Method and device for predicting heterogeneity of pore-vuggy carbonate reservoir

Abstract

The present invention discloses a method and device for predicting heterogeneity of pore-vuggy carbonate reservoirs, wherein the method comprises: obtaining an electrical imaging logging grayscale image of a pore-vuggy carbonate reservoir to be predicted; interpolating the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image with full borehole coverage; converting the electrical imaging logging grayscale image with full borehole coverage into a binary image of pore-vuggy distribution; performing digital image processing on the binary image of pore-vuggy distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-vuggy; determining the heterogeneity coefficient according to the surface porosity, roundness and pore aspect ratio parameters of the pore-vuggy and pre-established heterogeneity prediction model; determining the heterogeneity of the pore-vuggy carbonate reservoir to be predicted according to the heterogeneity coefficient. The present invention can improve the accuracy of heterogeneity prediction of pore-vuggy carbonate reservoirs.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum geological exploration and well logging interpretation, and in particular to a method and a device for predicting heterogeneity of pore-cavity type carbonate reservoirs. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] The quantitative characterization (evaluation and prediction) of the complex reservoir space composed of pores and caves in porous-cavity carbonate reservoirs and the reservoir heterogeneity (degree of heterogeneity) is crucial for the exploration and development of carbonate oil and gas reservoirs. It has always been a hot topic and difficulty in research. The existing characterization methods are roughly as follows:

[0004] 1. Heterogeneity comprehensive index method: Some scholars use the wave superposition principle to superimpose multiple parameters to obtain a comprehensive index. For example, parameters such as porosity, permeability, lithology and sedimentary phase type, which are closely related to reservoir heterogeneity, are normalized so that the parameter range is between 0 and 1. Multiple parameters are added to obtain a comprehensive index. The disadvantage of this method is that multiple parameters are linearly superimposed and cannot fully reflect reservoir heterogeneity. Reservoir heterogeneity is often a nonlinear problem.

[0005] 2. Production well information description method: The difference in production data of oil field production wells reflects the reservoir heterogeneity. Some scholars use the slope of the probability distribution of single well production and cumulative production to reflect reservoir heterogeneity. Since production wells are also affected by engineering measures, this method cannot accurately characterize the degree of reservoir heterogeneity.

[0006] 3. Conventional logging curve fractal dimension method: Some scholars quantitatively describe the pore-hole changes by calculating the fractal dimension of logging curves. First, the logging curve that is most sensitive to reservoir heterogeneity is selected, and the fractal dimension of the logging curve is calculated for the reservoir section using the variable scale analysis method (R / S analysis). The size and change of the fractal dimension can quantitatively describe the reservoir heterogeneity. The shortcoming of this method is that conventional logging reflects one-dimensional logging information of the formation, so it can only evaluate the one-dimensional heterogeneity of the reservoir.

[0007] In summary, existing methods cannot accurately predict the heterogeneity of pore-vuggy carbonate reservoirs. Summary of the invention

[0008] An embodiment of the present invention provides a method for predicting heterogeneity of a pore-vuggy carbonate reservoir, which is used to improve the prediction accuracy of the heterogeneity of the pore-vuggy carbonate reservoir. The method includes:

[0009] Obtaining an electrical imaging logging grayscale image of the porous-cavity carbonate reservoir to be predicted;

[0010] Interpolation processing is performed on the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image covering the entire wellbore;

[0011] The grayscale image of electrical imaging logging with full borehole coverage is converted into a binary image of pore-cavity distribution;

[0012] The binary image of pore-hole distribution is processed digitally to obtain the surface porosity, roundness and pore aspect ratio parameters of pore-hole.

[0013] The heterogeneity coefficient is determined based on the porosity, roundness and pore aspect ratio parameters of the pore-cavity surface and the pre-established heterogeneity prediction model; and the heterogeneity of the pore-cavity carbonate reservoir to be predicted is determined based on the heterogeneity coefficient.

