Method and system for constructing permeability model of fault control fracture-cavity type oil and gas reservoir

Abstract

The invention provides a fault control fracture-cavity type oil and gas reservoir permeability model construction method and system, and belongs to the technical field of oil and gas field development. The method comprises the steps that on the basis of permeability data, fitting is conducted on permeability normal distribution functions fusing all preset reservoir body types; based on the reservoir bodies, determining values of all target ranges of a preset variation function and porosity cooperative constraint functions corresponding to all preset reservoir body types; taking the values of each target range of the permeability normal distribution function and the preset variation function as modeling input parameters, and taking the porosity collaborative constraint function corresponding to each preset reservoir body type as a modeling constraint to obtain a fault control fracture-cavity type oil and gas reservoir permeability model; and on the basis of historical production data, iterative optimization and reservoir numerical simulation of the fault control fracture-vug type oil and gas reservoir permeability model are repeatedly executed until the fault control fracture-vug type oil and gas reservoir permeability model with a numerical simulation result meeting a preset standard is obtained. The purpose of establishing the accurate fault control fracture-cavity type oil and gas reservoir permeability model is achieved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, specifically to a method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, a system for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, a machine-readable storage medium, and an electronic device. Background Technology

[0002] Fault-controlled fractured-vuggy oil and gas reservoirs refer to those hosted in carbonate fractured-vuggy reservoirs controlled by fault zones and formed by tectonic fracturing along strike-slip fault zones. The reservoir development of fault-controlled fractured-vuggy oil and gas reservoirs is greatly influenced by tectonic movements and karst processes, resulting in complex spatial distribution characteristics and extremely high heterogeneity in porosity and permeability. Permeability is a key parameter reflecting the seepage capacity of reservoir fluids, affecting oil and gas well productivity evaluation and injection-production well pattern construction. Therefore, developing a permeability calculation method suitable for fault-controlled fractured-vuggy oil and gas reservoirs is of great significance for the effective development of such reservoirs.

[0003] In fractured-vuggy oil and gas reservoirs, drilling often results in large-scale air leakage due to encountering fractures and cavities, forcing premature well completion. Therefore, logging data for the target layer is limited, and reservoir characterization relies primarily on seismic data. Fractured-vuggy oil and gas reservoirs are generally classified into three types: caverns, pores, and fractures.

[0004] Stochastic simulation methods, based on geostatistics, are an important tool for permeability modeling. However, due to the scarcity of well data for fractured-vuggy reservoirs, existing stochastic simulation methods have failed to establish representative data distribution models. Currently, the main method for permeability modeling of fractured-vuggy reservoirs is to assign permeability values ​​to cavernous and porous reservoirs based on limited well test data, while assigning uniform permeability values ​​to fractured reservoirs based on experience. The main problem with this method is its high degree of subjectivity and large error.

[0005] Therefore, there is an urgent need for a method to establish an accurate permeability model for fault-controlled fracture-vuggy reservoirs in order to solve the problem of permeability modeling for such reservoirs, thereby providing technical support for the formulation of rolling development plans and the prediction of production indicators for these reservoirs. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, so as to at least solve the above-mentioned problem of how to establish an accurate permeability model for fault-controlled fracture-vuggy reservoirs.

[0007] To achieve the above objectives, the first aspect of the present invention provides a method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, comprising:

[0008] Based on the pre-acquired permeability data, the normal distribution function of permeability for each preset reservoir type is fitted;

[0009] Based on the reservoirs that integrate various preset reservoir types, the values ​​of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type are determined.

[0010] The values ​​of the normal distribution function of permeability and the target range of the preset variation function are used as modeling input parameters. The porosity co-constraint function corresponding to each preset reservoir type is used as modeling constraint. A permeability model corresponding to each preset reservoir type is established, and the permeability model of fault-controlled fracture-vuggy oil and gas reservoir is obtained by combining the permeability models corresponding to each preset reservoir type.

[0011] Based on historical production data, reservoir numerical simulations were performed on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Based on the results of the reservoir numerical simulations, the modeling input parameters were adjusted. Based on the adjusted modeling input parameters, the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs was iteratively optimized. Reservoir numerical simulations were then performed on the iteratively optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. The iterative optimization of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs and reservoir numerical simulations were repeated until a permeability model of fault-controlled fracture-vuggy oil and gas reservoirs with numerical simulation results that met the preset standards was obtained.

[0012] Optionally, the above-mentioned fitting of the normal distribution function of permeability for each preset reservoir type based on pre-acquired permeability data includes:

[0013] Based on the pre-acquired penetration rate data, determine the penetration rate data samples corresponding to each preset reservoir type;

[0014] Based on the Gaussian distribution, the permeability data samples corresponding to each preset reservoir type are fitted to obtain the Gaussian distribution function of permeability for each preset reservoir type.

[0015] Based on the Gaussian distribution function of permeability corresponding to each preset reservoir type, a Gaussian mixture model is used to fit the normal distribution function of permeability.

[0016] Optionally, the above-mentioned preset storage types include caves, holes, and cracks;

[0017] The expressions for the Gaussian distribution function of permeability corresponding to each preset reservoir type include:

[0018]

[0019] Where Ω represents the preset reservoir type as cave, void, or fracture, and K is the permeability data sample corresponding to each preset reservoir type. Ω The permeability value, μ, for each preset reservoir type.Ω σ is the average reservoir penetration rate for each preset reservoir type. Ω This represents the variance of the penetration rate of each preset reservoir type.

[0020] Optionally, the expression for the above Gaussian mixture model includes:

[0021]

[0022]

[0023] Where, φ i K represents the weight of each preset storage type. o This is the sampled data.

