Reservoir fluid identification method, device, equipment and medium

Abstract

The present application discloses a reservoir fluid identification method, device, equipment and medium, which relate to the field of oil and gas field exploration technology. This solution proposes a multi-level nested reservoir fluid identification method based on geological model constraints for dual-medium tight sandstone. For different reservoir types, a preset algorithm is used to fuse comprehensive logging parameters and the proposed fluid sensitivity factor applicable to dual-medium tight sandstone to construct fluid discrimination parameters applicable to different reservoir types. On this basis, the reservoir types are divided by comprehensively considering the effects of the coupling between the matrix and structural fractures, and the differences in fluid characteristics of different reservoir types are clarified. Based on the classification constraints of the geological model, different reservoir types are divided, and the integrated identification model is used to complete the identification of tight sandstone reservoir fluid types. The logging curve data used for fluid identification is easy to obtain, the fluid identification accuracy is high, and the identification efficiency is high, which can be widely used for reservoir fluid identification of dual-medium tight sandstone.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas field exploration, and in particular to a reservoir fluid identification method, device, equipment and medium. Background Art

[0002] Reservoir fluid identification refers to the process of analyzing and determining the type of fluid in an oil and gas reservoir through various technical means during oil and gas exploration and development. Reservoir fluids typically include crude oil, natural gas, and water, which may exist in different proportions and states within the reservoir.

[0003] Well logging interpretation of reservoir fluids aims to identify reservoir fluid types and response characteristics by analyzing conventional logging curves. However, for unconventional oil and gas reservoirs, such as dual-media tight sandstone gas reservoirs, complex pore structures and significant differences in logging responses make conventional methods such as curve overlap and crossplots less accurate. While array acoustic logging and nuclear magnetic resonance logging have improved accuracy, they are costly and difficult to scale.

[0004] In view of the above problems, how to solve the current conventional analysis methods for reservoir fluid identification, which have low identification accuracy and high cost, is an urgent problem to be solved by technicians in this field. Summary of the Invention

[0005] The purpose of this application is to provide a reservoir fluid identification method, device, equipment and medium to solve the problems of low recognition accuracy and high cost of current conventional analysis methods for reservoir fluid identification.

[0006] To solve the above technical problems, the present application provides a reservoir fluid identification method, comprising:

[0007] Obtaining well logging data of reservoirs within the study area and determining the inferred fluid type of the reservoirs;

[0008] Obtaining the original resistivity of the reservoir, and performing ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain a corrected resistivity of the reservoir;

[0009] constructing a fluid sensitivity factor based on the corrected resistivity, and generating a plurality of data sets representing different reservoir types based on the well logging data, the corrected resistivity, and the fluid sensitivity factor;

[0010] Generating fluid discrimination parameters applicable to different reservoir types according to each of the data sets and a preset algorithm;

[0011] Inputting the well logging data, the inferred fluid type and the fluid discrimination parameter into an integrated recognition model to output the fluid type of the reservoir in the study area;

[0012] The integrated identification model is a model for predicting reservoir fluid types constructed based on multiple integrated machine learning algorithms.

[0013] On the one hand, before obtaining the original resistivity of the reservoir and after obtaining the well logging curve data of the reservoir in the study area, the method further includes:

[0014] Acquiring core data and analytical materials of the reservoir, and performing depth offset correction on the logging curve data based on the core data and the analytical materials;

[0015] Eliminating erroneous data and / or spike data in the well logging curve data;

[0016] Processing abnormal values ​​and / or missing values ​​in the well logging curve data;

[0017] The well logging curve data of different order of magnitude sequences are converted into the same order of magnitude sequences that conform to the standard normal distribution through Z-score normalization, and the well logging curve data uniformly tested throughout the well or the well logging curve data tested in different formation sections are excluded to preprocess the well logging curve data.

[0018] On the other hand, performing ground stress resistivity correction and fracture resistivity correction on the original resistivity includes:

[0019] Obtaining formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, and overburden porosity of the reservoir;

[0020] Determine the work area empirical coefficient based on the formation factors at normal temperature and pressure, the porosity at normal temperature and pressure, the formation water resistivity, the original cementation index, the corrected cementation index, the formation factors under overburden pressure, the overburden porosity, and Archie's formula;

[0021] Performing geostress resistivity correction on the original resistivity according to the empirical coefficient of the work area to obtain an initial corrected resistivity;

[0022] Determining a fracture development layer section of the reservoir according to the imaging logging data in the logging curve data, and determining the fracture resistivity of the fracture development layer section;

[0023] The fracture parameters of the fracture-developed layer are evaluated using a dual lateral component method, fractures of different dip angles are decomposed into vertical and horizontal fracture components, and a lateral resistivity model is established in a wellbore environment based on the differential form of Ohm's law. The lateral resistivity model is solved using simultaneous deep and shallow dual lateral equations to obtain the fracture dip, fracture aperture, and fracture porosity.

[0024] Fracture resistivity correction is performed based on the initial corrected resistivity, the fracture resistivity, the fracture dip, the fracture aperture, and the fracture porosity to obtain the corrected resistivity of the reservoir.

[0025] On the other hand, constructing the fluid sensitivity factor according to the corrected resistivity includes:

[0026] Obtaining a maximum principal stress and a minimum principal stress at a formation level of the reservoir;

[0027] The fluid sensitivity factor is constructed according to the corrected resistivity, the maximum principal stress at the formation level, and the minimum principal stress at the formation level.

[0028] On the other hand, generating a plurality of data sets representing different reservoir types according to the well logging data, the corrected resistivity and the fluid sensitivity factor comprises:

[0029] Obtaining macroscopic lithologic data and microscopic rock physical structure parameter data of the reservoir;

[0030] Determining macroscopic lithologic differences and rock physical structure differences among different reservoirs based on the macroscopic lithologic data, and determining microscopic pore structure differences among different reservoirs based on the microscopic rock physical structure parameter data;

[0031] Classifying the porosity and permeability in the microscopic rock physical structure parameter data according to the macroscopic lithologic differences, the rock physical structure differences, and the microscopic pore structure differences to establish a reservoir type classification standard;

[0032] Calibrate the logging curve data, the corrected resistivity, and the fluid sensitivity factor according to the reservoir type classification standard, and perform a semi-supervised cluster analysis on the calibrated data using a DBSCAN algorithm to obtain a cluster analysis result;

[0033] The logging curve data, the corrected resistivity, and the fluid sensitivity factor are divided into a plurality of data sets according to the cluster analysis result.

