CN112834455A - Method for detecting water content in crude oil - Google Patents
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- CN112834455A CN112834455A CN202011616687.3A CN202011616687A CN112834455A CN 112834455 A CN112834455 A CN 112834455A CN 202011616687 A CN202011616687 A CN 202011616687A CN 112834455 A CN112834455 A CN 112834455A
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- 239000010779 crude oil Substances 0.000 title claims abstract description 64
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000001514 detection method Methods 0.000 claims abstract description 26
- 238000000411 transmission spectrum Methods 0.000 claims abstract description 22
- 230000003287 optical effect Effects 0.000 claims abstract description 18
- 238000012937 correction Methods 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 15
- 238000004458 analytical method Methods 0.000 claims abstract description 13
- 230000005540 biological transmission Effects 0.000 claims abstract description 7
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 238000012795 verification Methods 0.000 claims abstract description 7
- 230000000694 effects Effects 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 3
- 238000007689 inspection Methods 0.000 claims description 2
- 238000005259 measurement Methods 0.000 description 11
- 238000001228 spectrum Methods 0.000 description 7
- 239000003921 oil Substances 0.000 description 4
- 239000003129 oil well Substances 0.000 description 4
- 238000004497 NIR spectroscopy Methods 0.000 description 3
- 238000004140 cleaning Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- RTZKZFJDLAIYFH-UHFFFAOYSA-N Diethyl ether Chemical compound CCOCC RTZKZFJDLAIYFH-UHFFFAOYSA-N 0.000 description 2
- 238000002835 absorbance Methods 0.000 description 2
- 238000010521 absorption reaction Methods 0.000 description 2
- 238000012864 cross contamination Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000002329 infrared spectrum Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000005033 Fourier transform infrared spectroscopy Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003670 easy-to-clean Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 239000006193 liquid solution Substances 0.000 description 1
- 239000003960 organic solvent Substances 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000004506 ultrasonic cleaning Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
Abstract
The invention provides a method for detecting water in crude oil, which comprises a stage of establishing an analysis model and a stage of detecting the water in the crude oil, wherein the step of establishing the analysis model comprises the following steps: (A1) detecting light emitted by the light source passes through the plurality of colorimetric pools to respectively obtain transmission spectrums; the optical path L of the detection light in the colorimetric pool belongs to [0.8, 2.2] mm, different crude oil samples are filled in the colorimetric pool, and the water content of the crude oil samples is known; (A2) dividing a correction set verification set according to the transmission light source and the corresponding water content; (A3) preprocessing the transmission spectrum, wherein the preprocessing comprises centralization and weighting operation; (A4) extracting characteristic wave bands from the preprocessed transmission spectrum, and establishing a prediction model of the crude oil moisture content corresponding to the wavelength combination; (A5) and performing regression calculation by adopting an algorithm, and establishing a correction model between the transmission spectrum and the moisture content. The invention has the advantages of accurate detection and the like.
Description
Technical Field
The invention relates to crude oil detection, in particular to a method for detecting water in crude oil.
Background
With the increase of crude oil import and oil field development in China, the detection means of the crude oil moisture content which seriously influences the factors of each link in the whole process from crude oil exploitation to oil field sale is also increasingly emphasized. The water content of the crude oil is an important parameter in oil product transaction and production, and the measurement result of the water content of the crude oil not only can directly influence the judgment of the position of effluent and an oil outlet layer and the yield of an oil well, but also can continuously monitor the state of the oil well in development and judge whether the oil well has exploitation value, so that the method has very important practical significance in reducing the energy loss in the crude oil development, saving the production cost of the crude oil and the like.