[0014] The embodiment of the present invention further provides a device for predicting heterogeneity of pore-vuggy carbonate reservoirs, which is used to improve the prediction accuracy of heterogeneity of pore-vuggy carbonate reservoirs. The device includes:

[0015] An acquisition unit, used for acquiring an electrical imaging logging grayscale image of a porous-cavity carbonate reservoir to be predicted;

[0016] An interpolation processing unit is used to perform interpolation processing on the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image covering the entire wellbore;

[0017] A binary processing unit, used to convert the electrical imaging logging grayscale image of the full borehole coverage into a binary image of pore-hole distribution;

[0018] An image processing unit is used to perform digital image processing on the binary image of the pore-hole distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-hole;

[0019] The prediction unit is used to determine the heterogeneity coefficient according to the surface porosity, roundness and pore aspect ratio parameters of the pore-cavity and the pre-established heterogeneity prediction model; and determine the heterogeneity of the pore-cavity carbonate reservoir to be predicted according to the heterogeneity coefficient.

[0020] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting heterogeneity of pore-vuggy carbonate reservoirs when executing the computer program.

[0021] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above-mentioned method for predicting heterogeneity of pore-vuggy carbonate reservoirs.

[0022] In the embodiment of the present invention, the heterogeneity prediction scheme of pore-vuggy carbonate reservoir is compared with the technical schemes in the prior art, such as the heterogeneity comprehensive index method, the production well information description method and the conventional logging curve fractal dimension method, which cannot accurately predict the heterogeneity of pore-vuggy carbonate reservoir. The scheme includes: obtaining the electrical imaging logging grayscale image of the pore-vuggy carbonate reservoir to be predicted; interpolating the electrical imaging logging grayscale image to obtain the electrical imaging logging grayscale image covering the entire wellbore; converting the electrical imaging logging grayscale image covering the entire wellbore into the pore-vuggy carbonate reservoir; obtaining the heterogeneity prediction scheme of the pore-vuggy carbonate reservoir to be predicted ... -A binary image of pore-pore distribution; digital image processing is performed on the binary image of pore-pore distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-pore; the heterogeneity coefficient is determined according to the surface porosity, roundness and pore aspect ratio parameters of the pore-pore and a pre-established heterogeneity prediction model; the heterogeneity of the pore-pore type carbonate reservoir to be predicted is determined according to the heterogeneity coefficient, thereby achieving quantitative characterization of the heterogeneity of the pore-pore type carbonate reservoir and improving the accuracy of heterogeneity prediction of the pore-pore type carbonate reservoir. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0024] Figure 1 Schematic diagram of the process of the heterogeneity prediction method of pore-vuggy carbonate reservoir in an embodiment of the present invention;

[0025] Figure 2 It is the grayscale image of electrical imaging logging in the embodiment of the present invention;

[0026] Figure 3 It is an electrical imaging logging grayscale image of the entire wellbore covered after interpolation in an embodiment of the present invention;

[0027] Figure 4 is a binary image of pore-hole distribution after digital image processing in an embodiment of the present invention;

[0028] Figure 5a-5c The electrical imaging diagrams of different heterogeneous reservoir sections and the calculated heterogeneity coefficient T in the embodiment of the present invention;

[0029] Figure 6a-Figure 6b This is an example diagram of reservoir heterogeneity evaluation of Moxi 9 Well in an embodiment of the present invention;

[0030] Figure 7 Schematic diagram of the structure of the heterogeneity prediction device of pore-vuggy carbonate reservoir in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] Before introducing the embodiments of the present invention, the technical terms involved in the embodiments of the present invention are first introduced: heterogeneity refers to the uniformity of pore development. For the permeability parameter, strong heterogeneity means that the difference between horizontal permeability and vertical permeability is very large, and weak heterogeneity means that the difference between horizontal permeability and vertical permeability is small.