[0034] On the other hand, generating the fluid discrimination parameters applicable to different reservoir types according to each of the data sets and the preset algorithm includes:

[0035] Inputting the well log data, the corrected resistivity, the fluid sensitivity factor and each of the data sets into a Fisher algorithm model to determine an overall average and a classification average;

[0036] Determine an overall scatter matrix, an intra-class scatter matrix, and an inter-class scatter matrix according to the overall average and the classification average;

[0037] Calculating eigenvectors according to eigenvalues, the intra-class scatter matrix, and the inter-class scatter matrix, and determining the eigenvector corresponding to the maximum eigenvalue;

[0038] Generate an eigenvector matrix according to the eigenvector corresponding to the maximum eigenvalue;

[0039] The fluid discrimination parameter is determined according to the eigenvector matrix and each of the data sets.

[0040] On the other hand, the construction process of the integrated recognition model includes:

[0041] Get training set sample data and initialize weights;

[0042] Input the training set sample data and the initialization weights into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, to obtain the recognition results output by each algorithm module;

[0043] Inputting each of the recognition results into a weak classifier, and determining the error between each of the recognition results and the corresponding true result and the classifier weight through the weak classifier;

[0044] Adjusting the initialization weight according to the classifier weight to obtain a new initialization weight, and returning to the step of inputting the training set sample data and the initialization weight into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, until the minimum error is determined;

[0045] A strong learner is constructed according to the weights of each classifier and the weighted combination strategy, so as to output a final recognition result through the strong learner.

[0046] To solve the above technical problems, the present application also provides a reservoir fluid identification device, comprising:

[0047] An acquisition module, for acquiring well logging data of reservoirs in a study area and determining the inferred fluid type of the reservoirs;

[0048] a correction module, configured to obtain the original resistivity of the reservoir, and perform ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain a corrected resistivity of the reservoir;

[0049] A first generating module is configured to construct a fluid sensitivity factor based on the corrected resistivity, and generate a plurality of data sets representing different reservoir types based on the well logging data, the corrected resistivity and the fluid sensitivity factor;

[0050] A second generating module is used to generate fluid discrimination parameters applicable to different reservoir types according to each of the data sets and a preset algorithm;

[0051] an identification module, configured to input the well logging curve data, the inferred fluid type, and the fluid discrimination parameter into an integrated identification model to output the fluid type of the reservoir in the study area;

[0052] The integrated identification model is a model for predicting reservoir fluid types constructed based on multiple integrated machine learning algorithms.

[0053] To solve the above technical problems, the present application also provides a reservoir fluid identification device, comprising:

[0054] memory for storing computer programs;

[0055] A processor is used to implement the steps of the above-mentioned reservoir fluid identification method when executing the computer program.

[0056] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned reservoir fluid identification method are implemented.

[0057] The reservoir fluid identification method provided in this application obtains well logging data of the reservoir in the study area and determines the inferred fluid type of the reservoir; obtains the original resistivity of the reservoir and corrects the original resistivity for stress resistivity and fracture resistivity to obtain the corrected resistivity of the reservoir; constructs a fluid sensitivity factor based on the corrected resistivity, and generates multiple data sets representing different reservoir types based on the well logging data, corrected resistivity, and fluid sensitivity factor; generates fluid discrimination parameters applicable to different reservoir types based on each data set and a preset algorithm; inputs the well logging data, inferred fluid type, and fluid discrimination parameters into an integrated identification model to output the fluid type of the reservoir in the study area; wherein the integrated identification model is a model constructed based on multiple integrated machine learning algorithms for predicting reservoir fluid types. Therefore, this scheme proposes a multi-level nested reservoir fluid identification method based on geological model constraints for dual-medium tight sandstone. For different reservoir types, a preset algorithm is used to fuse comprehensive logging parameters and the proposed fluid sensitivity factor applicable to dual-medium tight sandstone to construct fluid discrimination parameters applicable to different reservoir types. On this basis, the authors comprehensively consider the effects of matrix and structural fracture coupling to classify reservoir types, clarify the differences in fluid properties among reservoir types, and classify reservoir types based on the classification constraints of geological models. Using an integrated identification model, they identify fluid types in tight sandstone reservoirs. The well logging data used for fluid identification is easy to obtain, and fluid identification is highly accurate and efficient, making it widely applicable to fluid identification in dual-medium tight sandstone reservoirs.

[0058] In addition, the present application also provides a reservoir fluid identification device, equipment and medium, with the same effects as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0060] Figure 1 A flow chart of a reservoir fluid identification method provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of reservoir fluid identification provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the preprocessing of well logging curve data provided in an embodiment of the present application;

[0063] Figure 4 Schematic diagram of the reservoir original resistivity correction provided in the embodiment of the present application;

[0064] Figure 5 A schematic diagram of a process for constructing a reservoir type sub-model based on geological knowledge provided in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of a Fisher-based curve reconstruction technology process provided in an embodiment of the present application;

[0066] Figure 7 A schematic diagram of the integrated recognition model construction and optimization process provided in the embodiment of the present application;

[0067] Figure 8 A schematic diagram of the Boosting-based integrated algorithm technology flow provided in the embodiment of this application;

[0068] Figure 9 Scatter plots of porosity and permeability of different reservoir types provided in the embodiments of this application;

[0069] Figure 10 A comparison chart of the blind well cross-check accuracy of the algorithm model provided in the embodiment of this application;

[0070] Figure 11 A comparison chart of the importance of algorithm model features provided in the embodiments of this application;

[0071] Figure 12 Schematic diagram of the blind well identification effect of the algorithm model provided in the embodiment of the present application;

[0072] Figure 13 A schematic diagram of a reservoir fluid identification device provided in an embodiment of the present application;

[0073] Figure 14 A schematic diagram of a reservoir fluid identification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0075] The core of this application is to provide a reservoir fluid identification method, device, equipment and medium to solve the problems of low recognition accuracy and high cost of current conventional analysis methods for reservoir fluid identification.

