Method and device for analyzing seepage capacity of deep carbonate gas reservoir

Abstract

The invention discloses a seepage capacity analysis method and device for a deep carbonate rock gas reservoir. The method comprises the steps that static data and dynamic data of the carbonate rock gas reservoir are obtained; extracting distribution characteristics and development characteristics of a reservoir medium from core pore structure analysis data, and constructing a plurality of core conceptual models; determining longitudinal and transverse distribution characteristics and development characteristics of a reservoir medium of the carbonate gas reservoir according to the seismic interpretation data and the logging data, and constructing reservoir conceptual models of different types in combination with a plurality of core conceptual models; within the maximum well control range of a single well, according to the reservoir conceptual models of different types and the multiple core conceptual models, a digital core single well conceptual model is constructed; finite element flow simulation test operation is conducted on the digital core single well conceptual model, a simulation well test curve is drawn and compared with a real well test curve, an optimized digital core single well conceptual model is obtained, and the difference between the macroscopic seepage capacity interpretation result such as oil test and well test and the actual productivity of a gas well is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas development, and in particular to a method and a device for analyzing the seepage capacity of a deep carbonate gas reservoir. Background Art

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

[0003] Due to the long burial-diagenesis history and the superposition and transformation of multi-cycle tectonic movements, the deep carbonate reservoir has undergone multiple stages of deposition, dissolution, and filling. It is also affected by factors such as stratum uplift, compaction, and faults, and the development of fractures and caves is highly heterogeneous. There is a large permeability difference between the near and far wells in the plane of gas wells; different types of reservoirs are vertically superimposed, and also show strong heterogeneity. In the process of oil testing, acidification transformation is usually carried out in the near-well area, and the scope of acidification transformation is limited. The well test fails to reflect the original seepage characteristics of the near-well area. Affected by factors such as the relationship between fractures and caves, the heterogeneity of the acid fracturing transformation range, and structural faults, gas reservoirs often show more complex permeability characteristics. It is impossible to use a single seepage mode to analyze and divide the real seepage law of the reservoir, and it is difficult to effectively distinguish the main factors affecting the reservoir seepage capacity, so it is impossible to provide accurate dynamic analysis basis for the targeted formulation of gas well production system and gas reservoir development countermeasures, and maximize the benefits of gas reservoir development.

[0004] When analyzing deep and highly heterogeneous carbonate gas reservoirs, traditional well test analysis methods show complex permeability characteristics due to the influence of factors such as fracture-cavity combination, acid fracturing transformation, and faults. The pressure derivatives on the double logarithmic curves of the pressure recovery well test of the gas well show that the radial flow difference between the near-well area and the far-well area is large. Based on core observation, imaging logging and other data, it is confirmed that there are a large number of fractures and caves in deep carbonate reservoirs, and the characteristics of multiple media are obvious. However, the existing well test interpretation of reservoir pressure derivative curves rarely observes the "depression" phenomenon unique to multiple media, and the well test interpretation model is difficult to interpret this mismatch between dynamic and static data. Moreover, although the existing well test interpretation results can generally describe the permeability of the reservoir encountered by the gas well, the changes in the seepage field and seepage parameters caused by different media, especially in analyzing the influence of the heterogeneous fracture zones in the reservoir on the seepage, make it difficult to accurately characterize the heterogeneous characteristics of the fractures and holes and the connectivity between the wellbore and the reservoir. The accuracy of the seepage analysis of carbonate gas reservoirs is not high, resulting in obvious differences between the well test interpretation results and the gas well production effects, which restricts the further in-depth understanding of the reservoir characteristics. Summary of the invention

[0005] The embodiment of the present invention provides a method for analyzing the seepage capacity of a deep carbonate gas reservoir, which is used to improve the accuracy of seepage analysis of carbonate gas reservoirs, reduce the difference between the interpretation results of macroscopic seepage capacity analysis such as oil testing and well testing and the actual production capacity of gas wells, and effectively support the formulation of a reasonable production system for gas wells and a gas reservoir development strategy. The method includes:

[0006] Obtaining static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation, wherein the static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil testing and well testing data;

[0007] Extracting distribution characteristic data and development characteristics of reservoir media from core pore structure analysis data of carbonate gas reservoirs, and constructing multiple core concept models based on the distribution characteristics and development characteristics of reservoir media, wherein the reservoir media includes pores, caves and fractures;

[0008] Determine the distribution characteristic data and development characteristics of the reservoir medium of the carbonate gas reservoir based on seismic interpretation data and well logging data, and construct different types of reservoir concept models based on the distribution characteristic data and development characteristics of the reservoir medium of the carbonate gas reservoir and multiple core concept models;

[0009] Determine the maximum well control range of a single well based on the seismic interpretation data, well logging data and oil test well testing data in the study area. Within the maximum well control range of a single well, construct a digital core single well concept model based on different types of reservoir concept models and multiple core concept models. The well logging data reflects the actual situation of a single well drilling through multiple reservoirs; the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range.

[0010] The digital core flow simulation analysis technology is used to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; a simulated well test curve is drawn based on the simulated well test data, and the simulated well test curve is compared with the real well test curve corresponding to the carbonate gas reservoir, and the deviation value of the digital core single well conceptual model is calculated based on the comparison results;

[0011] When the deviation value of the digital core single well conceptual model is not less than a preset threshold, the parameters of the digital core single well conceptual model are adjusted according to the deviation value, and the deviation value is continuously calculated using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold, thereby obtaining an optimized digital core single well conceptual model; and the seepage capacity of a deep carbonate gas reservoir is analyzed using the optimized digital core single well conceptual model.

[0012] The embodiment of the present invention also provides a seepage analysis device for deep carbonate gas reservoirs, which is used to improve the accuracy of seepage analysis of carbonate gas reservoirs, reduce the difference between the interpretation results of macroscopic seepage capacity such as oil testing and well testing and the actual production capacity of gas wells, and effectively support the formulation of reasonable production systems for gas wells and gas reservoir development strategies. The device includes:

[0013] A static data acquisition module is used to acquire static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation. The static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil testing and well testing data;

[0014] A core concept model building module is used to extract the distribution characteristics and development characteristics of the reservoir medium from the core pore structure analysis data of the carbonate gas reservoir reservoir, and to build multiple core concept models according to the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium includes pores, caves and fractures;

[0015] The reservoir concept model building module is used to determine the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir based on seismic interpretation data and well logging data, and to build different types of reservoir concept models based on the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of the carbonate gas reservoir;

[0016] A digital core single well conceptual model module is used to determine the maximum well control range of a single well based on the seismic interpretation data, well logging data and oil test well testing data in the study area. Within the maximum well control range of a single well, a digital core single well conceptual model is constructed based on different types of reservoir conceptual models and multiple core conceptual models. The well logging data reflects the actual situation of a single well drilling through multiple reservoirs, and the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range.

[0017] The curve drawing and comparison module is used to use the digital core flow simulation analysis technology to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; draw a simulated well test curve based on the simulated well test data, compare the simulated well test curve with the real well test curve corresponding to the carbonate gas reservoir, and calculate the deviation value of the digital core single well conceptual model based on the comparison results;

[0018] The model optimization module is used to adjust the parameters of the digital core single well conceptual model according to the deviation value when the deviation value of the digital core single well conceptual model is not less than a preset threshold value, and continue to calculate the deviation value using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold value to obtain the optimized digital core single well conceptual model; and use the optimized digital core single well conceptual model to analyze the seepage capacity of deep carbonate gas reservoirs.

