Continental facies shale oil lithofacies type identification method and device

Abstract

The invention provides a continental facies shale oil lithofacies type identification method and device, and the method comprises the steps: carrying out the preprocessing and image enhancement processing of the electric imaging logging original data of a to-be-identified lithofacies, and obtaining a first electric imaging static image, carrying out the histogram processing of the first electric imaging static image, and obtaining a first electric imaging static image histogram; determining stratum structure indexes and lithofacies discrimination parameters of the to-be-identified lithofacies, which are used for reflecting stratum structure characteristics, on the first electric imaging static image histogram; and comparing the stratigraphic structure index and the lithofacies discrimination parameter of the to-be-identified lithofacies with preset stratigraphic structure indexes and lithofacies discrimination parameter limit standards of various shale oil and lithofacies types, and determining the shale oil and lithofacies type. According to the method, the electrical characteristics of the stratum can be objectively and truly reflected, the fine structure characteristics of the stratum can be described more finely and efficiently, and the continental facies shale oil facies type can be accurately identified.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum exploration and well logging technology, and in particular relates to a method and a device for identifying the lithofacies type of continental shale oil. Background Art

[0002] The lithology of continental shale oil reservoirs is complex. The shale strata have developed a variety of lithologies and are highly heterogeneous. In addition to shale, siltstone, muddy siltstone, and sandy shale are also developed. These different lithologies, unequal thicknesses, and rapid changes in lithology increase the complexity of reservoir lithofacies to varying degrees. Different lithofacies mean differences in relevant parameters such as mineral composition, organic matter content, and brittleness index. These parameters play a vital role in the prediction of favorable shale oil areas and the calculation of resource quantities in the later stage. At the same time, it is difficult to identify favorable lithofacies, which seriously restricts the identification and optimization of sweet spots in shale oil reservoirs.

[0003] CN111577263A discloses a tuff logging identification method, which obtains a comprehensive identification index of tuff in the formation according to the relative value of natural gamma of the formation, the apparent acoustic porosity value of the pure rock skeleton of the formation, and the apparent density porosity value of the pure rock skeleton of the formation, and determines whether tuff exists in the formation according to the comprehensive identification index of tuff in the formation. Although this method determines whether tuff exists according to multiple conventional logging curves, it is not suitable for lithofacies identification of continental shale oil reservoirs with complex lithology.

[0004] CN113945992A discloses a mudstone and oil shale identification method, including determining the target logging curve and preprocessing it; determining the lithology identification end value; determining the lithology interpretation strategy according to the lithology identification end value; and performing lithology identification on each single well according to the lithology interpretation strategy to obtain the identification result. This method makes up for the deficiency caused by the small amount of rock logging data, and can predict the vertical and horizontal distribution characteristics of the source rock, but does not identify the reservoir facies in combination with the lithology analysis results. Summary of the invention

[0005] In order to effectively identify the lithofacies type of continental shale oil reservoirs, the present invention proposes a method and device for identifying the lithofacies type of continental shale oil.

[0006] A method for identifying the lithofacies type of continental shale oil comprises the following steps:

[0007] Acquire raw electrical imaging logging data of the lithofacies to be identified, perform preprocessing and image enhancement processing on the raw electrical imaging logging data of the lithofacies to be identified to obtain a first electrical imaging static image, wherein the first electrical imaging logging raw data at least includes electrical imaging pad data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves;

[0008] Performing histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determining a stratigraphic structure index and a lithofacies discrimination parameter of the lithofacies to be identified that are used to reflect stratigraphic structure characteristics on the first electrical imaging static image histogram;

[0009] The stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified are compared with the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the continental shale oil lithofacies type.

[0010] Furthermore, the preset stratigraphic structure index and lithofacies discrimination parameter boundary standards of various shale oil lithofacies types are obtained according to the following steps:

[0011] Collect core analysis data and electrical imaging logging raw data of target wells in the study area, perform preprocessing and image enhancement processing on the electrical imaging logging raw data, and obtain a second electrical imaging static image for reflecting formation lithology change characteristics;

[0012] Performing histogram processing on the obtained second electrographic static image to obtain a second electrographic static image histogram;

[0013] The preset coring wells, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are used for calibration to determine the stratigraphic structure index and lithofacies discrimination parameter boundary standards for various shale oil lithofacies types.

[0014] Furthermore, the preprocessing and image enhancement processing of the raw data of the electrical imaging logging of the lithofacies to be identified includes:

[0015] The original data of electrical imaging logging for the facies to be identified are subjected to velocity correction, equalization processing and abnormal electrode correction.

[0016] Further, the obtained first electro-imaging static image is subjected to histogram processing to obtain the first electro-imaging static image histogram, which includes:

[0017] The first electrographic static image is equally divided into a number of intervals, and the frequency of image data falling in each interval is counted point by point according to the depth to obtain a histogram of the first electrographic static image.

