A carbonate reservoir classification method and a reservoir type identification plate establishment method

Abstract

The present invention provides a carbonate reservoir classification method and a reservoir type identification plate establishment method. The identification plate establishment method includes: obtaining well logging data, core analysis data, pore throat median radius data and oil test data of coring wells for exploration and development of carbonate oil and gas fields; classifying the coring well section reservoirs; for each coring well section reservoir of each reservoir type, calculating the reservoir structure parameters and performing normalization processing; obtaining the intersection diagram of the normalized reservoir structure parameters and porosity to obtain a reservoir type identification plate. The carbonate reservoir classification method includes: using the above method to establish a carbonate reservoir type identification plate for the work area; obtaining well logging data and pore throat median radius data for the target reservoir in the work area; determining the target reservoir structure parameters and performing normalization processing; using the normalized reservoir structure parameters and porosity of the target reservoir to intersect, and referring to the established carbonate reservoir type identification plate for the work area to identify the target reservoir type.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbonate oil and gas field exploration and development, and particularly relates to a method for establishing a carbonate reservoir type identification plate and a carbonate reservoir classification method based on the carbonate reservoir type identification plate established by the method. Background Art

[0002] The physical properties of carbonate reservoirs are mainly controlled by sedimentary phases, diagenesis and tectonic movements. Carbonate rocks have the characteristics of diverse reservoir space, obvious secondary changes and strong heterogeneity. Carbonate reservoir classification is one of the basic tasks in the study of carbonate reservoirs, involving the entire process of oil field exploration and development. The exploration and development of complex carbonate reservoirs is difficult. With the deepening of oil and gas field exploration and development at home and abroad, the existing conventional logging reservoir classification methods are usually divided into two categories: one is the qualitative identification method of logging curve graphics, which mainly conducts qualitative identification of reservoir types based on the morphological distribution of conventional logging curves; the other is to classify reservoir types based on the size and distribution of porosity and permeability. Since it is difficult to obtain the permeability of complex carbonate reservoirs, the existing reservoir classification methods generally have large errors for complex carbonate reservoirs and are difficult to meet the technical requirements of oil and gas exploration and development. Summary of the invention

[0003] In view of the shortcomings of the prior art, the present invention aims to provide a method for establishing a carbonate reservoir type identification plate. The plate established by the method can be well used for carbonate oil and gas field reservoir classification.

[0004] Another object of the present invention is to provide a carbonate reservoir classification method, which realizes reservoir classification without the need for core permeability analysis or permeability calculation, plays an important guiding role in determining reserves, exploitation methods adopted for development, etc., and helps to guide the efficient exploration and development of oil and gas fields.

[0005] In order to achieve the above object, the present invention provides a method for establishing a carbonate reservoir type identification plate, wherein the method comprises:

[0006] Obtain well logging data, core analysis data, pore throat median radius data, oil testing data, etc. of coring wells in carbonate oil and gas field exploration and development;

[0007] Based on the core analysis data, well logging data and oil testing data, the reservoir of the coring section is classified;

[0008] For each cored well section of each reservoir type, the reservoir structure parameters are calculated based on the well logging data and pore throat median radius data, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters;

[0009] The cross-plot of normalized reservoir structural parameters and porosity of each cored well section of each reservoir type is obtained, and then a reservoir type identification chart is obtained.

[0010] In the method for establishing the above-mentioned carbonate reservoir type identification atlas, preferably, the types obtained by classifying the cored well section reservoir include: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0011] In the above-mentioned method for establishing a carbonate reservoir type identification plate, preferably, for each cored well section reservoir of each reservoir type, based on its logging data and pore throat median radius data, the reservoir structure parameters are calculated, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters including:

[0012] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on well logging data;

[0013] Based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined;

[0014] Normalizing the reservoir structure parameters of each coring section of each reservoir type to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0015] In this preferred solution, for each cored section of each reservoir type, based on its logging response characteristics and pore throat median radius data, reservoir permeability analysis parameters are introduced to calculate reservoir structural parameters and perform normalization processing on them.

[0016] In the method for establishing the carbonate reservoir type identification chart, preferably, for each cored well section reservoir of each reservoir type, based on its logging data, the reservoir permeability analysis parameters are determined in the following manner:

[0017] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0018] More preferably, the reservoir permeability analysis parameter is determined by the following formula:

[0019] Para_K=a·e b·PHIE +c

[0020] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0021] In the above-mentioned method for establishing the carbonate reservoir type identification atlas, preferably, based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section reservoir of each reservoir type, the reservoir structure parameters are determined in the following manner:

[0022] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0023] More preferably, the reservoir structure parameter is determined by the following formula:

[0024]

[0025] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0026] In the above-mentioned method for establishing a carbonate reservoir type identification atlas, preferably, normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type refers to: normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type.

[0027] In the above-mentioned method for establishing a carbonate reservoir type identification atlas, preferably, the pore throat median radius data of the coring wells in the exploration and development of carbonate oil and gas fields are obtained by means of mercury injection experiments.

[0028] The present invention also provides a carbonate reservoir classification method, wherein the method comprises:

[0029] The carbonate reservoir type identification plate is established by using the above-mentioned carbonate reservoir type identification plate establishment method;

[0030] Obtain logging data of target carbonate reservoirs in the work area;

[0031] Obtain the pore throat median radius data of the target carbonate reservoir in the work area;

[0032] Based on the well logging data and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined, and the reservoir structure parameters of the target carbonate reservoir are normalized to obtain normalized reservoir structure parameters;

[0033] The normalized reservoir structure parameters and porosity of the target carbonate reservoir are intersected, and the target carbonate reservoir type is identified by referring to the established carbonate reservoir type identification chart of the work area to complete the carbonate reservoir classification.

[0034] In the above carbonate reservoir classification method, the method used to determine the normalized reservoir structural parameters of the target carbonate reservoir is the same as the method used to determine the normalized reservoir structural parameters of each cored well section of each reservoir type in the process of establishing a carbonate reservoir type identification chart for the work area.

[0035] In the above carbonate reservoir classification method, preferably, based on the well logging data and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined, and the reservoir structure parameters of the target carbonate reservoir are normalized to obtain the normalized reservoir structure parameters, including:

[0036] For the target carbonate reservoir, determine the reservoir permeability analysis parameters based on its logging data;

[0037] Determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir;

[0038] Normalizing the reservoir structure parameters of the target carbonate reservoir to obtain normalized reservoir structure parameters of the target carbonate reservoir;

[0039] In this preferred solution, for the target carbonate reservoir, based on its logging response characteristics and pore throat median radius data, reservoir permeability analysis parameters are introduced to calculate reservoir structure parameters and perform normalization processing.

