Carbonate rock fracture-cavity body identification method based on clustering analysis technology

Abstract

The invention relates to a carbonate rock fracture-cavity body identification method based on a clustering analysis technology, and belongs to the technical field of oil exploration and development. According to the scheme, firstly, fractured-vuggy body classification identification parameters of all reservoirs in a target area are determined and calculated; carrying out clustering analysis on the basis of the fracture-cavity body classification and identification parameters of the reservoirs to obtain clustering categories of the reservoirs; and finally, determining fractured-vuggy body classification corresponding to each clustering category by combining the reservoir with the imaging logging information under each clustering category so as to obtain the fractured-vuggy body classification of the reservoir under the clustering category. According to the method, based on comprehensive evaluation of nonlinear and various conventional logging methods, the type of the fracture-cavity body can be reasonably and accurately identified.

Description

Technical Field

[0001] The present invention relates to a method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology, belonging to the technical field of oil exploration and development. Background Art

[0002] Carbonate oil and gas reservoirs play an important role in the global oil and gas distribution. Their oil and gas reserves account for about 50% of the total global oil and gas reserves, and the oil and gas production is as high as 60%. Although carbonate rocks account for a small proportion in sedimentary rocks, they contain large oil and gas reserves and are important exploration targets for increasing proven oil and gas reserves. The reservoir pore space is mainly divided into three categories: primary pores, karst caves and fractures. The reservoir space types corresponding to different oil and gas reservoirs are also different: gas reservoirs are mostly pore-type media, and oil reservoirs are mostly fracture-pore type and karst fracture-vug type media. Therefore, the classification of reservoirs is the key in the logging evaluation of carbonate rock reservoirs.

[0003] In the existing technology, for the problem of carbonate rock classification, explorers mainly classify and identify carbonate rock reservoirs through means such as seismic, logging, and coring. Since conventional logging tests each well, and a lot of old wells have conventional logging data, the data is very rich, so the research on conventional logging is particularly important.

[0004] Currently, the methods for identifying fracture-vug bodies based on logging parameters mainly include qualitative analysis, crossplot analysis, and quantitative analysis, etc. Qualitative analysis mainly judges the rock type and fracture characteristics by observing and describing the characteristics such as peaks and valleys in the logging curves. The analysis results of logging data by different personnel are often different; crossplot analysis is to determine the fracture characteristics and rock type by plotting the intersection points of the rock layer and the fracture layer. However, there is a large overlap of different lithologies on the bivariate crossplot, and other variables related to lithology are ignored, and the information utilization is not sufficient; quantitative analysis is to determine the rock type and fracture characteristics by calculating the porosity parameters in the logging curves, such as porosity, permeability, etc. For example, the published text of the Chinese invention patent application with the publication number CN116010789A discloses a method for identifying carbonate rock reservoir types, which identifies carbonate rock reservoir types based on porosity. However, due to factors such as the large variation in the size and shape of carbonate rock particles, the complex internal structure of biological particles, and the multi-stage and non-uniform filling of pores by carbonate cements and internal sediments, the pore structure is complicated, and this conventional quantitative analysis is not suitable for carbonate rocks.

[0005] Due to many factors such as formation fluid properties, mud properties and invasion conditions, sedimentary environment, lithology changes, the development degree and morphological structure of fractures and vugs, wellbore irregularities, and thin shale interbeds, etc., they affect the response of conventional logging to varying degrees, resulting in inaccurate problems in the above-mentioned conventional logging methods for classifying and identifying carbonate rock fracture-vug bodies in the prior art. Summary of the Invention

[0006] The object of the present invention is to provide a method for identifying carbonate fracture-vug bodies based on clustering analysis technology, so as to solve the problem of inaccuracy in the existing conventional logging methods for classifying and identifying carbonate fracture-vug bodies.

[0007] To achieve the above object, the solution of the present invention includes:

[0008] A technical solution of a method for identifying carbonate fracture-vug bodies based on clustering analysis technology of the present invention is to determine and calculate the classification and identification parameters of fracture-vug bodies in each reservoir of the target area; perform clustering analysis based on the classification and identification parameters of fracture-vug bodies in each reservoir to obtain the clustering categories of each reservoir; combine the reservoirs with imaging logging data under each clustering category to determine the fracture-vug body classification corresponding to each clustering category, and further obtain the fracture-vug body classification of the reservoirs under the clustering category.