[0033] The inventor discovered the technical problems existing in the prior art, and therefore proposed a heterogeneity prediction scheme for pore-vuggy carbonate reservoirs. The scheme is based on the inventor's idea: a large porosity aspect ratio of the reservoir indicates that the pores are irregular and the heterogeneity is strong; a large roundness of the pores indicates that the pore shape is more round and the heterogeneity is weak. The larger the porosity, the stronger the heterogeneity; if these parameters can be extracted, the reservoir heterogeneity evaluation can be achieved. Therefore, using the above relationship, the roundness is used as the denominator, the pore aspect ratio is used as the numerator, and an exponential function is used to amplify this difference, a formula is constructed to calculate the heterogeneity parameter T, and finally the heterogeneity parameter T is used to determine the heterogeneity of the pore-vuggy carbonate reservoir to be predicted. The following is a detailed introduction to the heterogeneity prediction scheme for the pore-vuggy carbonate reservoir.

[0034] Figure 1 FIG. 1 is a flow chart of a method for predicting heterogeneity of pore-vuggy carbonate reservoirs in an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0035] Step 101: obtaining an electrical imaging logging grayscale image of a porous-vuggy carbonate reservoir to be predicted;

[0036] Step 102: interpolating the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image covering the entire wellbore;

[0037] Step 103: converting the electrical imaging logging grayscale image covering the entire wellbore into a binary image of pore-hole distribution;

[0038] Step 104: digitally process the binary image of the pore-hole distribution to obtain the porosity, roundness and pore aspect ratio parameters of the pore-hole;

[0039] Step 105: Determine the heterogeneity coefficient according to the porosity, roundness and pore aspect ratio parameters of the pore-cavity surface and the pre-established heterogeneity prediction model; and determine the heterogeneity of the pore-cavity carbonate reservoir to be predicted according to the heterogeneity coefficient.

[0040] In the embodiments of the present invention, the heterogeneity prediction scheme of the pore-vuggy carbonate reservoir achieves quantitative characterization of the heterogeneity of the pore-vuggy carbonate reservoir, and improves the accuracy of heterogeneity prediction of the pore-vuggy carbonate reservoir, compared with the technical schemes in the prior art such as the heterogeneity comprehensive index method, the production well information description method and the conventional logging curve fractal dimension method, which cannot accurately predict the heterogeneity of the pore-vuggy carbonate reservoir.

[0041] In specific implementation, the carbonate reservoir heterogeneity prediction scheme provided in the embodiment of the present invention can be used not only for the prediction of pore-cavity type carbonate reservoirs, but also for the heterogeneity prediction of other types of carbonate reservoirs.

[0042] The following is a detailed description of each step involved in the embodiment of the present invention.

[0043] 1. First, the above step 101 is introduced.

[0044] In one embodiment, obtaining an electrical imaging logging grayscale image of a porous-vuggy carbonate reservoir to be predicted may include:

[0045] The formation microresistivity scanning imaging FMI resistivity imaging logging instrument is used to perform electrical imaging logging on the predicted pore-cavity carbonate reservoir to obtain the electrical imaging logging grayscale image of the predicted pore-cavity carbonate reservoir.

[0046] In the specific implementation, electrical imaging logging is carried out on the formation, and after logging data processing, the electrical imaging logging grayscale image is obtained. The selected logging instrument is Schlumberger's FMI resistivity imaging logging instrument, which has a high wellbore coverage rate. Under 8.5in wellbore conditions, the wellbore coverage rate is 85%, so it helps to further improve the accuracy of heterogeneity prediction of porous-cavity type carbonate reservoirs.

[0047] In specific implementation, the detailed implementation process of grayscale image acquisition is as follows:

[0048] The image obtained after electrical imaging logging is a color bitmap, which is composed of pixels. Each pixel is a single color, which is a mixture of three primary colors: red (R), green (G), and blue (B). There are many bitmap file formats. This article mainly uses the JJPEG (Joint Photographic Experts Group) format, which is called "digital compression and encoding of continuous-tone static images". The file suffix is ​​".jpg" or ".jpeg", which is the most commonly used image file format.

[0049] Color image to grayscale conversion: Grayscale images are usually divided into 256 levels from 0 to 255, where 0 is the darkest (completely black), 255 is the brightest (completely white), and the gray in between is transitional. Each pixel in a color image has three components: R, G, and B. Each pixel is converted to a grayscale value Grey using the following formula:

[0050] Gray=R×0.299+G×0.587+B×0.114.

