPC title

• G01N23/2076 Primary

  for spectrometry, i.e. using an analysing crystal, e.g. for measuring X-ray fluorescence spectrum of a sample with wavelength-dispersion, i.e. WDXFS · CPC title

• G01N33/2823

  Raw oil, drilling fluid or polyphasic mixtures · CPC title

• G01N23/2206

  Combination of two or more measurements, at least one measurement being that of secondary emission, e.g. combination of secondary electron [SE] measurement and back-scattered electron [BSE] measurement · CPC title

• G01N23/085

  X-ray absorption fine structure [XAFS], e.g. extended XAFS [EXAFS] · CPC title