[0024] Optionally, the target range mentioned above includes primary range, secondary range and vertical range, and the preset reservoir types include caves, holes and cracks;

[0025] The above-mentioned determination of the target range values ​​of the preset variation function based on the storage group that integrates various preset storage group types includes:

[0026] Based on a reservoir that integrates various preset reservoir types, the size data of multiple cavities, multiple holes, and multiple cracks are measured; among them, the size data includes length data, width data, and height data;

[0027] Based on the size data of multiple caves, multiple holes, and multiple cracks, the average size data corresponding to the caves, holes, and cracks is calculated; the average size data includes the average length, average width, and average height.

[0028] For each preset storage type, the average length is used as the main range of the corresponding preset variation function, the average width is used as the secondary range of the corresponding preset variation function, and the average height is used as the vertical range of the corresponding preset variation function.

[0029] Optionally, the above-mentioned preset storage types include caves, holes, and cracks;

[0030] The rules for determining the porosity co-constraint function corresponding to each preset reservoir type include:

[0031] Based on the relationship between reservoir porosity and permeability for each preset reservoir type, the porosity co-constraint function corresponding to each preset reservoir type is determined, including:

[0032] Based on the relationship between reservoir porosity and permeability of caves and pores, the logarithmic correlation coefficient between reservoir porosity and permeability for the corresponding preset reservoir type is determined as the coefficient of the porosity co-constraint function for the corresponding preset reservoir type.

[0033] Based on the relationship between reservoir porosity and permeability in fractured reservoirs, the linear correlation coefficient between fractured reservoir porosity and permeability is determined as the coefficient of the fracture porosity co-constraint function; where,

[0034] The relationship between reservoir porosity and permeability for both caverns and pores is as follows:

[0035] K p It is the permeability, φ p This refers to reservoir porosity, where a, b, and c are coefficients.

[0036] The relationship between reservoir porosity and permeability in fractured reservoirs is as follows:

[0037] K f It is the permeability, φ f is the reservoir porosity of the fracture, and w is the fracture width;

[0038] The coefficients of the porosity co-constraint function for cavities and voids are the logarithmic correlation coefficients between the corresponding reservoir porosity and permeability, while the coefficients of the porosity co-constraint function for fractures are the linear correlation coefficients between the reservoir porosity and permeability of fractures.

[0039] Optionally, the rules for establishing the penetration rate model corresponding to each preset reservoir type include:

[0040] Based on the values ​​of the target ranges of the preset variation functions corresponding to each preset storage type, set the variation functions corresponding to each preset storage type.

[0041] Based on the normal distribution function of permeability, the distribution range of permeability output results corresponding to each preset reservoir type is determined;

[0042] For each preset reservoir type, the corresponding porosity co-constraint function is used as a co-kriging constraint. Based on the corresponding variation function and the distribution range of permeability output results, the corresponding permeability model is established using phase control modeling and Gaussian stochastic equation simulation methods.

[0043] Optionally, the rules for establishing the above-mentioned storage collective that integrates various preset storage collective types include:

[0044] Obtain the sculpting results of each preset storage type;

[0045] Based on a preset priority order and deterministic modeling method, the sculpting results of each preset reservoir type are fused to obtain a reservoir that integrates each preset reservoir type.

[0046] A second aspect of this invention provides a permeability model construction system for fault-controlled fracture-vuggy oil and gas reservoirs, comprising:

[0047] The permeability normal distribution function fitting module is used to fit the permeability normal distribution function of various preset reservoir types based on pre-acquired permeability data.

[0048] The target range value determination module is used to determine the value of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type based on the reservoirs that integrate each preset reservoir type.

[0049] The permeability model building module is used to take the values ​​of the target ranges of the permeability normal distribution function and the preset variation function as modeling input parameters, take the porosity co-constraint function corresponding to each preset reservoir type as modeling constraints, build the permeability model corresponding to each preset reservoir type, and combine the permeability models corresponding to each preset reservoir type to obtain the permeability model of fault-controlled fracture-vuggy oil and gas reservoir.

[0050] The numerical simulation module is used to perform reservoir numerical simulation on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs based on historical production data. Based on the reservoir numerical simulation results, the modeling input parameters are adjusted. Based on the adjusted modeling input parameters, the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is iteratively optimized. The optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is then used for reservoir numerical simulation. The iterative optimization and reservoir numerical simulation of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs are repeatedly executed until a permeability model of fault-controlled fracture-vuggy oil and gas reservoirs with numerical simulation results that meet the preset standards is obtained.

[0051] In a third aspect, the present invention provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the above-described method for constructing a permeability model for fractured-vuggy oil and gas reservoirs.

[0052] In a fourth aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs.

[0053] The above technical solution provides a method and system for constructing a permeability model for fractured-vuggy oil and gas reservoirs. Using pre-acquired permeability data as the foundation, it fits the normal distribution function of permeability for various preset reservoir types, overcoming the problem of missing logging permeability data caused by well-logging losses in fractured-vuggy reservoirs, thus laying a data foundation for permeability modeling. By statistically analyzing the reservoir size of different reservoir types within the reservoir, the values ​​of each target range of the preset variogram are determined, and the porosity co-constraint function corresponding to each preset reservoir type is used as co-constraint data, reducing the uncertainty of the spatial distribution characteristics of permeability. Therefore, the values ​​of the normal distribution function of permeability and each target range of the preset variogram are used as modeling input parameters, and the porosity co-constraint function corresponding to each preset reservoir type is used as modeling constraints to establish permeability models corresponding to each preset reservoir type. Finally, the permeability models corresponding to each preset reservoir type are combined to obtain the permeability model for fractured-vuggy oil and gas reservoirs. Based on historical production data, numerical simulations of the permeability model for fault-controlled fracture-vuggy oil and gas reservoirs are conducted. Based on the simulation results, the permeability model is fitted and adjusted until a model conforming to preset standards is obtained. This method and system adjust the permeability model based on the numerical simulation results, inferring static permeability characteristics from dynamic production features and refining the model. The combination of dynamic and static data further enhances the reliability of the permeability model. This achieves the goal of establishing a relatively accurate permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, thus providing technical support for the formulation of rolling development plans and the prediction of production indicators.