[0076] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0077] Figure 1 This is a flow chart of a reservoir fluid identification method provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0078] S10: Obtain well logging data of the reservoir in the study area and determine the inferred fluid type of the reservoir;

[0079] Figure 2 This is a schematic diagram of the reservoir fluid identification principle provided in the embodiment of this application. Figure 2 As shown, the first step is to determine the possible fluid types in the study area based on gas testing data. Because single-well testing is short and cannot fully reflect the true state of the subsurface reservoir fluid, a comprehensive assessment is needed based on production data. Ultimately, the primary fluid type in the study area, or the inferred fluid type, is determined. Production data includes single-well gas and water test data, daily production data, and cumulative gas and water production data.

[0080] At the same time, it is necessary to obtain the logging curve data of the reservoir in the study area, including gamma ray data, deep lateral resistivity data, shallow lateral resistivity data, density data, sonic time difference data and neutron porosity data.

[0081] S11: Obtain the original resistivity of the reservoir, and perform ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain the corrected resistivity of the reservoir.

[0082] Furthermore, when stress changes, assuming that the resistivity change of the dual-medium reservoir mainly depends on the contraction of the small pore throats, and the small pores are mainly filled with bound water; there are a large number of fine particles in the small pores or throats, then according to the Archie formula, the resistivity change under stress change can be obtained. It can be obtained that the change in the physical properties of the tight sandstone is determined by the pore structure coefficient. At the same time, the fluid in the pores is squeezed out due to the stress, causing the resistivity to increase. This is extremely obvious in the reservoir tested as a dry layer during the gas test. At the same time, it will cause the resistivity curve value of some water layers to increase abnormally, giving the illusion of a high-resistance water layer. Therefore, it is necessary to perform geostress resistivity correction on the original resistivity of the reservoir.

[0083] Furthermore, in dual-medium tight sandstones, reservoir fractures are well-developed. When low-resistivity fluids such as formation water with a certain degree of mineralization or drilling mud or mud filtrate intrude and fill fractures and pores, the resistivity can be significantly reduced. The more developed the fractures, the greater the impact, causing the resistivity in gas-bearing intervals to decrease, thus interfering with logging interpretation. Therefore, it is necessary to correct the resistivity of fractured intervals in dual-medium reservoirs in addition to correcting for in-situ stress.

[0084] It should be noted that, in this embodiment, there is no limitation on the specific process of performing ground stress resistivity correction and crack resistivity correction on the original resistivity, which depends on the specific implementation situation.

[0085] S12: Construct a fluid sensitivity factor based on the corrected resistivity, and generate multiple data sets representing different reservoir types based on the well logging data, the corrected resistivity, and the fluid sensitivity factor.

[0086] Furthermore, in this embodiment, a fluid identification sensitive factor based on ground stress and resistivity is proposed, which can accurately identify the reservoir fluid type of dual-medium tight sandstone. The construction principle is mainly that for dual-medium tight sandstone, the matrix porosity, permeability, fracture pores and openings of its reservoir gradually decrease with the increase of effective stress and horizontal stress difference, while the pore fluid and gas will cause the elastic limit and strength to decrease, and the enhanced toughness will lead to a further decrease in stress difference. In addition, when the difference in reservoir rock skeleton is not large, the difference in fluid contained in the rock skeleton will lead to a significant difference in resistivity, among which the gas conductivity is much lower than the water conductivity, so the resistivity measured by logging in the pure gas layer is much higher than that in the pure water layer. In this embodiment, there is no restriction on the specific construction method of the fluid sensitive factor.

[0087] At the same time, multiple data sets representing different reservoir types are generated based on logging curve data, corrected resistivity, and fluid sensitivity factors, thereby realizing the construction of a reservoir type sub-model based on geological knowledge. It should be noted that the overall reservoir type classification based on geological knowledge serves as a soft constraint, and the data sets belong to the classification type established based on comprehensive macroscopic and microscopic differences. Each reservoir type includes multiple data sets. The establishment of this sub-model only facilitates the construction of a high-precision, highly stable sub-model with a relatively simple data structure and smaller variance. The specific process of data set construction is not limited in this embodiment.

[0088] S13: Generate fluid discrimination parameters suitable for different reservoir types based on each data set and a preset algorithm.

[0089] Furthermore, based on the multiple divided data sets and different reservoir types, a preset algorithm is used to fuse comprehensive logging parameters and fluid identification sensitivity factors, and low-correlation variables with little impact on the results are removed. Ultimately, the fusion results in a fluid discrimination parameter suitable for each reservoir type. This fluid discrimination parameter serves as input to the identification model. In this embodiment, there are no restrictions on the process of generating the fluid discrimination parameter.

[0090] S14: Input the well logging data, the inferred fluid type, and the fluid discrimination parameters into the integrated identification model to output the fluid type of the reservoir in the study area.

[0091] Among them, the integrated identification model is a model constructed based on multiple integrated machine learning algorithms to predict reservoir fluid types.

[0092] Finally, an integrated approach was employed to further refine reservoir fluid identification using multiple datasets constructed based on geologically informed reservoir types. Using only sampling points predicted to be reservoirs within these datasets, an integrated machine learning algorithm was used to perform deep learning on the fluids within each subdivided reservoir, identifying the fluid distribution characteristics within each reservoir type. This enabled fluid type identification.

[0093] It should be noted that the integrated identification model is a model for predicting reservoir fluid types constructed based on multiple integrated machine learning algorithms; in this embodiment, there is no restriction on the construction process of the integrated identification model, which depends on the specific implementation situation.

[0094] In this embodiment, a multi-level nested reservoir fluid identification method based on geological model constraints for dual-medium tight sandstone is proposed. For different reservoir types, a preset algorithm is used to integrate comprehensive logging parameters and the proposed fluid sensitivity factor applicable to dual-medium tight sandstone to construct fluid discrimination parameters applicable to different reservoir types. On this basis, the reservoir types are divided by comprehensively considering the effects of the coupling between matrix and structural fractures, and the differences in fluid properties of different reservoir types are clarified. Based on the classification constraints of the geological model, different reservoir types are classified, and the integrated identification model is used to complete the identification of tight sandstone reservoir fluid types. The logging curve data used for fluid identification is easy to obtain, and the fluid identification accuracy and efficiency are high. It can be widely used for reservoir fluid identification of dual-medium tight sandstone.