[0018] Furthermore, determining the stratigraphic structure index and the lithofacies discrimination parameters of the lithofacies to be identified and used to reflect the stratigraphic structure characteristics on the first electrical imaging static image histogram includes:

[0019] The image values ​​of the frequency points of the three preset positions of the image data at each depth point are defined in the first electro-imaging static image histogram as the minimum skewness, the centering degree and the maximum skewness, respectively, to serve as the formation structure index;

[0020] The difference between the maximum skewness and the minimum skewness is taken as the maximum-minimum skewness difference, the first-order derivative of the centering degree is taken as the centering degree variation, and the maximum-minimum skewness difference and the centering degree variation are the lithofacies discrimination parameters.

[0021] Furthermore, the image values ​​at three preset frequency positions of the image data at each depth point defined in the first electrical imaging static image histogram are respectively minimum skewness, centering and maximum skewness, including: the image values ​​at 15%, 50% and 85% frequency of the image data at each depth point defined in the first electrical imaging static image histogram are its minimum skewness, centering and maximum skewness.

[0022] Furthermore, the shale oil lithofacies types include at least homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale.

[0023] On the other hand, the present invention also discloses a device for identifying the lithofacies type of continental shale oil, comprising a first electro-imaging static image generation module, a feature generation module, and a comparison module, wherein:

[0024] A first electrical imaging static image generation module is used to obtain electrical imaging logging raw data of the lithofacies to be identified, and to perform preprocessing and image enhancement processing on the electrical imaging logging raw data of the lithofacies to be identified to obtain a first electrical imaging static image, wherein the first electrical imaging logging raw data at least includes electrical imaging pad data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves;

[0025] A feature generation module, used for performing histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determining a stratigraphic structure index and a lithofacies discrimination parameter of the lithofacies to be identified, which are used to reflect the stratigraphic structure characteristics, on the first electrical imaging static image histogram;

[0026] The comparison module is used to compare the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the continental shale oil lithofacies type.

[0027] In a third aspect, the present invention further discloses a method for generating a continental shale oil lithofacies type identification standard, which is used to provide a basis for automatic identification of continental shale oil reservoir lithofacies types, and the method comprises the following steps:

[0028] The core analysis data and electrical imaging logging raw data of the target wells in the study area are collected, and the electrical imaging logging raw data are preprocessed and image enhanced to obtain a second electrical imaging static image for reflecting the formation lithology change characteristics.

[0029] The obtained second electrographic static image is subjected to histogram processing to obtain a second electrographic static image histogram.

[0030] The preset coring wells, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are used for calibration to determine the stratigraphic structure index and lithofacies discrimination parameter boundary standards for various shale oil lithofacies types.

[0031] In a fourth aspect, the present invention further discloses a device for generating a standard for identifying lithofacies type of continental shale oil, which is used to provide a basis for automatic identification of lithofacies type of continental shale oil reservoirs, and is characterized in that the device comprises a second electrical imaging static image generating module, a second electrical imaging static image histogram generating module, and a boundary standard generating module, wherein:

[0032] The second electrical imaging static image generation module is used to collect core analysis data and electrical imaging logging raw data of the target well in the study area, and perform preprocessing and image enhancement processing on the electrical imaging logging raw data to obtain a second electrical imaging static image used to reflect the formation lithology change characteristics;

[0033] The second electrical imaging static image histogram generating module is used to perform histogram processing on the obtained second electrical imaging static image to obtain a second electrical imaging static image histogram;

[0034] The boundary standard generation module is used to calibrate using preset coring wells, collected core analysis data of target wells in the study area, and a second electrical imaging static image histogram to determine the boundary standards of stratigraphic structural indices and lithofacies discrimination parameters for various shale oil lithofacies types.

[0035] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0036] The present invention obtains the raw data of electrical imaging logging of the lithofacies to be identified, performs preprocessing and image enhancement processing on the raw data of electrical imaging logging of the lithofacies to be identified to obtain a first electrical imaging static image, performs histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, determines the formation structure index and lithofacies discrimination parameters of the lithofacies to be identified that are used to reflect the formation structure characteristics on the first electrical imaging static image histogram; compares the formation structure index and lithofacies discrimination parameters of the lithofacies to be identified with the formation structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the shale oil lithofacies type. The present invention can objectively and truly reflect the electrical characteristics of the formation, and the description of the fine structural characteristics of the formation is more precise and efficient, and can accurately identify the continental shale oil lithofacies type. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 This is a schematic flow chart of a method for identifying lithofacies types of continental shale oil in Embodiment 1 of the present invention;

[0039] Figure 2 It is a schematic diagram of an original electrical imaging logging image and a first electrical imaging static image of the lithofacies to be identified in the first embodiment of the present invention;