[0009] Furthermore, the classification and identification parameters of fracture-vug bodies include two or more of shale parameters, resistivity porosity parameters, compensated neutron, compensated acoustic porosity parameters, compensated density porosity parameters, and well diameter change.

[0010] Furthermore, the classification and identification parameters of fracture-vug bodies in each reservoir of the target area are calculated according to the logging curves of various logging indicators in the target area.

[0011] Furthermore, the logging curves include natural gamma curve, compensated neutron curve, compensated acoustic curve, compensated density curve, well diameter curve, and deep and shallow dual lateral resistivity curves.

[0012] Furthermore, the shale parameter K1 is calculated by the following formula according to the median GR, maximum GRmax, and minimum GRmin of the gamma curve:

[0013]

[0014] Furthermore, the resistivity porosity parameter K2 is calculated by establishing the following relationship between fracture porosity and deep and shallow resistivities through simplifying the Sibbit fracture void model:

[0015] K2 = RS*(RD - RS) / RD

[0016] Where: RS and RD are the medians of the deep and shallow dual lateral resistivity curves in the corresponding section of the carbonate reservoir.

[0017] Furthermore, the compensated neutron is obtained according to the median of the compensated neutron curve in the corresponding section of the carbonate reservoir.

[0018] Further, the compensated acoustic porosity parameter is obtained according to the median value of the compensated acoustic curve within the corresponding section of the carbonate reservoir.

[0019] Further, the compensated density porosity parameter is obtained according to the median value of the compensated density curve within the corresponding section of the carbonate reservoir.

[0020] Further, the borehole diameter is obtained according to the median value of the borehole diameter curve within the corresponding section of the carbonate reservoir.

[0021] The beneficial effects of the present invention are as follows:

[0022] 1. A method for identifying carbonate fracture-vug bodies based on clustering analysis technology according to the present invention is based on constructing classification and identification parameters for carbonate fracture-vug bodies. Based on the clustering algorithm, different carbonate reservoir types are obtained through non-linear clustering analysis of multiple porosity-related parameters and shale parameters, thereby ensuring the "accuracy, efficiency, and science" of the classification results;

[0023] 2. A method for identifying carbonate fracture-vug bodies based on clustering analysis technology according to the present invention synthesizes various parameters, classifies the fracture-vug bodies in the reservoir, and combines a small amount of imaging logging and other means to confirm different categories, improving the work efficiency and reducing the capital investment for rechecking old wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of a method for identifying carbonate fracture-vug bodies based on clustering analysis technology in an embodiment;

[0025] Figure 2 is a variety of logging curves in the target area of the example given in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0027] This embodiment provides a method for identifying carbonate fracture-vug bodies based on clustering analysis technology. The specific method includes the following steps:

[0028] 1) Obtain the logging curves of multiple logging indicators selected in the target area. First, analyze the principles of the influence of multiple logging indicators on the identification of carbonate fracture-vug bodies and select the logging indicators.

[0029] Since the rock has an extremely high resistivity value, that is, the conductivity of the rock without shale forms a huge contrast with the conductivity of shale, and the presence of shale will greatly enhance the conductive ability of the rock. As long as there is shale, the resistivity will change significantly. Therefore, in carbonate formations, the additional conductivity of shale cannot be ignored.

[0030] The sonic logging measures the first arrival wave of the longitudinal wave. According to the logging skimming wave theory, the first arrival wave of the longitudinal wave skims along the wellbore wall, with the fastest propagation speed and reaching the receiving head first. The sonic wave has basically no response to high-angle fractures and attenuates for low-angle fractures. However, a large number of practices have shown that fractures, dissolution pores with different states and characteristics have different responses to sonic logging.

[0031] The porosity of density logging is the total porosity within the detection range of the density logging tool. When the electrode plates encounter fractures or karst caves, it will change.

[0032] Resistivity measures the conductivity of rocks and is affected by clay minerals, some special conductive minerals, and the water-bearing space. The shallow lateral mainly reflects the resistivity of the invaded zone, while the deep lateral reflects the resistivity of the virgin formation. For porous reservoirs, the invasion of drilling fluid occurs, resulting in changes in the resistivity of the invaded zone. In the simulation experiment of the dual laterolog water tank in the Sichuan Logging Institute, the occurrence, aperture, and porosity of fractures have an impact on the difference and resistivity value of the deep and shallow dual laterologs.