[0054] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 This is a flowchart of a method for constructing a permeability model for a fractured-vuggy oil and gas reservoir according to one embodiment of the present invention;

[0057] Figure 2 This is a block diagram of a permeability model construction system for fracture-vuggy oil and gas reservoirs provided in one embodiment of the present invention;

[0058] Figure 3 This is a flowchart of another method for constructing a permeability model for fractured-vuggy oil and gas reservoirs according to one embodiment of the present invention;

[0059] Figure 4 This is the reservoir classification and characterization result of Unit A provided by one embodiment of the present invention;

[0060] Figure 5 This is the permeability distribution function of unit A provided in one embodiment of the present invention;

[0061] Figure 6 This is a range-changing statistical result of Unit A provided by one embodiment of the present invention;

[0062] Figure 7 This is a three-dimensional geological model of permeability of unit A provided in one embodiment of the present invention;

[0063] Figure 8 This is a permeability distribution histogram of Unit A provided in one embodiment of the present invention;

[0064] Figure 9 This is a typical well history fitting result before and after correction of the A-unit permeability model provided by one embodiment of the present invention;

[0065] Figure 10 This is a schematic diagram of an electronic device structure provided by a preferred embodiment of the present invention.

[0066] Explanation of reference numerals in the attached figures

[0067] 10 - Electronic device, 100 - Processor, 101 - Memory, 102 - Computer program. Detailed Implementation

[0068] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0069] Example 1

[0070] Figure 1 This is a flowchart illustrating a method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to one embodiment of the present invention. Figure 1 As shown, this invention provides a method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, comprising:

[0071] S110: Based on the pre-acquired permeability data, fit the normal distribution function of permeability of each preset reservoir type;

[0072] The preset reservoir types include caverns, pores, and fractures, and the pre-acquired permeability data includes permeability data from well test interpretations and empirical relationships. Specifically, the permeability range for each preset reservoir type is determined by statistically analyzing permeability data from well test interpretations and empirical relationships, and a Gaussian mixture model is applied to fit and integrate the normal distribution function of permeability for caverns, pores, and fractures.

[0073] In some embodiments of this example, the above-mentioned fitting of the normal distribution function of permeability for each preset reservoir type based on pre-acquired permeability data includes:

[0074] Based on the pre-acquired penetration rate data, determine the penetration rate data samples corresponding to each preset reservoir type;

[0075] Furthermore, based on the seismic attribute-based reservoir (including caverns, cavities, and fissures) sculpting results, a deterministic modeling method can be used to sample the seismic reservoir to determine the preset reservoir type (including caverns, cavities, and fissures).

[0076] Specifically, permeability data from well test interpretation and empirical relationships are compiled, and permeability data samples for different preset reservoir types (including caverns, pores, and fractures) are compiled based on the preset reservoir types controlled by the test wells.

[0077] Based on the Gaussian distribution, the permeability data samples corresponding to each preset reservoir type are fitted to obtain the Gaussian distribution function of permeability for each preset reservoir type.

[0078] Specifically, based on the permeability data samples corresponding to each preset reservoir type, a Gaussian distribution is used to fit the permeability in caves, pores, and fissures, and its probability density function (pdf) is expressed as follows: Where Ω represents the preset reservoir type as cave, void, or fracture, and K is the permeability data sample corresponding to each preset reservoir type. Ω The permeability value, μ, for each preset reservoir type. Ω σ is the average reservoir penetration rate for each preset reservoir type. Ω This represents the variance of the penetration rate of each preset reservoir type.

[0079] For example, Ω∈{1: cave; 2: hole; 3: crack}, that is, when Ω is 1, it represents a cave; when Ω is 2, it represents a hole; when Ω is 3, it represents a crack.

[0080] Based on the Gaussian distribution function of permeability corresponding to each preset reservoir type, a Gaussian mixture model is used to fit the normal distribution function of permeability.

[0081] Specifically, based on the Gaussian distribution function of permeability corresponding to each preset reservoir type, a Gaussian mixture model is used to comprehensively characterize the permeability distribution pattern of the entire region, resulting in a normal permeability distribution function; wherein, the Gaussian mixture model is:

[0082]

[0083]

[0084] In the formula, φ i K represents the weight of each preset storage type. o This is the sampled data.

[0085] S120: Based on the reservoirs that integrate various preset reservoir types, determine the values ​​of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type.

[0086] Specifically, the scale of reservoirs of various preset reservoir types is analyzed and integrated to determine the values ​​of each target range of the preset variation function, and the porosity-permeability relationship is analyzed to determine the porosity co-constraint function for permeability modeling corresponding to each preset reservoir type.