[0040] Figure 3 Schematic diagram of a first electro-imaging static image histogram in Embodiment 1 of the present invention;

[0041] Figure 4 This is a schematic diagram of a process for generating preset stratigraphic structure indexes and lithofacies discrimination parameter limit standards for various shale oil lithofacies types in the first embodiment of the present invention;

[0042] Figure 5 It is a schematic diagram of an electrical imaging static image and a static image histogram of homogeneous sandstone in the first embodiment of the present invention;

[0043] Figure 6 It is a schematic diagram of an electrical imaging static image and a static image histogram of a heterogeneous sandstone in the first embodiment of the present invention;

[0044] Figure 7 It is a schematic diagram of an electrical imaging static image and a static image histogram of dark mudstone in the first embodiment of the present invention;

[0045] Figure 8 This is a schematic diagram of an electrical imaging static image and a static image histogram of black shale in Embodiment 1 of the present invention;

[0046] Fig. 9 This is a schematic diagram of the results of shale oil lithofacies analysis of a lithofacies to be identified in a certain study area of ​​a certain basin using the method of this embodiment in Example 1 of the present invention;

[0047] Fig.10 This is a schematic structural diagram of a device for identifying lithofacies types of continental shale oil in Embodiment 3 of the present invention;

[0048] Fig.11 This is a schematic structural diagram of a device for generating a standard for identifying lithofacies type of continental shale oil in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0049] The inventors have found through extensive research that the existing conventional logging data and conventional logging curves reflect a single formation information, which presents many difficulties in identifying complex shale oil lithofacies. Electrical imaging logging is a new logging technology developed in the 1990s. It has multiple measuring plates, each of which is distributed with multiple measuring electrodes. During logging, the micro-resistivity measurement values ​​of more than one hundred points around the well can be measured at each depth by sticking to the well wall. It has the characteristics of high resolution and high coverage, and can clearly reflect the formation characteristics around the well. Therefore, the present invention intends to construct a method for effectively identifying the lithofacies type of continental shale oil reservoirs based on electrical imaging logging data.

[0050] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0051] Embodiment 1

[0052] A method for identifying lithofacies types of continental shale oil, combined with Figure 1 As shown, steps S110-S130 are included, specifically:

[0053] Step S110, obtaining the raw data of electrical imaging logging of the lithofacies to be identified, preprocessing and image enhancement processing are performed on the raw data of electrical imaging logging of the lithofacies to be identified to obtain a first electrical imaging static image, wherein the first raw data of electrical imaging logging at least includes electrical imaging pad data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves.

[0054] Specifically, the original electrical imaging logging data of the lithofacies to be identified is obtained, and the electrical imaging logging data mainly includes electrical imaging plate data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves, etc. The original electrical imaging logging data of the lithofacies to be identified is subjected to pre-processing such as velocity correction, equalization processing, abnormal electrode correction, and image enhancement processing to obtain the first electrical imaging static image. Figure 2 As shown, attached Figure 2 The original image of the electrical imaging logging and the first static image of the electrical imaging of the lithofacies to be identified. In the figure, the first track is the original image of the electrical imaging logging; the second track is the depth track; the third track is the processed static image; and the fourth track is the processed dynamic image. The static image reflects the changing characteristics of the lithology of the formation; the dynamic image reflects the changing characteristics of the sedimentary structure of the formation, which is the local detail feature of the formation and cannot be used to identify the lithofacies of the formation.

[0055] Step S120, performing histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determining the stratigraphic structure index and lithophase discrimination parameters of the lithophase to be identified that are used to reflect the stratigraphic structure characteristics on the first electrical imaging static image histogram.

[0056] Specific, combined Figure 2 As shown in the figure, based on the maximum and minimum value ranges of the first electrical imaging static image data, it is equally divided into several intervals, and the frequency of image data falling in each interval is counted point by point according to the depth to obtain the first electrical imaging static image histogram. In the process of electrical imaging logging, a plate with multiple electrodes is pushed against the well wall to measure the resistivity information of the formation around the well. Each depth point has n (144-192) measurement data (different types of electrical imaging logging instruments have different numbers of electrodes). The resistivity data measured by each electrode is represented by 0-255 color scale values ​​to reflect the resistivity changes of the formation around the well wall at each depth point. Since the RGB colors of the first electrical imaging static image are at most 256, the number of intervals into which the 256 color scale values ​​are divided mainly depends on whether it is conducive to the identification of lithofacies. If there are too few intervals (less than 10), it is not conducive to the identification of multiple lithofacies types; if there are too many intervals (greater than 50), the obtained discrimination parameter curve changes too much, which is not conducive to the determination of the discrimination standards of different lithofacies. The number of intervals can be between 10 and 50. In this embodiment, the first electrographic static image is divided into 20 intervals. The petrographic identification standard corresponds to the number of intervals. When the number of intervals changes, the petrographic identification standard also changes accordingly.