[0019] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for analyzing the permeability of deep carbonate gas reservoirs when executing the computer program.

[0020] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for analyzing the permeability of a deep carbonate gas reservoir is implemented.

[0021] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned method for analyzing the permeability of a deep carbonate gas reservoir is implemented.

[0022] In the embodiment of the present invention, static data and dynamic data of carbonate gas reservoirs in the process of exploration and development evaluation are obtained; the distribution characteristics and development characteristics of the reservoir medium are extracted from the core pore structure analysis data, and multiple core concept models are constructed according to the distribution characteristics and development characteristics of the reservoir medium; the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir are determined according to the seismic interpretation data and the logging data, and different types of reservoir concept models are constructed according to the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of the carbonate gas reservoir; the maximum well control range of a single well is determined according to the seismic interpretation data, the logging data and the oil and well testing data, and within the maximum well control range of the single well, a digital rock is constructed according to different types of reservoir concept models and multiple core concept models. The invention discloses a digital core single well conceptual model; adopts digital core flow simulation analysis technology to perform finite element flow simulation test operation on the digital core single well conceptual model to obtain simulated well test data; draws a simulated well test curve according to the simulated well test data, compares the simulated well test curve with the real well test curve corresponding to the carbonate gas reservoir, and calculates the deviation value of the digital core single well conceptual model according to the comparison result; when the deviation value of the digital core single well conceptual model is not less than a preset threshold value, adjusts the parameters of the digital core single well conceptual model according to the deviation value, and continues to calculate the deviation value using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold value to obtain an optimized digital core single well conceptual model; and uses the optimized digital core single well conceptual model to analyze the seepage capacity of the deep carbonate gas reservoir.

[0023] By making full use of data such as gas reservoir geological characteristics, basic reservoir physical properties, and core pore structure analysis, combined with digital core flow simulation analysis technology, a digital core single well conceptual model that accurately reflects the distribution of reservoir space such as pores and fractures is constructed. This can improve the accuracy of carbonate gas reservoir seepage analysis, reduce the difference between the interpretation results of macroscopic seepage capacity such as oil testing and well testing and the actual production capacity of gas wells, and effectively support the formulation of reasonable production systems for gas wells and gas reservoir development strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 1 is a processing flow chart of a method for analyzing the permeability of a deep carbonate gas reservoir in an embodiment of the present invention;

[0026] Figure 2 A flow chart of a method for constructing a core conceptual model in an embodiment of the present invention;

[0027] Figure 3 A flow chart of a method for verifying the permeability range of a core conceptual model in an embodiment of the present invention;

[0028] Figure 4 This is a permeability chart of different hole-slit ratios when the porosity is 5% in an embodiment of the present invention;

[0029] Figure 5 A flow chart of a method for constructing different types of reservoir conceptual models in an embodiment of the present invention;

[0030] Figure 6 Schematic diagram of the relationship between cross-scale reservoir media in an embodiment of the present invention;

[0031] Figure 7(a) is a single well simulation well test curve drawn based on the digital core single well conceptual model; Figure 7(b) is an actual single well pressure recovery well test curve;

[0032] FIG8(a) shows the core reservoir space distribution of a pore-vuggy reservoir with a porosity of 3.3%; FIG8(b) shows the core reservoir space distribution of a fracture-vuggy reservoir with a porosity of 3.6%;

[0033] Fig. 9 The combination and simplification process of the conceptual model of the reservoir in the near-wellbore area of ​​the digital core is shown;

[0034] Fig.10A schematic cross-sectional view of a single well drilling trajectory is shown;

[0035] FIG11( a ) shows a single-well reservoir physical model, and FIG11( b ) shows a single-well conceptual model;

[0036] Fig.12 Schematic diagram of the structure of a seepage analysis device for a deep carbonate gas reservoir in an embodiment of the present invention;

[0037] Fig.13 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0039] Figure 1 FIG. 1 is a processing flow chart of a method for analyzing the permeability of a deep carbonate gas reservoir in an embodiment of the present invention. Figure 1 As shown, the method for analyzing the permeability of a deep carbonate gas reservoir in an embodiment of the present invention may include:

[0040] Step 101, acquiring static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation, wherein the static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil test and well testing data;

[0041] Step 102: extracting the distribution characteristics and development characteristics of the reservoir medium from the core pore structure analysis data of the carbonate gas reservoir, and constructing multiple core concept models according to the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium includes pores, caves and fractures;

[0042] Step 103: determining the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir according to the seismic interpretation data and the well logging data, and constructing different types of reservoir concept models according to the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir and multiple core concept models;

[0043] Step 104: determine the maximum well control range of a single well based on the seismic interpretation data, well logging data and oil test well testing data in the study area, and construct a digital core single well concept model within the maximum well control range of the single well based on different types of reservoir concept models and multiple core concept models, wherein the well logging data reflects the actual situation of a single well drilling through multiple reservoirs, and the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range;

[0044] Step 105: using digital core flow simulation analysis technology, perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; draw a simulated well test curve based on the simulated well test data, compare the simulated well test curve with the real well test curve corresponding to the carbonate gas reservoir, and calculate the deviation value of the digital core single well conceptual model based on the comparison result;

[0045] Step 106: When the deviation value of the digital core single well conceptual model is not less than a preset threshold, the parameters of the digital core single well conceptual model are adjusted according to the deviation value, and the deviation value is continued to be calculated using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold, thereby obtaining an optimized digital core single well conceptual model; and the seepage capacity of the deep carbonate gas reservoir is analyzed using the optimized digital core single well conceptual model.

[0046] The following describes the execution steps of the seepage analysis method for deep carbonate gas reservoirs in an embodiment of the present invention:

[0047] First, step 101 may be performed to obtain static data and dynamic data of carbonate gas reservoirs during the exploration and development evaluation process, wherein the static data may include core pore structure analysis data, seismic interpretation data and well logging data, and wherein the dynamic data may include oil test and well testing data.

[0048] In specific implementation, static and dynamic data obtained in the early stage of gas reservoir development and evaluation can be used, such as drilling records and tests, coring records and core pore structure analysis data, seismic interpretation data, logging data, oil testing and well testing data, etc.

[0049] Then, step 102 may be performed to extract the distribution characteristics and development characteristics of the reservoir medium from the core pore structure analysis data of the carbonate gas reservoir, and construct multiple core concept models based on the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium may include pores, caves and fractures.

[0050] It should be noted that the core pore structure analysis data specifically include but are not limited to: CT scan results, capillary pressure curves, nuclear magnetic resonance analysis data, etc.; the pore structure of the core is comprehensively analyzed at multiple scales from micron to centimeter level, and a multi-scale digital core model can be constructed eventually.

[0051] The embodiment of the present invention conceptualizes the digital core model from these pore structure data. The existing technology usually develops in a more refined direction. However, the more refined the model is, the larger the amount of data will be, and faster computers with more storage capacity will be needed to assist in completion, and the time and financial costs will become higher and higher. The research direction of the present invention is the opposite, which is another research direction of scaling from micro to macro. A macro single well model is established through step-by-step conceptualization (simplification), and the main reservoir types and main influencing factors that constitute the gas well production capacity are found through comparative analysis with real gas well dynamic data.

[0052] In one embodiment, the distribution characteristics and development characteristics of the reservoir medium may include one or any combination of the following:

[0053] Cave size distribution curves in different porosity ranges, cave number distribution curves, fracture number and distribution curves, fracture inclination data, fracture length data, fracture thickness data, fracture width data, and normal distribution range of matrix porosity.

