A method and apparatus for measuring oil production from core percolation experiments based on gas chromatography.

Abstract

This invention relates to the field of chromatographic analysis technology, specifically to a method and apparatus for measuring oil yield in core percolation experiments based on gas chromatography. Core percolation experiments produce various hydrocarbons, and overlapping peaks are common in gas chromatographic data. This is due to differences in retention times caused by similar hydrocarbon properties and polarity, as well as tailing of highly polar hydrocarbons. After obtaining the initial gas chromatographic data of the core sample extract, the local peak shape characteristics of each peak segment are analyzed to calculate the local signal-to-noise ratio and symmetry index, reflecting peak blurring and deviation from the ideal Gaussian distribution, which are used to obtain an adaptive sharpening factor. Furthermore, the adaptive standard deviation is determined by combining the peak half-width and the separation degree of adjacent peak segments. Finally, the initial gas chromatographic data is sharpened based on these adaptive parameters to obtain gas chromatographic data that eliminates the influence of overlapping peaks, effectively improving the accuracy of calculations of relevant parameters in percolation experiments.

Description

Technical Field

[0001] This invention relates to the field of chromatographic analysis technology, specifically to a method and apparatus for measuring oil production from core percolation experiments based on gas chromatography. Background Technology

[0002] In the field of oil and gas development, core percolation experiments are an important means of assessing the spontaneous water (or oil) absorption capacity of reservoir rocks, and the oil production measurement results directly affect the formulation and optimization of reservoir development plans. High-temperature and high-pressure percolation processes can simulate the percolation of rocks under bottom conditions, and can read the visualized oil phase portion of the percolation experiment's oil production. However, in core percolation experiments of unconventional reservoirs, the oil content in the produced fluid is sometimes low. If physicochemical methods are used for demulsification, it is difficult to achieve the minimum emulsified oil content requirement, which also introduces errors into the analysis results.

[0003] After percolation experiments, core samples yield oil and water liquid phase products. These products are then extracted, diluted, and injected into a gas chromatograph. Hydrocarbon components with different carbon numbers are separated on the chromatographic column according to their differences in physicochemical properties and placed on the detector. Response signals are generated based on the retention time and content of the hydrocarbons, resulting in gas chromatographic data. Subsequently, the peak area of ​​each hydrocarbon component is calculated by integrating using the gas chromatographic internal standard method to obtain the hydrocarbon content in the liquid phase product. By converting these hydrocarbon contents, the oil yield of the core sample in the percolation experiment can be obtained. Therefore, gas chromatography technology, due to its high separation efficiency, high sensitivity, and quantitative accuracy, is widely used for the qualitative and quantitative analysis of the components of the extract in core percolation experiments.

[0004] When analyzing gas chromatography data, overlapping peaks can occur due to similar physicochemical properties of certain hydrocarbons, resulting in similar retention times, and interference from the aqueous phase caused by incomplete removal of water phases in oil-water emulsions. Existing methods typically rely on Gaussian sharpening algorithms for resolving overlapping peaks and employ fixed sharpening operators to mathematically sharpen the gas chromatography data, separating overlapping peaks into single peaks in the signal space. However, fixed sharpening operators often fail to adapt to the local characteristics and scales of different peaks, easily leading to over-sharpening, false peaks, or incomplete separation of overlapping peaks. This makes it difficult to effectively recover the true chromatographic peaks of each hydrocarbon, thus affecting the accuracy of subsequent calculations. Summary of the Invention

[0005] To address the technical problem that fixed sharpening operators often fail to adapt to the local characteristics and scales of different spectral peaks, easily leading to over-sharpening, false peaks, or incomplete separation of overlapping peaks, making it difficult to effectively recover the true chromatographic peaks of each hydrocarbon and thus affecting the accuracy of subsequent calculations, the present invention aims to provide a method and apparatus for measuring oil production in core percolation experiments based on gas chromatography. The specific technical solution adopted is as follows:

[0006] A method for measuring oil production from core percolation experiments based on gas chromatography, comprising:

[0007] Initial gas chromatographic data of the core sample extract were obtained and peaks were identified to obtain all peak segments;

[0008] The time-frequency localization characteristics of each spectral peak are analyzed to determine the local signal-to-noise ratio (SNR) index of each spectral peak. The peak shape variation characteristics of each spectral peak are analyzed and combined with the local SNR index of each spectral peak to adjust the preset sharpening factor and obtain the adaptive sharpening factor of each spectral peak.

[0009] Within each spectral peak segment, the temporal differences in intensity are analyzed to determine the peak half-width at half-maximum (HWHM) of each segment. Temporally, the numerical characteristics of the separation degree between adjacent spectral peak segments are analyzed to determine the separation index for each segment. Based on the HWHM and separation index of each spectral peak segment, the preset standard deviation is adjusted to obtain the adaptive standard deviation for each spectral peak segment.

[0010] The initial gas chromatographic data is sharpened based on the adaptive sharpening factor and adaptive standard deviation of the spectral peaks to obtain the sharpened gas chromatographic data.

[0011] The percolation efficiency and percolation oil production were calculated based on the sharpened gas chromatography data.

[0012] Furthermore, the method for obtaining the local signal-to-noise ratio includes:

[0013] In the initial gas chromatographic data, a data segment of a preset length is extracted centered on each spectral peak segment as the spectral peak interval for each spectral peak segment;

[0014] The gas chromatographic data in the spectral peak interval is decomposed using wavelet transform decomposition, with the number of decomposition layers set to a preset value, to obtain all sub-band signals;

[0015] The highest frequency sub-band signal among all sub-band signals is integrated using the trapezoidal integral method, and the area is used as the noise characteristic value.

[0016] The gas chromatographic data corresponding to each spectral peak segment is integrated using the trapezoidal integral method. The normalized value of the ratio of the area to the noise characteristic value is used as the local signal-to-noise ratio index for each spectral peak segment.

[0017] Furthermore, the method for obtaining the adaptive sharpening factor includes:

[0018] In each spectral peak segment, the peak shape variation characteristics are analyzed, and the symmetry index of each spectral peak segment is determined;

[0019] The value obtained by normalizing the mean of the local signal-to-noise ratio index and the symmetry index of each spectral peak is used as the first adjustment factor;

[0020] The difference between the preset maximum sharpening factor and the preset minimum sharpening factor is used as the second adjustment factor;

[0021] The product of the first adjustment factor and the second adjustment factor is used as the adjustment level value, and the sum of the preset minimum sharpening factor and the adjustment level value is used as the adaptive sharpening factor for each spectral peak segment.