[0057] The present invention first sorts the n electrode measurement data (color scale values) according to their size (i.e., divides 256 color scales into m intervals of histogram processing), then takes the image values ​​of the frequency points at three preset positions as the minimum skewness, the centering degree and the maximum skewness, respectively, as the stratigraphic structure index, and takes the difference between the maximum skewness and the minimum skewness as the maximum minimum skewness difference, takes the first-order derivative of the centering degree as the centering degree variation, and the maximum minimum skewness difference and the centering degree variation as the lithofacies discrimination parameters. Among them, the frequency refers to the number of n electrode color scale values ​​falling into each interval of the m color scale intervals.

[0058] For ease of understanding, Figure 3 As shown in the figure, the first track is the static image of electrical imaging logging, the second track is the depth track, and the third track is the first static image histogram of electrical imaging at the depth point indicated by the arrow. In the first static image histogram of electrical imaging, the image values ​​of 15%, 50% and 85% frequency of image data at each depth point are defined as its minimum skewness, centering and maximum skewness, that is, the color scale value of the n*0.15th electrode is taken as the minimum skewness, the color scale value of the n*0.5th electrode is taken as the centering, and the color scale value of the n*0.85th electrode is taken as the maximum centering.

[0059] Step S130, comparing the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the preset stratigraphic structure index and lithofacies discrimination parameter boundary standards of various shale oil lithofacies types to determine the continental shale oil lithofacies type.

[0060] The preset stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types can be empirical values ​​or constructed from electrical imaging logging data according to the ideas created by the present invention. Figure 4 As shown, the preset stratigraphic structure index and lithofacies discrimination parameter boundary standards of various shale oil lithofacies types are obtained according to steps S210 to S230:

[0061] Step S210, collecting core analysis data and electrical imaging logging raw data of target wells in the study area, preprocessing and image enhancement processing are performed on the electrical imaging logging raw data to obtain a second electrical imaging static image for reflecting formation lithology change characteristics.

[0062] The core analysis data and electrical imaging logging raw data of the target wells in the study area are collected. The electrical imaging logging raw data mainly include electrical imaging plate data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves, etc. The electrical imaging logging raw data are pre-processed and image enhanced by velocity correction, equalization, abnormal electrode correction, etc. to obtain the second electrical imaging static image. The static image reflects the changing characteristics of the lithology of the formation, and can therefore be used for the analysis of the facies type of the formation. The schematic diagram of the second electrical imaging static image can also be referred to the attached Figure 2 .

[0063] Step S220, performing histogram processing on the obtained second electrographic static image to obtain a second electrographic static image histogram.

[0064] The obtained second electrical imaging static image is processed and interpreted by histogram, that is, based on the maximum and minimum value ranges of the image data, it is divided into 20 intervals, and the frequency of the image data falling in each partition is counted point by point according to the depth. From the histogram distribution of the image data, the image values ​​of 15%, 50% and 85% frequency of the image data at each depth point are defined as its minimum skewness, centering and maximum skewness as the stratigraphic structure index, and the maximum and minimum skewness difference and centering variation are calculated as the lithofacies discrimination parameters. The histogram of the second electrical imaging static image can also be referred to in the attached Figure 3 .

[0065] Step S230, using the preset coring well, the collected core analysis data of the target well in the study area, and the second electrical imaging static image histogram for calibration, to determine the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various shale oil lithofacies types.

[0066] Specifically, the preset coring well has core analysis data results. The preset coring well, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are calibrated to obtain the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types such as homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale.

[0067] The static images of the electric imaging of the lithofacies of the target well, the static image histogram, the stratigraphic structure index of various shale oil lithofacies types and the limit standards of lithofacies discrimination parameters obtained by the above method are as follows:

[0068] Figure 5 Including static images and static image histograms of electrical imaging of homogeneous sandstone. The static image of electrical imaging of homogeneous sandstone is uniform and blocky, and the static image histogram is a left-skewed single-peak narrow-range distribution with a smooth low value of centering (centering is generally <70, centering variation is <5), and the maximum and minimum skewness differences are small (generally <35).

[0069] Figure 6 Including static images and static image histograms of electrical imaging of heterogeneous sandstone. The static image of electrical imaging of heterogeneous sandstone is non-uniform blocky, and the static image histogram is a multi-peak wide-domain distribution. Its centering index is a serrated medium-high value (the centering is generally between 70 and 175, and the centering variation is between 5 and 10), and the maximum and minimum skewness are greatly different (generally between 35 and 140).

[0070] Figure 7 Including static images and static image histograms of electrical imaging of dark mudstone. The static image of dark mudstone is laminar, and its image histogram is a right-biased multi-peak wide-domain distribution. Its centering is a serrated high value (the centering is generally between 175 and 245, and the centering varies between 5 and 10), and the maximum and minimum skewnesses are greatly different (generally between 0 and 175).