[0054] In one embodiment, the distribution characteristics and development characteristics of the reservoir medium can be obtained in the following manner: using electronic computer tomography to obtain the distribution characteristics and development characteristics of the matrix, wherein the matrix is ​​tiny pores with a porosity less than a preset threshold; using electronic computer X-ray tomography technology to obtain the distribution characteristics and development characteristics of the core scale; according to seismic interpretation data, the distribution characteristics and development characteristics of the reservoir medium at the reservoir scale are obtained; according to the maximum extension direction of the fracture, the fracture is flattened for analysis to obtain the distribution characteristics and development characteristics of the fracture; using the maximum sphere method to obtain the distribution characteristics and development characteristics of the cave.

[0055] In specific implementation, the microscopic development patterns of reservoir media such as fractures and caves and the basic characteristics of fracture-cave reservoirs encountered by gas wells can be analyzed and summarized, and the distribution and development characteristics of the reservoir media can be extracted. The distribution characteristics and development characteristics of the reservoir media may include but are not limited to: the size distribution curve of caves in different porosity ranges, the number distribution curve of caves / fractures, the statistical distribution of fracture inclination, length, thickness, and width, and the normal distribution range of matrix porosity.

[0056] Among them, the pore size distribution of the matrix is ​​mainly obtained by micro-CT (computer tomography) or scanning electron microscope analysis results, but due to the influence of heterogeneity, the representativeness of the sample will be biased; since carbonate reservoirs usually use tiny pores with a porosity of less than 2% as the matrix, when the pore size of the matrix may not be fully identified due to scanning accuracy or scale, the pore distribution of the matrix can be simulated by the normal random function of Randn (0, 2); the main parameters of the cave are statistically analyzed by the maximum sphere method; the fracture parameters are obtained by flattening the fracture according to the maximum extension direction of the fracture.

[0057] It should be noted that in the embodiment of the present invention, the model mainly reflects the reservoir medium: fractures and caves. Small pores are treated as matrix, similar to the "background" of the model.

[0058] The following are the specific implementation steps for building a core conceptual model:

[0059] Figure 2 Flow chart of the method for constructing a core conceptual model in an embodiment of the present invention. Figure 2 As shown, in one embodiment, multiple core concept models are constructed according to the distribution characteristics and development characteristics of the reservoir medium, which may include:

[0060] Step 201, presetting the porosity classification range and the permeability classification range of the reservoir medium;

[0061] Step 202: According to the porosity classification range and the permeability classification range of the reservoir medium, normal function fitting is performed on the distribution characteristics and development characteristics of the reservoir medium in different classification ranges to obtain the classification statistical characteristic parameter range of the reservoir medium;

[0062] Step 203: using a normal random function to generate a three-dimensional spatial distribution parameter of the reservoir medium according to the hierarchical statistical characteristic parameter range of the reservoir medium;

[0063] Step 204: construct multiple core conceptual models based on the three-dimensional spatial distribution parameters of the reservoir medium.

[0064] In specific implementation, the carbonate reservoir of the reservoir medium can be divided into at least 3 levels or more according to its core porosity and permeability, and normal function fitting is performed respectively. The stronger the heterogeneity of the reservoir, the more the number of levels should be. The reference of the classification standard is as follows:

[0065] Porosity: <2%, (2-6)%, >6%;

[0066] Permeability: <0.01mD, (0.01-0.1)mD, >0.1mD.

[0067] The pore radius satisfies the normal distribution function. According to the CT analysis of the core and the statistical results of the pore size distribution, the size of the cave, the length, width and thickness of the cracks in the core can be fitted with normal functions:

[0068] F(x)=N(μ, σ 2 ) (1)

[0069] Where: μ—cavern radius, fracture length, width and thickness normal distribution average value;

[0070] σ 2—Cave radius, standard deviation of normal distribution of crack length, width and thickness.

[0071] The distribution characteristics and development characteristics of the above-mentioned reservoir media can be used: the average diameter of the cave, the standard deviation of the cave diameter distribution, the number of fractures, the length, width, thickness mean and its standard deviation, the range of fracture inclination, etc., and the parameter range of the three-dimensional spatial distribution structure of caves and fractures under different porosities can be determined according to the following normal random function formula.

[0072] R h =Randn{min[(R a -3S gh ), R hmin ],max[(R a +3S gh ), R hmax ]} (2)

[0073] L bi =min[(C Li -3S gi ), C rmi ] (3)

[0074] U bi =max[(C Li +3S gi) , C rxi ] (4)

[0075] Where: R h —Radius of the cave, mm; R a —average radius of the cave, mm; R hmin —Minimum cave radius, mm; R hmax —Maximum cave radius, mm; S gh —Standard deviation of cave radius, mm; i—1 is crack length, 2 is crack width, 3 is crack thickness, mm; L bi —Lower limit of crack length / width / thickness, mm; U bi — Upper limit of crack length / width / thickness, mm; C Li —Average length / width / thickness of cracks, mm; C rmi —Minimum seam (length, width, thickness), mm; C rxi —Maximum seam (length, width, thickness), mm; S gi —Standard deviation of crack length / width / thickness, mm. Randn—Normal random function.

[0076] After constructing multiple core conceptual models, in order to verify whether the permeability of the established core conceptual models is within a reasonable range, digital core simulation flow technology can be used to perform flow simulation to calculate the permeability value and determine whether it is within the classification range. If so, it is considered reasonable. Otherwise, the conceptual model needs to be regenerated and the permeability verification is performed again.

[0077] Figure 3 Flow chart of the method for verifying the permeability range of the core conceptual model in an embodiment of the present invention. Figure 3 As shown, in one embodiment, after constructing multiple core concept models, the following steps may also be included:

[0078] Step 301, using digital core flow simulation analysis technology to determine the permeability of each core conceptual model in three dimensions;

[0079] Step 302: when the permeability of the core conceptual model does not conform to the preset permeability classification range, a normal random function is used to regenerate the three-dimensional spatial distribution parameters of the reservoir medium;

[0080] Step 303: Continue to construct multiple core concept models according to the regenerated three-dimensional spatial distribution parameters of the reservoir medium until the permeability of each core concept model meets the pre-set permeability classification range.

[0081] In specific implementation, digital core flow simulation analysis technology can also be used to analyze the permeability of these core conceptual models, and to establish triangular graphs and data tables of core permeability under different porosity conditions and different reservoir medium distributions. Figure 4 : This is a permeability chart of different hole-slit ratios when the porosity is 5% in the embodiment of the present invention. Figure 4 As shown, Figure 4 This is an application example generated during the analysis of the present invention, which can be used to establish a chart of the proportion of different reservoir media at different porosities in the region (gas reservoir), and this chart can be improved as the core pore structure analysis data is continuously enriched. After the chart is formed, when randomly generating a core conceptual model, a comparison can be made to check whether the random model is within a reasonable range, thereby providing a reasonable guarantee for the establishment of a higher-scale conceptual model to reflect the actual situation of the underground reservoir.

[0082] After constructing multiple core concept models, step 103 is executed to determine the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir based on the seismic interpretation data and the logging data, and to construct different types of reservoir concept models based on the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of the carbonate gas reservoir.

[0083] The following are the specific implementation steps for constructing a reservoir conceptual model:

[0084] It should be noted that the reservoir conceptual model here is different from the aforementioned core conceptual model. The aforementioned is a first-level conceptualized first-level (core-level) conceptual model, while this is a second-level conceptualized reservoir-level conceptual model.