[0022] Furthermore, the method for obtaining the symmetry index includes:

[0023] In each spectral peak segment, 10 percent of the maximum intensity value is taken as the reference intensity value. In the gas chromatographic data on both sides of the maximum intensity value, the two times corresponding to the values ​​of the reference intensity value are taken as reference times.

[0024] The absolute value of the difference between each reference time and the time corresponding to the maximum intensity value is used as the time distance factor. The absolute value of the difference between the two time distance factors is negatively correlated and normalized, and then used as the symmetry index corresponding to each spectral peak segment. If there are no two reference times in a certain spectral peak segment, the symmetry index of that spectral peak segment is set to 0.

[0025] Furthermore, the method for obtaining the peak half-width includes:

[0026] In each spectral peak segment, half of the maximum intensity value is taken as the half-peak value. In the gas chromatographic data on both sides of the maximum intensity value, the two times corresponding to the half-peak value are taken as the half-peak times.

[0027] The absolute value of the difference between two half-peak times is used as the peak half-height width of each spectral peak segment; if there is only one half-peak time corresponding to a certain spectral peak segment, then twice the absolute value of the difference between the time corresponding to the maximum intensity value and the half-peak time is used as the peak half-height width of that spectral peak segment.

[0028] Furthermore, the method for obtaining the separation index includes:

[0029] In the initial gas chromatography data, obtain the resolution of each peak segment;

[0030] Among all the spectral peaks, the first and last spectral peaks in time are taken as endpoints, and all the remaining spectral peaks are taken as intermediate segments.

[0031] The separation degree between each endpoint segment and its adjacent spectral peak segment is used as the separation index of each endpoint segment, and the average separation degree between each intermediate segment and its two adjacent spectral peak segments is used as the separation index of each intermediate segment.

[0032] Furthermore, the method for obtaining the adaptive standard deviation includes:

[0033] For any spectral peak segment, when the separation index of the spectral peak segment is greater than or equal to the preset separation threshold, the preset separation threshold is used as the separation factor of the spectral peak segment; when the separation index of the spectral peak segment is less than the preset separation threshold, the separation index of the spectral peak segment is used as the separation factor.

[0034] The normalized value of the peak half-width of each spectral peak segment and the normalized value of the mean of the separation factor of each spectral peak segment are used as the third adjustment factor for each spectral peak segment.

[0035] The difference between the preset maximum standard deviation and the preset minimum standard deviation is used as the fourth adjustment factor;

[0036] The product of the third adjustment factor and the fourth adjustment factor is used as the adjustment parameter, and the sum of the adjustment parameter and the preset minimum standard deviation is used as the adaptive standard deviation for each spectral peak segment.

[0037] Furthermore, the method for acquiring the sharpened gas chromatographic data includes:

[0038] Gaussian sharpening was used to sharpen the spectral peaks based on the adaptive sharpening factor and adaptive standard deviation of each peak segment, thereby obtaining the sharpened gas chromatographic data corresponding to the initial gas chromatographic data.

[0039] Furthermore, the calculation of percolation efficiency and percolation oil production based on sharpened gas chromatographic data includes:

[0040] In the sharpened gas chromatographic data, the peak area of ​​each hydrocarbon component is compared with the peak area of ​​the standard sample to determine the amount of oil produced during each percolation process of the extract.

[0041] The percolation efficiency of the extract is determined based on the amount of oil produced during each percolation process and the preset initial oil content.

[0042] A gas chromatography-based core permeation experiment oil production measurement device includes a core permeation experiment device body and a control module. The control module includes a processor and a memory. The memory stores at least one instruction, at least one program, code set or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set or instruction set, it implements the steps of the gas chromatography-based core permeation experiment oil production measurement method.

[0043] The present invention has the following beneficial effects:

[0044] Initial gas chromatographic data of the core extract were obtained and peaks were identified to divide all peak segments. The product components of the core percolation experiment include various hydrocarbons. Due to the similarity of the physicochemical properties of some hydrocarbons, overlapping peaks can easily occur in the chromatographic data of different hydrocarbon components. Some hydrocarbons have different polarities, so their retention times are also different. Hydrocarbons with higher polarity are prone to tailing on polar columns and are also prone to overlapping with the chromatographic peaks of other hydrocarbons. Traditional Gaussian sharpening-based overlapping peak analysis uses a fixed Gaussian sharpening factor and standard deviation to decompose overlapping peaks in the entire gas chromatographic data. This can easily lead to over-sharpening, resulting in false peaks or incomplete separation of overlapping peaks. Therefore, in this invention, by analyzing the local peak shape characteristics and data features of each spectral peak segment, an adaptive sharpening factor and standard deviation for each peak segment are determined. Given that the signal-to-noise ratio reflects the contrast between the peak intensity and the presence of noise interference, and can reveal the fuzzy peak signal characteristics caused by water interference from oil-water emulsions, and that peak symmetry reflects the degree of deviation of the peak shape from the ideal Gaussian distribution, poorer peak symmetry makes sharpening separation more difficult, the time-frequency localization characteristics and peak shape variation characteristics of each peak segment are analyzed, and the adaptive sharpening factor for each peak segment is calculated. Furthermore, the peak half-width at half-maximum (HWHM) and the separation degree between adjacent peaks were analyzed. These two factors were combined to determine the adaptive standard deviation for each peak segment. This ensured that the separated peaks in the subsequent sharpening process better matched the true peak shape characteristics. Finally, the initial gas chromatographic data was sharpened based on the adaptive sharpening factor and adaptive standard deviation of the peak segments, resulting in sharpened gas chromatographic data. This data eliminated the influence of overlapping peaks, thus making the calculated percolation efficiency and percolation oil yield more accurate. Attached Figure Description

[0045] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a schematic diagram of the main body of a core deep aspiration experimental device according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the structure of a control module provided in one embodiment of the present invention;

[0048] Figure 3A flowchart of a method for measuring oil production from a core percolation experiment based on gas chromatography, provided as an embodiment of the present invention;

[0049] Figure 4 A flowchart illustrating a method for obtaining an adaptive sharpening factor according to an embodiment of the present invention;