[0095] Figure 3 Schematic diagram of the preprocessing of well logging curve data provided in the embodiment of the present application. Based on the above embodiment, in some embodiments, before obtaining the original resistivity of the reservoir, after obtaining the well logging curve data of the reservoir in the study area, such as Figure 3 As shown, it also includes:

[0096] S15: Obtaining core data and analytical test materials of the reservoir, and performing depth offset correction on the well logging curve data based on the core data and analytical test materials;

[0097] S16: Eliminating erroneous data and / or spike data in the well logging curve data;

[0098] S17: Processing abnormal values ​​and / or missing values ​​in the well logging curve data;

[0099] S18: The logging curve data of different orders of magnitude sequences are converted into the same order of magnitude sequences that conform to the standard normal distribution through Z-score normalization, and the logging curve data uniformly tested in the entire well or the logging curve data tested in different formation sections are excluded to preprocess the logging curve data.

[0100] Due to geological factors and the influence of logging instruments, well logging data can exhibit depth offsets. Furthermore, the range of the same logging data varies across different wells, impacting subsequent identification accuracy. To eliminate the influence of the logging instruments themselves and ensure data reliability, well logging data preprocessing is necessary.

[0101] Specifically, all well logging data were depth-shifted using core data and analytical laboratory data. Visual inspection of the well logging data was performed to remove obvious data errors and / or spikes. Outliers and missing values ​​in the well logging data were further processed, and the curves were baseline-corrected and smoothed. Z-score normalization was used to convert well logging data with different magnitude sequences to sequences of the same magnitude that conform to a standard normal distribution. Furthermore, by screening out all well logging data that had undergone magnitude sequence processing and excluding those that were uniformly tested throughout the well or tested in different stratigraphic intervals, errors were effectively avoided and the true logging response of the main fluid types in the subsurface reservoirs of the study area was determined.

[0102] Figure 4 The principle diagram of reservoir original resistivity correction provided in the embodiment of the present application. Based on the above embodiment, in some embodiments, such as Figure 4 As shown, the original resistivity is corrected for ground stress resistivity and crack resistivity, including:

[0103] S111: Obtain the reservoir's formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, and overburden porosity;

[0104] S112: Determine the empirical coefficient of the work area based on formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, overburden porosity, and Archie's formula;

[0105] S113: performing ground stress resistivity correction on the original resistivity according to the empirical coefficient of the work area to obtain the initial corrected resistivity;

[0106] S114: determining a fracture development layer section of the reservoir according to the imaging logging data in the logging curve data, and determining the fracture resistivity of the fracture development layer section;

[0107] S115: Use the dual lateral component method to evaluate fracture parameters in fracture-developed intervals. Fractures with varying inclination angles are decomposed into vertical and horizontal components. A lateral resistivity model is established in the wellbore environment based on the differential form of Ohm's law. The lateral resistivity model is solved using simultaneous deep and shallow dual lateral equations to determine fracture inclination, fracture aperture, and fracture porosity.

[0108] S116: performing fracture resistivity correction based on the initial corrected resistivity, fracture resistivity, fracture dip, fracture aperture, and fracture porosity to obtain a corrected resistivity of the reservoir.

[0109] When stress changes, it is assumed that the resistivity change of the dual-medium reservoir mainly depends on the contraction of the small pore throats, and the small pores are mainly filled with bound water; there are a large number of fine particles in the small pores or throats. The resistivity change formula under stress change can be obtained by deforming the Archie formula. Therefore, the change in the physical properties of dense sandstone is determined by the pore structure coefficient. At the same time, the fluid in the pores is squeezed out due to stress, causing the resistivity to increase. This is extremely obvious in the reservoir tested as a dry layer during the gas test. At the same time, it will cause the resistivity curve value of some water layers to increase abnormally, appearing as a high-resistance water layer. Therefore, the empirical coefficient of the work area is introduced. .

[0110] Specifically, the reservoir's formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, and overburden porosity are obtained; based on the formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, overburden porosity, and Archie's formula, the empirical coefficient of the work area is determined. Archie's formula is as follows:

[0111] ;

[0112] The resistivity change formula is as follows:

[0113] ;

[0114] Furthermore, the geostress-corrected resistivity formula of the study area can be established based on the empirical coefficient of the work area. The geostress-corrected resistivity formula is performed on the original resistivity according to the empirical coefficient of the work area. The corrected resistivity formula is as follows:

[0115] ;

[0116] Among them, F is the formation factor at normal temperature and pressure; It is the overburden stratum factor; is the formation water resistivity, (Ω.m); is the porosity at normal temperature and pressure, %; is the overburden porosity, %; is the original cementation index, dimensionless; is the corrected cementation index under strong stress, dimensionless; is the original resistivity, (Ω.m); is the initial corrected resistivity after correction for geostress resistivity, (Ω.m); It is the empirical coefficient of the work area and is dimensionless.

[0117] It should be noted that the corrected cementation index under strong stress and the underlying stratum factors It can be obtained through rock electrical experiments under high temperature and high pressure conditions, and the overburden porosity It can be obtained through overburden porosity experiment.

[0118] Furthermore, in dual-medium tight sandstones, reservoir fractures are well-developed. When low-resistivity fluids such as formation water with a certain degree of mineralization or drilling mud or mud filtrate intrude and fill fractures and pores, the resistivity can be significantly reduced. The more developed the fractures, the greater the impact, causing the resistivity in gas-bearing intervals to decrease, thus interfering with logging interpretation. Therefore, it is necessary to correct the resistivity of fractured intervals in dual-medium reservoirs in addition to correcting for in-situ stress.

[0119] Specifically, the fracture development layer of the reservoir is determined based on the imaging logging data in the logging curve data, and the fracture resistivity of the fracture development layer is determined. The double lateral component method is further used to evaluate the fracture parameters of the fracture development layer, and the fractures with different inclination angles are decomposed into two fracture components, vertical and horizontal. The lateral resistivity model under the wellbore environment is established based on the differential form of Ohm's law. The lateral resistivity model is solved by the deep and shallow double lateral equations to obtain the fracture inclination angle. , crack opening and fracture porosity In the specific implementation, the imaging logging results can be used to verify and compare the fracture parameters and calculate the average error. If the error is greater than 30%, the wellbore environmental parameters are adjusted and the calculation model is rebuilt. The initial corrected resistivity after ground stress correction is converted into and crack resistivity The difference in logarithms of and the crack parameters obtained using the double lateral component method establish a common relationship, as follows:

[0120] ;

[0121] Finally, the above common relationship formula is transformed and the fracture resistivity is corrected according to the initial corrected resistivity, fracture resistivity, fracture dip, fracture aperture and fracture porosity to obtain the corrected resistivity of the reservoir. The specific formula is as follows:

[0122] ;

[0123] in, is the initial corrected resistivity after ground stress correction, (Ω.m); is the final corrected resistivity, (Ω.m); is the crack resistivity, (Ω.m); is the crack inclination, °; is the crack opening, μm; is the fracture porosity, %; a, b, c, e are dimensionless multivariate fitting coefficients.