[0085] Figure 5 Flow chart of the method for constructing different types of reservoir conceptual models in the embodiment of the present invention. Figure 5 As shown, in one embodiment, based on the vertical and horizontal distribution characteristics and development characteristics of the carbonate gas reservoir reservoir medium and multiple core concept models, different types of reservoir concept models are constructed, which may include:

[0086] Step 501: classify carbonate gas reservoirs according to the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir to obtain multiple reservoir types;

[0087] Step 502: Overlay multiple core conceptual models within the same type of reservoir to construct different types of reservoir conceptual models.

[0088] In one embodiment, the reservoir concept model may include one or any combination of the following models: a fracture-pore reservoir concept model, a fracture reservoir concept model, a cave reservoir concept model and a pore reservoir concept model.

[0089] After constructing different types of reservoir concept models, execute step 104 to determine the maximum well control range of a single well based on the seismic interpretation data, logging data and oil test data in the study area. Within the maximum well control range of the single well, a digital core single well concept model is constructed based on different types of reservoir concept models and multiple core concept models, wherein the logging data reflects the actual situation of a single well drilling through multiple reservoirs.

[0090] It should be noted that this maximum well control range is only a possible maximum range. Due to the early development, the geological conditions around the single well are not very clear, and only a rough model can be constructed. From the data of the flow simulation process, we can analyze which areas of reserves have been mobilized within the well control range of the single well model, so as to continuously correct the well control range of the single well. If the model is built too small, the mobilized area and the actual production capacity of the single well will be underestimated.

[0091] The embodiment of the present invention carries out conceptualization at three different levels, that is, at least three simplifications. In this simplification process, different reasonable simplification techniques and methods are used for different reservoir media, so that the simplified single well model can reflect the main characteristics of the reservoir, rather than including all the detailed characteristics into the model.

[0092] The following are the specific implementation steps for building a digital core single well conceptual model:

[0093] The different fracture-cavity combination conceptual models of the study area can be combined to establish a digital core single well conceptual model based on the distribution and development of reservoir media in seismic logging, drilling testing and coring data and the actual situation of reservoirs encountered by typical gas wells.

[0094] ① Determine the reservoir type encountered by the gas well according to the data displayed by seismic, well logging and drilling;

[0095] ② Segment and count the reservoir types encountered by the gas wells to determine the main reservoir and conductivity types and reservoir physical parameters (such as distribution range, thickness, porosity and permeability range, etc.) that have the main contribution to the actual productivity of the gas wells;

[0096] ③ The digital core conceptual models representing different fracture-cavity reservoir types are combined according to the main reservoir types encountered by the gas wells;

[0097] ④ In order to improve the efficiency of flow simulation calculation, the combined model superimposed with the digital core conceptual model is conceptualized again. The main steps of simplification are as follows:

[0098] The distribution of caves is reclassified according to the reservoir type in the single well range area, and the porosity distribution characteristics of caves at all levels (including porosity range, pore size range, heterogeneity coefficient, etc.) are simulated using normal random functions, which are reflected in the model as multiple characteristic parameters such as porosity, permeability parameters and distribution area. When the permeability heterogeneity is strong and the number of samples is small, the weighted average is used instead of the geometric mean. The weighted parameters include but are not limited to: sample length, sample volume, and the thickness of the reservoir subdivision layer represented by the sample.

[0099] It should be noted that the classification standard is to classify caves according to reservoir types, and fractures are a special reservoir medium that plays a key role in flow energy. Therefore, the modeling mainly reflects its distribution and scale. If it is a microcrack, it has been included in the core-level conceptual model and does not need to be considered here. If it is a fracture above the meter level, it is included in the single well model and modeled according to its shape and size.

[0100] Random fitting of the i-th type cave distribution:

[0101] Φ Rfi =Φ i +(Φ imax -Φ imin )×a Φ ×Randn(0, 1) (5)

[0102] Where: Φ Rfi—porosity of a point in the i-th reservoir type; Φ i —Geometric mean value of the cave porosity in this type of reservoir; Φ imax —The maximum value of the cave porosity in this type of reservoir; Φ imin —Minimum porosity of this type of cave; a Φ —The heterogeneity coefficient of the cave porosity in this type of reservoir, a Φ =Φ a / Φ i ; Φ a —The arithmetic mean of the cave porosity in this type of reservoir; Randn(0,1)—a normal random function between 0 and 1.

[0103] K Rfi =aK i0 exp[((K imax / K imin )×a K ×Randn(0, 1)] (6)

[0104] Where: K Rfi —permeability of a point in the i-th reservoir type; K i0 —The fitting function form of the cave permeability index in this type of reservoir K=aK0 -b The base K0 (geometric mean); K imax —The maximum value of cave permeability in this type of reservoir; K imin —The minimum value of cave permeability in this type of reservoir; a K —Heterogeneity coefficient of cave permeability in this type of reservoir, a K =log 10 (K a / K i0 );K a —The arithmetic mean of the cave permeability in this type of reservoir.

[0105] All classified reservoir caves are superimposed to form a cave set in a single well area. Fractures with a length greater than 10 times the accuracy must maintain their geometric shape according to their length range, distribution and model establishment accuracy requirements. For example: when the accuracy is 0.01m, fractures with a length of more than 0.1m must maintain their geometric shape, and fractures with a length of less than 0.1m can be simplified according to the principle of conceptualization of cave areas. The scale of the digital core single well conceptual model conforms to the credible scale range that can be provided by seismic and drilling test data, so as to reflect the development and distribution of reservoir media in real reservoirs.

[0106] Figure 6It is a schematic diagram of the relationship between cross-scale reservoir media in an embodiment of the present invention, showing the process of using different reasonable simplification technologies and methods for different reservoir media to establish a digital core single well conceptual model, the change process and correlation of reservoir medium units.

[0107] In order to verify whether the established digital core single well conceptual model is accurate, step 105 is executed to use the digital core flow simulation analysis technology to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; a simulated well test curve is drawn based on the simulated well test data, the simulated well test curve is compared with the real well test curve corresponding to the carbonate gas reservoir, and the deviation value of the digital core single well conceptual model is calculated based on the comparison result.

[0108] Next, the digital core single well conceptual model is further optimized, and step 106 is executed. When the deviation value of the digital core single well conceptual model is not less than a preset threshold, the parameters of the digital core single well conceptual model are adjusted according to the deviation value, and the deviation value is continued to be calculated using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold, thereby obtaining an optimized digital core single well conceptual model; and the seepage capacity of the deep carbonate gas reservoir is analyzed using the optimized digital core single well conceptual model.

[0109] In specific implementation, the accuracy of the digital core single well conceptual model can be analyzed and optimized by following the steps below:

[0110] (1) Using the established digital core single well conceptual model, the digital core flow simulation analysis technology that can reflect the seepage flow state of different media in holes, caves and fractures is used to perform finite element flow simulation trials and calculate the pressure decay curve over time during the mining process under real high temperature and high pressure formation conditions. The accuracy of the time acquisition point needs to reach 10 -5 s or lower.

[0111] (2) After debugging and calculating the pressure continuous change data that meets the accuracy requirements of the well test interpretation, based on the actual on-site well test process, the pressure and production changes during the well test, such as the shut-in and recovery, are simulated in the digital core single well conceptual model, and the pressure and bottom hole flow values ​​in the simulated well test at different times are recorded.