[0050] Figure 5 A flowchart illustrating an adaptive standard deviation acquisition method according to an embodiment of the present invention;

[0051] Reference numerals: 1-Displacement pump; 2-Percolator container; 3-Percolation reactor; 4-Heating jacket; 5-Sampling device; 6-Temperature control system; 7-First valve; 8-Second valve; 9-Pressure sensor; 10-Fifth valve; 11-Second three-way valve; 12-Sixth valve; 13-Temperature probe; 14-First three-way valve; 15-Third valve; 16-Fourth valve; 17-Inner vessel temperature display; 18-Experimental temperature display; 19-Bolt; 31-Percolation reactor body; 311-Extended male connector; 312-Reservoir support; 32-Reservoir cover. Detailed Implementation

[0052] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a gas chromatography-based method and apparatus for measuring oil production in core percolation experiments according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0054] The following detailed description, in conjunction with the accompanying drawings, illustrates the specific scheme of the method and apparatus for measuring oil production from core percolation experiments based on gas chromatography provided by this invention.

[0055] A gas chromatography-based core permeation experiment oil production measurement device includes a core permeation experiment device body and a control module. Please refer to [link / reference]. Figure 1This diagram illustrates a schematic of the core percolation experimental apparatus in one embodiment of the present invention, including a percolator container 2, a percolation reaction vessel 3, and a heating jacket 4. One end of the percolator container 2 is connected to a displacement pump 1, and the other end of the percolator container 2 is connected to the percolation reaction vessel 3. The percolation reaction vessel 3 is placed inside the heating jacket 4, and a sampling device 5 is connected to the upper end of the percolation reaction vessel 3. The heating jacket 4 is equipped with a temperature control system 6 for controlling the temperature. A first valve 7 is provided on the pipeline between the percolator container 2 and the displacement pump 1. A second valve 8, a pressure sensor 9, a fifth valve 10, and a second three-way valve 11 are provided on the pipeline between the percolator container 2 and the percolator reaction vessel 3.

[0056] The percolation reactor 3 includes a reactor body 31 and a reactor cover 32. The lower end of the reactor body 31 is provided with an extended male connector 311, which is connected to the percolating agent container 2. The extended male connector 311 is provided with a sixth valve 12. The reactor body 31 has a reactor support 312, on which a temperature probe 13 is provided. The reactor cover 32 is connected to the reactor body 31 by bolts 19. The diameter of the reactor cover 32 is the same as that of the end cap. The lower opening of the reactor cover 32 is conical, and its size is the same as that of the opening of the reactor body 31. A first three-way valve 14, a third valve 15, and a fourth valve 16 are provided on the pipeline between the reactor cover 32 and the sampling device 5.

[0057] The heating jacket 4 and the temperature control system 6 are connected by an internal temperature display 17 and an experimental temperature display 18.

[0058] Please see Figure 2 The diagram illustrates the structure of a control module in one embodiment of the present invention, including a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 may include a high-speed random access memory, and the bus 202 may be an ISA bus, a PCI bus, or an EISA bus, etc. The processor 200 may be an integrated circuit chip with signal processing capabilities. The memory 201 stores at least one instruction, at least one program, a code set, or an instruction set. When the processor loads and executes the at least one instruction, at least one program, a code set, or an instruction set, it implements the steps of a method for measuring oil production from a core percolation experiment based on gas chromatography.

[0059] Please see Figure 3 The diagram illustrates a flowchart of a method for measuring oil production from core percolation experiments based on gas chromatography, according to an embodiment of the present invention. The method includes the following steps:

[0060] Step S1: Obtain the initial gas chromatographic data of the core sample extract and identify the peaks to obtain all the peak segments.

[0061] Core samples were obtained, and the core samples were subjected to percolation and extraction experiments using the core percolation experimental apparatus to obtain core sample extract.

[0062] Specifically, in this embodiment of the invention, the core sample is a raw oil-bearing core extracted under pressure. Further, the fourth valve 16 and the fifth valve 10 of the core percolation experimental apparatus are closed. The prepared percolation solution, Gulong No. 1 emulsion system, is loaded into the percolation agent container 2. Before starting the displacement pump 1, the first valve 7, the second valve 8, the fourth valve 16, and the fifth valve 10 are opened to inject the percolation solution from the percolation agent container 2 into the percolation reaction vessel 3. Once the percolation reaction vessel 31 is full of percolation solution, pressure is continued until the pressure in the percolation reaction vessel 3 reaches the required experimental pressure of 30 MPa. Then, the first valve 7, the second valve 8, the fourth valve 16, the fifth valve 10, and the displacement pump 1 are closed. The percolation reaction vessel 3 is heated to the required experimental temperature of 110°C using the temperature control system 6. The cycle is set to one month, first cooling to room temperature and then depressurizing to atmospheric pressure. After connecting the sampling device 5, open the displacement pump 1, first valve 7, second valve 8, third valve 15, fourth valve 16, and fifth valve 10 to inject the Gulong No. 1 emulsion system of the percolating fluid, driving the produced gas and liquid into the sampling device 5 for collection. Repeat the above steps multiple times for eight cycles of percolation experiments until the percolation oil production no longer changes. Conducting multiple cycles of percolation experiments is beneficial for analyzing changes in percolation efficiency and the changes in oil phase mobility in the core. After the percolation experiment is completed, open the sixth valve 12 to discharge the percolating fluid from the percolation reactor.

[0063] To improve crude oil recovery rates, various surfactants are often added to percolators to emulsify with the crude oil. This can lead to water-in-oil emulsions in the visible oil phase or oil-in-water emulsions in the aqueous phase, resulting in significant measurement errors. When the produced liquid is severely emulsified, a demulsifier should be added for further treatment. In this embodiment, the percolator is a Gulong No. 1 emulsion system. A demulsifier should be added to the produced liquid, and the mixture should be stirred for 10 minutes. The demulsifier should then be in contact with the produced liquid for 40 minutes. The demulsifier-treated produced liquid should be transferred to a centrifuge tube, and the centrifuge speed should be set to 5000 rpm for 20 minutes. It should be noted that the demulsification treatment method varies depending on the type of percolator and can be adjusted according to the specific implementation scenario.