[0124] In summary, the original resistivity can be corrected for ground stress resistivity and crack resistivity.

[0125] Based on the above embodiments, in some embodiments, constructing a fluid sensitivity factor according to the corrected resistivity includes:

[0126] S121: Obtaining the maximum principal stress and the minimum principal stress at the formation level of the reservoir;

[0127] S122: Construct a fluid sensitivity factor based on the corrected resistivity, the maximum principal stress at the formation level, and the minimum principal stress at the formation level.

[0128] For dual-medium tight sandstone, the matrix porosity, permeability, and fracture pores and openings of its reservoir gradually decrease with the increase of effective stress and horizontal stress difference, while the pore fluid and gas will cause the elastic limit and strength to decrease, and the enhanced toughness will lead to a further decrease in stress difference. In addition, when the difference in the reservoir rock skeleton is not large, the difference in the fluid contained in the rock skeleton will lead to a significant difference in resistivity. The conductivity of gas is much lower than that of water. Therefore, the resistivity measured by logging in pure gas layers is much higher than that in pure water layers. Combining the above principles, the ratio method is used to amplify the gas-containing response. A fluid sensitivity factor suitable for dual-medium tight sandstone is constructed, and the formula is as follows:

[0129] ;

[0130] in, To correct for resistivity, is the stress difference of the formation, MPa; SDXM is the maximum horizontal principal stress of the formation, MPa; SDYM is the minimum horizontal principal stress of the formation, MPa.

[0131] Figure 5 The flowchart of the reservoir type sub-model construction based on geological knowledge provided in the embodiment of the present application is as follows. Based on the above embodiment, in some embodiments, such as Figure 5As shown in Figure 1, multiple data sets representing different reservoir types are generated based on well log data, corrected resistivity, and fluid sensitivity factors, including:

[0132] S123: Obtaining macroscopic lithologic data and microscopic rock physical structure parameter data of the reservoir;

[0133] S124: determining macroscopic lithologic differences and rock physical structure differences among different reservoirs based on macroscopic lithologic data, and determining microscopic pore structure differences among different reservoirs based on microscopic rock physical structure parameter data;

[0134] S125: Based on macroscopic lithologic differences, rock physical structure differences, and microscopic pore structure differences, the porosity and permeability in the microscopic rock physical structure parameter data are divided to establish a reservoir type classification standard;

[0135] S126: Calibrate the well logging curve data, corrected resistivity, and fluid sensitivity factor according to the reservoir type classification standard, and perform semi-supervised cluster analysis on the calibrated data using the DBSCAN algorithm to obtain cluster analysis results;

[0136] S127: Divide the logging curve data, the corrected resistivity, and the fluid sensitivity factor into multiple data sets according to the cluster analysis results.

[0137] Specifically, macroscopic lithologic data of the reservoir is first obtained through geological observations such as cores, thin sections, and electron microscope images. Microscopic rock physical structure parameter data are then obtained using porosity and permeability. Macroscopic lithologic and rock physical structure differences between different reservoirs are determined based on the macroscopic lithologic data, and microscopic pore structure differences between different reservoirs are determined based on the microscopic rock physical structure parameter data.

[0138] Furthermore, based on the differences in macroscopic lithology, rock physical structure and microscopic pore structure, the porosity and permeability in the microscopic rock physical structure parameter data were divided, and pore structures with similar porosity and permeability relationships were grouped together. By integrating the macroscopic and microscopic differences, a classification standard for dual-medium tight sandstone reservoir types was established.

[0139] A semi-supervised cluster analysis was then performed. The well logging data, corrected resistivity, and fluid sensitivity factors were calibrated according to reservoir type classification criteria. After data cleaning, these data were used as input. A semi-supervised cluster analysis was then performed on the calibrated data using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to obtain cluster analysis results. Based on the cluster analysis results, the well logging data, corrected resistivity, and fluid sensitivity factors were further divided into multiple datasets, each representing a potential reservoir type within a broad category. It is important to note that the overall reservoir classification based on geological knowledge serves as a soft constraint, with datasets belonging to classification types established based on comprehensive macroscopic and microscopic differences, and each reservoir type includes multiple datasets. The establishment of this sub-model facilitates the construction of a high-precision, highly stable sub-model with relatively simple data structures and smaller variance.

[0140] Figure 6 A flow chart of a curve reconstruction technique based on Fisher provided in an embodiment of the present application. Based on the above embodiments, in some embodiments, such as Figure 6 As shown in the figure, fluid discrimination parameters suitable for different reservoir types are generated based on each data set and preset algorithm, including:

[0141] S131: inputting the well log data, the corrected resistivity, the fluid sensitivity factor and each data set into the Fisher algorithm model to determine the overall average and the classification average;

[0142] S132: Determine the overall scatter matrix, the intra-class scatter matrix, and the inter-class scatter matrix based on the overall mean and the classification mean;

[0143] S133: Calculate eigenvectors according to the eigenvalues, the intra-class scatter matrix, and the inter-class scatter matrix, and determine the eigenvector corresponding to the maximum eigenvalue;

[0144] S134: Generate an eigenvector matrix according to the eigenvector corresponding to the maximum eigenvalue;

[0145] S135: Determine fluid discrimination parameters based on the eigenvector matrix and each data set.

[0146] In this embodiment, based on the divided data set, for different reservoir types, the Fisher linear discriminant analysis (Fisher) algorithm is used to fuse comprehensive logging parameters and fluid identification sensitivity factors, and low-correlation variables with less impact on the results are removed. Finally, the fluid discrimination parameters suitable for different reservoir types are calculated by fusion.

[0147] Specifically, the logging curve data, corrected resistivity, fluid sensitivity factor and each data set (X1, X2, X2, ..., X k ) is input into the Fisher algorithm model to determine the overall mean and the category mean Based on the overall average and the category mean Determine the overall scatter matrix , intra-class dispersion matrix and the inter-class dispersion matrix .