[0112] (3) According to the bottom hole flow pressure value, wellhead pressure value, bottom hole temperature, fluid density, viscosity and other conditions, the bottom hole flow rate is converted into the production under the standard wellhead conditions. According to the change of wellhead production and bottom hole pressure value with the test time, a test curve (such as double logarithmic curve of pressure recovery) of simulated well test is made.

[0113] (4) Compare the obtained well test curve with the corresponding typical well test curve in the study area, and analyze the differences between the two and the reasons for their occurrence. If the difference between the two is large except for the near-well area (the single well may have been acidified), the digital core single well model must be adjusted and improved, and compared with the seismic, well logging and core test data in the study area. Analyze the main reason for the difference, whether it is caused by the lack of fractures and holes or the representativeness of heterogeneous samples due to the accuracy of these data analysis, or whether it is caused by the excessive coarsening of the conceptual model during the modeling process.

[0114] ① Determine whether the difference is caused by the excessive coarsening of the conceptual model during the modeling process

[0115] In the original single well conceptual model, the second-level conceptual simplification is no longer performed, and the combination of reservoir conceptual models is directly adopted; and at the far end of the wellbore, a certain scale of fracture or cave geometric entities are added. The geometric entity shape and scale of the fracture and cave refer to the average value of the fracture and cave shape and scale shown in the basic data of this area. A simulated well test is conducted again under the same conditions as the original single well model to observe whether the curve change is closer to the real typical gas well test curve. If it is closer, it is necessary to adjust the geometric entity shape and scale of the fracture and cave again to obtain a single well model that is closest to the real well test curve. If repeated adjustments are made, only the direction and trend are close, but the amplitude changes are small, it is possible to consider improving the refinement of different types of reservoir classification, such as subdividing the cave type into small cave type, medium cave type and large cave type, and subdividing the fracture type into fracture-hole type and fracture-pore type, etc. After improving the subdivision, the reservoir concept model and the single well model under different types of reservoir combinations are established again, and simulated well tests are conducted to observe whether the curve change is closer to the real typical gas well test curve.

[0116] ② Determine whether the difference is caused by the representativeness of the non-homogeneous sample of the basic data.

[0117] Change the distribution and scale (density) of the reservoir medium in the reservoir concept model respectively, and the adjustment range shall not exceed the extreme value range in the pore-cavity fracture statistical data of this area. Carry out the same simulated well test as the original single well model again to observe whether the curve change is closer to the actual typical gas well test curve. If it is closer, it is necessary to adjust the distribution and scale (density) parameters of the reservoir medium again to obtain the single well model that is closest to the actual well test curve.

[0118] ③ Determine whether the difference is caused by the lack of seams due to the accuracy of basic data analysis during modeling

[0119] The basic parameters of the conceptual model are adjusted separately, including the main parameters reflecting the length, width, strike (inclination) of the fractures in the reservoir, the size distribution and development density of the caves, and the statistical heterogeneity of the matrix porosity. The adjustment range does not exceed the extreme value range of the relevant statistical data in this area. The same simulated well test as the original single well model is carried out again to observe whether the curve change is closer to the actual typical gas well test curve. If it is closer, it is necessary to adjust the corresponding parameters again to obtain a single well model that is closest to the actual well test curve.

[0120] After judging and analyzing the reasons for the differences, in the process of re-improving the digital core single well model, reference can be made to the geological seismic data, coring data, gas well logging test and production dynamic data of adjacent wells or the gas reservoir if necessary.

[0121] (5) Continuously modify and improve the digital core single well model, and conduct simulated well test analysis again until the simulated test well curve is consistent with the general trend and direction of the test curve in the simulated area. This process of debugging and improving the digital core single well model can reflect the main factors affecting gas well productivity in terms of geological reservoirs.

[0122] (6) Considering the strong heterogeneity of deep carbonate gas reservoirs, after conducting a simulated well test analysis of a single well, it is recommended that, if conditions permit, digital core single well modeling simulated well test analysis of 3-5 wells in the adjacent area be conducted to enhance the accuracy of the simulated well test analysis.

[0123] (7) Based on the classification of different types of gas wells encountering different reservoir combinations, simulated well test analysis of different types of gas wells encountering reservoir types is carried out.

[0124] Figure 7(a) is a simulated well test curve of the digital core single well conceptual model, in which multiple fracture-cavity reservoirs are connected during drilling; Figure 7(b) is an actual single well pressure recovery well test curve. By comparing the curve trends shown in Figures 7(a) and 7(b), the difference information (deviation value) between the single well simulated well test curve and the actual single well pressure recovery well test curve can be obtained.

[0125] The effects of the present invention are mainly reflected in the following aspects:

[0126] (1) When analyzing deep carbonate gas reservoirs, the existing traditional well test interpretation model fails to accurately reflect the distribution of reservoir spaces such as fractures and holes, as well as the changes in the seepage field and seepage parameters between different reservoir media, especially the impact of fracture zones on seepage, due to the geological characteristics of deep reservoir burial, reservoir basic physical properties, core pore structure analysis and other data. In combination with digital core flow simulation analysis technology, the present invention proposes a simulation well test analysis method that can quantitatively analyze the impact of various reservoir media combinations and heterogeneous distribution conditions such as pores, caves, and fractures within the well control range of the gas well on the dynamic production of the gas well.

[0127] (2) For reservoirs with strong heterogeneity, in terms of multi-scale reservoir characterization and modeling methods, the present invention uses the main storage space that affects fluid seepage as the description subject, including pores, caves and fractures, etc., ignoring the influence of small throats and pores with less seepage effect on the flow, and uses them as the matrix background for a unified scientific homogeneous or random heterogeneous description. This is different from the development trend of more detailed characterization of existing digital cores. This method is conducive to the overall description of the macroscopic flow of gas reservoirs or single wells, rapid analysis of the main contradictions affecting seepage, and identification of the main influencing factors affecting flow. It is also conducive to reducing the amount of calculation when performing macroscopic flow analysis in the context of big data, saving time in analyzing seepage and production problems. In this regard, the present invention has obvious advantages.

[0128] The technical solution of the present invention is explained in detail below through a specific embodiment:

[0129] Based on the S101 drilling records and tests, coring records, core pore structure analysis results, production logging, seismic, imaging logging data and other data of a deep carbonate DG gas reservoir in the S basin, the distribution and development characteristics of the reservoir medium are extracted. The two types of reservoirs, the pore-vuggy type and the fracture-pore-vuggy type, are the main production reservoirs. The physical property parameters of the pore-vuggy reservoir are porosity>2% and permeability>0.01mD; the physical property parameters of the fracture-pore-vuggy reservoir are porosity>2% and permeability>0.1mD. Except for the main production layer within the well control range, the reservoirs in most areas are pore-type reservoirs with a porosity of less than 2% and a permeability of less than 0.01mD. The pore size distribution of the reservoir core is mainly obtained by micro-CT or scanning electron microscopy analysis results.

[0130] (1) Establishment of digital core conceptual model

[0131] The main parameters of the caves in the pore-type reservoirs were statistically analyzed by the maximum sphere method. Using the statistical distribution range of the above reservoir media, the three-dimensional spatial distribution parameters of pores, caves and fractures of different types of reservoirs were randomly generated according to the above formula, and then multiple core conceptual models were constructed. Table 1 shows the distribution range of the core reservoir media of a deep carbonate DG gas reservoir in the S basin.

[0132] R h =Randn(0.03,8.23);L b1 =3.45mm, U b1 =12.34mm; L b2 =2.35mm, U b2 =10.23mm,

[0133] L b3 =0.07mm, U b3 =2.45mm.