[0040] In the above carbonate reservoir classification method, preferably, for the target carbonate reservoir, based on its logging data, the reservoir permeability analysis parameters are determined in the following manner:

[0041] For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0042] More preferably, the reservoir permeability analysis parameter is determined by the following formula:

[0043] Para_K=a·e b·PHIE +c

[0044] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0045] In the above carbonate reservoir classification method, preferably, based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined in the following manner:

[0046] Based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity, reservoir structural parameters are determined;

[0047] More preferably, the reservoir structure parameter is determined by the following formula:

[0048]

[0049] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0050] In the above-mentioned carbonate reservoir classification method, preferably, the normalizing the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir refers to: normalizing the reservoir structure parameters of the target carbonate reservoir to [0, 1] to obtain the normalized reservoir structure parameters of the target carbonate reservoir.

[0051] In the above carbonate reservoir classification method, preferably, obtaining the pore throat median radius data of the target carbonate reservoir in the work area includes:

[0052] Establish the fitting relationship between logging response and pore throat median radius;

[0053] Based on the well logging data of the target carbonate reservoir in the work area, the median radius of the pore throat of the target carbonate reservoir in the work area is determined by using the fitting relationship between the well logging response and the median radius of the pore throat.

[0054] More preferably, the establishment of the fitting relationship between the logging response and the pore throat median radius is performed using a neural network algorithm; in a specific embodiment, it includes:

[0055] Constructing a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include pore throat median radius;

[0056] Obtaining training samples for the neural network model based on a plurality of historical well logging data and corresponding pore throat median radius data;

[0057] The training samples are used to train the neural network model, and the trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

[0058] The present invention also provides a system for establishing a carbonate reservoir type identification chart, wherein the system comprises:

[0059] Data acquisition module: used to obtain well logging data, core analysis data, pore throat median radius data, oil testing data, etc. of coring wells in carbonate oil and gas field exploration and development;

[0060] Reservoir classification module: used to classify the reservoir of the coring section based on the core analysis data, well logging data and oil testing data;

[0061] Reservoir structure parameter processing module: used for calculating reservoir structure parameters for each cored well section of each reservoir type based on its logging data and pore throat median radius data, and normalizing the reservoir structure parameters to obtain normalized reservoir structure parameters;

[0062] Identification plate construction module: used to obtain the intersection diagram of normalized reservoir structure parameters and porosity of each cored well section of each reservoir type, and then obtain the reservoir type identification plate.

[0063] In the above-mentioned carbonate reservoir type identification atlas establishment system, preferably, the reservoir classification module classifies the core section reservoir into types including: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0064] In the above-mentioned carbonate reservoir type identification chart establishment system, preferably, the reservoir structure parameter processing module includes:

[0065] Permeability analysis parameter determination submodule: used to determine reservoir permeability analysis parameters for each cored well section of each reservoir type based on its logging data;

[0066] Reservoir structure parameter determination submodule: used to determine the reservoir structure parameters based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type;

[0067] Normalization processing submodule: used for normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type.

[0068] In the above-mentioned carbonate reservoir type identification chart establishment system, preferably, the permeability analysis parameter determination submodule determines the reservoir permeability analysis parameters in the following manner:

[0069] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0070] More preferably, the reservoir permeability analysis parameter is determined by the following formula:

[0071] Para_K=a·e b·PHIE +c

[0072] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0073] In the above-mentioned carbonate reservoir type identification chart establishment system, preferably, the reservoir structure parameter determination submodule determines its reservoir structure parameters in the following manner:

[0074] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0075] More preferably, the reservoir structure parameter is determined by the following formula:

[0076]

[0077] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0078] In the above-mentioned carbonate reservoir type identification chart establishment system, preferably, the normalization processing submodule obtains the normalized reservoir structure parameters of each coring section reservoir of each reservoir type in the following manner:

[0079] The reservoir structural parameters of each coring section of each reservoir type are normalized to [0, 1] to obtain the normalized reservoir structural parameters of each coring section of each reservoir type.

[0080] In the above-mentioned carbonate reservoir type identification atlas establishment system, preferably, the pore throat median radius data of carbonate oil and gas field exploration and development core wells are obtained by mercury injection experiment.

[0081] The present invention also provides a carbonate reservoir classification system, wherein the system comprises:

[0082] The above carbonate reservoir type identification chart establishment system is used to establish a carbonate reservoir type identification chart for a work area;

[0083] Target reservoir logging data acquisition module: used to obtain logging data of target carbonate reservoirs in the work area;

[0084] Target reservoir pore throat median data acquisition module: used to obtain pore throat median radius data of target carbonate reservoir in the work area;

[0085] Target reservoir structure parameter calculation module: used to determine the reservoir structure parameters of the target carbonate reservoir based on the well logging data and pore throat median radius data of the target carbonate reservoir, and normalize the reservoir structure parameters of the target carbonate reservoir to obtain normalized reservoir structure parameters;

[0086] Target reservoir type identification module: It is used to use the normalized reservoir structure parameters and porosity of the target carbonate reservoir to intersect, refer to the established carbonate reservoir type identification chart of the work area to identify the target carbonate reservoir type, and complete the carbonate reservoir classification.

[0087] In the above carbonate reservoir classification system, the method used to determine the normalized reservoir structural parameters of the target carbonate reservoir is the same as the method used to determine the normalized reservoir structural parameters of each cored well section of each reservoir type in the process of establishing the carbonate reservoir type identification chart of the work area.

[0088] In the above carbonate reservoir classification system, preferably, the target reservoir structure parameter calculation module includes:

[0089] Target reservoir permeability analysis parameter calculation submodule: used to determine reservoir permeability analysis parameters for target carbonate reservoirs based on their logging data;

[0090] Target reservoir structure parameter calculation submodule: used to determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir;

[0091] Target reservoir normalization calculation submodule: used to normalize the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir.

[0092] In the above carbonate reservoir classification system, preferably, the target reservoir permeability analysis parameter calculation submodule determines the reservoir permeability analysis parameters in the following manner:

[0093] For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0094] More preferably, the reservoir permeability analysis parameter is determined by the following formula:

[0095] Para_K=a·e b·PHIE +c

[0096] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0097] In the above carbonate reservoir classification system, preferably, the target reservoir structure parameter calculation submodule determines its reservoir structure parameters in the following manner:

[0098] Based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity, reservoir structural parameters are determined;

[0099] More preferably, the reservoir structure parameter is determined by the following formula:

[0100]

[0101] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0102] In the above carbonate reservoir classification system, preferably, the target reservoir normalization calculation submodule obtains the normalized reservoir structure parameters of the target carbonate reservoir in the following manner:

[0103] The reservoir structural parameters of the target carbonate reservoir are normalized to [0, 1] to obtain the normalized reservoir structural parameters of the target carbonate reservoir.