[0033] Neutron porosity is a reflection of the total hydrogen index of rocks, that is, in addition to the hydrogen index of the fluid in the pores, it also includes the hydrogen index of clay crystal water, bound water, and the equivalent hydrogen index of the rock matrix. Therefore, shale correction, lithology correction, and gas-bearing correction are required.

[0034] Karst caves are the result of dissolution along fractures or pores underground. Therefore, there is always a certain pore thickness connected to the fractures, which also causes differences in conductivity.

[0035] In addition, since other types of logging curves are not very sensitive to the changes in fracture-cave bodies, seven logging curves are mainly selected as reference curves in multiple logging curves, including 1a) natural gamma curve, 1b) compensated neutron curve, 1c) compensated sonic curve, 1d) compensated density curve, 1e) well diameter, and 1f) deep and shallow dual lateral resistivity curves.

[0036] 1a) Natural gamma curve: A curve that measures the intensity of natural gamma rays of rock formations along the wellbore.

[0037] 1b) Compensated neutron curve: A curve that measures the hydrogen index of the formation by sticking to the wellbore wall.

[0038] 1c) Compensated sonic curve: A curve that shows the time required for sound waves to pass through a 1-meter formation as the depth changes.

[0039] 1d) Compensated density curve: A curve that shows the change in the intensity of scattered gamma rays as the depth changes.

[0040] 1e) Well diameter curve: A curve that shows the change in the well diameter as the depth changes.

[0041] 1f) Deep and shallow laterolog resistivity curves: Curves showing the variation of the resistivity of the virgin formation and the invaded zone resistivity with depth.

[0042] 2) Selection of characteristic parameters for each curve of carbonate rocks.

[0043] Based on the 7 logging curves obtained in step 1) above, respectively read the median GR, maximum GR max and minimum GR min of the natural gamma curve in the corresponding section of the carbonate reservoir; the median CNL of the compensated neutron curve in the corresponding section of the carbonate reservoir; the median DT of the compensated acoustic curve in the corresponding section of the carbonate reservoir; the median RHOB of the compensated density curve in the corresponding section of the carbonate reservoir; the median CAL of the borehole diameter curve in the corresponding section of the carbonate reservoir; the median RD and RS of the deep and shallow laterolog resistivity curves in the corresponding section of the carbonate reservoir. Take the above 9 read values as the characteristic parameters of the carbonate section.

[0044] 3) Construct classification and identification parameters for fracture-vug bodies.

[0045] Due to factors such as the multi-stage and non-uniform filling of pores by carbonate cements and internal sediments, it has a certain impact on porosity. Therefore, the gamma curve is selected and the shale parameter K1 is obtained through the simplification of the shale parameter formula. The porosities of different fracture-vug bodies in carbonate rocks are different. Since porosity can be calculated from resistivity, acoustic travel time, compensated neutron, and litho-density, and the meaning of the porosity calculated by each parameter is different, some or all of these parameters can be used as important reference criteria for cluster analysis.

[0046] As the best implementation method, all of the above 6 parameters are selected as the classification and identification parameters for fracture-vug bodies, including: shale parameter K1, resistivity porosity parameter K2, compensated neutron, compensated acoustic porosity parameter, compensated density porosity parameter, and borehole diameter. Specifically as follows:

[0047] 3a) Shale parameter K1: Calculate according to the median GR, maximum GR max and minimum GR min of the gamma curve obtained in step 2). From

[0048]

[0049] the gamma extreme ratio K1 can be obtained, which represents the overall change trend of the gamma curve and reflects the shale change situation of the reservoir.

[0050] 3b) Resistivity porosity parameter K2: By simplifying the Sibbit fracture void model, establish a relationship between fracture porosity and deep and shallow resistivities:

[0051] K2 = RS * (RD - RS) / RD

[0052] 3c) Compensated Neutron: In this embodiment, the compensated neutron is reflected by the compensated neutron median CNL. The total porosity can be calculated based on the shale parameter and the compensated neutron. Therefore, according to the compensated neutron median CNL obtained in step 2), it reflects the overall pore development degree of the reservoir.

[0053] 3d) Compensated Acoustic Porosity Parameter: In this embodiment, the compensated acoustic porosity parameter is reflected by the compensated acoustic median DT. The matrix porosity of the rock can be calculated based on the shale parameter and the compensated acoustic wave. Therefore, the compensated acoustic median DT obtained in step 2) reflects the secondary porosity of the reservoir and can evaluate the development degree of secondary fractures and caves.