[0134] Table 1 Statistics of core reservoir medium distribution range

[0135]

[0136] FIG8(a) shows the storage space distribution of the core of the vug-type reservoir with a porosity of 3.3%; FIG8(b) shows the storage space distribution of the core of the fracture-vug-type reservoir with a porosity of 3.6%.

[0137] (2) Conceptual models of different types of reservoirs in the near-wellbore area of ​​digital cores

[0138] According to the microscopic development pattern of the above-mentioned reservoir media and the basic characteristics of fracture-cavity reservoirs encountered by gas wells, the situations of gas wells encountering different reservoirs are also classified. The seismic and imaging logging test results of Well S101 show that the fracture zone near the wellbore is connected with the fracture-cavity in the far wellbore area, and the scale of the fracture-cavity body is limited. The digital core models of different types of reservoirs established at the microscopic level are superimposed within the production layer according to the logging results, and the scale is expanded to the meter level to form different types of reservoir models near the wellbore area. Due to the large number of model grid nodes, it is necessary to simplify and construct a reservoir conceptual model.

[0139] Fig. 9 The simplified process of the reservoir model encountered by a single well in the near-wellbore zone of the digital core (second-level conceptual model) is shown. This conceptual model is a simplified second-level conceptual model based on the numerical simulation of different fracture-cavity reservoirs, taking into account factors such as the length, width, strike (inclination), size distribution and development density of the caves, and statistical heterogeneity of the matrix porosity in a certain type of reservoir encountered by the gas well. In order to facilitate the observation of the combination, the schematic diagram is displayed in the form of an oblique half section.

[0140] (3) Established digital core single well conceptual model

[0141] According to the different fracture-cavity combination conceptual models established in the study area, the distribution and development of reservoir media in seismic logging, drilling testing and coring data, and the actual situation of typical gas wells encountering multiple reservoirs, various types of reservoir conceptual models are combined to establish a digital core single well conceptual model.

[0142] The porosity and permeability of the single well conceptual model are calculated according to the following fitting formula:

[0143] Φ Rfi =Φ i +(Φ imax -Φ imin )×a Φ ×Randn(0,1)

[0144] K Rfi =aK i0 exp[((K imax / K imin )×a K ×Randn(0,1)

[0145] The main reservoirs of the well are divided into five categories, and their physical properties (porosity and permeability) are shown in Table 2:

[0146] Table 2 Reservoir classification and distribution of physical property parameters

[0147]

[0148] When establishing a digital core single well conceptual model, the model can be conceptualized and simplified for the sake of high efficiency of flow simulation calculation. For example, the distribution of caves in the single well range area can be classified again according to the degree of cave development, and the porosity distribution characteristics of caves at each level are simulated by normal random functions, which are reflected in the model as their porosity, permeability parameters and distribution areas. Fractures with a length of more than 0.1m must maintain their geometric shape according to their length range, distribution and model establishment accuracy requirements, and fractures with a length of less than 0.1m can be simplified according to the principle of conceptualization of cave areas. The scale of the digital core single well conceptual model is constructed according to the maximum well control radius of 1500m. The model accuracy is consistent with the range of credible scales of 20m provided by seismic and drilling test data. The maximum grid unit in the model hole and fracture-cave development area is less than 20m to reflect the development and distribution of reservoir media in real reservoirs.

[0149] Fig.10The schematic diagram of the single well drilling trajectory is shown. The drilling trajectory shows that the near-well fracture zone is connected with the fracture-cavity in the far-well area, and the fracture-cavity body is limited in size. Figure 11 (a) shows the single-well reservoir physical model, and Figure 11 (b) shows the single-well conceptual model. Combining Figures 11 (a) and 11 (b), the generation process of the single-well digital core forward physical model of Well S101 can be obtained (third-level conceptual model).

[0150] The digital core simulation flow technology that can reflect the seepage flow state of different media in holes, caves and cracks is used to conduct finite element flow simulation trials and calculate the pressure attenuation change curve during mining under real formation conditions. The pressure value measurement accuracy reaches 10 -5 s. After debugging and calculating the pressure continuous change data that meets the accuracy requirements of the well test interpretation, based on the actual production and well test conditions on site, first simulate the pressure decay process of the well control range during the well opening process, then simulate the pressure and production change process during the well test such as the well shut-in recovery, record the pressure, bottom hole flow, fluid density, viscosity and other data in the simulated well test at different times, and convert them into the production of the wellhead standard conditions. According to the changes in the wellhead production and bottom hole pressure values ​​with the test time, a double logarithmic curve of the pressure recovery of the simulated well test is drawn.

[0151] The obtained well test curve is compared with the typical well test curve corresponding to the study area, and the digital core single well model is continuously modified and improved. The simulated well test analysis is performed again until the simulated test curve is consistent with the general trend and direction of the well test curve in the simulated area. The well test curve of this well reflects that the two curves are parallel in the early stage, the linear flow characteristics of the fracture appear near the well, and the transition section shows the radial flow of the horizontal section. Due to the very limited scale of connectivity, the well test pressure derivative curve will quickly rise and tend to gradually close after the transition section appears. Starting from the model analysis, the high production of this well in the early stage is because the fractures are connected in the near-well zone, the reserves in the near-well area are mobilized, and a short-term high production is formed, but the subsequent production is difficult to maintain a high level. It can also be proposed that the main influencing factor restricting the production capacity is the low permeability of the fluid in the far-well reservoir. The production allocation of this well should not be set too high, so as to avoid the inability to continue stable production in the later stage.

[0152] The present invention also provides a seepage analysis device for deep carbonate gas reservoirs, as described in the following embodiments. Since the principle of the device to solve the problem is similar to the seepage analysis method for deep carbonate gas reservoirs, the implementation of the device can refer to the implementation of the seepage analysis method for deep carbonate gas reservoirs, and the repeated parts will not be repeated.

[0153] Fig.12 Schematic diagram of the structure of a seepage analysis device for a deep carbonate gas reservoir in an embodiment of the present invention.

[0154] like Fig.12As shown, the seepage analysis device for deep carbonate gas reservoir in the embodiment of the present invention may specifically include:

[0155] Static data acquisition module 1201, used to acquire static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation, wherein the static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil testing and well testing data;

[0156] The core concept model building module 1202 is used to extract the distribution characteristics and development characteristics of the reservoir medium from the pore structure analysis data of the carbonate gas reservoir core, and to build multiple core concept models according to the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium includes pores, caves and fractures;

[0157] The reservoir concept model building module 1203 is used to determine the vertical and horizontal distribution characteristics and development characteristics of the carbonate gas reservoir reservoir medium according to the seismic interpretation data and the well logging data, and to build different types of reservoir concept models according to the vertical and horizontal distribution characteristic data and development characteristic data of the carbonate gas reservoir reservoir medium and multiple core concept models;

[0158] The digital core single well concept model module 1204 is used to determine the maximum well control range of the single well according to the seismic interpretation data, well logging data and oil test well testing data of the study area, and to construct a digital core single well concept model within the maximum well control range of the single well according to different types of reservoir concept models and multiple core concept models, wherein the well logging data reflects the actual situation of a single well drilling through multiple reservoirs, and the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range;

[0159] The curve drawing and comparison module 1205 is used to use the digital core flow simulation analysis technology to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; draw a simulated well test curve based on the simulated well test data, compare the simulated well test curve with the real well test curve corresponding to the carbonate gas reservoir, and calculate the deviation value of the digital core single well conceptual model based on the comparison result;

[0160] The model optimization module 1206 is used to adjust the parameters of the digital core single well conceptual model according to the deviation value when the deviation value of the digital core single well conceptual model is not less than a preset threshold value, and continue to calculate the deviation value using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold value to obtain the optimized digital core single well conceptual model; and use the optimized digital core single well conceptual model to analyze the seepage capacity of deep carbonate gas reservoirs.