Near infrared spectroscopy (NIR) is a nondestructive, fast and accurate detection technique, has a series of advantages such as no need of pretreatment, convenience and rapidness, accurate result, short analysis time, and overcomes some defects in the conventional chemical detection method, and has an obvious absorption characteristic in the Near infrared band range based on moisture, so that an attempt to apply the NIR to the measurement of the moisture content of crude oil has been made at present, but there are many problems in the Near infrared detection of the moisture content of crude oil at present:
1. most of near infrared instruments measure crude oil by adopting a transmission accessory optical path with the length of about 5mm or a smaller optical path, for example, a CCD near infrared spectrometer is used for measuring crude oil (Avaspec-2048, Avants, Netherlands) and the sample cell optical path range of a Nicolet 6700 Fourier transform infrared spectrometer for measuring crude oil is 0.05-0.6 mm. Research shows that when the transmission mode is adopted for measurement, the optical path of the sample cell is too large, so that moisture is absorbed too much in a near infrared region, and saturation is caused, so that the measurement cannot be carried out. Too small an optical path will result in too little information being carried by the spectrum, which all have a significant impact on the detection result.
2 according to the lambert-beer law, the absorbance value of a uniform non-scattering substance mainly depends on the absorption coefficient of a sample at a specific wavelength and the optical path length, wherein the optical path is one of the main factors influencing the absorbance, and the influence of the optical path change on the transmission measurement precision of a liquid solution is studied. However, the effect of various preprocessing methods on the near infrared measurement accuracy is not basically studied. In the external measurement of crude oil, the consistency of the colorimetric pool with short optical path is not easy to control, which causes noise and error in measurement.
3. Crude oil is a substance which has high viscosity and is not easy to dissolve in common organic solvents, so that a colorimetric pool after detection is not easy to clean, cross contamination among samples caused by incomplete cleaning is easy to greatly influence the accuracy of a measurement result, and the crude oil is generally cleaned in a petroleum ether and ultrasonic cleaning mode at present, but the operation not only consumes long time, but also cannot ensure that the interior of the colorimetric pool is completely cleaned.
In view of the above technical problems, the near infrared technology has not been effectively applied to the detection of crude oil moisture.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method for detecting the moisture in the crude oil.
The purpose of the invention is realized by the following technical scheme:
the method for detecting the water in the crude oil comprises a stage of establishing an analysis model and a stage of detecting the water in the crude oil, wherein the step of establishing the analysis model comprises the following steps:
(A1) detecting light emitted by the light source passes through the plurality of colorimetric pools to respectively obtain transmission spectrums;
the optical path L of the detection light in the colorimetric pool belongs to [0.8, 2.2] mm, different crude oil samples are filled in the colorimetric pool, and the water content of the crude oil samples is known;
(A2) dividing a correction set verification set according to the transmission light source and the corresponding water content;
(A3) preprocessing the transmission spectrum, wherein the preprocessing comprises centralization and weighting operation;
(A4) extracting characteristic wave bands from the preprocessed transmission spectrum, and establishing a prediction model of the crude oil moisture content corresponding to the wavelength combination;
(A5) and performing regression calculation by adopting an algorithm, and establishing a correction model between the transmission spectrum and the moisture content.
Compared with the prior art, the invention has the beneficial effects that:
in order to solve the application problem of the near infrared spectrum technology in the detection of the moisture in the crude oil, the applicant makes corresponding innovation on the consistency of the short optical path and the optical path, so that the mature near infrared spectrum analysis technology can be really applied to the detection of the moisture in the crude oil, and the aim of good detection accuracy is fulfilled;
the specification of the colorimetric pool is optimized, so that the optical path L of detection light in the colorimetric pool is within 0.8, 2.2mm, the crude oil sample in the colorimetric pool cannot absorb the detection light too much or too little, and the detection accuracy is improved;
the preprocessing algorithm corrects the optical path difference of the short-optical-path colorimetric pool, eliminates the influence of the surface of crude oil on the spectrum, eliminates the baseline drift and partial background noise irrelevant to the wavelength in the transmission spectrum, and improves the quality of the spectrum and the near-infrared modeling effect;
and a disposable colorimetric pool is adopted in the measurement stage, so that the operation of cleaning is reduced, the analysis time is greatly shortened, and the inaccuracy of the measurement result caused by the unclean cleaning is avoided.