[0054] 3e) Compensated Density Porosity Parameter: In this embodiment, the compensated density porosity parameter is reflected by the compensated density median RHOB. The total porosity can be calculated based on the shale parameter and the compensated density. However, due to the influence of mud invasion, it is necessary to compare with the total porosity calculated by neutrons at the same time. Therefore, according to the compensated density median RHOB obtained in step 2), it reflects the overall pore development degree of the reservoir.

[0055] 3f) Hole Diameter: In this embodiment, the hole diameter is reflected by the hole diameter median CAL. According to the hole diameter median CAL obtained in step 2), it reflects the development of holes and caves.

[0056] 4) Cluster analysis to identify the types of reservoir fracture-cave bodies.

[0057] Based on the fracture-cave body classification and identification parameters in the above step 3), this step determines the corresponding fracture-cave body classification and identification parameters for each reservoir, and then establishes a data matrix of 6 index systems for n reservoirs. Based on the K-Means algorithm, clustering analysis is performed on it to distinguish similar data samples in a large amount of data, minimizing the types within the group and maximizing the distance between groups. Thus, classification processing is carried out among different reservoirs, and the density function of the fracture-cave body identification parameters in each category is obtained.

[0058] 5) Determination of various reservoir types.

[0059] Through the density function, the corresponding values of the larger density of the identification parameters in each type of reservoir can be obtained. By comparing with the empirical parameters of fracture-cave body identification in the target area, the types of fracture-cave bodies to which each type of reservoir belongs are determined.

[0060] Generally speaking, a method for identifying carbonate rock fracture-cave bodies based on clustering analysis technology in the present invention has the following specific technical key points:

[0061] First, conduct in-depth research on the relevant logging curves of carbonate reservoirs, preprocess the data, eliminate noise data, extract characteristic parameters, and construct identification parameters for carbonate fracture-vug bodies, specifically including shale parameters, resistivity porosity parameters, compensated neutron, compensated acoustic wave, compensated density, and well diameter.

[0062] Then, based on the identification parameters for carbonate fracture-vug bodies, construct a basic matrix and perform clustering analysis on it using the K-Means algorithm, so as to classify different reservoirs and obtain the clustering categories of each reservoir.

[0063] Finally, for the reservoirs under each clustering category obtained by classification, determine the type of this category of reservoirs according to the reservoirs containing imaging logging in each category.

[0064] The following beneficial effects are obtained by using a method for identifying carbonate fracture-vug bodies based on clustering analysis technology of the present invention:

[0065] 1. A method for identifying carbonate fracture-vug bodies based on clustering analysis technology of the present invention is based on constructing classification and identification parameters for carbonate fracture-vug bodies. Based on the K-Means algorithm, different carbonate reservoir types are obtained through non-linear clustering analysis of multiple porosity-related parameters and shale parameters, thus ensuring the "accuracy, efficiency, and science" of the classification results;

[0066] 2. A method for identifying carbonate fracture-vug bodies based on clustering analysis technology of the present invention synthesizes various parameters, classifies the fracture-vug bodies of the reservoirs, combines a small amount of imaging logging and other means to confirm different categories, improves the work efficiency for rechecking old wells, and reduces the capital investment.

[0067] Next, taking the carbonate fracture-vug gas reservoir in the Maokou Formation in the Puguang area as an example, the technical solution and achieved effects of a method for identifying carbonate fracture-vug bodies based on clustering analysis technology of the present invention will be further described in conjunction with the accompanying drawings.

[0068] A method for identifying carbonate fracture-vug bodies based on clustering analysis technology, as Figure 1 shown, includes the following specific steps:

[0069] 1) Obtaining multiple logging curves in the target area.

[0070] Select seven curves from the logging curves, including 1a) natural gamma curve, 1b) compensated neutron curve, 1c) compensated acoustic wave curve, 1d) compensated density curve, 1e) well diameter, and 1f) deep and shallow dual lateral resistivity curves, a total of seven curves.

[0071] 1a) Natural gamma curve: A curve that measures the intensity of natural gamma rays of rock formations along the wellbore.

[0072] 1b) Compensated neutron curve: A curve for measuring the hydrogen index of the formation adjacent to the borehole wall.

[0073] 1c) Compensated acoustic curve: A curve representing the time required for acoustic waves to pass through a 1-meter formation as depth changes.

[0074] 1d) Compensated density curve: A curve showing the variation of the scattered gamma-ray intensity as depth changes.

[0075] 1e) Borehole diameter curve: A curve depicting the change in borehole diameter as depth changes.