[0064] Then, the produced liquid after demulsification was vortexed to disperse the oil droplets evenly. 100 mL of the produced liquid was placed in a separatory funnel, and 30 mL of extractant (n-hexane) was added to the produced liquid. The separatory funnel was shaken to mix the produced liquid and the extractant thoroughly. The mixture was then allowed to stand until it was completely separated into layers. The upper extract was collected, and 30 mL of extractant was added to the raffinate. The extraction operation was repeated 3 times. Finally, the upper extracts were collected and combined to obtain the core sample extract.

[0065] Furthermore, initial gas chromatographic data were obtained from the core sample extract using gas chromatography. Specifically, anthracene standard was added to the extract and dissolved to form the test solution. High-purity nitrogen was used as the carrier gas, and the test solution was instantaneously vaporized at the 300°C injection port and carried into the chromatographic column by nitrogen. The column was heated in a gradient (50°C → 290°C) to separate hydrocarbons according to their boiling points from low to high. The separated components were then sequentially introduced into the FID (310°C) to convert them into initial gas chromatographic data (baseline correction can be performed on this data, which is a well-known technique and will not be elaborated here). Peak identification was performed on the initial gas chromatographic data (derivative method to identify special points, such as the start point, end point, and peak point of a peak), thereby dividing the gas chromatographic data into all peak segments.

[0066] The products of core percolation experiments include various hydrocarbons. Due to the similarity of the physicochemical properties of some hydrocarbons, such as their boiling points, fluctuations in the rate of temperature rise can easily lead to overlapping peaks in the chromatographic data of different hydrocarbon components. Some hydrocarbons have different polarities, so their retention times are also different. Hydrocarbons with higher polarity are prone to tailing on polar columns and are more likely to overlap with the chromatographic peaks of other hydrocarbons.

[0067] Overlapping peak analysis is an effective method that decomposes overlapping peaks into single peaks, thereby better restoring the peak shape data of each component and improving the accuracy of subsequent quantitative and qualitative analysis of the components. Traditional overlapping peak analysis based on Gaussian sharpening uses a fixed Gaussian sharpening factor and standard deviation, which can easily lead to unexpected decomposition of overlapping peaks in the entire gas chromatography data, resulting in false peaks or incomplete separation of overlapping peaks. Therefore, in subsequent steps, adaptive sharpening factors and standard deviations are constructed by analyzing the data characteristics of each peak shape in the gas chromatography data to obtain different Gaussian sharpening operators. For overlapping peaks that have different data characteristics due to different factors, the adaptive-scaled Gaussian sharpening operators (sharpening factor and standard deviation) are used to sharpen and decompose the overlapping peaks, ultimately restoring the overlapping peaks to single peak data that can reflect the characteristics of each component, thus improving the accuracy of subsequent quantitative analysis of core percolation experimental products using gas chromatography data.

[0068] Step S2: Analyze the time-frequency localization characteristics of each spectral peak segment to determine the local signal-to-noise ratio index of each spectral peak segment; analyze the peak shape change characteristics of each spectral peak segment and combine them with the local signal-to-noise ratio index of each spectral peak segment to adjust the preset sharpening factor and obtain the adaptive sharpening factor of each spectral peak segment.

[0069] Because many hydrocarbons have similar physicochemical properties, some overlapping chromatographic peaks exist in the initial gas chromatography data, which interferes with the assessment of the content of each hydrocarbon. Gaussian sharpening controls the sharpening intensity of the peak shape through sharpening factors. However, a fixed sharpening factor will prevent these overlapping peaks with different characteristics from being accurately sharpened and separated, which can easily lead to over-sharpening and false peaks or insufficient sharpening effect that cannot completely separate overlapping peaks.

[0070] In the spectral peak segment, noise may exhibit a peak-like shape. Therefore, the signal-to-noise ratio (SNR) can reflect the contrast between the spectral peak intensity and the presence of noise interference, thus revealing the related characteristics of peak-shaped signal ambiguity. Therefore, time-frequency localization feature analysis is performed in each spectral peak segment to determine the local SNR index for each segment, which characterizes the degree of noise interference in each peak segment. On the other hand, peak symmetry can reflect the degree of deviation of the peak shape from the ideal Gaussian distribution. Poorer peak symmetry makes sharpening and separation more difficult. For example, when overlapping peaks with tailing characteristics have severely asymmetrical peak shapes, an excessively large sharpening factor can cause the tailing signal to be truncated, resulting in peak distortion and false peaks after sharpening. Therefore, analyzing the peak symmetry of the spectral peak segment can be used as one of the indicators for adjusting the sharpening factor.

[0071] Therefore, in each spectral peak segment, the peak shape variation characteristics are analyzed and combined with the local signal-to-noise ratio index of the spectral peak segment to adjust the preset sharpening factor and obtain the adaptive sharpening factor for each spectral peak segment.

[0072] Preferably, in one embodiment of the present invention, the method for obtaining the adaptive sharpening factor includes:

[0073] Please see Figure 4 The diagram illustrates a method flowchart for obtaining an adaptive sharpening factor according to an embodiment of the present invention. The method includes the following steps:

[0074] Step S401: Analyze the time-frequency localization characteristics of each spectral peak segment to determine the local signal-to-noise ratio index of each spectral peak segment.

[0075] Given that water-in-oil emulsions can cause some hydrocarbons to exhibit water-in-oil properties, resulting in water-phase interference in the peaks of individual hydrocarbon components in gas chromatography, and not all hydrocarbon components will experience this interference, a pre-defined peak interval is extracted from the initial gas chromatographic data, centered on each peak segment. This allows for the estimation of chromatographic data characteristics surrounding the hydrocarbon component to determine the presence of water-phase interference in its peaks, avoiding the distortion of the true noise interference level in each peak due to noise estimation based on global chromatographic data. It should be noted that in this embodiment of the invention, the specific range of the peak interval is expressed as... ,in, Indicates the start time of the spectral peak segment; Indicates the end time of the spectral peak segment; , which represents the peak width of the spectral peak segment, i.e., the time length; This indicates the extent to which the spectral peak expands to both sides, and can be adjusted according to the implementation scenario; no limitation is made here.

[0076] Gas chromatography noise may include high-frequency random noise (such as detector electronic noise). Wavelet decomposition can separate these components at multiple scales, and in particular, high-frequency subbands can effectively capture random noise. Therefore, wavelet transform decomposition is used to decompose the gas chromatography data in the spectral peak interval. The number of decomposition layers is a preset value (set to 5 in this embodiment of the invention; the specific number of layers can be adjusted according to the implementation scenario and is not limited here), thereby obtaining all subband signals.