[0148] The eigenvector is calculated based on the eigenvalue, intra-class scatter matrix and inter-class scatter matrix. The generalized eigenmatrix formula is as follows:

[0149] ;

[0150] in, is the eigenvalue, is the feature vector.

[0151] Further determine the eigenvector corresponding to the maximum eigenvalue, and generate the eigenvector matrix based on the eigenvector corresponding to the maximum eigenvalue .

[0152] Finally, according to the eigenvector matrix and each data set (X1, X2, X2, ..., X k ) Determine the fluid discrimination parameter, the formula is as follows:

[0153] ;

[0154] in, is the fluid discrimination parameter, i∈[1,2,3,…,k].

[0155] Figure 7 The schematic diagram of the integrated recognition model construction and optimization process provided in the embodiment of the present application. Based on the above embodiment, in some embodiments, such as Figure 7 As shown in Figure 2, the construction process of the integrated recognition model includes:

[0156] S141: Obtain training set sample data and initialize weights;

[0157] S142: Inputting the training set sample data and the initialization weights into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, to obtain the recognition results output by each algorithm module;

[0158] S143: Input each recognition result into a weak classifier, and determine the error between each recognition result and the corresponding true result and the classifier weight through the weak classifier;

[0159] S144: adjusting the initialization weight according to the classifier weight to obtain a new initialization weight, and returning to the step of inputting the training set sample data and the initialization weight into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, until the minimum error is determined;

[0160] S145: Constructing a strong learner according to the weights of each classifier and the weighted combination strategy, so as to output a final recognition result through the strong learner.

[0161] In this embodiment, multiple sub-models, i.e., multiple data sets, are constructed based on geological knowledge of reservoir types, and an integrated approach is further employed to achieve more refined identification of reservoir fluids. Based on the multiple sub-models constructed based on geological knowledge of reservoir types, only the sampling points predicted to be reservoirs are used as input. An integrated machine learning algorithm is then used to perform deep learning on the fluids in each subdivided reservoir, identifying the fluid distribution characteristics under different reservoir types. The following is a detailed description of the model construction process:

[0162] Figure 8 The following is a flowchart of the Boosting-based integrated algorithm technology provided in the embodiment of this application. Figure 8 As shown in the figure, first, the training set sample data and initialization weight ω are obtained, and the training set sample data and initialization weight ω are respectively input into the Fully Connected Neural Network (FCN) algorithm module, the Extreme Gradient Boosting Tree (eXtreme Gradient Boosting, XGBoost) algorithm module, the Category Feature Gradient Boosting (CatBoost) algorithm module, the Decision Tree (Decision Tree, DT) algorithm module and the Random Forest (Random Forest, RF) algorithm module to obtain the recognition results output by each algorithm module. Each recognition result is input into the weak classifier Ci, and the error r between each recognition result and the corresponding true result is determined by the weak classifier. i and classifier weight α i . According to the classifier weight α i Adjust the initialization weight ω to get the new initialization weight ω i, returning to the step of inputting the training set sample data and initialization weights into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, until the minimum error is determined. Finally, a strong learner is constructed based on the weights of each classifier and the weighted combination strategy, which outputs the final recognition result through the strong learner. It is important to note that different weights are constructed for different datasets (i.e., different reservoir types), resulting in different ensemble recognition models.

[0163] In addition, due to the presence of multiple algorithm modules in the integrated identification model, the model parameters are large, and manual parameter adjustment is inefficient and ineffective. In the specific implementation, the Bayesian hyperparameter intelligent optimization algorithm can be used to optimize the model parameters. Specifically, the established integrated identification model is fine-tuned through cross-validation and Bayesian hyperparameter optimization to improve the accuracy and robustness of fluid identification. At the same time, in order to evaluate the actual application effect of the model, the algorithm model is tested on blind wells in the application study area that did not participate in the training to ensure that the model meets the application requirements. Finally, all identification results of all levels are integrated to form a comprehensive understanding of the reservoir fluid, ensuring that the fluid type of each reservoir unit can be accurately identified.

[0164] In summary, the construction of the integrated recognition model is achieved.

[0165] The present invention is described in detail below with reference to specific embodiments:

[0166] Reservoir fluid identification was performed using data from a tight sandstone gas reservoir in a certain area. The specific steps include:

[0167] (I) Analysis of fluid types in the study area. Based on the gas test data in the study area, the possible fluid types in the study area were determined. Since the single-well test time is relatively short and cannot fully reflect the actual situation of the underground reservoir fluid, a comprehensive assessment was conducted based on production data, including single-well test gas and water data, single-well daily production, and cumulative gas and water production data. Finally, the main fluid types of the underground reservoir in the study area were determined, including gas layers, poor gas layers, gas-water layers, water layers, and dry layers.

[0168] (2) Well logging curve preprocessing: The well logging curve data were preprocessed by depth offset correction, visual inspection, outlier and missing value processing, baseline correction and smoothing, and Z-score normalization. Samples that were uniformly tested throughout the well or tested in different formation sections were screened out.

[0169] (3) Resistivity parameter correction. The resistivity parameters of the study area are corrected for ground stress and crack effects. The ground stress correction formula and the crack effect correction formula constructed in this embodiment are as follows:

[0170] ;

[0171] ;

[0172] in, is the porosity at normal temperature and pressure, %; is the overburden porosity, %; is the original cementation index, dimensionless; is the corrected cementation index under strong stress, dimensionless; is the original formation resistivity, (Ω.m); is the formation resistivity after correction for ground stress, (Ω.m); is the final corrected resistivity, (Ω.m); is the formation resistivity at the fracture, (Ω.m); is the crack inclination, °; is the crack opening, μm; is the fracture porosity, %; a, b, c, e are dimensionless multivariate fitting coefficients.

[0173] (IV) Construction of a new fluid sensitivity factor. First, based on well logging, gas testing, gas logging and geological data, the different response characteristics of fluids in different reservoir types are analyzed in detail. By using geological knowledge and the rock physical response of the fluid, the interference signal is suppressed, the contribution of the gas-bearing fluid is highlighted, and a fluid identification sensitivity factor is preliminarily constructed. This application uses the ratio method to amplify the gas-bearing response, and for the dual-medium tight sandstone, a fluid identification sensitivity factor A1 based on ground stress and resistivity is proposed for the first time. In addition, this embodiment also introduces two new logging curve characteristics, K1 and K2, based on the data of the study area. The formula is as follows:

[0174] ;

[0175] ;

[0176] Among them, R D is the deep lateral formation resistivity, (Ω.m); R S is the shallow lateral formation resistivity, (Ω.m).