[0071] Figure 8 Including static images and static image histograms of electrical imaging of black shale. The static image of black shale is thin interlayered, and its image histogram is a left-biased multi-peak wide-domain distribution. Its centering is a serrated high value (the centering is generally between 0 and 245, and the centering variation is >10), and the maximum and minimum skewness are greatly different (generally between 0 and 245).

[0072] It is understandable that the limits of lithofacies discrimination standards in different regions are different, but the limits in the same region are determined. Therefore, the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types such as homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale obtained in the present invention can be applied to the lithofacies to be identified in the same region. By comparing the stratigraphic structure index of the lithofacies to be identified obtained in step S102 with the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types obtained in step S203, the continental shale oil lithofacies type of the lithofacies to be identified can be determined.

[0073] The inventor used the solution of this embodiment to conduct an experiment on the lithofacies to be identified in a certain study area of ​​a certain basin. The experimental results are as follows: Fig. 9 As shown. In the figure, the first track is the lithology and borehole curve track, including well logging curves such as wellbore diameter, natural gamma and well deviation; the second track is the core lithology profile track (core analysis result); the third track is the result track of lithofacies type identification using the embodiment of the present invention; the fourth track is the depth track; the fifth track is the first electrical imaging static image track using the embodiment of the present invention; the sixth track is the first static image characteristic curve track using the embodiment of the present invention, including three curves of maximum skewness, minimum skewness and centering; the seventh track is the lithofacies discrimination parameter curve track using the embodiment of the present invention, including two curves of maximum and minimum skewness difference and centering variation; the eighth track is the first static image histogram track using the embodiment of the present invention. Considering that there is a certain depth difference between lithofacies and well logging, the lithofacies type of shale oil formation identified by this method (track 3) is well consistent with the core analysis result (track 2). The results obtained using the solution of the present invention show that there is a small amount of black shale at the top and bottom of this section; the middle part is mainly composed of heterogeneous sandstone with thin layers of black shale, which is consistent with the actual situation, indicating that the present invention can accurately identify the lithofacies type of continental shale oil.

[0074] The present invention obtains formation structural characteristic parameters and histogram distribution through in-depth processing and interpretation of static images of electrical imaging logging, and then constructs three structural indexes such as minimum skewness, maximum skewness, and centering, and two lithofacies discrimination parameters such as maximum and minimum skewness difference and centering variation, and combines lithofacies analysis to determine the structural index boundaries of various lithofacies types of continental shale oil reservoirs, thereby forming a method for identifying favorable lithofacies of continental shale oil reservoirs using electrical imaging logging data. Electrical imaging logging has high resolution, and can be processed quickly and efficiently using the embodiments of the present invention, which can objectively and truly reflect the electrical characteristics of the formation, and the description of the subtle structural characteristics of the formation is more detailed and efficient than subjective observation by the human eye, which is more conducive to the identification and evaluation of favorable lithofacies of shale oil.

[0075] Embodiment 2

[0076] In other embodiments, the present invention further discloses a method for generating a standard for identifying lithofacies types of continental shale oil, which is used to provide a basis for automatic identification of various lithofacies types of continental shale oil reservoirs. Specifically, the standard generation method is such as steps S210-S230 in the embodiment.

[0077] The static images of the electric imaging of the lithofacies of the target well, the static image histogram, the stratigraphic structure index of various shale oil lithofacies types and the limit standards of lithofacies discrimination parameters obtained by the above method are as follows:

[0078] (1) The static image of the electrical imaging of homogeneous sandstone is uniform and blocky. The static image histogram is a left-skewed single-peak narrow-range distribution with a smooth low value of centering (centering is generally <70, centering variation is <5), and the maximum and minimum skewness are small (generally <35).

[0079] (2) The static image of the electrical imaging of heterogeneous sandstone is non-uniform and blocky, and the static image histogram is a multi-peak wide-domain distribution. Its centering index is a serrated medium-high value (the centering index is generally between 70 and 175, and the centering index varies between 5 and 10), and the maximum and minimum skewness are greatly different (generally between 35 and 140).

[0080] (3) The static image of dark mudstone is laminar, and its image histogram is a right-biased multi-peak wide-range distribution. Its centering degree is a tooth-like high value (the centering degree is generally between 175 and 245, and the centering degree varies between 5 and 10). The maximum and minimum skewness are greatly different (generally between 0 and 175).

[0081] (4) The static image of black shale is thin interlayered, and its image histogram is a left-biased multi-peak wide-range distribution. Its centering is a tooth-like high value (the centering is generally between 0 and 245, and the centering variation is >10), and the maximum and minimum skewness are greatly different (generally between 0 and 245).