[0077] Then, the trapezoidal integral method is used to integrate the highest frequency sub-band signal among all sub-band signals, and the area (energy) is used as the noise characteristic value. The trapezoidal integral method is also used to integrate the gas chromatographic data corresponding to each spectral peak segment to obtain the area value. This area value characterizes the signal energy. Therefore, the normalized value of the ratio of this area value to the noise characteristic value is used as the local signal-to-noise ratio (SNR) index for each spectral peak segment. The larger the local SNR index, the less the spectral peak segment is affected by noise interference; conversely, the smaller the local SNR index, the greater the spectral peak segment is affected by noise interference. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0078] It should be noted that wavelet transform decomposition and trapezoidal integral method are well-known techniques, and the specific process will not be elaborated here.

[0079] Step S402: In each spectral peak segment, analyze the peak shape variation characteristics and determine the symmetry index of each spectral peak segment.

[0080] The symmetry of a peak segment represents the degree of uniformity of mass transfer and diffusion of a certain hydrocarbon component in the chromatographic column of a gas chromatograph. If the peak shape is symmetrical, it indicates that the adsorption and desorption rates of the hydrocarbon component in the chromatographic column are consistent, the component content is consistent, and the detection signal is stable. When the peak shape shows obvious tailing or forward signal, the symmetry of the peak is destroyed, indicating that there are other matrix interferences in the hydrocarbon component, which leads to a decrease in chromatographic separation effect.

[0081] Since the peak height is usually calculated as 10% when analyzing the symmetry of the peak shape, 10% of the maximum intensity value is used as the reference intensity value in each peak segment, and the two times corresponding to the values ​​of the reference intensity value on both sides of the maximum intensity value are used as reference times.

[0082] The absolute value of the difference between each reference time and the time corresponding to the maximum intensity value is used as the time distance factor. The time distance factor indicates the time distance between the reference time and the time corresponding to the peak height. The absolute value of the difference between the two time distance factors is calculated. The smaller the absolute value of the difference, the more symmetrical the peak shape. Therefore, the absolute value of the difference here is negatively correlated and normalized to correct the logical relationship and obtain the symmetry index corresponding to each spectral peak segment. At this time, the larger the symmetry index, the more symmetrical the peak shape, that is, the more similar the adsorption and desorption rates of hydrocarbon components in the chromatographic column. Theoretically, the chromatographic peaks should conform to the Gaussian curve distribution. However, in reality, any chromatographic peak will have a certain deviation from the Gaussian curve distribution, that is, asymmetry. Therefore, the negative correlation mapping and normalization here can be directly applied using the formula. )or Where x represents the independent variable, This represents an exponential function with base e. This represents the normalization function.

[0083] It should be noted that if there are no two reference times in a certain spectral peak segment, that is, if two intensity values ​​of 10% of the peak height cannot be found on both sides of the maximum intensity value of the spectral peak segment, then it means that the symmetry of the spectral peak segment is extremely poor, and the symmetry index of the spectral peak segment can be set to 0 directly.

[0084] Step S403: The local signal-to-noise ratio and symmetry index of each spectral peak are fused to adjust the preset sharpening factor and obtain the adaptive sharpening factor for each spectral peak.

[0085] A higher local signal-to-noise ratio (SNR) indicates less noise interference, suggesting more genuine overlapping peak features. In this case, increasing the sharpening factor improves the algorithm's sharpening and separation of overlapping peaks. Conversely, a lower SNR indicates more noise interference, requiring a lower sharpening factor to maintain sharpening stability. Similarly, a higher symmetry index indicates better symmetry, allowing for increased sharpening factor to improve overlapping peak separation. Conversely, a lower symmetry index indicates poorer symmetry, necessitating a lower sharpening factor to avoid over-sharpening distorted or trailing features and creating false peaks. Therefore, the normalized value of the local SNR and symmetry indices for each peak is used as the first adjustment factor. Based on the aforementioned logic, a larger first adjustment factor allows for a greater increase in the sharpening factor. Normalization is a well-known technique, and the normalization function can be linear or standard, etc. The specific normalization method is not limited here.

[0086] Excessive sharpening factor may lead to excessive peak distortion, while insufficient sharpening factor may prevent the separation of overlapping peaks. Therefore, the difference between the preset maximum and minimum sharpening factors is used as the second adjustment factor. This second adjustment factor reflects the dynamic adjustment potential of the sharpening factor, which can be considered the adjustable space of the sharpening factor. Then, the product of the first and second adjustment factors is used as the adjustment degree value. This adjustment degree value can convert the proportion of the first adjustment factor into the actual change in the sharpening factor within the adjustable space of the sharpening factor. Finally, the sum of the preset minimum sharpening factor and the adjustment degree value is used as the adaptive sharpening factor for each spectral peak segment. This adaptive sharpening factor can better conform to the peak shape characteristics of the spectral peak segment itself, thereby improving the sharpening quality in subsequent processes.

[0087] It should be noted that in this embodiment of the present invention, the preset minimum sharpening factor is taken as an empirical value of 0.05, and the preset maximum sharpening factor is taken as an empirical value of 0.85.

[0088] Step S3: In each spectral peak segment, analyze the temporal difference characteristics of intensity and determine the peak half-width of each spectral peak segment; in terms of time, analyze the numerical characteristics of the separation degree of adjacent spectral peak segments and determine the separation index of each spectral peak segment; based on the peak half-width and separation index of each spectral peak segment, adjust the preset standard deviation to obtain the adaptive standard deviation of each spectral peak segment.

[0089] In the analysis of overlapping peaks based on Gaussian sharpening, the standard deviation controls the smoothing scale of the Gaussian function. If the standard deviation is too large, the overlapping peaks will be over-smoothed, the valley values ​​between peaks will increase, and the separation of overlapping peaks will become difficult. When the standard deviation is too small, the Gaussian function will amplify some interference, causing the spectral signal to oscillate, and false peaks will easily appear during the sharpening process. In the core percolation experiment, the overlap of various hydrocarbon characteristic peaks makes the overlapping peaks have different shapes. A fixed standard deviation is difficult to accurately retain the true structure of each peak shape after the overlapping peaks are separated.