[0087] In some embodiments of this example, the target range includes a primary range, a secondary range, and a vertical range, and the preset reservoir types include caves, cavities, and fissures. The determination of the values ​​of each target range of the preset variation function based on the reservoirs of each preset reservoir type includes: measuring the size data of multiple caves, multiple cavities, and multiple fissures based on the reservoirs of each preset reservoir type; wherein the size data includes length data, width data, and height data; calculating the average size data corresponding to the caves, the average size data corresponding to the cavities, and the average size data corresponding to the fissures based on the size data of the multiple caves, the multiple cavities, and the multiple fissures; wherein the average size data includes the average length, the average width, and the average height; for each preset reservoir type, the average length is used as the primary range of the corresponding preset variation function, the average width is used as the secondary range of the corresponding preset variation function, and the average height is used as the vertical range of the corresponding preset variation function.

[0088] Specifically, in determining the values ​​of each target range of the preset variation function, the size data (including length, width, and height) of multiple caves, holes, and cracks are measured multiple times. The average size data (including average length, average width, and average height) of each cave, hole, and crack are calculated. The average length, average width, and average height are used as the main range, secondary range, and vertical range of the variation function, respectively.

[0089] In some embodiments of this example, the aforementioned preset reservoir types include caverns, pores, and fractures; the rules for determining the porosity co-constraint function corresponding to each preset reservoir type include: determining the porosity co-constraint function corresponding to each preset reservoir type based on the relationship between reservoir porosity and permeability, including: determining the logarithmic correlation coefficient between reservoir porosity and permeability of the corresponding preset reservoir type as the coefficient of the porosity co-constraint function of the corresponding preset reservoir type based on the relationship between reservoir porosity and permeability of caverns and pores; determining the linear correlation coefficient between reservoir porosity and permeability of fractures as the coefficient of the porosity co-constraint function of fractures; wherein, the relationship between reservoir porosity and permeability of caverns and pores is as follows: K p It is the permeability, φ p This refers to reservoir porosity, where a, b, and c are coefficients; the relationship between reservoir porosity and permeability in fractured reservoirs is as follows: K f It is the permeability, φ f is the reservoir porosity of the fracture, and w is the fracture width; the coefficient of the co-constraint function of porosity for caverns and pores is the logarithmic correlation coefficient between the corresponding reservoir porosity and permeability, and the coefficient of the co-constraint function of porosity for fractures is the linear correlation coefficient between the reservoir porosity and permeability for fractures.

[0090] Specifically, based on the relationship between reservoir porosity and permeability for each preset reservoir type, a porosity co-constraint function is established for each preset reservoir type. Among these, the porosity and permeability of cavernous reservoirs (including mechanically deposited filling, collapsed colloidal filling, and chemically deposited filling) and porous reservoirs exhibit the following relationships: The exponential relationship shown indicates that porosity and permeability in fractured reservoirs exhibit a relationship similar to that shown in the diagram. The linear relationship is shown. Therefore, for the logarithmic correlation coefficient between permeability and porosity in cavern and pore-type reservoirs, the corresponding logarithmic correlation coefficient between reservoir porosity and permeability is used as the coefficient of the porosity co-constraint function for caverns and pores. For the correlation coefficient between permeability and porosity in fracture-type reservoirs, the linear correlation coefficient between reservoir porosity and permeability in fractures is used as the coefficient of the porosity co-constraint function for fractures.

[0091] S130: The values ​​of the target ranges of the permeability normal distribution function and the preset variation function are used as modeling input parameters. The porosity co-constraint function corresponding to each preset reservoir type is used as modeling constraint. The permeability model corresponding to each preset reservoir type is established, and the permeability model of the fault-controlled fracture-vuggy oil and gas reservoir is obtained by combining the permeability models corresponding to each preset reservoir type.

[0092] Specifically, firstly, a phase-controlled Gaussian stochastic equation algorithm is used to establish permeability models corresponding to each preset reservoir type. Then, the permeability models corresponding to each preset reservoir type are combined to obtain a permeability model for fault-controlled fracture-vuggy reservoirs. Specifically, a phase-controlled modeling approach and a Gaussian stochastic equation simulation algorithm are used, with the porosity model as co-kriging data, to establish permeability models for caverns, pores, and fractures respectively. This method uses permeability obtained from production testing as input data. Based on the phase-controlled principle, a three-dimensional spatial distribution model of permeability (i.e., the permeability model for fault-controlled fracture-vuggy reservoirs) is established through a Gaussian simulation algorithm combined with porosity isotopic co-constraints.

[0093] In some implementations of this embodiment, the rules for establishing the permeability model corresponding to each preset reservoir type include:

[0094] Based on the values ​​of the target ranges of the preset variation functions corresponding to each preset storage type, set the variation functions corresponding to each preset storage type.

[0095] Specifically, a variation function is set. For example, the nugget value is set to 0.1, the variation function type is set to spherical, and the values ​​of the main range, secondary range, and vertical range of the variation function are the average length, average width, and average height corresponding to the preset reservoir type. The azimuth of the main range is the azimuth of the strike-slip fault, and the dip angle is the dip angle of the Yijianfang Formation of the Ordovician.

[0096] Based on the normal distribution function of permeability, the distribution range of permeability output results corresponding to each preset reservoir type is determined;

[0097] Specifically, a distribution function is set. The seed number is set, and the permeability output result distribution range for each facies zone (a lithological zone within a rock mass composed of different lithologies, with gradual transitions between zones, can only be roughly defined) is set according to the normal permeability distribution function of each preset reservoir type (this normal permeability distribution function is a Gaussian mixture model). This determines the permeability output result distribution range corresponding to each preset reservoir type, ensuring consistency between the Gaussian equation simulation results and the results established by the Gaussian mixture model.

[0098] For each preset reservoir type, the corresponding porosity co-constraint function is used as a co-kriging constraint. Based on the corresponding variation function and the distribution range of permeability output results, the corresponding permeability model is established using phase control modeling and Gaussian stochastic equation simulation methods.