[0104] In the above carbonate reservoir classification system, preferably, the target reservoir pore throat median data acquisition module includes:

[0105] Relationship construction submodule: used to establish the fitting relationship between logging response and pore throat median radius;

[0106] Target reservoir pore throat median calculation submodule: used to determine the pore throat median radius of the target carbonate reservoir in the work area based on the well logging data of the target carbonate reservoir in the work area, using the fitting relationship between the well logging response and the pore throat median radius;

[0107] More preferably, the relationship building submodule uses a neural network algorithm to establish a fitting relationship between the well logging response and the pore throat median radius; in a specific embodiment, the relationship building submodule includes:

[0108] Neural network model building unit: used to build a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include pore throat median radius;

[0109] A training sample determination unit is used to obtain training samples of a neural network model according to a plurality of historical well logging data and corresponding pore throat median radius data;

[0110] Relationship construction unit: used to apply the training samples to perform model training on the neural network model, and the trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

[0111] The present invention also provides a device for establishing a carbonate reservoir type identification chart, comprising a processor and a memory; wherein:

[0112] Memory, used to store computer programs;

[0113] The processor is used to implement the steps of the method for establishing the above-mentioned carbonate reservoir type identification plate when executing the program stored in the memory.

[0114] The present invention also provides a carbonate reservoir classification device, comprising a processor and a memory; wherein:

[0115] Memory, used to store computer programs;

[0116] The processor is used to implement the steps of the above-mentioned carbonate reservoir classification method when executing the program stored in the memory.

[0117] The present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the method for establishing the above-mentioned carbonate reservoir type identification plate.

[0118] An embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned carbonate reservoir classification method.

[0119] In the process of oil and gas exploration and development in carbonate reservoirs, reservoir type is one of the basic studies, which plays a guiding role in determining reserves and the mining methods used for development. For example, in the exploration process, in order to more accurately calculate the new reserves, the reservoir type is first divided, and the appropriate reserve calculation method is selected according to the reservoir type to more accurately calculate the reserves, ensuring the reliability and economy of the reserves; in the development process, the reservoir type is first divided, and the appropriate mining method is selected according to the reservoir type, which is more conducive to the development of the reservoir; in addition, when evaluating water-flooded layers and remaining reserves, reservoir type division has its advantages. For different reservoir types, water-flooding analysis and remaining reserve distribution analysis are carried out respectively, which is more conducive to the summary of water-flooding mechanism and remaining oil distribution mechanism. It can be seen that only by establishing a reasonable carbonate reservoir type division and identification chart can we better analyze and guide the efficient exploration and development of oil and gas fields.

[0120] The technical solution provided by the present invention can effectively realize the classification of carbonate reservoirs, and solve the problems of low classification accuracy and difficulty in classification of complex carbonate reservoirs in the prior art. The reservoir type identification chart established by the technical solution provided by the present invention can classify reservoirs without core permeability analysis or permeability calculation, which reduces the difficulty of reservoir classification and has a high classification accuracy; it can distinguish reservoirs with similar porosity and permeability but different pore structures; and provide effective technical support for oil and gas exploration and development. BRIEF DESCRIPTION OF THE DRAWINGS

[0121] Figure 1A schematic flow chart of a method for establishing a carbonate reservoir type identification chart according to an embodiment of the present invention.

[0122] Figure 2 A schematic diagram of a process flow of a carbonate reservoir classification method provided in one embodiment of the present invention.

[0123] Figure 3 A reservoir identification plate established by a method for establishing a carbonate reservoir type identification plate provided in an embodiment of the present invention.

[0124] Figure 4 Schematic diagram of the neural network model structure in Example 1 of the present invention.

[0125] Figure 5 This is the reservoir identification plate of the MARK layer of the K oil field established in Example 1 of the present invention.

[0126] Figure 6 This is a schematic diagram of the reservoir classification results of the MARK layer of the X77 well in the K oil field in Example 1 of the present invention.

[0127] Figure 7 A schematic diagram of the structure of a system for establishing a carbonate reservoir type identification plate provided in one embodiment of the present invention.

[0128] Figure 8 A schematic diagram of the structure of a carbonate reservoir classification system provided in one embodiment of the present invention.

[0129] Fig. 9 A schematic diagram of the structure of a device for establishing a carbonate reservoir type identification plate provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0130] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0131] The principle and spirit of the present invention are described in detail below with reference to several representative embodiments of the present invention.

[0132] See also Figure 1 In order to achieve the above-mentioned object, the present invention provides a method for establishing a carbonate reservoir type identification chart, wherein the method comprises:

[0133] Step S1: obtaining well logging data, core analysis data, pore throat median radius data, and oil testing data of coring wells for exploration and development of carbonate oil and gas fields;

[0134] Step S2: classifying the reservoir of the coring section based on the core analysis data, well logging data and oil testing data;

[0135] Step S3: for each cored well section reservoir of each reservoir type, based on its logging data and pore throat median radius data, the reservoir structure parameters are calculated, and the reservoir structure parameters are normalized to obtain normalized reservoir structure parameters;

[0136] Step S4: Obtain a cross-plot of normalized reservoir structural parameters and porosity of each cored well section reservoir of each reservoir type, and then obtain a reservoir type identification plate.

[0137] In one embodiment, the reservoir types obtained by classifying the coring section include: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0138] In one embodiment, for each cored well section reservoir of each reservoir type, the reservoir structure parameters are calculated based on the well logging data and the pore throat median radius data, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters including:

[0139] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on well logging data;

[0140] Based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined;

[0141] Normalizing the reservoir structure parameters of each coring section of each reservoir type to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0142] In this preferred solution, for each cored section of each reservoir type, based on its logging response characteristics and pore throat median radius data, reservoir permeability analysis parameters are introduced to calculate reservoir structural parameters and perform normalization processing on them.

[0143] In one embodiment, for each cored well section of each reservoir type, based on its logging data, the reservoir permeability analysis parameters are determined in the following manner:

[0144] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0145] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0146] Para_K=a·e b·PHIE +c

[0147] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0148] In one embodiment, based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined in the following manner:

[0149] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0150] Furthermore, the reservoir structural parameters are determined by the following formula:

[0151]

[0152] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0153] In one embodiment, normalizing the reservoir structure parameters of each coring well section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring well section reservoir of each reservoir type refers to: normalizing the reservoir structure parameters of each coring well section reservoir of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring well section reservoir of each reservoir type;

[0154] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0155] Obtain the maximum and minimum values ​​of reservoir structural parameters;

[0156] Based on the maximum and minimum values ​​of the reservoir structure parameters, the reservoir structure parameters of each cored well section of each reservoir type are normalized using the following formula;

[0157]

[0158] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0159] In one embodiment, the pore throat median radius data of the coring wells in the exploration and development of carbonate oil and gas fields are obtained by mercury injection testing.