[0051] 2. Next, the above step 102 is introduced.

[0052] In specific implementation, the electrical imaging logging grayscale image is interpolated to obtain an image with full borehole coverage. The electrical imaging processing module of the Ciflog software is used to interpolate the imaging data using geostatistical methods to obtain an electrical imaging logging grayscale image with full borehole coverage (100% wellbore coverage), which further improves the accuracy of heterogeneity prediction of pore-vuggy carbonate reservoirs.

[0053] 3. Next, the above step 103 is introduced.

[0054] In one embodiment, converting the electrical imaging logging grayscale image covering the entire wellbore into a binary image of pore-hole distribution may include: converting the grayscale value image into a binary image according to a preset segmentation value and the grayscale value of each pixel in the electrical imaging logging grayscale image.

[0055] In one embodiment, the segmentation value may range from 28 to 32.

[0056] In one embodiment, the segmentation value may be 30.

[0057] Specifically, a segmentation value preset unit can be set to preset the segmentation value, and a binary image generation unit can be set to segment the grayscale value according to the preset segmentation value to convert the grayscale value image into a binary image, wherein the segmentation value is 30, which helps to further improve the accuracy of heterogeneity prediction of pore-cavity type carbonate reservoirs.

[0058] In the specific implementation, the grayscale image of the electrical imaging logging with full borehole coverage is converted into a binary image, the data of the electrical imaging logging image with full borehole coverage is processed using Maltab software, and the im2bw() function is used to process the data to obtain a binary image, that is, an image of pore-hole distribution.

[0059] In specific implementation, the detailed implementation process of grayscale image conversion binary image is as follows:

[0060] The grayscale image is converted into a binary image. By setting the segmentation threshold, a binary image is obtained. A binary image has two values, namely 0 and 1. 0 is black, representing the dissolution pores, and 1 is white, which is the background value. Through binarization, effective pore-pore information can be extracted, that is, a pore-pore image can be obtained.

[0061] 4. Next, the above step 104 is introduced.

[0062] The binary image is processed by digital image processing technology, and the porosity of the pore-hole surface is obtained by using the function m , roundness RD and pore aspect ratio AR parameters;

[0063]

[0064] Among them, φ m - Porosity value of the surface of the electrical RD image logging image, %;

[0065] S k — is the total number of pixels with a value of 0 in the electrical imaging logging image, dimensionless;

[0066] S all — is the total number of pixels in the electrical imaging logging image, dimensionless;

[0067]

[0068] Among them, RD - electrical imaging logging image pore-hole roundness, decimal;

[0069] A—is the average area of ​​all holes in the electrical imaging logging image, dimensionless;

[0070] D max — is the diameter of the largest hole in the electrical imaging logging image, dimensionless;

[0071] Note: The area size is actually the sum of the number of pixels in the image.

[0072]

[0073] Among them, AR-electric imaging logging pore aspect ratio, decimal;

[0074] D max— is the diameter of the largest hole in the electrical imaging logging image, dimensionless;

[0075] D min — is the diameter of the smallest hole in the electrical imaging logging image, dimensionless.

[0076] For step 104, the digital processing function of Maltab software is used to obtain a binary image, which is the corresponding pore development, and the surface porosity, roundness and pore aspect ratio parameters of the dissolution pores can be calculated.

[0077] 5. Next, the above step 105 is introduced.

[0078] In one embodiment, determining the heterogeneity coefficient according to the porosity, roundness and pore aspect ratio parameters of the pore-hole and a pre-established heterogeneity prediction model may include: determining the heterogeneity coefficient according to the following heterogeneity prediction model:

[0079]

[0080] Where: T is the heterogeneity coefficient, a decimal; φ m is the surface porosity value of the electrical imaging logging image, %; RD is the pore-hole roundness of the electrical imaging logging image, decimal; AR is the pore aspect ratio parameter value of the electrical imaging logging, decimal.