[0099] Specifically, co-kriging parameters are set: using the porosity model data volume as co-kriging constraints, a normal score transformation is performed on the porosity data volume, and the isotopic co-kriging algorithm is used to constrain the spatial distribution trend of the permeability model. Thus, for each preset reservoir type, the corresponding porosity co-kriging constraint function is used as the co-kriging constraint, and the corresponding variogram function and the permeability output result distribution range are used as modeling inputs to sequentially establish the permeability model corresponding to each preset reservoir type.

[0100] S140: Based on historical production data, perform reservoir numerical simulation on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Based on the reservoir numerical simulation results, adjust the modeling input parameters. Based on the adjusted modeling input parameters, iteratively optimize the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Perform reservoir numerical simulation on the iteratively optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Repeat the iterative optimization of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs and the reservoir numerical simulation until the numerical simulation results meet the preset standards for the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs.

[0101] Specifically, a reservoir numerical model can be established, and then, based on historical production data, the permeability model for fault-controlled fractured-vuggy oil and gas reservoirs can be fitted and adjusted. The establishment of the reservoir numerical model includes: applying static data volumes from the 3D geological model, such as reservoir volume, porosity model, permeability model, and oil saturation model, and combining them with relative permeability curves, phase parameters, well completion data, and production dynamic data to establish the reservoir numerical model. The fitting and adjustment process for the permeability model of the fault-controlled fractured-vuggy oil and gas reservoir includes: analyzing the differences between historical production data (such as production and pressure) and the results of the reservoir numerical simulation to determine the adjustment direction of the permeability model (including the range of the variogram, and the average, maximum, and minimum values ​​of the distribution). The modeling input parameters of the permeability model of the fault-controlled fractured-vuggy oil and gas reservoir are reset, and S130 is re-executed to establish a new permeability model for the fault-controlled fractured-vuggy oil and gas reservoir, and the production history fitting of the reservoir numerical model is performed again. After multiple iterations until the production history fits well, the established permeability model for fault-controlled fracture-vuggy oil and gas reservoirs is considered to conform to the actual underground conditions.

[0102] In the above implementation process, this method uses pre-acquired permeability data as the basic data. It fits the normal distribution function of permeability for each preset reservoir type using a Gaussian mixture model, overcoming the problem of missing logging permeability data caused by leakage in fault-controlled fractured-vuggy reservoirs, thus laying the data foundation for permeability modeling. By statistically analyzing the reservoir size of different reservoir types within the reservoir, the values ​​of each target range of the preset variogram are determined. The porosity co-constraint function corresponding to each preset reservoir type is used as the co-constraint data, reducing the uncertainty of the spatial distribution characteristics of permeability. Therefore, the values ​​of the normal distribution function of permeability and each target range of the preset variogram are used as modeling input parameters, and the porosity co-constraint function corresponding to each preset reservoir type is used as modeling constraints to establish permeability models for each preset reservoir type. Finally, the permeability models corresponding to each preset reservoir type are combined to obtain the permeability model for fault-controlled fractured-vuggy oil and gas reservoirs. Based on historical production data, this method conducts numerical simulations of the permeability model for fault-controlled fracture-vuggy oil and gas reservoirs. Based on the simulation results, the permeability model is fitted and adjusted until the simulation results meet preset standards. This method adjusts the permeability model based on the numerical simulation results, inferring static permeability characteristics from dynamic production features and refining the permeability model. The combination of dynamic and static data further enhances the reliability of the permeability model. This achieves the goal of establishing a relatively accurate permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, providing technical support for the formulation of rolling development plans and the prediction of production indicators. This method fully utilizes the reservoir permeability data characteristics reflected in dynamic and static data such as seismic sculpting, production testing, and production data, and uses geostatistical methods to establish the permeability model.

[0103] It should be noted that existing technologies offer a method for modeling oil and gas reservoir permeability by discretizing the permeability interpreted from well logging into a modeling grid to match permeability data with reservoir type. By analyzing the discretized permeability data for different reservoir types separately, a normal distribution function of permeability for each reservoir type is fitted. However, due to the scarcity of well logging data for fractured-vuggy oil and gas reservoirs, analysis cannot be performed based on conventional permeability modeling workflows. Therefore, the permeability data used in this method is obtained from dynamic tests such as production well tests. Considering that the obtained permeability data is the comprehensive permeability of a single well, and the reservoir type it reflects is uncertain or not unique, this method further uses a Gaussian mixture model to obtain a comprehensive permeability distribution function for different reservoir types, thereby achieving matching between the permeability distribution function and the source of the permeability data and reducing modeling errors caused by input data errors.

[0104] In some embodiments of this example, the rules for establishing a storage group that integrates various preset storage group types include: obtaining the storage group carving results of each preset storage group type; and performing storage group fusion on the storage group carving results of each preset storage group type based on a preset priority order and a deterministic modeling method to obtain a storage group that integrates various preset storage group types.

[0105] Specifically, a reservoir fusion of various preset reservoir types is established: based on the sculpting results of cave, void, and fracture reservoirs with seismic attributes, and according to the priority order of cave > void > fracture, a deterministic modeling method is used to sample the seismic reservoirs and establish a reservoir fusion of cave, void, and fracture.