[0160] In one embodiment, the reservoir type identification chart established is as follows: Figure 3 As shown,

[0161] AA' line: the normalized reservoir structure parameter identification value a corresponding to the lower limit of the reservoir pore throat median radius RC50. When the value a is lower than this, the reservoir has no connectivity and can be divided into some isolated pores or unconnected pore bodies;

[0162] BB' line: normalized reservoir structure parameter identification value b corresponding to the median radius of the pore throat of the vug reservoir RC50. According to the calculation of relevant standard parameters in the petroleum industry and the statistical analysis of a large number of oil and gas field examples, this value b is about twice the AA' value a.

[0163] AB line: pore type identification line;

[0164] CC' line: reservoir porosity lower limit, PHIE_cutoff, obtained based on core analysis, oil testing, etc.;

[0165] BD line: vug porosity line, obtained based on core statistical analysis.

[0166] See also Figure 2 The present invention provides a carbonate reservoir classification method, wherein the method comprises:

[0167] Step S21: obtaining well logging data, core analysis data, pore throat median radius data, and oil testing data of coring wells for exploration and development of carbonate oil and gas fields;

[0168] Step S22: classifying the reservoir of the coring section based on the core analysis data, well logging data and oil testing data;

[0169] Step S23: for each cored well section reservoir of each reservoir type, based on its logging data and pore throat median radius data, the reservoir structure parameters are calculated, and the reservoir structure parameters are normalized to obtain normalized reservoir structure parameters;

[0170] Step S24: obtaining a cross-plot of normalized reservoir structural parameters and porosity of each cored well section reservoir of each reservoir type, thereby obtaining a reservoir type identification plate;

[0171] Step S25: Acquire well logging data of the target carbonate reservoir in the work area;

[0172] Step S26: obtaining pore throat median radius data of the target carbonate reservoir in the work area;

[0173] Step S27: determining the reservoir structure parameters of the target carbonate reservoir based on the well logging data and the pore throat median radius data of the target carbonate reservoir, and normalizing the reservoir structure parameters of the target carbonate reservoir to obtain normalized reservoir structure parameters;

[0174] Step S28: The target carbonate reservoir type is identified by intersecting the normalized reservoir structure parameters and porosity of the target carbonate reservoir, and the carbonate reservoir type identification chart established in the work area is used to complete the carbonate reservoir classification.

[0175] In one embodiment, the reservoir types obtained by classifying the coring section include: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0176] In one embodiment, for each cored well section reservoir of each reservoir type, the reservoir structure parameters are calculated based on the well logging data and the pore throat median radius data, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters including:

[0177] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on well logging data;

[0178] Based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined;

[0179] Normalizing the reservoir structure parameters of each coring section of each reservoir type to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0180] In this preferred solution, for each cored section of each reservoir type, based on its logging response characteristics and pore throat median radius data, reservoir permeability analysis parameters are introduced to calculate reservoir structural parameters and perform normalization processing on them.

[0181] In one embodiment, for each cored well section of each reservoir type, based on its logging data, the reservoir permeability analysis parameters are determined in the following manner:

[0182] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0183] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0184] Para_K=a·e b·PHIE +c

[0185] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0186] In one embodiment, based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined in the following manner:

[0187] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0188] Furthermore, the reservoir structural parameters are determined by the following formula:

[0189]

[0190] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0191] In one embodiment, normalizing the reservoir structure parameters of each coring well section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring well section reservoir of each reservoir type refers to: normalizing the reservoir structure parameters of each coring well section reservoir of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring well section reservoir of each reservoir type;

[0192] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0193] Obtain the maximum and minimum values ​​of reservoir structural parameters;

[0194] Based on the maximum and minimum values ​​of the reservoir structure parameters, the reservoir structure parameters of each cored well section of each reservoir type are normalized using the following formula;

[0195]

[0196] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0197] In one embodiment, the pore throat median radius data of the coring wells in the exploration and development of carbonate oil and gas fields are obtained by mercury injection testing.

[0198] In one embodiment, the method used to determine the normalized reservoir structural parameters of the target carbonate reservoir is the same as the method used to determine the normalized reservoir structural parameters of each cored well section reservoir of each reservoir type in the process of establishing a carbonate reservoir type identification chart for the work area.

[0199] In one embodiment, the method used to determine the normalized reservoir structural parameters of the target carbonate reservoir is the same as the method used to determine the normalized reservoir structural parameters of each cored well section reservoir of each reservoir type in the process of establishing a carbonate reservoir type identification chart for the work area.

[0200] In one embodiment, based on the well logging data and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined, and the reservoir structure parameters of the target carbonate reservoir are normalized to obtain the normalized reservoir structure parameters, including:

[0201] For the target carbonate reservoir, determine the reservoir permeability analysis parameters based on its logging data;

[0202] Determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir;

[0203] Normalizing the reservoir structure parameters of the target carbonate reservoir to obtain normalized reservoir structure parameters of the target carbonate reservoir;

[0204] In this preferred solution, for the target carbonate reservoir, based on its logging response characteristics and pore throat median radius data, reservoir permeability analysis parameters are introduced to calculate reservoir structure parameters and perform normalization processing.

[0205] In one embodiment, for a target carbonate reservoir, based on its well logging data, reservoir permeability analysis parameters are determined in the following manner:

[0206] For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0207] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0208] Para_K=a·e b·PHIE +c

[0209] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0210] In one embodiment, based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined by:

[0211] Based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity, reservoir structural parameters are determined;

[0212] Furthermore, the reservoir structural parameters are determined by the following formula:

[0213]

[0214] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0215] In one embodiment, normalizing the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir refers to: normalizing the reservoir structure parameters of the target carbonate reservoir to [0, 1] to obtain the normalized reservoir structure parameters of the target carbonate reservoir;

[0216] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0217] Obtaining the maximum and minimum values ​​of the reservoir structural parameters that are the same as when the reservoir structural parameters of each cored well section of each reservoir type are normalized to [0, 1];

[0218] Based on the maximum and minimum values ​​of the obtained reservoir structure parameters, the reservoir structure parameters of the target carbonate reservoir are normalized using the following formula;

[0219]

[0220] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0221] In one embodiment, obtaining pore throat median radius data of a target carbonate reservoir in a work area includes:

[0222] Establish the fitting relationship between logging response and pore throat median radius;

[0223] Based on the well logging data of the target carbonate reservoir in the work area, the median radius of the pore throat of the target carbonate reservoir in the work area is determined by using the fitting relationship between the well logging response and the median radius of the pore throat.