[0081] In the specific implementation, the heterogeneity coefficient T is calculated according to the heterogeneity evaluation formula to evaluate the heterogeneity of the reservoir section. Specifically, different heterogeneities can be quantitatively divided by threshold segmentation of the T value. Taking Moxi Well 108 as an example to represent different heterogeneous reservoir sections (see Appendix Figure 5a-5c ):

[0082] Attached Figure 5a Mainly isolated holes, with a porosity of φ m =6.8%, roundness RD=0.39, pore aspect ratio AR=3.08, T=6.01, strong heterogeneity;

[0083] Attached Figure 5b The number of holes gradually increases, and the porosity φ m =7.2%, roundness RD=0.47, pore aspect ratio AR=2.02, T=5.02, and medium heterogeneity;

[0084] Attached Figure 5c , the number of holes increases further, and the porosity φ m =7.5%, roundness RD=0.51, pore aspect ratio AR=2, T=4.97, and weak heterogeneity;

[0085] Based on the calibration of the calculation parameters of the typical heterogeneous reservoir section of Well Moxi 108, the segmentation thresholds for different heterogeneities are divided as follows: when T >= 6, the heterogeneity is strong; when 5 < T < 6, the heterogeneity is medium; when T <= 5, the heterogeneity is weak. It should be noted here that the determination of the above thresholds is based on core calibration and has regional empirical nature. The threshold division criteria may be different in different regions, but the heterogeneous prediction model remains unchanged.

[0086] The construction principle of the above formula (4) (heterogeneous prediction model) is as follows: a large aspect ratio of porosity indicates irregular pores and strong heterogeneity; a large roundness of pores indicates a more circular pore shape and weak heterogeneity. A large areal porosity indicates strong heterogeneity. Therefore, using the above relationship, with roundness as the denominator and the aspect ratio of porosity as the numerator, and using an exponential function to amplify this difference, formula (1) is constructed. Taking the actual electrical imaging logging images and core data of the Dengying Formation in the Hechuan area of the Sichuan Basin in the actual work area as an example, the heterogeneity level can be quantitatively divided as follows:

[0087] When T <= 5, the heterogeneity is weak and the pores and caves are well developed;

[0088] When 5 < T < 6, the heterogeneity is medium and the pores are relatively developed;

[0089] When T >= 6, the heterogeneity is strong, the pores are not very developed, and the pores and caves are relatively isolated.

[0090] That is, in one embodiment, according to the heterogeneity coefficient, determining the heterogeneity of the pore-cave type carbonate reservoir to be predicted may include:

[0091] Calibrating the heterogeneity coefficient parameters according to the core, and determining the threshold for heterogeneity evaluation, that is, determining the evaluation criteria (for example: when the heterogeneity coefficient T is less than or equal to 5, determining the heterogeneity of the pore-cave type carbonate reservoir to be predicted as the weak level (the first level); when T is greater than 5 and less than 6, determining the heterogeneity as the medium level (the second level); when T is greater than or equal to 6, determining the heterogeneity as the strong level of the medium level (the third level)), and then determining the heterogeneity of the pore-cave type carbonate reservoir to be predicted in the non-core-taking section.

[0092] Next, another example is given to illustrate how the present invention is implemented for easy understanding.

[0093] Taking the Dengying Formation in the Hechuan area of the Sichuan Basin as an example, taking Well Moxi 9 as the core-taking well to verify the heterogeneity evaluation effect, the present invention is elaborated in detail:

[0094] 1. Basic geological conditions and regional parameter conditions:

[0095] The Dengying Formation in Hechuan area is a marine carbonate formation, with the main lithology being dolomite and a small amount of quartz. There are three types of reservoirs in this formation: porous type, vug type, and pore-vug type. Heterogeneity refers to the uneven development of pores, which is reflected in the permeability. The vertical permeability and horizontal permeability are very different. The ratio of vertical permeability to horizontal permeability can represent the uneven development of pores in two directions. Logging parameter acquisition: The formation electrical imaging logging images are measured using Schlumberger's FMI imaging logging series instruments.