[0082] So far, the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types such as homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale have been obtained. In future use, it is only necessary to compare the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types obtained to determine the continental shale oil lithofacies type of the lithofacies to be identified.

[0083] Embodiment 3

[0084] On the other hand, corresponding to the first embodiment, Fig.10 As shown, the present invention also discloses a device for identifying the lithofacies type of continental shale oil, comprising a first electro-imaging static image generation module 10, a feature generation module 20, and a comparison module 30, wherein:

[0085] The first electrical imaging static image generation module 10 is used to obtain the electrical imaging logging raw data of the lithofacies to be identified, and to perform preprocessing and image enhancement processing on the electrical imaging logging raw data of the lithofacies to be identified to obtain the first electrical imaging static image, wherein the first electrical imaging logging raw data at least includes electrical imaging pad data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves.

[0086] Specifically, the original electrical imaging logging data of the lithofacies to be identified are obtained, and the electrical imaging logging data mainly include electrical imaging plate data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves, etc. The original electrical imaging logging data of the lithofacies to be identified are subjected to pre-processing such as velocity correction, equalization processing, abnormal electrode correction, and image enhancement processing to obtain a first electrical imaging static image.

[0087] The feature generation module 20 is used to perform histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determine the stratigraphic structure index and lithophase discrimination parameters of the lithophase to be identified that reflect the stratigraphic structure characteristics on the first electrical imaging static image histogram.

[0088] Specifically, based on the maximum and minimum value ranges of the first electrical imaging static image data, it is equally divided into several intervals, and the frequency of image data falling in each interval is counted point by point according to the depth to obtain the first electrical imaging static image histogram. In this embodiment, the first electrical imaging static image is selected to be divided into 20 intervals. In the first electrical imaging static image histogram, the image values ​​of 15%, 50% and 85% frequency of image data at each depth point are defined as its minimum skewness, centering and maximum skewness, that is, the color scale value of the nth (electrode measurement data)*0.15 electrode is taken as the minimum skewness, the color scale value of the nth*0.5 electrode is taken as the centering, and the color scale value of the nth*0.85 electrode is taken as the maximum centering, and the difference between the maximum skewness and the minimum skewness is taken as the maximum minimum skewness difference, and the first-order derivative of the centering is taken as the centering variation, and the maximum minimum skewness difference and the centering variation are the lithofacies discrimination parameters.

[0089] The comparison module 30 is used to compare the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the continental shale oil lithofacies type.

[0090] The preset boundary standards of stratigraphic structure index and lithofacies discrimination parameters for various shale oil lithofacies types are:

[0091] (1) The static image of the electrical imaging of homogeneous sandstone is uniform and blocky. The static image histogram is a left-skewed single-peak narrow-range distribution with a smooth low value of centering (centering is generally <70, centering variation is <5), and the maximum and minimum skewness are small (generally <35).

[0092] (2) The static image of the electrical imaging of heterogeneous sandstone is non-uniform and blocky, and the static image histogram is a multi-peak wide-domain distribution. Its centering index is a serrated medium-high value (the centering index is generally between 70 and 175, and the centering index varies between 5 and 10), and the maximum and minimum skewness are greatly different (generally between 35 and 140).

[0093] (3) The static image of dark mudstone is laminar, and its image histogram is a right-biased multi-peak wide-range distribution. Its centering degree is a tooth-like high value (the centering degree is generally between 175 and 245, and the centering degree varies between 5 and 10). The maximum and minimum skewness are greatly different (generally between 0 and 175).

[0094] (4) The static image of black shale is thin interlayered, and its image histogram is a left-biased multi-peak wide-range distribution. Its centering is a tooth-like high value (the centering is generally between 0 and 245, and the centering variation is >10), and the maximum and minimum skewness are greatly different (generally between 0 and 245).

[0095] The specific working principle of this device can refer to Example 1, which will not be repeated here. The present invention obtains the raw data of the electrical imaging logging of the lithofacies to be identified, performs preprocessing and image enhancement processing on the raw data of the electrical imaging logging of the lithofacies to be identified to obtain a first electrical imaging static image, performs histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determines the formation structure index and lithofacies discrimination parameters of the lithofacies to be identified that are used to reflect the formation structure characteristics on the first electrical imaging static image histogram; the formation structure index and lithofacies discrimination parameters of the lithofacies to be identified are compared with the formation structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the shale oil lithofacies type. The present invention can objectively and truly reflect the electrical characteristics of the formation, and the description of the fine structural characteristics of the formation is more precise and efficient, and can accurately identify the continental shale oil lithofacies type.