[0224] Furthermore, the fitting relationship between the logging response and the pore throat median radius is established by using a neural network algorithm; in a specific implementation, it includes:

[0225] Constructing a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include the median radius of pore throat;

[0226] Obtaining training samples for the neural network model based on a plurality of historical well logging data and corresponding pore throat median radius data;

[0227] The training samples are used to train the neural network model, and the trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

[0228] Example 1

[0229] This embodiment provides a carbonate reservoir classification method, which is used to identify the reservoir type of the MARK layer of the X77 well in the K oil field; wherein the method comprises:

[0230] Step 1: Obtain the well logging data, core analysis data, pore throat median radius data, and oil testing data of the core wells for exploration and development of the MARK layer carbonate oil and gas field in the K oil field;

[0231] Among them, the pore throat median radius data is obtained through mercury injection experiment;

[0232] Step 2: Based on the core analysis data, logging data and oil test data, the reservoir of the cored well section is classified into fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers);

[0233] Step 3:

[0234] 3.1. For each cored section of each reservoir type, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data; the reservoir permeability analysis parameters are determined by the following formula:

[0235] Para_K=a·e b·PHIE +c

[0236] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ;

[0237] 3.2. Based on the reservoir permeability analysis parameters and pore throat median radius data, combined with the reservoir porosity logging data, the reservoir structure parameters are determined; wherein the reservoir structure parameters are determined by the following formula:

[0238]

[0239] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um;

[0240] 3.3. Normalizing the reservoir structure parameters of each coring section of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0241] Among them, the normalization process to [0, 1] is performed in the following way:

[0242] Obtain the maximum and minimum values ​​of reservoir structural parameters;

[0243] Based on the maximum and minimum values ​​of the reservoir structure parameters, the reservoir structure parameters of each cored well section of each reservoir type are normalized using the following formula;

[0244]

[0245] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0246] Step 4: Obtain the cross-plot of normalized reservoir structure parameters and porosity of each cored well section of each reservoir type, and then obtain the reservoir type identification chart, such as Figure 5 shown.

[0247] Step 5: Obtain logging data of the target carbonate reservoir in the work area. The results are as follows: Figure 6 As shown;

[0248] Step 6: Obtain the pore throat median radius data of the target carbonate reservoir in the work area:

[0249] 6.1. Establish the fitting relationship between the logging response and the median radius of the pore throat; this is done using a neural network algorithm, specifically including:

[0250] 6.1.1. Construct a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include pore throat median radius;

[0251] like Figure 4 As shown, input parameters: porosity PHIE, v / v, resistivity ratio RS / RD, (RS-RXO) / RD, natural gamma GR, api; output parameters: pore throat median radius RC50, um;

[0252] 6.1.2. Obtain training samples for the neural network model based on multiple historical logging data and the corresponding pore throat median radius data;

[0253] 6.1.3. Using the training samples to train the neural network model, the trained neural network model is the fitting relationship between the logging response and the pore throat median radius;

[0254] 6.2. Based on the logging data of the target carbonate reservoir in the work area, the pore throat median radius of the target carbonate reservoir in the work area is determined by using the fitting relationship between the logging response and the pore throat median radius. The results are as follows: Figure 6 As shown;

[0255] Step 7:

[0256] 7.1. For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data; wherein the reservoir permeability analysis parameters are determined by the following formula:

[0257] Para_K=a·e b·PHIE +c

[0258] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ;

[0259] 7.2. Based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters are determined in combination with the reservoir porosity; wherein the reservoir structure parameters are determined by the following formula:

[0260]

[0261] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um;

[0262] 7.3. The reservoir structure parameters of the target carbonate reservoir are normalized to [0, 1] to obtain the normalized reservoir structure parameters of the target carbonate reservoir. The results are as follows: Figure 6 As shown;

[0263] The normalization process to [0, 1] is performed in the following manner:

[0264] Based on the maximum and minimum values ​​of the reservoir structure parameters obtained when the reservoir structure parameters of each cored well section of each reservoir type are normalized to [0, 1], the reservoir structure parameters of the target carbonate reservoir are normalized using the following formula;

[0265]

[0266] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter; Step 8: Use the normalized reservoir structure parameter and porosity of the target carbonate reservoir to intersect, refer to the established carbonate reservoir type identification chart of the work area to identify the target carbonate reservoir type, and complete the carbonate reservoir classification;

[0267] The reservoir type identification results of the MARK layer in the X77 well of the K oil field are as follows: Figure 6 shown.

[0268] The embodiment of the present invention further provides a system for establishing a carbonate reservoir type identification plate. Preferably, the system is used to implement the above method embodiment.

[0269] Figure 7 is a structural block diagram of a system for establishing a carbonate reservoir type identification chart according to an embodiment of the present invention. Figure 7 As shown, the system includes:

[0270] Data acquisition module 71: used to obtain well logging data, core analysis data, pore throat median radius data, oil testing data, etc. of coring wells for exploration and development of carbonate oil and gas fields;

[0271] Reservoir classification module 72: used to classify the reservoir of the coring section based on core analysis data, well logging data and oil testing data;

[0272] Reservoir structure parameter processing module 73: for calculating reservoir structure parameters for each cored well section of each reservoir type based on its logging data and pore throat median radius data, and performing normalization processing on the reservoir structure parameters to obtain normalized reservoir structure parameters;

[0273] The identification plate construction module 74 is used to obtain the cross-plot of normalized reservoir structure parameters and porosity of each cored well section reservoir of each reservoir type, and then obtain a reservoir type identification plate.

[0274] In one embodiment, the reservoir classification module 72 classifies the reservoir of the coring section into types including: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0275] In one embodiment, the reservoir structure parameter processing module 73 includes:

[0276] Permeability analysis parameter determination submodule: used to determine reservoir permeability analysis parameters for each cored well section of each reservoir type based on its logging data;

[0277] Reservoir structure parameter determination submodule: used to determine the reservoir structure parameters based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type;

[0278] Normalization processing submodule: used for normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type.

[0279] In one embodiment, the permeability analysis parameter determination submodule determines the reservoir permeability analysis parameter in the following manner:

[0280] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0281] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0282] Para_K=a·e b·PHIE +c

[0283] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0284] In one embodiment, the reservoir structure parameter determination submodule determines its reservoir structure parameters in the following manner:

[0285] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0286] Furthermore, the reservoir structural parameters are determined by the following formula:

[0287]

[0288] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0289] In one embodiment, the normalization processing submodule obtains the normalized reservoir structure parameters of each coring section reservoir of each reservoir type in the following manner:

[0290] Normalizing the reservoir structure parameters of each coring section of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0291] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0292] Obtain the maximum and minimum values ​​of reservoir structural parameters;

[0293] Based on the maximum and minimum values ​​of the reservoir structure parameters, the reservoir structure parameters of each cored well section of each reservoir type are normalized using the following formula;

[0294]

[0295] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0296] In one embodiment, the pore throat median radius data of the coring wells in the exploration and development of carbonate oil and gas fields are obtained by mercury injection testing.