[0161] In one embodiment, the distribution characteristics and development characteristics of the reservoir medium include one or any combination of the following:

[0162] Cave size distribution curves, cave number distribution curves, fracture number and distribution data, fracture inclination data, fracture length data, fracture thickness data, fracture width data, and normal distribution range of matrix porosity in different porosity ranges.

[0163] In one embodiment, the distribution characteristics and development characteristics of the reservoir medium are obtained as follows:

[0164] Computerized X-ray tomography is used to obtain the distribution and development characteristics of the matrix, where the matrix is ​​tiny pores with a porosity less than a preset threshold;

[0165] Computer X-ray tomography is used to obtain the distribution and development characteristics of core-scale reservoir media;

[0166] According to the maximum extension direction of the crack, the crack is flattened for analysis to obtain the distribution and development characteristics of the crack;

[0167] The maximum sphere method is used to obtain the distribution and development characteristics of caves in the core.

[0168] In one embodiment, the core conceptual model building module 1202 is specifically used to:

[0169] Preset the porosity classification range and permeability classification range of the reservoir medium;

[0170] According to the porosity classification range and permeability classification range of the reservoir medium, normal function fitting is performed on the distribution characteristics and development characteristics of the reservoir medium in different classification ranges to obtain the classification statistical characteristic parameter range of the reservoir medium;

[0171] Using normal random function, according to the range of hierarchical statistical characteristic parameters of reservoir media, the three-dimensional spatial distribution parameters of reservoir media are generated;

[0172] According to the three-dimensional spatial distribution parameters of the reservoir medium, multiple core conceptual models are constructed.

[0173] In one embodiment, a three-dimensional spatial distribution parameter regeneration module is further included, which is used after the core concept model building module 1202 builds multiple core concept models:

[0174] Digital core flow simulation analysis technology is used to determine the permeability of each core conceptual model in three dimensions;

[0175] When the permeability of the core conceptual model does not conform to the pre-set permeability classification range, a normal random function is used to regenerate the three-dimensional spatial distribution parameters of the reservoir medium;

[0176] According to the regenerated three-dimensional spatial distribution parameters of the reservoir medium, multiple core concept models are continuously constructed until the permeability of each core concept model meets the pre-set permeability classification range.

[0177] In one embodiment, the reservoir conceptual model building module 1203 is specifically used to:

[0178] According to the vertical and horizontal distribution characteristics and development characteristics of carbonate gas reservoir reservoir media, carbonate gas reservoir reservoirs are classified to obtain multiple reservoir types;

[0179] Multiple core conceptual models are superimposed and combined within the same type of reservoir to construct different types of reservoir conceptual models.

[0180] In one embodiment, the reservoir conceptual model includes one or any combination of the following models:

[0181] Fracture-pore type reservoir conceptual model, fracture type reservoir conceptual model, cave type reservoir conceptual model and pore type reservoir conceptual model.

[0182] Based on the above invention concept, Fig.13 As shown, the present invention also proposes a computer device 1300, including a memory 1310, a processor 1320, and a computer program 1330 stored in the memory 1310 and executable on the processor 1320, wherein the processor 1320 implements the aforementioned method for analyzing the permeability of deep carbonate gas reservoirs when executing the computer program 1330.

[0183] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned seepage analysis method for deep carbonate gas reservoirs is implemented.

[0184] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the above-mentioned seepage analysis method for deep carbonate gas reservoir is implemented.

[0185] In summary, in the embodiments of the present invention, static data and dynamic data of carbonate gas reservoirs in the process of exploration and development evaluation are obtained; the distribution characteristics and development characteristics of the reservoir medium are extracted from the core pore structure analysis data, and multiple core concept models are constructed according to the distribution characteristics and development characteristics of the reservoir medium; the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir are determined according to the seismic interpretation data and the logging data, and different types of reservoir concept models are constructed according to the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of the carbonate gas reservoir; the maximum well control range of a single well is determined according to the seismic interpretation data, the imaging logging data and the oil and well testing data imaging logging data, and within the maximum well control range of the single well, different types of reservoir concept models and multiple core concept models are constructed. The digital core single well conceptual model is constructed; the digital core flow simulation analysis technology is used to perform finite element flow simulation test operations on the digital core single well conceptual model to obtain simulated well test data; a simulated well test curve is drawn according to the simulated well test data, the simulated well test curve is compared with the real well test curve corresponding to the carbonate gas reservoir, and the deviation value of the digital core single well conceptual model is calculated according to the comparison result; when the deviation value of the digital core single well conceptual model is not less than a preset threshold value, the parameters of the digital core single well conceptual model are adjusted according to the deviation value, and the deviation value is continuously calculated using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold value to obtain an optimized digital core single well conceptual model; and the seepage capacity of the deep carbonate gas reservoir is analyzed using the optimized digital core single well conceptual model.

[0186] By making full use of data such as gas reservoir geological characteristics, basic reservoir physical properties, and core pore structure analysis, combined with digital core flow simulation analysis technology, a digital core single well conceptual model that accurately reflects the distribution of reservoir space such as pores and fractures is constructed. This can improve the accuracy of carbonate gas reservoir seepage analysis, reduce the difference between the interpretation results of macroscopic seepage capacity such as oil testing and well testing and the actual production capacity of gas wells, and effectively support the formulation of reasonable production systems for gas wells and gas reservoir development strategies.

[0187] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0191] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for analyzing the permeability of a deep carbonate gas reservoir, characterized in that: include: Obtaining static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation, wherein the static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil testing and well testing data; Extracting the distribution characteristics and development characteristics of the reservoir medium from the core pore structure analysis data of the carbonate gas reservoir, and constructing multiple core conceptual models based on the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium includes pores, caves and fractures; Determine the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of carbonate gas reservoirs based on seismic interpretation data and well logging data, and construct different types of reservoir concept models based on the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of carbonate gas reservoirs; Determine the maximum well control range of a single well based on the seismic interpretation data, well logging data and oil test well testing data in the study area, and build a digital core single well concept model within the maximum well control range of the single well based on different types of reservoir concept models and multiple core concept models. The well logging data reflects the actual situation of a single well drilling through multiple reservoirs, and the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range. The digital core flow simulation analysis technology is used to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; a simulated well test curve is drawn based on the simulated well test data, and the simulated well test curve is compared with the real well test curve corresponding to the carbonate gas reservoir, and the deviation value of the digital core single well conceptual model is calculated based on the comparison results; When the deviation value of the digital core single well conceptual model is not less than a preset threshold, the parameters of the digital core single well conceptual model are adjusted according to the deviation value, and the deviation value is continuously calculated using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold, thereby obtaining an optimized digital core single well conceptual model; and the seepage capacity of a deep carbonate gas reservoir is analyzed using the optimized digital core single well conceptual model.

2. The method according to claim 1, characterized in that The distribution characteristics and development characteristics of the reservoir medium include one or any combination of the following: Cave size distribution curves, cave number distribution curves, fracture number and distribution data, fracture inclination data, fracture length data, fracture thickness data, fracture width data, and normal distribution range of matrix porosity in different porosity ranges.