[0177] (V) Construction of reservoir type sub-models based on geological knowledge. Using geological observation methods such as cores, thin sections, electron microscope images, and imaging logging data, the macroscopic lithologic and rock physical structural differences between different reservoirs are determined. On this basis, core analysis test data such as porosity and permeability experiments are used to clarify the microscopic pore structure differences between different reservoirs. Classification criteria for dual-medium tight sandstone reservoir types are established based on the relationship between different pore and throat combinations. Finally, a semi-supervised cluster analysis is performed using the DBSCAN algorithm. Based on the semi-supervised results, the fluid identification dataset is divided into multiple sub-datasets, each representing a potential reservoir type within the larger category. Figure 9 The porosity and permeability scatter plots of different reservoir types provided in the examples of this application are as follows. Figure 9 As shown in the figure, based on the above method, this study divides the reservoir types in the study area into four types: fracture reservoirs, matrix pore reservoirs, microfracture-pore composite reservoirs and non-reservoirs, among which non-reservoirs are not identified.

[0178] (VI) Fluid discrimination parameters constructed based on the Fisher algorithm. The data set is divided based on the divided reservoir types. For different reservoir types, the Fisher algorithm is used to fuse comprehensive logging parameters and fluid identification sensitivity factors, and low-correlation variables with little impact on the results are removed. Finally, the fluid discrimination parameters suitable for different reservoir types are calculated. The fluid discrimination parameters finally constructed for fracture-type reservoirs, matrix pore-type reservoirs, and microfracture-pore composite reservoirs are F1, F2, and F3, and the formulas are as follows:

[0179] ;

[0180] ;

[0181] ;

[0182] (VII) Integrated identification of reservoir fluids. Based on multiple sub-models constructed based on geological knowledge of reservoir types, an integrated method is further used to identify reservoir fluids in a more refined manner. Based on multiple sub-models constructed based on geological knowledge of reservoir types, only the sampling points predicted to be reservoirs are used as input, and an integrated machine learning algorithm is used to conduct deep learning on the fluids of each subdivided reservoir to identify the fluid distribution characteristics under different reservoir types. The data input in this embodiment includes R D 、R S, AC, CNL, GR, SDXM, SDYM, K1, K2, A1. This example model is primarily constructed using five algorithm modules: FCN, XGBoost, CatBoost, DT, and RF. Three models were constructed using different integrated model weights based on geological constraints for fractured reservoirs, porous reservoirs, and microfracture-porous composite reservoirs.

[0183] Figure 10 This is a comparison chart of the accuracy of the blind well cross-check of the algorithm model provided in the embodiment of this application. Figure 10 As shown in the figure, through cross-checking of blind wells in the study area, the average recognition accuracy of the final model reached 85.2%, among which the highest recognition accuracy could reach more than 90%, effectively improving the recognition accuracy. Figure 11 This is a comparison chart of the importance of the algorithm model features provided in the embodiment of this application. Figure 11 As shown in Figure 3, the XGBoost algorithm module is used to output feature importance, which proves the effectiveness of the proposed recognition factor.

[0184] (8) Model output and quality control. Figure 12 This is a schematic diagram of the blind well identification effect of the algorithm model provided in the embodiment of this application. Figure 12 As shown in FIG, the output of the qualified fluid identification integrated algorithm model is applied to complete the reservoir fluid logging interpretation of a single well.

[0185] In the above embodiments, reservoir fluid identification is described in detail. The present application also provides corresponding embodiments of a reservoir fluid identification device.

[0186] Figure 13 This is a schematic diagram of a reservoir fluid identification device provided in an embodiment of the present application. Figure 13 As shown, the device includes:

[0187] An acquisition module 10 is used to obtain well logging data of the reservoir in the study area and determine the inferred fluid type of the reservoir;

[0188] The correction module 11 is used to obtain the original resistivity of the reservoir and perform ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain the corrected resistivity of the reservoir;

[0189] A first generating module 12 is configured to construct a fluid sensitivity factor based on the corrected resistivity, and generate multiple data sets representing different reservoir types based on the well logging data, the corrected resistivity, and the fluid sensitivity factor;

[0190] The second generating module 13 is used to generate fluid discrimination parameters applicable to different reservoir types based on each data set and a preset algorithm;

[0191] Identification module 14, for inputting well logging data, inferred fluid type and fluid discrimination parameters into the integrated identification model to output the fluid type of the reservoir in the study area;

[0192] Among them, the integrated identification model is a model constructed based on multiple integrated machine learning algorithms to predict reservoir fluid types.

[0193] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and they will not be repeated here.

[0194] Figure 14 This is a schematic diagram of a reservoir fluid identification device provided in an embodiment of the present application. Figure 14 As shown, reservoir fluid identification includes:

[0195] Memory 20, for storing computer programs;

[0196] The processor 21 is configured to implement the steps of the reservoir fluid identification method mentioned in the above embodiment when executing the computer program.

[0197] The reservoir fluid identification device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer, or a desktop computer.

[0198] The processor 21 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 21 may be implemented in at least one hardware form: a digital signal processor (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content required to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is responsible for processing computing operations related to machine learning.

[0199] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the reservoir fluid identification method disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include but is not limited to data related to the reservoir fluid identification method.

[0200] In some embodiments, the reservoir fluid identification device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0201] Those skilled in the art will understand that Figure 14 The structure shown in the figure does not constitute a limitation of the reservoir fluid identification device, and may include more or fewer components than shown in the figure.

[0202] Finally, the present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiment.

[0203] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0204] The above is a detailed introduction to a reservoir fluid identification method, device, equipment and medium provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of this application.

[0205] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A reservoir fluid identification method, characterized in that: include: Obtaining well logging data of reservoirs within the study area and determining the inferred fluid type of the reservoirs; Obtaining the original resistivity of the reservoir, and performing ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain a corrected resistivity of the reservoir; constructing a fluid sensitivity factor based on the corrected resistivity, and generating a plurality of data sets representing different reservoir types based on the well logging data, the corrected resistivity, and the fluid sensitivity factor; Generating fluid discrimination parameters applicable to different reservoir types according to each of the data sets and a preset algorithm; Inputting the well logging data, the inferred fluid type and the fluid discrimination parameter into an integrated recognition model to output the fluid type of the reservoir in the study area; The integrated identification model is a model for predicting reservoir fluid types constructed based on multiple integrated machine learning algorithms.