[0090] The peak half-width is a key parameter describing the degree of peak broadening and directly affects the setting of the standard deviation. The separation of adjacent spectral peaks can reflect the temporal overlap of adjacent spectral peaks and determine whether the standard deviation needs to be adjusted to enhance the separation capability. Therefore, in each spectral peak, the temporal difference characteristics of intensity and the numerical characteristics of the separation degree of adjacent spectral peaks are analyzed and the two are combined to adjust the preset standard deviation, thereby obtaining the adaptive standard deviation of each spectral peak.

[0091] Preferably, in one embodiment of the present invention, the method for obtaining the adaptive standard deviation includes:

[0092] Please see Figure 5 The diagram illustrates a method flowchart for obtaining adaptive standard deviation in one embodiment of the present invention, which includes the following steps:

[0093] Step S501: In each spectral peak segment, analyze the temporal difference characteristics of the intensity and determine the peak half-width of each spectral peak segment.

[0094] In each spectral peak segment, half of the maximum intensity value is taken as the half-peak value. In the gas chromatographic data on both sides of the maximum intensity value, the two times corresponding to the half-peak value are taken as the half-peak times. The absolute value of the difference between the two half-peak times is taken as the peak half-height width of each spectral peak segment. The peak half-height width shows the degree of broadening of the spectral peak segment. For spectral peak segments with different peak half-height widths, different standard deviations have different resolutions for smoothing. If the peak half-height width is very small, then a smaller standard deviation should be set to match the broadening of the spectral peak segment to improve the ability to identify narrow peak features during smoothing. If the peak half-height width is large, then the standard deviation should be increased to maintain the smoothness of the overall peak shape and avoid the magnification of wide peaks causing over-sharpening. Therefore, the peak half-height width can be used as a reference factor for subsequent adjustment of the preset standard deviation.

[0095] It should be noted that for some special cases in the peak shape characteristics, such as the appearance of a back shoulder peak, which causes only one half-peak time to exist for a certain spectral peak segment, twice the absolute value of the difference between the time corresponding to the maximum intensity value and the half-peak time can be used as the peak half-width of that spectral peak segment.

[0096] Step S502: In terms of time sequence, analyze the numerical characteristics of the separation degree of adjacent spectral peak segments and determine the separation index of each spectral peak segment.

[0097] In the initial gas chromatography data, the resolution of each peak segment is obtained, and the resolution formula is a known technique: ,in, This represents the separation degree between the nth spectral peak and the (n+1)th spectral peak. This represents the time corresponding to the peak point of the nth spectral segment. This represents the time corresponding to the peak point of the (n+1)th spectral peak segment. Indicates the first Peak width of each spectrum This represents the peak width of the (n+1)th spectral peak.

[0098] Among all spectral peak segments, the first and last spectral peak segments in time sequence are taken as endpoint segments, and all remaining spectral peak segments are taken as intermediate segments.

[0099] Because the aliasing at the endpoints originates from a single source and is only affected by the adjacent peak on one side, while the aliasing at the middle peaks is affected by the adjacent peaks on both sides, the resolution between each endpoint and its adjacent peak is used as the separation index for each endpoint. The mean resolution between each middle peak and its two adjacent peaks on both sides is used as the separation index for each middle peak. A larger separation index indicates better resolution between the peak and its adjacent peaks, resulting in clear chromatographic separation of the hydrocarbon component from other hydrocarbon components in the chromatographic data. Conversely, a smaller separation index indicates mutual influence between different hydrocarbon components, leading to peak aliasing in the chromatographic data. The more severe the aliasing, the more difficult it is to resolve the peaks. Therefore, a smaller standard deviation is needed to smooth the characteristics of overlapping peaks for separation.

[0100] Step S503: Integrate the peak half-width and separation index of each spectral peak segment to adjust the preset standard deviation and obtain the adaptive standard deviation of each spectral peak segment.

[0101] According to prior knowledge, when the resolution is greater than or equal to 1.5, it indicates that the two adjacent peaks are completely separated by baseline, with a separation degree greater than or equal to 99.7%. When the resolution is equal to 1, it is considered that there is still some overlap between the peaks. When the resolution is less than 1, it is considered that the peaks are severely overlapped. Therefore, in this embodiment of the present invention, for any spectral peak segment, when the separation index of the spectral peak segment is greater than or equal to the preset separation threshold (with a value of 1), the preset separation threshold is used as the separation factor of the spectral peak segment. When the separation index of the spectral peak segment is less than the preset separation threshold, the separation index of the spectral peak segment is used as the separation factor. The larger the separation factor, the better the separation degree between the spectral peak segment and the adjacent spectral peak segments. In the chromatographic data, the hydrocarbon component is clearly separated from other hydrocarbon components. The standard deviation value can be appropriately increased to better maintain the smooth characteristics of the overall peak shape.

[0102] When the half-maximum width (WHM) of a spectral peak segment is larger, the standard deviation should also increase to maintain the smoothness of the overall peak shape and avoid over-sharpening caused by magnification of wide peaks. Therefore, the normalized WHM value of each spectral peak segment is combined with the normalized value of the mean of the separation factor for each spectral peak segment, and used as the third adjustment factor for each spectral peak segment. Based on the aforementioned logic, a larger third adjustment factor indicates a greater degree of adjustment to the standard deviation. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0103] Excessive standard deviation may lead to over-smoothing of overlapping peaks, making it difficult to separate them. Conversely, excessively small standard deviations may result in false peaks during sharpening. Therefore, the difference between the preset maximum and minimum standard deviations is used as the fourth adjustment factor. This fourth adjustment factor reflects the adjustable space of the standard deviation. The product of the third and fourth adjustment factors is then used as the adjustment parameter. This parameter converts the proportion of the third adjustment factor into the actual adjustment amount within the adjustable space of the standard deviation. Finally, the sum of the adjustment parameter and the preset minimum standard deviation is used as the adaptive standard deviation for each spectral peak segment. This adaptive standard deviation better adapts to the peak shape of each spectral peak segment, thereby improving the sharpening quality in subsequent processes.

[0104] It should be noted that in this embodiment of the present invention, the preset minimum standard deviation is 0.5 and the preset maximum standard deviation is 0.5.

[0105] Step S4: Sharpen the initial gas chromatographic data according to the adaptive sharpening factor and adaptive standard deviation of the spectral peaks to obtain the sharpened gas chromatographic data.