[0297] The embodiment of the present invention further provides a carbonate reservoir classification system. Preferably, the system is used to implement the above method embodiment.

[0298] Figure 8 is a structural block diagram of a carbonate reservoir classification system according to an embodiment of the present invention. Figure 8 As shown, the system includes:

[0299] Data acquisition module 81: used to obtain well logging data, core analysis data, pore throat median radius data, oil testing data, etc. of coring wells in carbonate oil and gas field exploration and development;

[0300] Reservoir classification module 82: used to classify the reservoir of the coring section based on core analysis data, well logging data and oil testing data;

[0301] Reservoir structure parameter processing module 83: for calculating reservoir structure parameters for each cored well section of each reservoir type based on its logging data and pore throat median radius data, and performing normalization processing on the reservoir structure parameters to obtain normalized reservoir structure parameters;

[0302] Identification chart construction module 84: used to obtain the cross-plot of normalized reservoir structure parameters and porosity of each cored well section reservoir of each reservoir type, and then obtain a reservoir type identification chart;

[0303] Target reservoir logging data acquisition module 85: used to acquire logging data of target carbonate reservoir in the work area;

[0304] The target reservoir pore throat median data acquisition module 86 is used to acquire the pore throat median radius data of the target carbonate reservoir in the work area;

[0305] The target reservoir structure parameter calculation module 87 is used to determine the reservoir structure parameters of the target carbonate reservoir based on the well logging data and the pore throat median radius data of the target carbonate reservoir, and to normalize the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters;

[0306] The target reservoir type identification module 88 is used to identify the target carbonate reservoir type by intersecting the normalized reservoir structure parameters and porosity of the target carbonate reservoir, referring to the established carbonate reservoir type identification chart of the work area, and completing the carbonate reservoir classification.

[0307] In one embodiment, the reservoir classification module 82 classifies the reservoir of the coring section into types including: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers (i.e., isolated pore layers).

[0308] In one embodiment, the reservoir structure parameter processing module 83 includes:

[0309] Permeability analysis parameter determination submodule: used to determine reservoir permeability analysis parameters for each cored well section of each reservoir type based on its logging data;

[0310] Reservoir structure parameter determination submodule: used to determine the reservoir structure parameters based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type;

[0311] Normalization processing submodule: used for normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type.

[0312] In one embodiment, the permeability analysis parameter determination submodule determines the reservoir permeability analysis parameter in the following manner:

[0313] For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0314] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0315] Para_K=a·e b·PHIE +c

[0316] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0317] In one embodiment, the reservoir structure parameter determination submodule determines its reservoir structure parameters in the following manner:

[0318] Determine the reservoir structure parameters based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity logging data;

[0319] Furthermore, the reservoir structural parameters are determined by the following formula:

[0320]

[0321] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0322] In one embodiment, the normalization processing submodule obtains the normalized reservoir structure parameters of each coring section reservoir of each reservoir type in the following manner:

[0323] Normalizing the reservoir structure parameters of each coring section of each reservoir type to [0, 1] to obtain the normalized reservoir structure parameters of each coring section of each reservoir type;

[0324] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0325] Obtain the maximum and minimum values ​​of reservoir structural parameters;

[0326] Based on the maximum and minimum values ​​of the reservoir structure parameters, the reservoir structure parameters of each cored well section of each reservoir type are normalized using the following formula;

[0327]

[0328] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0329] In one embodiment, the pore throat median radius data of the coring wells in the exploration and development of carbonate oil and gas fields are obtained by mercury injection testing.

[0330] In one embodiment, the method used to determine the normalized reservoir structural parameters of the target carbonate reservoir is the same as the method used to determine the normalized reservoir structural parameters of each cored well section reservoir of each reservoir type in the process of establishing a carbonate reservoir type identification chart for the work area.

[0331] In one embodiment, the target reservoir structure parameter calculation module 87 includes:

[0332] Target reservoir permeability analysis parameter calculation submodule: used to determine reservoir permeability analysis parameters for target carbonate reservoirs based on their logging data;

[0333] Target reservoir structure parameter calculation submodule: used to determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir;

[0334] Target reservoir normalization calculation submodule: used to normalize the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir.

[0335] In one embodiment, the target reservoir permeability analysis parameter calculation submodule determines the reservoir permeability analysis parameters in the following manner:

[0336] For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data;

[0337] Furthermore, the reservoir permeability analysis parameters are determined by the following formula:

[0338] Para_K=a·e b·PHIE +c

[0339] Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is the correction constant; PHIE is the porosity (v / v); RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 .

[0340] In one embodiment, the target reservoir structure parameter calculation submodule determines its reservoir structure parameters in the following manner:

[0341] Based on reservoir permeability analysis parameters, pore throat median radius data, and reservoir porosity, reservoir structural parameters are determined;

[0342] Furthermore, the reservoir structural parameters are determined by the following formula:

[0343]

[0344] Where: IPS_Str is the reservoir structure parameter; Para_K is the permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um.

[0345] In one embodiment, the target reservoir normalization calculation submodule obtains the normalized reservoir structure parameters of the target carbonate reservoir in the following manner:

[0346] Normalizing the reservoir structure parameters of the target carbonate reservoir to a value between [0, 1] to obtain the normalized reservoir structure parameters of the target carbonate reservoir;

[0347] The normalization process to [0, 1] can be performed in a conventional manner, for example:

[0348] Obtaining the maximum and minimum values ​​of the reservoir structural parameters that are the same as when the reservoir structural parameters of each cored well section of each reservoir type are normalized to [0, 1];

[0349] Based on the maximum and minimum values ​​of the obtained reservoir structure parameters, the reservoir structure parameters of the target carbonate reservoir are normalized using the following formula;

[0350]

[0351] Wherein, MAX_IPS_Str is the maximum value of the reservoir structure parameter; MIN_IPS_Str is the minimum value of the reservoir structure parameter; IPS_Str is the reservoir structure parameter; IPS_Str_St is the normalized reservoir structure parameter.