2. The reservoir fluid identification method according to claim 1, characterized in that: Before obtaining the original resistivity of the reservoir and after obtaining the well logging curve data of the reservoir in the study area, the method further includes: Acquiring core data and analytical materials of the reservoir, and performing depth offset correction on the logging curve data based on the core data and the analytical materials; Eliminating erroneous data and / or spike data in the well logging curve data; Processing abnormal values ​​and / or missing values ​​in the well logging curve data; The well logging curve data of different order of magnitude sequences are converted into the same order of magnitude sequences that conform to the standard normal distribution through Z-score normalization, and the well logging curve data uniformly tested throughout the well or the well logging curve data tested in different formation sections are excluded to preprocess the well logging curve data.

3. The reservoir fluid identification method according to claim 1, characterized in that: Performing ground stress resistivity correction and fracture resistivity correction on the original resistivity includes: Obtaining formation factors at normal temperature and pressure, porosity at normal temperature and pressure, formation water resistivity, original cementation index, corrected cementation index, formation factors under overburden pressure, and overburden porosity of the reservoir; Determine the work area empirical coefficient based on the formation factors at normal temperature and pressure, the porosity at normal temperature and pressure, the formation water resistivity, the original cementation index, the corrected cementation index, the formation factors under overburden pressure, the overburden porosity, and Archie's formula; Performing geostress resistivity correction on the original resistivity according to the empirical coefficient of the work area to obtain an initial corrected resistivity; Determining a fracture development layer section of the reservoir according to the imaging logging data in the logging curve data, and determining the fracture resistivity of the fracture development layer section; The fracture parameters of the fracture-developed layer are evaluated using a dual lateral component method, fractures of different dip angles are decomposed into vertical and horizontal fracture components, and a lateral resistivity model is established in a wellbore environment based on the differential form of Ohm's law. The lateral resistivity model is solved using simultaneous deep and shallow dual lateral equations to obtain the fracture dip, fracture aperture, and fracture porosity. Fracture resistivity correction is performed based on the initial corrected resistivity, the fracture resistivity, the fracture dip, the fracture aperture, and the fracture porosity to obtain the corrected resistivity of the reservoir.

4. The reservoir fluid identification method according to claim 1, characterized in that: Constructing the fluid sensitivity factor according to the corrected resistivity includes: Obtaining a maximum principal stress and a minimum principal stress at a formation level of the reservoir; The fluid sensitivity factor is constructed according to the corrected resistivity, the maximum principal stress at the formation level, and the minimum principal stress at the formation level.

5. The reservoir fluid identification method according to claim 1, characterized in that: Generating a plurality of data sets representing different reservoir types according to the well logging data, the corrected resistivity and the fluid sensitivity factor comprises: Obtaining macroscopic lithologic data and microscopic rock physical structure parameter data of the reservoir; Determining macroscopic lithologic differences and rock physical structure differences among different reservoirs based on the macroscopic lithologic data, and determining microscopic pore structure differences among different reservoirs based on the microscopic rock physical structure parameter data; Classifying the porosity and permeability in the microscopic rock physical structure parameter data according to the macroscopic lithologic differences, the rock physical structure differences, and the microscopic pore structure differences to establish a reservoir type classification standard; Calibrate the logging curve data, the corrected resistivity, and the fluid sensitivity factor according to the reservoir type classification standard, and perform a semi-supervised cluster analysis on the calibrated data using a DBSCAN algorithm to obtain a cluster analysis result; The logging curve data, the corrected resistivity, and the fluid sensitivity factor are divided into a plurality of data sets according to the cluster analysis result.

6. The reservoir fluid identification method according to claim 1, characterized in that: Generating the fluid discrimination parameters applicable to different reservoir types according to each of the data sets and the preset algorithm includes: Inputting the well log data, the corrected resistivity, the fluid sensitivity factor and each of the data sets into a Fisher algorithm model to determine an overall average and a classification average; Determine an overall scatter matrix, an intra-class scatter matrix, and an inter-class scatter matrix according to the overall average and the classification average; Calculating eigenvectors according to eigenvalues, the intra-class scatter matrix, and the inter-class scatter matrix, and determining the eigenvector corresponding to the maximum eigenvalue; Generate an eigenvector matrix according to the eigenvector corresponding to the maximum eigenvalue; The fluid discrimination parameter is determined according to the eigenvector matrix and each of the data sets.

7. The reservoir fluid identification method according to any one of claims 1 to 6, characterized in that: The construction process of the integrated recognition model includes: Get training set sample data and initialize weights; Input the training set sample data and the initialization weights into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, to obtain the recognition results output by each algorithm module; Inputting each of the recognition results into a weak classifier, and determining the error between each of the recognition results and the corresponding true result and the classifier weight through the weak classifier; Adjusting the initialization weight according to the classifier weight to obtain a new initialization weight, and returning to the step of inputting the training set sample data and the initialization weight into the fully convolutional neural network algorithm module, the extreme gradient boosting tree algorithm module, the categorical feature gradient boosting algorithm module, the decision tree algorithm module, and the random forest algorithm module, respectively, until the minimum error is determined; A strong learner is constructed according to the weights of each classifier and the weighted combination strategy, so as to output a final recognition result through the strong learner.

8. A reservoir fluid identification device, characterized in that: include: An acquisition module, for acquiring well logging data of reservoirs in a study area and determining the inferred fluid type of the reservoirs; a correction module, configured to obtain the original resistivity of the reservoir, and perform ground stress resistivity correction and fracture resistivity correction on the original resistivity to obtain a corrected resistivity of the reservoir; A first generating module is configured to construct a fluid sensitivity factor based on the corrected resistivity, and generate a plurality of data sets representing different reservoir types based on the well logging data, the corrected resistivity and the fluid sensitivity factor; A second generating module is used to generate fluid discrimination parameters applicable to different reservoir types according to each of the data sets and a preset algorithm; an identification module, configured to input the well logging curve data, the inferred fluid type, and the fluid discrimination parameter into an integrated identification model to output the fluid type of the reservoir in the study area; The integrated identification model is a model for predicting reservoir fluid types constructed based on multiple integrated machine learning algorithms.

9. A reservoir fluid identification device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the reservoir fluid identification method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the reservoir fluid identification method according to any one of claims 1 to 7 are implemented.

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