3. The method according to claim 1, characterized in that The distribution and development characteristics of the reservoir medium are obtained as follows: Computerized X-ray tomography is used to obtain the distribution and development characteristics of the matrix, where the matrix is ​​tiny pores with a porosity less than a preset threshold; Computerized X-ray tomography is used to obtain the distribution and development characteristics at the core scale; Obtain reservoir-scale distribution and development characteristics based on seismic interpretation data; According to the maximum extension direction of the crack, the crack is flattened for analysis to obtain the distribution and development characteristics of the crack; The maximum sphere method is used to obtain the distribution and development characteristics of caves.

4. The method according to claim 1, characterized in that According to the distribution and development characteristics of the reservoir medium, multiple core conceptual models are constructed, including: Preset the porosity classification range and permeability classification range of the reservoir medium; According to the porosity classification range and permeability classification range of the reservoir medium, normal function fitting is performed on the distribution characteristics and development characteristics of the reservoir medium in different classification ranges to obtain the classification statistical characteristic parameter range of the reservoir medium; Using normal random function, according to the range of hierarchical statistical characteristic parameters of reservoir media, the three-dimensional spatial distribution parameters of reservoir media are generated; According to the three-dimensional spatial distribution parameters of the reservoir medium, multiple core conceptual models are constructed.

5. The method according to claim 4, characterized in that After building multiple core conceptual models, it also includes: Digital core flow simulation analysis technology is used to determine the permeability of each core conceptual model in three dimensions; When the permeability of the core conceptual model does not conform to the pre-set permeability classification range, a normal random function is used to regenerate the three-dimensional spatial distribution parameters of the reservoir medium; According to the regenerated three-dimensional spatial distribution parameters of the reservoir medium, multiple core concept models are continuously constructed until the permeability of each core concept model meets the pre-set permeability classification range.

6. The method according to claim 1, characterized in that According to the vertical and horizontal distribution characteristics and development characteristics of carbonate gas reservoir reservoir media and multiple core conceptual models, different types of reservoir conceptual models are constructed, including: According to the vertical and horizontal distribution characteristics and development characteristics of carbonate gas reservoir reservoir media, carbonate gas reservoir reservoirs are classified to obtain multiple reservoir types; Multiple core conceptual models are superimposed and combined within the same type of reservoir to construct different types of reservoir conceptual models.

7. The method according to claim 6, characterized in that The reservoir conceptual model includes one or any combination of the following models: Fracture-pore type reservoir conceptual model, fracture type reservoir conceptual model, cave type reservoir conceptual model and pore type reservoir conceptual model.

8. A device for analyzing the permeability of a deep carbonate gas reservoir, characterized in that: include: Static data acquisition module, used to acquire static data and dynamic data of carbonate gas reservoirs during exploration and development evaluation, the static data includes core pore structure analysis data, seismic interpretation data and well logging data, and the dynamic data includes oil test and well test data; A core concept model building module is used to extract the distribution characteristics and development characteristics of the reservoir medium from the core pore structure analysis data of the carbonate gas reservoir reservoir, and to build multiple core concept models according to the distribution characteristics and development characteristics of the reservoir medium, wherein the reservoir medium includes pores, caves and fractures; The reservoir concept model building module is used to determine the vertical and horizontal distribution characteristics and development characteristics of the reservoir medium of the carbonate gas reservoir based on seismic interpretation data and logging data, and to build different types of reservoir concept models based on the vertical and horizontal distribution characteristics, development characteristics and multiple core concept models of the reservoir medium of the carbonate gas reservoir. A digital core single well conceptual model module is used to determine the maximum well control range of a single well based on the seismic interpretation data, well logging data and oil test well testing data in the study area. Within the maximum well control range of a single well, a digital core single well conceptual model is constructed based on different types of reservoir conceptual models and multiple core conceptual models. The well logging data reflects the actual situation of a single well drilling through multiple reservoirs, and the oil test well testing data reflects the macroscopic seepage capacity of different areas within the well control range. The curve drawing and comparison module is used to use the digital core flow simulation analysis technology to perform finite element flow simulation test calculations on the digital core single well conceptual model to obtain simulated well test data; draw a simulated well test curve based on the simulated well test data, compare the simulated well test curve with the real well test curve corresponding to the carbonate gas reservoir, and calculate the deviation value of the digital core single well conceptual model based on the comparison results; The model optimization module is used to adjust the parameters of the digital core single well conceptual model according to the deviation value when the deviation value of the digital core single well conceptual model is not less than a preset threshold value, and continue to calculate the deviation value using the adjusted digital core single well conceptual model until the deviation value is less than the preset threshold value to obtain the optimized digital core single well conceptual model; and use the optimized digital core single well conceptual model to analyze the seepage capacity of deep carbonate gas reservoirs.

9. The device according to claim 8, characterized in that The distribution characteristics and development characteristics of the reservoir medium include one or any combination of the following: Cave size distribution curves, cave number distribution curves, fracture number and distribution data, fracture inclination data, fracture length data, fracture thickness data, fracture width data, and normal distribution range of matrix porosity in different porosity ranges.

10. The device according to claim 8, characterized in that The distribution and development characteristics of the reservoir medium are obtained as follows: Computerized X-ray tomography is used to obtain the distribution and development characteristics of the matrix, where the matrix is ​​tiny pores with a porosity less than a preset threshold; Computerized X-ray tomography is used to obtain the distribution and development characteristics at the core scale; According to the maximum extension direction of the crack, the crack is flattened for analysis to obtain the distribution and development characteristics of the crack; The maximum sphere method is used to obtain the distribution and development characteristics of caves.

11. The device according to claim 8, characterized in that The core conceptual model building module is specifically used for: Preset the porosity classification range and permeability classification range of the reservoir medium; According to the porosity classification range and permeability classification range of the reservoir medium, normal function fitting is performed on the distribution characteristics and development characteristics of the reservoir medium in different classification ranges to obtain the classification statistical characteristic parameter range of the reservoir medium; Using normal random function, according to the range of hierarchical statistical characteristic parameters of reservoir media, the three-dimensional spatial distribution parameters of reservoir media are generated; According to the three-dimensional spatial distribution parameters of the reservoir medium, multiple core conceptual models are constructed.

12. The device according to claim 11, characterized in that It also includes a three-dimensional spatial distribution parameter regeneration module, which is used after the core concept model building module builds multiple core concept models: Digital core flow simulation analysis technology is used to determine the permeability of each core conceptual model in three dimensions; When the permeability of the core conceptual model does not conform to the pre-set permeability classification range, a normal random function is used to regenerate the three-dimensional spatial distribution parameters of the reservoir medium; According to the regenerated three-dimensional spatial distribution parameters of the reservoir medium, multiple core concept models are continuously constructed until the permeability of each core concept model meets the pre-set permeability classification range.

13. The device according to claim 8, characterized in that The reservoir conceptual model building module is specifically used for: According to the vertical and horizontal distribution characteristics and development characteristics of carbonate gas reservoir reservoir media, carbonate gas reservoir reservoirs are classified to obtain multiple reservoir types; Multiple core conceptual models are superimposed and combined within the same type of reservoir to construct different types of reservoir conceptual models.

14. The device according to claim 8, characterized in that The reservoir conceptual model includes one or any combination of the following models: Fracture-pore type reservoir conceptual model, fracture type reservoir conceptual model, cave type reservoir conceptual model and pore type reservoir conceptual model.

15. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

16. 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 method according to any one of claims 1 to 7 is implemented.

17. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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