[0352] In one embodiment, the target reservoir pore throat median data acquisition module 86 includes:

[0353] Relationship construction submodule: used to establish the fitting relationship between logging response and pore throat median radius;

[0354] Target reservoir pore throat median calculation submodule: used to determine the pore throat median radius of the target carbonate reservoir in the work area based on the well logging data of the target carbonate reservoir in the work area, using the fitting relationship between the well logging response and the pore throat median radius;

[0355] Furthermore, the relationship building submodule uses a neural network algorithm to establish a fitting relationship between the well logging response and the pore throat median radius; in a specific embodiment, the relationship building submodule includes:

[0356] Neural network model building unit: used to build a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer; the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma; the neurons set in the output layer include the median radius of pore throat;

[0357] A training sample determination unit is used to obtain training samples of a neural network model according to a plurality of historical well logging data and corresponding pore throat median radius data;

[0358] Relationship construction unit: used to apply training samples to train the neural network model. The trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

[0359] Fig. 9 It is a schematic diagram of a device for establishing a carbonate reservoir type identification plate according to an embodiment of the present invention. Fig. 9The device for establishing the carbonate reservoir type identification plate shown is a general data processing device, which includes a general computer hardware structure, which at least includes a processor 1000 and a memory 1111; the processor 1000 is used to execute the establishment program of the carbonate reservoir type identification plate stored in the memory to implement the method for establishing the carbonate reservoir type identification plate of each method embodiment (for the specific method, please refer to the description of the above method embodiment, which will not be repeated here).

[0360] An embodiment of the present invention provides a carbonate reservoir classification device, which includes a general computer hardware structure, which includes at least a processor and a memory; the processor is used to execute a carbonate reservoir classification program stored in the memory to implement the carbonate reservoir classification method of each method embodiment (for specific methods, please refer to the description of the above method embodiments, which will not be repeated here).

[0361] An embodiment of the present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement a method for establishing a carbonate reservoir type identification map in each method embodiment (for specific methods, please refer to the description of the above method embodiments, which will not be repeated here).

[0362] An embodiment of the present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the carbonate reservoir classification method of each method embodiment (for specific methods, please refer to the description of the above method embodiments, which will not be repeated here).

[0363] The preferred embodiments of the present invention are described above with reference to the accompanying drawings. Many features and advantages of these embodiments are clear from this detailed description, and therefore the claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. In addition, since many modifications and changes are easily conceivable to those skilled in the art, it is not intended to limit the embodiments of the present invention to the precise structure and operation illustrated and described, but all suitable modifications and equivalents falling within its scope may be covered.

[0364] 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.

[0365] 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.

[0366] 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 including 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.

[0367] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0368] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for establishing a carbonate reservoir type identification chart, wherein: The method includes: Obtain well logging data, core analysis data, pore throat median radius data, and oil testing data for carbonate oil and gas field exploration and development core wells; Based on the core analysis data, well logging data and oil testing data, the reservoir of the coring section is classified; For each cored well section of each reservoir type, the reservoir structure parameters are calculated based on the well logging data and pore throat median radius data, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters; the reservoir structure parameters are determined by the following formula: Where: IPS_Str is the reservoir structure parameter; Para_K is the reservoir permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um; Among them, the reservoir permeability analysis parameters are determined by the following formula: For_K=a·e b·PHIE +c Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is a correction constant; RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ; The cross-plot of normalized reservoir structural parameters and porosity of each cored well section of each reservoir type is obtained, and then a reservoir type identification chart is obtained.

2. The method according to claim 1, wherein: The types obtained by classifying the reservoir of the coring section include: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers.

3. The method according to claim 1 or 2, wherein: For each cored well section of each reservoir type, the reservoir structure parameters are calculated based on the well logging data and pore throat median radius data, and the reservoir structure parameters are normalized to obtain the normalized reservoir structure parameters including: For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on well logging data; Based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined; The reservoir structural parameters of each coring section of each reservoir type are normalized to obtain the normalized reservoir structural parameters of each coring section of each reservoir type.

4. The method according to claim 3, wherein: For each cored section of each reservoir type, based on its logging data, the reservoir permeability analysis parameters are determined in the following way: For each cored section of each reservoir type, reservoir permeability analysis parameters are determined based on the flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data.

5. The method according to claim 3, wherein: Based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type, the reservoir structure parameters are determined in the following way: The reservoir structural parameters are determined based on reservoir permeability analysis parameters, pore throat median radius data and reservoir porosity logging data.

6. The method according to claim 3, wherein: Normalizing the reservoir structural parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structural parameters of each coring section reservoir of each reservoir type means: normalizing the reservoir structural parameters of each coring section reservoir of each reservoir type to [0, 1] to obtain the normalized reservoir structural parameters of each coring section reservoir of each reservoir type.

7. A method for classifying carbonate reservoirs, wherein: The method includes: A carbonate reservoir type identification chart is established in the work area using the method for establishing a carbonate reservoir type identification chart as described in any one of claims 1 to 6; Obtain logging data of target carbonate reservoirs in the work area; Obtain the pore throat median radius data of the target carbonate reservoir in the work area; Based on the well logging data and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined, and the reservoir structure parameters of the target carbonate reservoir are normalized to obtain the normalized reservoir structure parameters; wherein, the reservoir structure parameters are determined by the following formula: Where: IPS_Str is the reservoir structure parameter; Para_K is the reservoir permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um; Among them, the reservoir permeability analysis parameters are determined by the following formula: For_K=a·e b·PHIE +c Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is a correction constant; RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ; The normalized reservoir structure parameters and porosity of the target carbonate reservoir are intersected, and the target carbonate reservoir type is identified by referring to the established carbonate reservoir type identification chart of the work area to complete the carbonate reservoir classification.

8. The carbonate reservoir classification method according to claim 7, wherein: Based on the well logging data and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined, and the reservoir structure parameters of the target carbonate reservoir are normalized to obtain the normalized reservoir structure parameters including: For the target carbonate reservoir, determine the reservoir permeability analysis parameters based on its logging data; Determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir; The reservoir structural parameters of the target carbonate reservoir are normalized to obtain the normalized reservoir structural parameters of the target carbonate reservoir.

9. The carbonate reservoir classification method according to claim 8, wherein: For the target carbonate reservoir, based on its logging data, the reservoir permeability analysis parameters are determined in the following way: For the target carbonate reservoir, the reservoir permeability analysis parameters are determined based on its flushing zone resistivity logging data, shallow lateral resistivity logging data, deep lateral resistivity logging data and porosity logging data.

10. The carbonate reservoir classification method according to claim 8, wherein: Based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir, the reservoir structure parameters of the target carbonate reservoir are determined in the following way: The reservoir structural parameters are determined based on reservoir permeability analysis parameters, pore throat median radius data and reservoir porosity.

11. The carbonate reservoir classification method according to claim 8, wherein: The normalizing the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir refers to normalizing the reservoir structure parameters of the target carbonate reservoir to [0, 1] to obtain the normalized reservoir structure parameters of the target carbonate reservoir.

12. The carbonate reservoir classification method according to claim 8, wherein: The pore throat median radius data of the target carbonate reservoir in the work area include: Establish the fitting relationship between logging response and pore throat median radius; Based on the logging data of the target carbonate reservoir in the work area, the pore throat median radius of the target carbonate reservoir in the work area is determined by using the fitting relationship between the logging response and the pore throat median radius.