[0076] 1f) Deep and shallow laterolog resistivity curves: Curves showing the variation of the resistivity of the virgin formation and the resistivity of the invaded zone as depth changes.

[0077] Multiple logging curves obtained in the target area are as Figure 2 shown.

[0078] 2) Selection of characteristic parameters for each curve of carbonate rocks.

[0079] Based on the 7 logging curves obtained in step 1) above, respectively read the median GR, maximum GR max and minimum GR min of the natural gamma curve within the corresponding section of the carbonate reservoir; the median CNL of the compensated neutron curve within the corresponding section of the carbonate reservoir; the median DT of the compensated acoustic curve within the corresponding section of the carbonate reservoir; the median RHOB of the compensated density curve within the corresponding section of the carbonate reservoir; the median CAL of the borehole diameter curve within the corresponding section of the carbonate reservoir; the median RD and RS of the deep and shallow laterolog resistivity curves within the corresponding section of the carbonate reservoir. Take the above 9 read values as the characteristic parameters of the carbonate section, as shown in Table 1 specifically.

[0080] Table 1 Characteristic parameters of each curve in Puguang area

[0081] Layer number GR <![CDATA[GR min > <![CDATA[GR max > CAL CNL RHOB DT RD RS 146 50.491 41.642 60.844 9.594 5.727 2.647 59.475 45.333 45.175 147 27.958 26.77 28.891 9.525 2.207 2.737 50.641 252.832 236.476 148 31.872 27.302 37.577 9.584 0.243 2.636 50.937 471.959 422.259 149 38.374 23.688 55.19 9.455 2.848 2.567 58.989 87.668 76.412 150 40.66 34.627 49.768 9.585 0.148 2.68 49.038 2243.746 1850.803 151 24.624 21.041 32.104 9.487 3.798 2.593 52.303 160.41 144.321 152 44.003 34 60 9.52 3.768 2.703 49.537 1340.576 1210.039 153 48.031 39.949 54.585 9.539 3.114 2.652 50.591 197.738 197.199 154 39.857 33.514 43.747 9.537 1.819 2.665 50.502 1509.88 1441.32 155 43.979 35.185 51.466 9.58 1.187 2.653 52.2 430.507 420.288

[0082] 3) Construction of classification and identification parameters for fracture-vug bodies.

[0083] The classification and identification parameters for fracture-vug bodies include 6: gamma extreme ratio K1, deep and shallow resistivity ratio K2, compensated neutron, compensated acoustic, compensated density, and borehole diameter. Specifically:

[0084] 3a) Gamma extreme ratio K1: According to the median GR, maximum GR max and minimum GR min of the gamma curve obtained in step 2), from

[0085]

[0086] The gamma extreme value ratio K1 can be obtained, which represents the overall change trend of the gamma curve and reflects the lithology change of the reservoir.

[0087] 3b) Deep and shallow resistivity ratio K2: According to the deep and shallow resistivities RD and RS obtained in step 2), from

[0088] K2 = RS * (RD - RS) / RD

[0089] The deep and shallow resistivity ratio K2 can be obtained, which represents the conductivity of the reservoir and reflects the dissolution degree of the reservoir.

[0090] 3c) Compensated neutron: In this embodiment, the compensated neutron is reflected according to the compensated neutron median CNL. The total porosity can be calculated based on the shale parameter and the compensated neutron. Therefore, according to the compensated neutron median CNL obtained in step 2), it reflects the overall pore development degree of the reservoir.

[0091] 3d) Compensated acoustic porosity parameter: In this embodiment, the compensated acoustic porosity parameter is reflected according to the compensated acoustic median DT. The rock matrix porosity can be calculated based on the shale parameter and the compensated acoustic wave. Therefore, the compensated acoustic median DT obtained in step 2) reflects the secondary porosity of the reservoir and can evaluate the development degree of secondary fractures and caves.

[0092] 3e) Compensated density porosity parameter: In this embodiment, the compensated density porosity parameter is reflected according to the compensated density median RHOB. The total porosity can be calculated based on the shale parameter and the compensated density. However, due to the influence of mud invasion, it is necessary to compare it with the total porosity calculated by neutrons at the same time. Therefore, according to the compensated density median RHOB obtained in step 2), it reflects the overall pore development degree of the reservoir.

[0093] 3f) Well diameter: In this embodiment, the well diameter is reflected according to the well diameter median CAL. According to the well diameter median CAL obtained in step 2), it reflects the development of holes and caves.