[0096] Embodiment 4

[0097] Corresponding to the second embodiment, combined with Fig.11 As shown, the present invention also discloses a device for generating a standard for identifying lithofacies types of continental shale oil, which is used to provide a basis for automatically identifying various lithofacies types of continental shale oil reservoirs, including a second electrical imaging static image generating module 40, a second electrical imaging static image histogram generating module 50, and a boundary standard generating module 60, specifically:

[0098] The second electrical imaging static image generation module 40 is used to collect core analysis data and electrical imaging logging raw data of the target well in the study area, pre-process and image enhance the electrical imaging logging raw data, and obtain a second electrical imaging static image for reflecting the formation lithology change characteristics.

[0099] The core analysis data and electrical imaging logging raw data of the target wells in the study area are collected. The electrical imaging logging raw data mainly include electrical imaging plate data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves, etc. The electrical imaging logging raw data are pre-processed and image enhanced by velocity correction, equalization, abnormal electrode correction, etc. to obtain the second electrical imaging static image. The static image reflects the changing characteristics of the formation lithology and can therefore be used for formation lithofacies type analysis.

[0100] The second electro-imaging static image histogram generating module 50 is used to perform histogram processing on the obtained second electro-imaging static image to obtain a second electro-imaging static image histogram.

[0101] The obtained second electrical imaging static image is subjected to histogram processing and interpretation, that is, based on the maximum and minimum value ranges of the image data, it is equally divided into 20 intervals, and the frequency of the image data falling in each partition is counted point by point according to the depth. From the histogram distribution of the image data, the image values ​​of 15%, 50% and 85% frequency of the image data at each depth point are defined as its minimum skewness, centering and maximum skewness as the stratigraphic structure index, and the maximum and minimum skewness difference and centering variation are calculated as lithofacies discrimination parameters.

[0102] The boundary standard generation module 60 is used to calibrate using the preset coring wells, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram to determine the boundary standards of the stratigraphic structure index and lithofacies discrimination parameters of various shale oil lithofacies types.

[0103] Specifically, the preset coring well has core analysis data results. The preset coring well, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are calibrated to obtain the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types such as homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale.

[0104] The static images of the electric imaging of the lithofacies of the target well, the static image histogram, the stratigraphic structure index of various shale oil lithofacies types and the limit standards of lithofacies discrimination parameters obtained by the above method are as follows:

[0105] (1) The static image of the electrical imaging of homogeneous sandstone is uniform and blocky. The static image histogram is a left-skewed single-peak narrow-range distribution with a smooth low value of centering (centering is generally <70, centering variation is <5), and the maximum and minimum skewness are small (generally <35).

[0106] (2) The static image of the electrical imaging of heterogeneous sandstone is non-uniform and blocky, and the static image histogram is a multi-peak wide-domain distribution. Its centering index is a serrated medium-high value (the centering index is generally between 70 and 175, and the centering index varies between 5 and 10), and the maximum and minimum skewness are greatly different (generally between 35 and 140).

[0107] (3) The static image of dark mudstone is laminar, and its image histogram is a right-biased multi-peak wide-range distribution. Its centering degree is a tooth-like high value (the centering degree is generally between 175 and 245, and the centering degree varies between 5 and 10). The maximum and minimum skewness are greatly different (generally between 0 and 175).

[0108] (4) The static image of black shale is thin interlayered, and its image histogram is a left-biased multi-peak wide-range distribution. Its centering is a tooth-like high value (the centering is generally between 0 and 245, and the centering variation is >10), and the maximum and minimum skewness are greatly different (generally between 0 and 245).

[0109] So far, the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types such as homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale have been obtained. In future use, it is only necessary to compare the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the stratigraphic structure index and lithofacies discrimination parameter limit standards of various shale oil lithofacies types obtained to determine the continental shale oil lithofacies type of the lithofacies to be identified.

[0110] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0111] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is encompassed in a manner similar to the term "including", as explained by "including," used as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A method for identifying the lithofacies type of continental shale oil, characterized in that: The following steps are involved: Acquire raw electrical imaging logging data of the lithofacies to be identified, perform preprocessing and image enhancement processing on the raw electrical imaging logging data of the lithofacies to be identified to obtain a first electrical imaging static image; Performing histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determining a stratigraphic structure index and a lithofacies discrimination parameter of the lithofacies to be identified that are used to reflect stratigraphic structure characteristics on the first electrical imaging static image histogram; The stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified are compared with the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the continental shale oil lithofacies type.

2. The identification method according to claim 1, characterized in that: The preset stratigraphic structure index and lithofacies discrimination parameter boundary standards of various shale oil lithofacies types are obtained according to the following steps: Collect core analysis data and electrical imaging logging raw data of target wells in the study area, perform preprocessing and image enhancement processing on the electrical imaging logging raw data, and obtain a second electrical imaging static image for reflecting formation lithology change characteristics; Performing histogram processing on the obtained second electrographic static image to obtain a second electrographic static image histogram; The preset coring wells, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are used for calibration to determine the stratigraphic structure index and lithofacies discrimination parameter boundary standards for various shale oil lithofacies types.