[0096] 2. Implementation steps:

[0097] Step 1: Conduct electrical imaging logging on the formation. The logging instrument is the FMI resistivity imaging logging instrument of Schlumberger. The instrument has a high wellbore coverage rate. Under the condition of 8.5in wellbore, the wellbore coverage rate is 85%, which is applicable to the present invention. Figure 2 It is the grayscale image of electrical imaging logging, which is the grayscale image of the electrical imaging image after grayscale conversion.

[0098] Step 2: Use the electrical imaging processing module of Ciflog software to perform geostatistical interpolation on the imaging data to obtain an electrical imaging logging grayscale image with full borehole coverage (wellbore coverage rate of 100%); Figure 3 This is the interpolated electrical imaging logging grayscale image of the entire borehole. This figure shows the electrical imaging logging processing and interpretation module of the Ciflog logging software. The geostatistical method is used to interpolate the electrical imaging logging grayscale image to obtain a grayscale image covering the entire borehole.

[0099] Step 3: Use Maltab software to process the grayscale image of the full-bore electrical imaging logging, and use the im2bw() function to process the data to obtain a binary image, that is, an image of pore-hole distribution. Figure 4 Binary image after digital image processing. This figure shows the binary image of reaction pore-hole development after digital image processing.

[0100] Step 4: Using the digital processing function of Maltab software, the porosity, roundness and pore aspect ratio parameters of the pore-cavity image and the dissolution pores are obtained;

[0101] The surface porosity of the 5430.02m-5430.5m reservoir section of Moxi 9 Well was calculated to be 15.06%, roundness = 0.41, and pore aspect ratio = 2.59. The surface porosity of the 5444.1m-5444.6m reservoir section of Moxi 9 Well was calculated to be 3%, roundness = 0.71, and pore aspect ratio = 1.42.

[0102] Step 5: Calculate the heterogeneity coefficient T according to the heterogeneity evaluation formula. Figure 5a-5cThe figure shows the electrical imaging of different heterogeneous reservoir sections and the calculated heterogeneity coefficient T. The figure shows the electrical imaging of different heterogeneous reservoir sections in Moxi 108 well and the calculated heterogeneity coefficient T. To evaluate the heterogeneity of the reservoir section, the pore-cavity image in step 104 and the surface porosity, roundness and pore aspect ratio parameters of the dissolved pores are used to calculate the heterogeneity coefficient T (see Appendix Figure 6a-Figure 6b ).

[0103] The coring section from 5430.02m to 5430.5m of Moxi Well 9 (attached Figure 6a ), the horizontal permeability of the core measured in this section is 1.67mD, and the vertical permeability is 0.183mD. The horizontal permeability is 9.1 times the vertical permeability, and the heterogeneity is strong. The calculated heterogeneity coefficient T = 8.66 (Appendix Figure 6a ), and was evaluated as highly heterogeneous. The core section of Moxi Well 9 at 5444.1m-5444.6m (attached Figure 6b ), the horizontal permeability is 1.38mD, the vertical permeability is 0.327mD, the horizontal permeability is 4.2 times the vertical permeability, and the heterogeneity is weak. The calculated heterogeneity coefficient T = 2.36 is evaluated as weak heterogeneity, which is basically consistent with the core permeability analysis results. From the electrical imaging image, Figure 6b The holes are more uniform. Figure 6a The holes in the deposits have the characteristics of bedding dissolution and therefore have strong heterogeneity.

[0104] In summary, the heterogeneity prediction method for pore-vuggy carbonate reservoirs provided by the embodiment of the present invention is applied to the actual well data of the Dengying Formation in the Hechuan area of ​​the Sichuan Basin, which can improve the prediction accuracy.

[0105] The present invention also provides a device for predicting heterogeneity of pore-vuggy carbonate reservoirs, as described in the following embodiments. Since the principle of solving the problem by the device is similar to the method for predicting heterogeneity of pore-vuggy carbonate reservoirs, the implementation of the device can refer to the implementation of the method for predicting heterogeneity of pore-vuggy carbonate reservoirs, and the repeated parts will not be repeated.