[0094] The classification and identification parameters of fracture-cavity bodies in each reservoir in the Puguang area are shown in Table 2.

[0095] Table 2 Classification and identification parameters of fracture-cavity bodies in each reservoir in the Puguang area

[0096]

[0097]

[0098] 4) Cluster analysis to identify the types of fracture-cavity bodies in the reservoir.

[0099] Based on the fracture-cavity body classification and identification parameters in step 3) above, this step determines the corresponding fracture-cavity body classification and identification parameters for each reservoir, and then establishes a data matrix of 6 index systems for n reservoirs, and performs cluster analysis on it based on the K-Means algorithm.

[0100] 4a) Establish a data matrix for the six index systems and perform data standardization processing. The standard deviation method is mainly used, and its model is:

[0101]

[0102] In the formula: i is the layer number, representing the i-th layer, i = 1, 2,..., n; j is the identification parameter, representing the j-th identification parameter, j = 1, 2,..., m. In this embodiment, six classification and identification parameters of fracture-vug bodies are selected, so m = 6; represents the average value of the j-th identification parameter; S j represents the standard deviation of the j-th identification parameter.

[0103] 4b) Select K centers and perform iterative calculations on the loss function

[0104]

[0105] until convergence.

[0106] 4c) For each sample x i , assign it to the center with the closest distance. For each class center x j , recalculate the center.

[0107] Through the above calculations, the clustering analysis results shown in Table 3 can be obtained. It can be seen that there are 4 categories: Category I: 149, 151; Category II: 147, 148, 153, 154, 155; Category III: 150, 152; Category IV: 146.

[0108] Table 3 Clustering analysis results

[0109]

[0110]

[0111] Combined with Figure 2 the imaging logging data in, it can be obtained that the No. 149 reservoir in Category I is cave type, and the No. 147 reservoir in Category II is fracture-vug type. Therefore, it can be determined that Category I is cave type and Category II is fracture-vug type. Since there is no imaging logging for the reservoirs in Category III and Category IV, they are uniformly classified as fracture-porosity type. By comparing with the later reservoir analysis results, it is found that the No. 146 reservoir is cave type, and the reservoir types of the rest are consistent with the clustering analysis results. Therefore, the accuracy rate of classification by this method can reach 90%.

Claims

1. A method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology, characterized in that, Determine and calculate the classification and identification parameters of fracture-vug bodies in each reservoir in the target area; conduct cluster analysis based on the classification and identification parameters of fracture-vug bodies in each reservoir to obtain the cluster categories of each reservoir; combine the reservoirs with imaging logging data under each cluster category to determine the fracture-vug body classification corresponding to each cluster category, and further obtain the fracture-vug body classification of the reservoirs under the cluster category.

2. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 1, characterized in that, The classification and identification parameters of fracture-vug bodies include two or more of shale parameters, resistivity porosity parameters, compensated neutron, compensated acoustic porosity parameters, compensated density porosity parameters, and well diameter variation.

3. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 2, characterized in that, The classification and identification parameters of fracture-vug bodies in each reservoir in the target area are calculated based on the logging curves of various logging indicators in the target area.

4. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 3, characterized in that, The logging curves include natural gamma curve, compensated neutron curve, compensated acoustic curve, compensated density curve, well diameter curve, and deep and shallow dual lateral resistivity curves.

5. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The shale parameter K1 is calculated based on the median GR, maximum GR max and minimum GR min of the gamma ray curve by the following formula:

6. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The resistivity porosity parameter K2 is calculated by simplifying the Sibbit fracture void model and establishing the following relationship between fracture porosity and deep and shallow resistivities: K2 = RS * (RD - RS) / RD Where: RS and RD are the median values of the deep and shallow dual lateral resistivity curves in the corresponding section of the carbonate reservoir.

7. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The compensated neutron is obtained based on the median value of the compensated neutron curve in the corresponding section of the carbonate reservoir.

8. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The compensated acoustic porosity parameter is obtained based on the median value of the compensated acoustic curve in the corresponding section of the carbonate reservoir.

9. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The compensated density porosity parameter is obtained based on the median value of the compensated density curve in the corresponding section of the carbonate reservoir.

10. The method for identifying carbonate rock fracture-vug bodies based on clustering analysis technology according to claim 4, characterized in that, The well diameter is obtained based on the median value of the well diameter curve in the corresponding section of the carbonate reservoir.

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