13. The carbonate reservoir classification method according to claim 12, wherein: The establishment of the fitting relationship between the logging response and the pore throat median radius is performed using a neural network algorithm, including: Constructing a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include pore throat median radius; Obtaining training samples for the neural network model based on a plurality of historical well logging data and corresponding pore throat median radius data; The training samples are used to train the neural network model, and the trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

14. A system for establishing a carbonate reservoir type identification chart, wherein: The system includes: Data acquisition module: used to obtain well logging data, core analysis data, pore throat median radius data, and oil testing data of coring wells in carbonate oil and gas field exploration and development; Reservoir classification module: used to classify the reservoir of the coring section based on the core analysis data, well logging data and oil testing data; Reservoir structure parameter processing module: for each cored well section of each reservoir type, based on its logging data and pore throat median radius data, calculate the reservoir structure parameters, and normalize the reservoir structure parameters to obtain normalized reservoir structure parameters; wherein, the reservoir structure parameters are determined by the following formula: Where: IPS_Str is the reservoir structure parameter; Para_K is the reservoir permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um; Among them, the reservoir permeability analysis parameters are determined by the following formula: For_K=a·e b·PHIE +c Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is a correction constant; RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ; Identification plate construction module: used to obtain the intersection diagram of normalized reservoir structure parameters and porosity of each cored well section of each reservoir type, and then obtain the reservoir type identification plate.

15. The system for establishing a reservoir type identification plate according to claim 14, wherein: The reservoir classification module classifies the reservoir of the coring section into the following types: fracture type, pore type, hole type, pore-fracture composite type and non-reservoir type, wherein the non-reservoir type includes unconnected pore layers.

16. The system for establishing a reservoir type identification plate according to claim 14 or 15, wherein: The reservoir structure parameter processing module includes: Permeability analysis parameter determination submodule: used to determine reservoir permeability analysis parameters for each cored well section of each reservoir type based on its logging data; Reservoir structure parameter determination submodule: used to determine the reservoir structure parameters based on the reservoir permeability analysis parameters and pore throat median radius data of each cored well section of each reservoir type; Normalization processing submodule: used for normalizing the reservoir structure parameters of each coring section reservoir of each reservoir type to obtain the normalized reservoir structure parameters of each coring section reservoir of each reservoir type.

17. A carbonate reservoir classification system, wherein: The system includes: The system for establishing a carbonate reservoir type identification chart according to any one of claims 14 to 16, which is used to establish a carbonate reservoir type identification chart for a work area; Target reservoir logging data acquisition module: used to obtain logging data of target carbonate reservoirs in the work area; Target reservoir pore throat median data acquisition module: used to obtain pore throat median radius data of target carbonate reservoir in the work area; Target reservoir structure parameter calculation module: used to determine the reservoir structure parameters of the target carbonate reservoir based on the well logging data and pore throat median radius data of the target carbonate reservoir, and normalize the reservoir structure parameters of the target carbonate reservoir to obtain normalized reservoir structure parameters; wherein the reservoir structure parameters are determined by the following formula: Where: IPS_Str is the reservoir structure parameter; Para_K is the reservoir permeability analysis parameter; PHIE is the porosity (v / v); RC50 is the median radius of the pore throat, um; Among them, the reservoir permeability analysis parameters are determined by the following formula: For_K=a·e b·PHIE +c Where: Para_K is the permeability analysis parameter; a is a constant, ranging from 0.01 to 0.05; c is a correction constant; RXO is the resistivity of the flushing zone, ohmm; RS is the shallow lateral resistivity, ohmm; RD is the deep lateral resistivity, ohmm; if but like Then b = 0.25; if Then b = (1-PHIE) 2 ; Target reservoir type identification module: It is used to use the normalized reservoir structure parameters and porosity of the target carbonate reservoir to intersect, refer to the established carbonate reservoir type identification chart of the work area to identify the target carbonate reservoir type, and complete the carbonate reservoir classification.

18. The reservoir classification system according to claim 17, wherein: The target reservoir structure parameter calculation module includes: Target reservoir permeability analysis parameter calculation submodule: used to determine reservoir permeability analysis parameters for target carbonate reservoirs based on their logging data; Target reservoir structure parameter calculation submodule: used to determine the reservoir structure parameters of the target carbonate reservoir based on the reservoir permeability analysis parameters and pore throat median radius data of the target carbonate reservoir; Target reservoir normalization calculation submodule: used to normalize the reservoir structure parameters of the target carbonate reservoir to obtain the normalized reservoir structure parameters of the target carbonate reservoir.

19. The reservoir classification system of claim 17, wherein: The target reservoir pore throat median data acquisition module includes: Relationship construction submodule: used to establish the fitting relationship between logging response and pore throat median radius; Target reservoir pore throat median calculation submodule: used to determine the pore throat median radius of the target carbonate reservoir in the work area based on the logging data of the target carbonate reservoir in the work area and using the fitting relationship between the logging response and the pore throat median radius.

20. The reservoir classification system of claim 19, wherein: The relationship building submodule uses a neural network algorithm to establish a fitting relationship between the logging response and the pore throat median radius. The relationship building submodule includes: Neural network model building unit: used to build a neural network model; wherein the topological structure of the neural network model includes an input layer, a pattern layer, a summation layer and an output layer, the neurons set in the input layer include porosity, the ratio of shallow lateral resistivity to deep lateral resistivity, the ratio of the difference between shallow lateral resistivity and flushing zone resistivity to deep lateral resistivity and natural gamma, and the neurons set in the output layer include pore throat median radius; A training sample determination unit is used to obtain training samples of a neural network model according to a plurality of historical well logging data and corresponding pore throat median radius data; Relationship construction unit: used to apply the training samples to perform model training on the neural network model, and the trained neural network model is the fitting relationship between the logging response and the pore throat median radius.

21. A device for establishing a carbonate reservoir type identification chart, comprising a processor and a memory; wherein: Memory, used to store computer programs; The processor is used to implement the steps of the method for establishing a carbonate reservoir type identification plate as described in any one of claims 1 to 6 when executing the program stored in the memory.

22. A carbonate reservoir classification method and device, comprising a processor and a memory; wherein: Memory, used to store computer programs; The processor is used to implement the steps of the carbonate reservoir classification method described in any one of claims 7 to 13 when executing the program stored in the memory.

23. A computer-readable storage medium storing one or more programs, wherein the one or more programs can be executed by one or more processors to implement the steps of the method for establishing a carbonate reservoir type identification plate as described in any one of claims 1 to 6.

24. A computer-readable storage medium storing one or more programs, wherein the one or more programs can be executed by one or more processors to implement the steps of the carbonate reservoir classification method according to any one of claims 7 to 13.

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