[0106] Figure 7 FIG. 1 is a schematic diagram of the structure of a device for predicting heterogeneity of pore-vuggy carbonate reservoirs in an embodiment of the present invention. Figure 7 As shown, the device comprises:

[0107] An acquisition unit 01 is used to acquire an electrical imaging logging grayscale image of a porous-cavity carbonate reservoir to be predicted;

[0108] The interpolation processing unit 02 is used to perform interpolation processing on the electrical imaging logging grayscale image to obtain the electrical imaging logging grayscale image covering the entire wellbore;

[0109] Binarization processing unit 03, used to convert the electrical imaging logging grayscale image of the whole borehole coverage into a binary image of pore-hole distribution;

[0110] The image processing unit 04 is used to perform digital image processing on the binary image of the pore-hole distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-hole;

[0111] Prediction unit 05 is used to determine the heterogeneity coefficient according to the surface porosity, roundness and pore aspect ratio parameters of the pore-cavity and the pre-established heterogeneity prediction model; and determine the heterogeneity of the pore-cavity carbonate reservoir to be predicted according to the heterogeneity coefficient.

[0112] In one embodiment, the acquisition unit can be specifically used to: use the formation microresistivity scanning imaging FMI resistivity imaging logging instrument to perform electrical imaging logging on the predicted pore-cavity type carbonate reservoir to obtain the electrical imaging logging grayscale image of the predicted pore-cavity type carbonate reservoir.

[0113] In one embodiment, the prediction unit may be specifically used to determine the heterogeneity coefficient according to the following heterogeneity prediction model:

[0114]

[0115] Where: T is the heterogeneity coefficient, φ m is the porosity value of the electrical imaging logging image, RD is the pore-hole roundness of the electrical imaging logging image, and AR is the pore aspect ratio parameter value of the electrical imaging logging image.

[0116] In one embodiment, the binarization processing unit may be specifically used to convert the grayscale image into a binary image according to a preset segmentation value and the grayscale value of each pixel in the electrical imaging logging grayscale image.

[0117] In one embodiment, the prediction unit can be specifically used for: when the heterogeneity coefficient T is less than or equal to 5, determining that the heterogeneity of the pore-vug type carbonate reservoir to be predicted is a weak level; when T is greater than 5 and less than 6, determining that the heterogeneity is a medium level; when T is greater than or equal to 6, determining that the heterogeneity is a medium-strong level.

[0118] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting heterogeneity of pore-vuggy carbonate reservoirs when executing the computer program.

[0119] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program for executing the above-mentioned method for predicting heterogeneity of pore-vuggy carbonate reservoirs.

[0120] In the embodiment of the present invention, the heterogeneity prediction scheme of pore-vuggy carbonate reservoir is compared with the technical schemes in the prior art, such as the heterogeneity comprehensive index method, the production well information description method and the conventional logging curve fractal dimension method, which cannot accurately predict the heterogeneity of pore-vuggy carbonate reservoir. The scheme includes: obtaining the electrical imaging logging grayscale image of the pore-vuggy carbonate reservoir to be predicted; interpolating the electrical imaging logging grayscale image to obtain the electrical imaging logging grayscale image covering the entire wellbore; converting the electrical imaging logging grayscale image covering the entire wellbore into the pore-vuggy carbonate reservoir; obtaining the heterogeneity prediction scheme of the pore-vuggy carbonate reservoir to be predicted ... -A binary image of pore-pore distribution; digital image processing is performed on the binary image of pore-pore distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-pore; the heterogeneity coefficient is determined according to the surface porosity, roundness and pore aspect ratio parameters of the pore-pore and a pre-established heterogeneity prediction model; the heterogeneity of the pore-pore type carbonate reservoir to be predicted is determined according to the heterogeneity coefficient, thereby achieving quantitative characterization of the heterogeneity of the pore-pore type carbonate reservoir and improving the accuracy of heterogeneity prediction of the pore-pore type carbonate reservoir.