3. The identification method according to claim 1, characterized in that: The preprocessing and image enhancement processing of the raw data of the electrical imaging logging of the lithofacies to be identified includes: The original data of electrical imaging logging for the facies to be identified are subjected to velocity correction, equalization processing and abnormal electrode correction.

4. The identification method according to claim 1, characterized in that: Performing histogram processing on the obtained first electro-imaging static image to obtain the first electro-imaging static image histogram includes: The first electrographic static image is equally divided into a number of intervals, and the frequency of image data falling in each interval is counted point by point according to the depth to obtain a histogram of the first electrographic static image.

5. The identification method according to claim 1, characterized in that: The step of determining the stratigraphic structure index and the lithofacies discrimination parameters of the lithofacies to be identified and used for reflecting the stratigraphic structure characteristics on the first electro-imaging static image histogram comprises: The image values ​​of the frequency points of the three preset positions of the image data at each depth point are defined in the first electro-imaging static image histogram as the minimum skewness, the centering degree and the maximum skewness, respectively, to serve as the formation structure index; The difference between the maximum skewness and the minimum skewness is taken as the maximum-minimum skewness difference, the first-order derivative of the centering degree is taken as the centering degree variation, and the maximum-minimum skewness difference and the centering degree variation are the lithofacies discrimination parameters.

6. The identification method according to claim 1, characterized in that: The image values ​​of the three preset frequency positions of the image data at each depth point defined in the first electro-imaging static image histogram are respectively the minimum skewness, the centering degree and the maximum skewness, including: In the first electro-imaging static image histogram, the image values ​​of 15%, 50% and 85% frequency of the image data at each depth point are defined as its minimum skewness, centering and maximum skewness.

7. The identification method according to claim 1, characterized in that: The shale oil lithofacies types include at least homogeneous sandstone, heterogeneous sandstone, dark mudstone and black shale.

8. A device for identifying the lithofacies type of continental shale oil, characterized in that: It includes a first electro-imaging static image generation module, a feature generation module, and a comparison module, wherein: A first electrical imaging static image generation module is used to obtain electrical imaging logging raw data of the lithofacies to be identified, and to perform preprocessing and image enhancement processing on the electrical imaging logging raw data of the lithofacies to be identified to obtain a first electrical imaging static image, wherein the first electrical imaging logging raw data at least includes electrical imaging pad data, velocity and acceleration curves, well diameter curves, well inclination and azimuth curves; A feature generation module, used for performing histogram processing on the first electrical imaging static image to obtain a first electrical imaging static image histogram, and determining a stratigraphic structure index and a lithofacies discrimination parameter of the lithofacies to be identified, which are used to reflect the stratigraphic structure characteristics, on the first electrical imaging static image histogram; The comparison module is used to compare the stratigraphic structure index and lithofacies discrimination parameters of the lithofacies to be identified with the stratigraphic structure index and lithofacies discrimination parameter boundary standards of various preset shale oil lithofacies types to determine the continental shale oil lithofacies type.

9. A method for generating a standard for identifying lithofacies types of continental shale oil, used to provide a basis for automatic identification of lithofacies types of continental shale oil reservoirs, characterized in that: The method comprises the following steps: The core analysis data and electrical imaging logging raw data of the target wells in the study area are collected, and the electrical imaging logging raw data are preprocessed and image enhanced to obtain a second electrical imaging static image for reflecting the formation lithology change characteristics. The obtained second electrographic static image is subjected to histogram processing to obtain a second electrographic static image histogram. The preset coring wells, the collected core analysis data of the target wells in the study area, and the second electrical imaging static image histogram are used for calibration to determine the stratigraphic structure index and lithofacies discrimination parameter boundary standards for various shale oil lithofacies types.

10. A device for generating a standard for identifying lithofacies type of continental shale oil, used to provide a basis for automatic identification of lithofacies type of continental shale oil reservoirs, characterized in that: The device comprises a second electro-imaging static image generating module, a second electro-imaging static image histogram generating module, and a boundary standard generating module, wherein: The second electrical imaging static image generation module is used to collect core analysis data and electrical imaging logging raw data of the target well in the study area, and perform preprocessing and image enhancement processing on the electrical imaging logging raw data to obtain a second electrical imaging static image used to reflect the formation lithology change characteristics; The second electrical imaging static image histogram generating module is used to perform histogram processing on the obtained second electrical imaging static image to obtain a second electrical imaging static image histogram; The boundary standard generation module is used to calibrate using preset coring wells, collected lithofacies analysis data of target wells in the study area, and a second electrical imaging static image histogram to determine the boundary standards of stratigraphic structure index and lithofacies discrimination parameters for various shale oil lithofacies types.

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