[0106] Based on the aforementioned steps, the adaptive sharpening factor and adaptive standard deviation of each peak segment in the initial gas chromatography data can be obtained. These factors have a better fit with the peak shape characteristics of each peak segment. Therefore, by sharpening and separating each peak segment based on these two indicators, better smoothing can be achieved, the accuracy of peak segment separation can be improved, and the sharpened gas chromatography data can be more consistent with the true peak shape characteristics.

[0107] Preferably, in one embodiment of the present invention, the method for obtaining sharpened gas chromatographic data includes:

[0108] An adaptive sharpening factor controls the sharpening intensity, and an adaptive standard deviation determines the local range of the sharpening filter. Gaussian sharpening is used, and the peak segments are sharpened based on the adaptive sharpening factor and adaptive standard deviation of each peak segment to obtain the sharpened gas chromatographic data corresponding to the initial gas chromatographic data.

[0109] It should be noted that Gaussian sharpening is a well-known technique, and the specific process will not be described in detail here.

[0110] Step S5: Calculate the percolation efficiency and percolation oil production based on the sharpened gas chromatography data.

[0111] After obtaining the sharpened gas chromatographic data based on the aforementioned steps, the crude oil can be quantitatively characterized using the gas chromatographic internal standard method. The gas chromatographic internal standard method for quantitative analysis of chromatographic data is an existing technology, and the specific process will not be elaborated here. Then, based on this, the percolation efficiency and percolation oil production can be calculated using the sharpened gas chromatographic data.

[0112] Preferably, in one embodiment of the present invention, calculating the percolation efficiency and percolation oil production based on sharpened gas chromatographic data includes:

[0113] In the sharpened gas chromatographic data, the peak area of ​​each hydrocarbon component is compared with the peak area of ​​the standard sample to determine the amount of oil produced during each percolation process of the extract: First, the actual content of each hydrocarbon component is calculated:

[0114] in, This represents the correction factor, which is a dimensionless parameter. Indicates the first Peak areas of hydrocarbon components; Indicates the peak area of ​​the standard sample; Indicates the volumetric content of the standard sample; Indicates the first Volume content of hydrocarbon components.

[0115] In this formula model, As a correction factor, it is set to 1 here after experimental verification; this formula is based on the first... There is a linear relationship between the peak area of ​​a hydrocarbon component and its actual content. Therefore, the peak area can be obtained by comparing it with a standard sample of known concentration. The actual content of each hydrocarbon component.

[0116] Based on the actual content of each hydrocarbon component and the ratio of hydrocarbons to non-hydrocarbons, the percolation oil production of one cycle of percolation experiment is obtained. In this embodiment of the invention, the proportion of hydrocarbons is assumed to be 0.8. Therefore, the percolation oil production of one cycle of percolation experiment is:

[0117]

[0118] In the formula, Indicates the first Volume content of hydrocarbon components; This indicates the volume of liquid in the osmosis experiment (which can be obtained by measurement during the experiment). Indicates the percentage of hydrocarbons in the oil; This represents the amount of oil produced by percolation in one cycle of percolation experiment; Indicates the types of hydrocarbon components.

[0119] In this formula model, The unit is , The unit is , As a conversion factor for volume units Converted to G level, This is a dimensionless parameter. By calculating the actual content of each hydrocarbon and the total volume of the dialysis solution, and then converting the volume of each hydrocarbon into mass form according to the hydrocarbon ratio and unit conversion factor, the oil production of each dialysis cycle can be obtained by summing all hydrocarbon components.

[0120] Furthermore, the percolation efficiency of the extract can be determined based on the amount of oil produced during each percolation process and the preset initial oil content.

[0121] Therefore, the percolation efficiency for each percolation cycle can be expressed as:

[0122] in, This indicates the percolation efficiency for each percolation cycle; This indicates the amount of oil produced by the percolation in each cycle of the percolation experiment; This indicates the original oil content of the core sample, as shown in this embodiment. It is 18.53g (which can be calculated using porosity and oil saturation, etc.); constant Used to convert its ratio to a percentage.

[0123] In this formula, the oil production efficiency of a deep absorption cycle can be measured by statistically analyzing the ratio of the oil production to the initial oil content in each cycle of the deep absorption experiment.

[0124] Ultimately, the above steps can be used to obtain the percolation efficiency and oil production of the core sample under each percolation effect.

[0125] In summary, initial gas chromatographic data of the core extract were obtained and peaks were identified to divide all peak segments. The product components of the core percolation experiment include various hydrocarbons. Due to the similarity of the physicochemical properties of some hydrocarbons, overlapping peaks can easily occur in the chromatographic data of different hydrocarbon components. Some hydrocarbons have different polarities, so their retention times also vary. Hydrocarbons with higher polarity are prone to tailing on polar columns and are also prone to overlapping with the chromatographic peaks of other hydrocarbons. Traditional Gaussian sharpening-based overlapping peak analysis uses a fixed Gaussian sharpening factor and standard deviation to decompose overlapping peaks in the entire gas chromatographic data. This can easily lead to over-sharpening, resulting in false peaks or incomplete separation of overlapping peaks. Therefore, in this embodiment of the invention, by analyzing the local peak shape characteristics and data characteristics of each spectral peak segment, an adaptive sharpening factor and standard deviation for each spectral peak segment are determined. Given that the signal-to-noise ratio can reflect the contrast between the peak intensity and the presence of noise interference, and can reveal the fuzzy peak signal characteristics caused by water interference from oil-water emulsions, and that peak symmetry can reflect the degree of deviation of the peak shape from the ideal Gaussian distribution, the worse the peak shape symmetry, the more difficult it is to perform sharpening separation; therefore, the time-frequency localization characteristics and peak shape change characteristics of each spectral peak segment are analyzed, and the adaptive sharpening factor for each spectral peak segment is calculated. Furthermore, the peak half-width at half-maximum (HWHM) and the separation degree between adjacent peaks were analyzed. These two factors were combined to determine the adaptive standard deviation for each peak segment. This ensured that the separated peaks in the subsequent sharpening process better matched the true peak shape characteristics. Finally, the initial gas chromatographic data was sharpened based on the adaptive sharpening factor and adaptive standard deviation of the peak segments, resulting in sharpened gas chromatographic data. This data eliminated the influence of overlapping peaks, thus making the calculated percolation efficiency and percolation oil yield more accurate.