Drawings
The disclosure of the present invention will become more readily understood with reference to the accompanying drawings. As is readily understood by those skilled in the art: these drawings are only for illustrating the technical solutions of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a flow chart of establishing an analytical model according to an embodiment of the present invention.
Detailed Description
Fig. 1 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and reproduce the invention. Some conventional aspects have been simplified or omitted for the purpose of teaching the present invention. Those skilled in the art will appreciate that variations or substitutions from these embodiments will be within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the present invention is not limited to the following alternative embodiments, but is only limited by the claims and their equivalents.
Example 1:
the method for detecting the water in the crude oil comprises a stage of establishing an analysis model and a stage of detecting the water in the crude oil, wherein the step of establishing the analysis model comprises the following steps of:
(A1) detecting light emitted by the light source passes through the plurality of colorimetric pools to respectively obtain transmission spectrums;
the optical path L of the detection light in the colorimetric pool belongs to [0.8, 2.2] mm, different crude oil samples are filled in the colorimetric pool, and the water content of the crude oil samples is known;
(A2) dividing a correction set verification set according to the transmission light source and the corresponding water content;
(A3) preprocessing the transmission spectrum, wherein the preprocessing comprises centralization and weighting operation;
(A4) extracting characteristic wave bands from the preprocessed transmission spectrum, and establishing a prediction model of the crude oil moisture content corresponding to the wavelength combination;
(A5) and performing regression calculation by adopting an algorithm, and establishing a correction model between the transmission spectrum and the moisture content.
In order to avoid cross contamination and improve detection accuracy, the cuvette is further disposable during the detection phase.
To improve the modeling and detection accuracy, further, in step (a2), a gradient is performed according to the moisture content, and the ratio of the calibration set to the verification set is 3: 1.
In order to improve the quality of the spectrum and the near infrared modeling effect, further, in step (a3), the preprocessing further includes a detrending algorithm, and the preprocessing result is evaluated using a correction standard deviation, a prediction standard deviation, or a decision coefficient, the lower the correction standard deviation or the prediction standard deviation, and the higher the decision coefficient, the better the preprocessing effect.
In order to improve the near infrared modeling effect, further, in step (a4), a continuous projection algorithm is used to extract a characteristic wavelength band, and an optimal modeling wavelength is screened using the prediction standard deviation as an evaluation criterion.
In order to improve the near infrared modeling effect, further, in step (a5), a partial least squares algorithm is used to perform regression calculation.
In order to improve the near infrared modeling effect, further, in the step (a1), the crude oil sample is added into the cuvette by the following method:
a crude oil sample is added along the side wall of the cuvette having a smaller width and a side wall having a larger width, and the inspection light passes through the side wall having the larger width.
Example 2:
an application example of the method for detecting moisture in crude oil according to embodiment 1 of the present invention.
In this application example, the method for detecting moisture in crude oil according to the embodiment of the present invention includes a stage of establishing an analysis model and a stage of detecting moisture in crude oil, as shown in fig. 1, where the step of establishing the analysis model includes the following steps:
(A1) preparing a near-infrared spectrometer with a wavelength range of 1000 plus or minus 1800nm, a wavelength accuracy of plus or minus 0.2nm, a resolution of 10.9 plus or minus 0.3nm @1529.5nm and a spot diameter of 5 mm;
preparing 70 crude oil samples of the same oil well, basically keeping the water content range of the samples within 0-1%, bottling the samples and marking corresponding numbers;
the cuvette has a side wall with a smaller width and a side wall with a larger width, the detection light passes through the side wall with the larger width, and the distance (optical path) L between the two opposite side walls with the larger width belongs to [0.8, 2.2] mm, such as 0.8mm, 1mm, 1.6mm, 2mm and 2.2 mm;
the crude oil sample was injected into the cuvette along the side wall of the cuvette with the smaller width using a 2mL syringe (with a needle) to fill the cuvette with the sample requirements: the liquid sample loading amount is 2/3 of the volume of the colorimetric pool, so that the smooth surface of the colorimetric pool is clean and has no fingerprint or stain;