[0106] Example 2

[0107] This invention provides a method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs. This embodiment provides an example of applying this method to a fault-controlled fracture-vuggy oil and gas reservoir in Unit A of the P fault zone in the T oilfield. Unit A is located on the Shaya Uplift structural belt in the Tarim Basin, with the reservoir stratigraphic level being the Middle-Lower Ordovician Unified Interstellar Formation, with an average burial depth of over 6000 meters. The S fault zone trends northeast-southwest, exhibiting extensional, translational, and compressional characteristics, and showing obvious segmentation. Three-dimensional seismic data has been acquired for Unit A, with a cumulative drilling of 20 wells and a cumulative oil production of 2.27 million tons.

[0108] Figure 3 This is a flowchart of another method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to one embodiment of the present invention, as shown below. Figure 3 As shown, the specific execution process of this embodiment is as follows:

[0109] S1: Statistical well test interpretation and empirical relationships are used to determine the permeability range. A Gaussian mixture model is applied to fit the normal distribution function of permeability for caverns, pores, and fractures. The reservoir type of Unit A is classified into three types: caverns, pores, and fractures. Caverns are characterized by residual impedance, pores by energy envelope, and fractures by AFE (energy envelope effect). Figure 4 As shown, Figure 4 This is the reservoir classification and characterization result of Unit A provided by one embodiment of the present invention. The permeability of single-well tests in the T oilfield was statistically interpreted, and the permeability distribution functions of caverns, pores, and fractures were fitted using a Gaussian mixture model. The permeability of caverns was 200±100 mD, the permeability of pores was 50±40 mD, and the permeability of fractures was 300±140 mD. Figure 5 As shown, Figure 5 This is the permeability distribution function of unit A provided in one embodiment of the present invention.

[0110] S2: Reservoir size analysis determines the primary and secondary ranges of the variogram, and porosity-permeability relationship analysis determines the co-constraint function for permeability modeling. Based on the lithofacies modeling results, the geometric dimensions of cavities, voids, and fractures are measured multiple times to determine the primary, secondary, and vertical ranges. The results are as follows: the primary, secondary, and vertical ranges of the cavities are 120m, 80m, and 60m, respectively. Figure 6 As shown, Figure 6 The results are statistical results of the range variation of Unit A provided by one embodiment of the present invention; the main range variation, secondary range variation, and vertical range variation of the pores are 300m, 180m, and 100m, respectively; the main range variation, secondary range variation, and vertical range variation of the fractures are 800m, 50m, and 80m, respectively. Based on the empirical relationship in the study area, the correlation coefficient between porosity and permeability is determined to be 0.7.

[0111] S3: Phased-Controlled Gaussian Stochastic Equation Algorithm for Permeability Model Establishment. Based on the phased-controlled principle, Gaussian stochastic equation simulation methods were used to establish permeability models for caves, cavities, and fissures. The nugget value of the cave variogram was 0.1, a spherical variogram was selected, with ranges of 120m, 80m, and 60m, an azimuth angle of 60°, and a dip angle of 5°; the minimum value of the distribution function was 50mD, the maximum value was 500mD, a standard normal distribution was adopted, with a mean of 200mD and a standard deviation of 100mD; co-kriging was used with the porosity data volume as a constraint, and the in-situ co-kriging algorithm was adopted, with a correlation coefficient of 0.7. The nugget value of the porosity variability function is 0.1. A spherical variability function is selected, with ranges of 300m, 180m, and 100m, an azimuth of 60°, and a dip angle of 5°. The minimum value of the distribution function is 10mD, the maximum value is 150mD, a standard normal distribution is adopted, with a mean of 50mD and a standard deviation of 40mD. Co-kriging is used with the porosity data volume as a constraint, employing the same-position co-kriging algorithm, with a correlation coefficient of 0.7. The nugget value of the crack variability function is 0.1. A spherical variability function is selected, with ranges of 800m, 50m, and 80m, an azimuth of 60°, and a dip angle of 5°. The minimum value of the distribution function is 100mD, the maximum value is 500mD, a standard normal distribution is adopted, with a mean of 300mD and a standard deviation of 140mD. Co-kriging is used with the porosity data volume as a constraint, employing the same-position co-kriging algorithm, with a correlation coefficient of 0.7. Simulations are conducted based on the above parameters to obtain the permeability model as follows. Figure 7 As shown, the permeability distribution is as follows: Figure 8 As shown.

[0112] S4: Iterative optimization of the permeability model in reservoir numerical simulation. A numerical model for element A is established based on the geological model (including the permeability model), and historical data fitting is performed. The initial fitting results for a typical well are shown below. Figure 9As shown in Figure a, the simulated water production of this well was lower than the actual water production, while the simulated flowing pressure was higher than the actual flowing pressure throughout the entire production period. This was attributed to the well encountering a cavernous reservoir, which resulted in leakage. Analysis suggests that the high permeability of the cavern prevented bottom water from flowing along the fractures, leading to the lower water production and higher flowing pressure. Based on this analysis, the permeability distribution function of the cavern was adjusted to 150±50 mD. A new numerical model was established using the adjusted geological model, and history fitting was performed. The results are as follows: Figure 9 As shown in Figure b, the well's production and pressure fitting results are clearly significantly improved.

[0113] Example 3

[0114] Figure 2 This is a block diagram of a permeability model construction system for fracture-vuggy oil and gas reservoirs provided in one embodiment of the present invention. Figure 2 As shown, this invention provides a permeability model construction system for fault-controlled fracture-vuggy oil and gas reservoirs, comprising:

[0115] The permeability normal distribution function fitting module is used to fit the permeability normal distribution function of various preset reservoir types based on pre-acquired permeability data.

[0116] The target range value determination module is used to determine the value of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type based on the reservoirs that integrate each preset reservoir type.