[0126] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0127] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for measuring oil production in a core imbibition experiment based on gas chromatography, characterized by, The method comprises: Obtaining initial gas chromatography data of the core sample extraction liquid and performing spectral peak identification to obtain all spectral peak segments; Performing time-frequency localization feature analysis on each spectral peak segment to determine the local signal-to-noise ratio index of each spectral peak segment; analyzing the peak shape change feature of each spectral peak segment, and combining the local signal-to-noise ratio index of each spectral peak segment to adjust the preset sharpening factor, thereby obtaining the adaptive sharpening factor of each spectral peak segment; In each spectral peak segment, the time sequence difference feature of the intensity is analyzed to determine the peak half-height width of each spectral peak segment; in the time sequence, the numerical feature of the separation degree of adjacent spectral peak segments is analyzed to determine the separation index of each spectral peak segment; based on the peak half-height width and the separation index of each spectral peak segment, the preset standard deviation is adjusted to obtain the adaptive standard deviation of each spectral peak segment; According to the adaptive sharpening factor and the adaptive standard deviation of the spectral peak segment, the initial gas chromatography data is sharpened to obtain the sharpened gas chromatography data; Based on the sharpened gas chromatography data, the imbibition efficiency and the imbibition oil production are calculated; The method for obtaining the local signal-to-noise ratio comprises: In the initial gas chromatography data, a data segment of a preset length is intercepted as a spectral peak interval of each spectral peak segment; The gas chromatography data in the spectral peak interval is decomposed by wavelet transform decomposition, and the decomposition layer number is a preset value to obtain all sub-band signals; The highest frequency sub-band signal in all sub-band signals is integrated by using the trapezoidal integral method, and the area is taken as a noise characteristic value; The gas chromatography data corresponding to each spectral peak segment is integrated by using the trapezoidal integral method, and the ratio of the area to the noise characteristic value after normalization is taken as the local signal-to-noise ratio index of each spectral peak segment; The method for obtaining the adaptive sharpening factor comprises: In each spectral peak segment, the peak shape change feature is analyzed to determine the symmetry index of each spectral peak segment; The mean value of the local signal-to-noise ratio index and the symmetry index of each spectral peak segment after normalization is taken as a first adjustment factor; The difference between the preset maximum sharpening factor and the preset minimum sharpening factor is taken as a second adjustment factor; The product of the first adjustment factor and the second adjustment factor is taken as an adjustment degree value, and the sum of the preset minimum sharpening factor and the adjustment degree value is taken as the adaptive sharpening factor of each spectral peak segment; The method for obtaining the symmetry index comprises: In each spectral peak segment, ten percent of the maximum intensity value is taken as a reference intensity value, and in the gas chromatography data on both sides of the maximum intensity value, the two time points corresponding to the numerical value of the reference intensity value are taken as reference time points; The absolute value of the difference between each reference time point and the time point corresponding to the maximum intensity value is taken as a time distance factor, and the absolute value of the difference between the two time distance factors is negatively correlated and normalized to obtain the symmetry index corresponding to each spectral peak segment, wherein if there are no two reference time points in a certain spectral peak segment, the symmetry index of the spectral peak segment is set to 0; The method for obtaining the separation index comprises: In the initial gas chromatography data, the separation degree of each spectral peak segment is obtained; In all the spectral peak segments, the first spectral peak segment and the last spectral peak segment are taken as end point segments, and the remaining spectral peak segments are taken as intermediate segments; The separation degree between each end point segment and an adjacent spectral peak segment is taken as the separation index of each end point segment, and the average of the separation degrees between each intermediate segment and two adjacent spectral peak segments is taken as the separation index of each intermediate segment; The adaptive standard deviation obtaining method comprises: For any spectral peak segment, when the separation index of the spectral peak segment is greater than or equal to a preset separation threshold, the preset separation threshold is taken as the separation factor of the spectral peak segment, and when the separation index of the spectral peak segment is less than the preset separation threshold, the separation index of the spectral peak segment is taken as the separation factor; The normalized value of the peak half-height width of each spectral peak segment and the normalized value of the average of the separation factors of each spectral peak segment are taken as the third adjustment factor of each spectral peak segment; The difference between the preset maximum standard deviation and the preset minimum standard deviation is taken as the fourth adjustment factor; The product of the third adjustment factor and the fourth adjustment factor is taken as an adjustment parameter, and the sum of the adjustment parameter and the preset minimum standard deviation is taken as the adaptive standard deviation of each spectral peak segment; The sharpened gas chromatography data obtaining method comprises: The spectral peak segments are sharpened by using a Gaussian sharpening method and based on the adaptive sharpening factors and the adaptive standard deviations of the spectral peak segments, so that the sharpened gas chromatography data corresponding to the initial gas chromatography data are obtained.

2. The method of claim 1, wherein, The peak half-height width obtaining method comprises: In each spectral peak segment, half of the maximum intensity value is taken as a half-peak value, and in the gas chromatography data on both sides of the maximum intensity value, two time points corresponding to the half-peak value are taken as half-peak time points; The absolute value of the difference between the two half-peak time points is taken as the peak half-height width of each spectral peak segment; if there is only one half-peak time point corresponding to a spectral peak segment, twice the absolute value of the difference between the time point corresponding to the maximum intensity value and the half-peak time point is taken as the peak half-height width of the spectral peak segment.

3. The method of claim 1, wherein, The method for calculating the infiltration efficiency and the infiltration oil production based on the sharpened gas chromatography data comprises: In the sharpened gas chromatography data, the peak area of each hydrocarbon component is compared with the standard sample peak area to determine the infiltration oil production of the extraction liquid in each infiltration process; Based on the infiltration oil production in each infiltration process and the preset original oil content, the infiltration efficiency of the extraction liquid is determined.

4. The method of claim 1, wherein, The range of the spectrum peak interval is represented as ; wherein, represents the start time of the spectrum peak segment; represents the end time of the spectrum peak segment; represents the time length of the spectrum peak segment; represents the range of the spectrum peak segment expanding to both sides.

5. A device for measuring oil production in a core imbibition experiment based on gas chromatography, comprising a core imbibition experiment device body and a control module, characterized in that, The control module comprises a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, which are loaded and executed by the processor to implement the steps of the core infiltration experiment oil production measurement method based on gas chromatography according to any one of claims 1-4.

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