detection light emitted by the light source respectively penetrates through the 70 colorimetric pools, penetrates through the side walls with larger width and opposite to the positions of the colorimetric pools, and respectively obtains transmission spectrums; taking a space ratio color pool as reference, and acquiring a background spectrum before acquiring a transmission spectrum of a sample each time;
the water content of the crude oil sample is obtained by measuring the water content of the crude oil sample by a national standard method GB/T89282006;
(A2) introducing the collected transmission spectrum with the background spectrum subtracted and the measured moisture content into a Matlab platform, and performing gradient division according to the moisture content, wherein the ratio of a correction set to a verification set is 3: 1;
(A3) preprocessing the transmission spectrum data with the background spectrum removed in Matlab, wherein the preprocessing mode comprises centralization and weighting operation and a trend removing algorithm, and evaluating the processing effect from a correction standard deviation (RMSEC), a prediction standard deviation (RMSEP) and a decision coefficient (r); the lower the corrected standard deviation (RMSEC), predicted standard deviation (RMSEP), and the higher the correlation coefficient (r) represents the more significant the pretreatment effect;
(A4) screening the characteristic wave bands of the crude oil by adopting a continuous projection algorithm (SPA) in a Matlab platform, establishing a quantitative prediction model of the water content of the crude oil according to wavelength combinations, and screening the optimal modeling wavelength of the crude oil by taking a prediction standard deviation (RMSEP) as an evaluation standard;
(A5) finally, performing regression calculation by adopting a partial least square algorithm, operating in a Matlab platform, and establishing a quantitative correction model of the transmission spectrum and the moisture content;
in the detection stage, the colorimetric pool is used as a consumable for one time.
Claims (7)
1. The method for detecting the water in the crude oil comprises a stage of establishing an analysis model and a stage of detecting the water in the crude oil, wherein the step of establishing the analysis model comprises the following steps:
(A1) detecting light emitted by the light source passes through the plurality of colorimetric pools to respectively obtain transmission spectrums;
the optical path L of the detection light in the colorimetric pool belongs to [0.8, 2.2] mm, different crude oil samples are filled in the colorimetric pool, and the water content of the crude oil samples is known;
(A2) dividing a correction set verification set according to the transmission light source and the corresponding water content;
(A3) preprocessing the transmission spectrum, wherein the preprocessing comprises centralization and weighting operation;
(A4) extracting characteristic wave bands from the preprocessed transmission spectrum, and establishing a prediction model of the crude oil moisture content corresponding to the wavelength combination;
(A5) and performing regression calculation by adopting an algorithm, and establishing a correction model between the transmission spectrum and the moisture content.
2. The method of claim 1, wherein the cuvette is disposable during the detection stage.
3. The method for detecting moisture in crude oil according to claim 1, wherein in the step (A2), the gradient division is performed according to the moisture content, and the ratio of the calibration set to the verification set is 3: 1.
4. The method for detecting moisture in crude oil according to claim 1, wherein in step (A3), the pretreatment further comprises a de-trending algorithm, and the pretreatment result is evaluated by using a correction standard deviation, a prediction standard deviation or a determination coefficient, wherein the lower the correction standard deviation or the prediction standard deviation is, and the higher the determination coefficient is, the better the pretreatment effect is.
5. The method for detecting moisture in crude oil according to claim 1, wherein in the step (A4), the characteristic wavelength band is extracted by using a continuous projection algorithm, and the optimum modeling wavelength is screened by using the predicted standard deviation as an evaluation criterion.
6. The method for detecting moisture in crude oil according to claim 1, wherein in the step (A5), the regression calculation is performed by using a partial least squares algorithm.
7. The method for detecting moisture in crude oil according to claim 1, wherein in the step (A1), the crude oil sample is added to the cuvette by:
a crude oil sample is added along the side wall of the cuvette having a smaller width and a side wall having a larger width, and the inspection light passes through the side wall having the larger width.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115144350A (en) * | 2022-09-06 | 2022-10-04 | 中国科学院地理科学与资源研究所 | Hyperspectral similar pixel comparison-based site hydrocarbon pollution identification method and system |
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