[0121] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0125] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting heterogeneity of pore-vuggy carbonate reservoirs, characterized in that: include: Obtaining an electrical imaging logging grayscale image of the porous-cavity carbonate reservoir to be predicted; Interpolation processing is performed on the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image covering the entire wellbore; The grayscale image of electrical imaging logging with full borehole coverage is converted into a binary image of pore-cavity distribution; The binary image of pore-hole distribution is processed digitally to obtain the surface porosity, roundness and pore aspect ratio parameters of pore-hole. Determine the heterogeneity coefficient based on the porosity, roundness and pore aspect ratio parameters of the pore-cavity surface and the pre-established heterogeneity prediction model; determine the heterogeneity of the pore-cavity carbonate reservoir to be predicted based on the heterogeneity coefficient; According to the porosity, roundness and pore aspect ratio parameters of the pore-hole surface and the pre-established heterogeneity prediction model, the heterogeneity coefficient is determined, including: determining the heterogeneity coefficient according to the following heterogeneity prediction model: Where: T is the heterogeneity coefficient, is the porosity value of the electrical imaging logging image, RD is the pore-hole roundness of the electrical imaging logging image, and AR is the pore aspect ratio parameter value of the electrical imaging logging image.

2. The method for predicting heterogeneity of pore-vuggy carbonate reservoirs according to claim 1, characterized in that: Obtain the grayscale image of the electrical imaging logging of the porous-vuggy carbonate reservoir to be predicted, including: The formation microresistivity scanning imaging FMI resistivity imaging logging instrument is used to perform electrical imaging logging on the predicted pore-cavity carbonate reservoir to obtain the electrical imaging logging grayscale image of the predicted pore-cavity carbonate reservoir.

3. The method for predicting heterogeneity of pore-vuggy carbonate reservoirs according to claim 1, characterized in that: The electrical imaging logging grayscale image covering the entire wellbore is converted into a binary image of pore-hole distribution, including: converting the grayscale image into a binary image according to a preset segmentation value and the grayscale value of each pixel in the electrical imaging logging grayscale image.

4. The method for predicting heterogeneity of pore-vuggy carbonate reservoirs according to claim 3, characterized in that: The segmentation value ranges from 28 to 32.

5. The method for predicting heterogeneity of pore-vuggy carbonate reservoirs according to claim 4, characterized in that: The segmentation value is:

30.

6. A device for predicting heterogeneity of pore-vuggy carbonate reservoirs, characterized in that: include: An acquisition unit, used for acquiring an electrical imaging logging grayscale image of a porous-cavity carbonate reservoir to be predicted; An interpolation processing unit is used to perform interpolation processing on the electrical imaging logging grayscale image to obtain an electrical imaging logging grayscale image covering the entire wellbore; A binary processing unit, used to convert the electrical imaging logging grayscale image of the full borehole coverage into a binary image of pore-hole distribution; An image processing unit is used to perform digital image processing on the binary image of pore-hole distribution to obtain the surface porosity, roundness and pore aspect ratio parameters of the pore-hole; The prediction unit is used to determine the heterogeneity coefficient according to the surface porosity, roundness and pore aspect ratio parameters of the pore-cavity and the pre-established heterogeneity prediction model; and determine the heterogeneity of the pore-cavity carbonate reservoir to be predicted according to the heterogeneity coefficient; The prediction unit is specifically used to determine the heterogeneity coefficient according to the following heterogeneity prediction model: Where: T is the heterogeneity coefficient, is the porosity value of the electrical imaging logging image, RD is the pore-hole roundness of the electrical imaging logging image, and AR is the pore aspect ratio parameter value of the electrical imaging logging image.

7. The heterogeneity prediction device for pore-vuggy carbonate reservoir according to claim 6, characterized in that: The acquisition unit is specifically used to: use the formation microresistivity scanning imaging FMI resistivity imaging logging instrument to perform electrical imaging logging on the predicted pore-cavity type carbonate reservoir to obtain the electrical imaging logging grayscale image of the predicted pore-cavity type carbonate reservoir.

8. The heterogeneity prediction device for pore-vuggy carbonate reservoir according to claim 6, characterized in that: The binarization processing unit is specifically used for converting the grayscale image into a binary image according to a preset segmentation value and the grayscale value of each pixel in the electrical imaging well logging grayscale image.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 5.

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