[0117] The permeability model building module is used to take the values ​​of the target ranges of the permeability normal distribution function and the preset variation function as modeling input parameters, take the porosity co-constraint function corresponding to each preset reservoir type as modeling constraints, build the permeability model corresponding to each preset reservoir type, and combine the permeability models corresponding to each preset reservoir type to obtain the permeability model of fault-controlled fracture-vuggy oil and gas reservoir.

[0118] The numerical simulation module is used to perform reservoir numerical simulation on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs based on historical production data. Based on the reservoir numerical simulation results, the modeling input parameters are adjusted. Based on the adjusted modeling input parameters, the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is iteratively optimized. The optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is then used for reservoir numerical simulation. The iterative optimization and reservoir numerical simulation of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs are repeatedly executed until a permeability model of fault-controlled fracture-vuggy oil and gas reservoirs with numerical simulation results that meet the preset standards is obtained.

[0119] Specifically, the system uses pre-acquired permeability data as its foundation, fitting the normal distribution function of permeability to various preset reservoir types. This overcomes the problem of missing logging permeability data caused by leakage in fault-controlled fractured-vuggy reservoirs, laying the data foundation for permeability modeling. By statistically analyzing the reservoir size of different reservoir types within the reservoir, the system determines the values ​​of each target range of the preset variation function. The system also uses the porosity co-constraint function corresponding to each preset reservoir type as co-constraint data, reducing the uncertainty of the spatial distribution characteristics of permeability. Therefore, the system uses the values ​​of the normal distribution function of permeability and each target range of the preset variation function as modeling input parameters, and the porosity co-constraint function corresponding to each preset reservoir type as modeling constraints. This allows for the establishment of permeability models for each preset reservoir type, and the synthesis of these models yields a permeability model for fault-controlled fractured-vuggy oil and gas reservoirs. Based on historical production data, numerical simulations are performed on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Based on the simulation results, the permeability model is fitted and adjusted until the simulation results meet preset standards. This system adjusts the permeability model based on the numerical simulation results, and can infer static permeability characteristics from dynamic production features to improve the model. The combination of dynamic and static data further enhances the reliability of the permeability model. This achieves the goal of establishing a relatively accurate permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, thus providing technical support for the formulation of rolling development plans and the prediction of production indicators.

[0120] Example 4

[0121] The present invention also provides a machine-readable storage medium storing instructions that, when executed by a processor 100, configure the processor 100 to perform the above-described method for constructing a permeability model for fractured-vuggy oil and gas reservoirs.

[0122] Machine-readable storage media include both permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0123] The present invention also provides an electronic device 10, which includes a memory 101, a processor 100, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the above-described method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs.

[0124] like Figure 10 The diagram shown is a schematic representation of an electronic device according to an embodiment of the present invention. Figure 10 As shown, the electronic device 10 of this embodiment includes a processor 100, a memory 101, and a computer program 102 stored in the memory 101 and executable on the processor 100. When the processor 100 executes the computer program 102, it implements the steps in the method embodiment described above. Alternatively, when the processor 100 executes the computer program 102, it implements the functions of each module / unit in the device embodiment described above.

[0125] For example, computer program 102 can be divided into one or more modules / units, one or more of which are stored in memory 101 and executed by processor 100 to complete the present invention. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 102 in electronic device 10. For example, computer program 102 can be divided into a permeability normal distribution function fitting module, a target range value determination module, a permeability model establishment module, and a numerical simulation module.

[0126] Electronic device 10 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Electronic device 10 may include, but is not limited to, processor 100 and memory 101. Those skilled in the art will understand that... Figure 10 This is merely an example of electronic device 10 and does not constitute a limitation on electronic device 10. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0127] The processor 100 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0128] The memory 101 can be an internal storage unit of the electronic device 10, such as a hard disk or RAM of the electronic device 10. The memory 101 can also be an external storage device of the electronic device 10, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the electronic device 10. Furthermore, the memory 101 can include both internal and external storage units of the electronic device 10. The memory 101 is used to store computer programs and other programs and data required by the electronic device 10. The memory 101 can also be used to temporarily store data that has been output or will be output.

[0129] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as a method, system, or computer program 102 product. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program 102 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.

[0131] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program 102 products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program 102 instructions. These computer program 102 instructions can be provided to a processor 100 of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor 100 of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program 102 instructions may also be stored in a computer-readable storage medium 101 that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium 101 produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program 102 instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0135] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs, characterized in that, include: Based on the pre-acquired permeability data, the normal distribution function of permeability for each preset reservoir type is fitted; Based on the reservoirs that integrate various preset reservoir types, the values ​​of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type are determined. The values ​​of the target ranges of the permeability normal distribution function and the preset variation function are used as modeling input parameters. The porosity co-constraint function corresponding to each preset reservoir type is used as modeling constraint. A permeability model corresponding to each preset reservoir type is established, and the permeability models corresponding to each preset reservoir type are combined to obtain a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs. Based on historical production data, reservoir numerical simulations were performed on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. Based on the results of the reservoir numerical simulations, the modeling input parameters were adjusted. Based on the adjusted modeling input parameters, the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs was iteratively optimized. Reservoir numerical simulations were then performed on the iteratively optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs. The iterative optimization of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs and reservoir numerical simulations were repeated until a permeability model of fault-controlled fracture-vuggy oil and gas reservoirs with numerical simulation results that met the preset standards was obtained.

2. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 1, characterized in that, The fitting of the normal distribution function of permeability for each preset reservoir type based on pre-acquired permeability data includes: Based on the pre-acquired penetration rate data, determine the penetration rate data samples corresponding to each preset reservoir type; Based on the Gaussian distribution, the permeability data samples corresponding to each preset reservoir type are fitted to obtain the Gaussian distribution function of permeability for each preset reservoir type. Based on the Gaussian distribution function of permeability corresponding to each preset reservoir type, a Gaussian mixture model is used to fit the normal distribution function of permeability.

3. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 2, characterized in that, The preset storage types include caves, holes, and cracks; The expressions for the Gaussian distribution function of permeability corresponding to each preset reservoir type include: Where Ω represents the preset reservoir type as cave, void, or fracture, and K is the permeability data sample corresponding to each preset reservoir type. Ω The permeability value, μ, for each preset reservoir type. Ω σ is the average reservoir penetration rate for each preset reservoir type. Ω This represents the variance of the penetration rate of each preset reservoir type.

4. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 3, characterized in that, The expression for the Gaussian mixture model includes: Where, φ i K represents the weight of each preset storage type. o This is the sampled data.

5. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 1, characterized in that, The target range includes the main range, the secondary range, and the vertical range; the preset storage type includes caves, holes, and cracks. The determination of the target range values ​​of the preset variation function based on the storage group that integrates various preset storage group types includes: Based on a reservoir that integrates various preset reservoir types, the size data of multiple cavities, multiple holes, and multiple cracks are measured; wherein, the size data includes length data, width data, and height data; Based on the size data of multiple caves, multiple holes, and multiple cracks, the average size data corresponding to the caves, holes, and cracks is calculated; the average size data includes the average length, average width, and average height. For each preset storage type, the average length is used as the main range of the corresponding preset variation function, the average width is used as the secondary range of the corresponding preset variation function, and the average height is used as the vertical range of the corresponding preset variation function.

6. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 1, characterized in that, The preset storage types include caves, holes, and cracks; The rules for determining the porosity co-constraint function corresponding to each preset reservoir type include: Based on the relationship between reservoir porosity and permeability for each preset reservoir type, the porosity co-constraint function corresponding to each preset reservoir type is determined, including: Based on the relationship between reservoir porosity and permeability of caves and pores, the logarithmic correlation coefficient between reservoir porosity and permeability for the corresponding preset reservoir type is determined as the coefficient of the porosity co-constraint function for the corresponding preset reservoir type. Based on the relationship between reservoir porosity and permeability in fractured reservoirs, the linear correlation coefficient between fractured reservoir porosity and permeability is determined as the coefficient of the fracture porosity co-constraint function; where, The relationship between reservoir porosity and permeability for both caverns and pores is as follows: K p It is the permeability, φ p This refers to reservoir porosity, where a, b, and c are coefficients. The relationship between reservoir porosity and permeability in fractured reservoirs is as follows: K f It is the permeability, φ f is the reservoir porosity of the fracture, and w is the fracture width; The coefficients of the porosity co-constraint function for cavities and voids are the logarithmic correlation coefficients between the corresponding reservoir porosity and permeability, while the coefficients of the porosity co-constraint function for fractures are the linear correlation coefficients between the reservoir porosity and permeability of fractures.

7. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 1, characterized in that, The rules for establishing the penetration rate model for each preset reservoir type include: Based on the values ​​of the target ranges of the preset variation functions corresponding to each preset storage type, set the variation functions corresponding to each preset storage type. Based on the permeability normal distribution function, the distribution range of permeability output results corresponding to each preset reservoir type is determined; For each preset reservoir type, the corresponding porosity co-constraint function is used as a co-kriging constraint. Based on the corresponding variation function and the distribution range of permeability output results, the corresponding permeability model is established using phase control modeling and Gaussian stochastic equation simulation methods.

8. The method for constructing a permeability model for fault-controlled fracture-vuggy oil and gas reservoirs according to claim 1, characterized in that, The rules for establishing a storage group that integrates various preset storage group types include: Obtain the sculpting results of each preset storage type; Based on a preset priority order and deterministic modeling method, the sculpting results of each preset reservoir type are fused to obtain a reservoir that integrates each preset reservoir type.

9. A permeability model construction system for fault-controlled fracture-vuggy oil and gas reservoirs, characterized in that, include: The permeability normal distribution function fitting module is used to fit the permeability normal distribution function of various preset reservoir types based on pre-acquired permeability data. The target range value determination module is used to determine the value of each target range of the preset variation function and the porosity co-constraint function corresponding to each preset reservoir type based on the reservoirs that integrate each preset reservoir type. The permeability model building module is used to take the values ​​of the target ranges of the permeability normal distribution function and the preset variation function as modeling input parameters, take the porosity co-constraint function corresponding to each preset reservoir type as modeling constraints, build the permeability model corresponding to each preset reservoir type, and combine the permeability models corresponding to each preset reservoir type to obtain the permeability model of fault-controlled fracture-vuggy oil and gas reservoir. The numerical simulation module is used to perform reservoir numerical simulation on the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs based on historical production data. Based on the reservoir numerical simulation results, the modeling input parameters are adjusted. Based on the adjusted modeling input parameters, the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is iteratively optimized. The optimized permeability model of fault-controlled fracture-vuggy oil and gas reservoirs is then used for reservoir numerical simulation. The iterative optimization and reservoir numerical simulation of the permeability model of fault-controlled fracture-vuggy oil and gas reservoirs are repeatedly executed until a permeability model of fault-controlled fracture-vuggy oil and gas reservoirs with numerical simulation results that meet the preset standards is obtained.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the method for constructing a permeability model for a fractured-vuggy oil and gas reservoir as described in any one of claims 1 to 8.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for constructing a permeability model for fractured-vuggy oil and gas reservoirs as described in any one of claims 1 to 8.

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