Low-porosity and low-permeability reservoir pore structure parameter construction method and device and storage medium

Abstract

The invention relates to a low-porosity and low-permeability reservoir pore structure parameter construction method and device and a storage medium. The method comprises the following steps: acquiring formation test data; constructing a porosity logging interpretation model according to the formation test data; constructing a permeability model according to the formation test data; establishing lithofacies coefficients according to the formation test data; and forming hole structure parameters according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient. According to the low-porosity and low-permeability reservoir pore structure parameter construction method and device and the storage medium provided by the invention, on the basis of experimental analysis and formation test data, rock physical analysis is carried out, and the pore structure parameters representing the effectiveness of the low-porosity and low-permeability reservoir based on lithofacies recognition are innovatively constructed by utilizing an artificial intelligence algorithm; the problems that the heterogeneity of the clastic rock low-porosity and low-permeability reservoir is high and the effective reservoir is difficult to identify and predict are solved, and important parameters are provided for effective reservoir distribution prediction in the clastic rock low-porosity and low-permeability reservoir.

Description

Technical Field

[0001] The present application relates to the field of network monitoring, and in particular to a method, device and storage medium for constructing pore structure parameters of a low-porosity and low-permeability reservoir. Background Art

[0002] my country's low-porosity and low-permeability reservoirs are widely distributed, have great resource potential, and are at a low level of exploration and development. They have become an important direction for exploration and development. However, due to the influence of various factors such as sedimentation and diagenesis, compared with conventional reservoirs, their reservoir physical properties are significantly deviated and heterogeneous. The physical properties affect the oil content and directly affect the oil field production. How to effectively identify and predict effective reservoirs in low-porosity and low-permeability reservoirs, find favorable reservoir distribution areas, and thus guide the implementation of favorable closures and well deployment, and discover sustainable development of enriched oil reservoirs is an important scientific and technological problem that needs to be solved.

[0003] At present, there are mainly the following methods to predict effective reservoirs in low-porosity and low-permeability reservoirs: first, to use seismic response characteristics to select sensitive attributes to predict the planar distribution of effective reservoirs; second, to select sensitive parameters based on rock physical analysis to evaluate parameter characteristics of effective reservoir physical properties; third, to use multi-level reconstruction logging methods to evaluate lithology, physical properties, and oil and gas content step by step, and predict the distribution of low-porosity and low-permeability "sweet spots"; fourth, to evaluate favorable reservoirs by calculating the logging slope using the storage coefficient and the seepage coefficient based on the porosity and permeability curve.

[0004] However, in the context of low-porosity and low-permeability reservoirs, the geophysical response characteristics of effective reservoirs and poor reservoirs are slightly different. The method of seismic attribute prediction has strong multi-solutions for predicting effective reservoirs and cannot predict the vertical distribution of effective reservoirs in low-porosity and low-permeability reservoirs. The parameter evaluation method cannot usually establish a unified parameter standard for low-porosity and low-permeability reservoirs with strong heterogeneity in multiple wells, and cannot achieve three-dimensional conversion. The multi-level reconstruction logging method uses a step-by-step stacking method to predict the "sweet spot" and there is a certain deviation in the results. The evaluation method based on storage coefficient and seepage coefficient mainly uses conventional logging curve calculations, and experimental parameters are rarely used. The calculation results are mainly used for the evaluation of drilled reservoirs, and there is a lack of effective guidance for the prediction of effective reservoirs in undrilled areas. Summary of the invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a method, device and storage medium for constructing pore structure parameters of a low-porosity and low-permeability reservoir.

[0006] In a first aspect, the present application provides a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir, the method comprising the steps of:

[0007] Obtain formation test data;

[0008] constructing a porosity logging interpretation model based on the formation test data;

[0009] constructing a permeability model based on the formation test data;

[0010] Establishing lithofacies coefficients based on the formation test data;

[0011] The pore structure parameters are formed according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

[0012] Preferably, the step of obtaining formation test data comprises the following steps:

[0013] Obtain rock samples;

[0014] Obtaining measured data of the rock sampling;

[0015] Get well logging data.

[0016] Preferably, the expression of the porosity logging interpretation model is:

[0017]

[0018] Among them, φ is the porosity, AC is the logging acoustic time difference value, DEN is the logging density value, CNL is the logging neutron porosity value, and a, b, c, and d are constants.

[0019] Preferably, the step of constructing a permeability model according to the formation test data comprises the following steps:

[0020] Constructing a low-porosity and low-permeability dual-precision permeability model based on the formation test data;

[0021] An ultra-low porosity and low permeability dual-precision permeability model is constructed based on the formation test data.

[0022] Preferably, the expression of the low porosity and low permeability dual-precision permeability model is:

[0023]

[0024] Among them, K is permeability, φ is porosity, φ<10%, and m, n, e are constants.

[0025] Preferably, the expression of the ultra-low porosity and low permeability dual-precision permeability model is:

[0026]

[0027] Where K is the permeability, φ is the porosity, 10%≤φ<15%, and e, x, and y are constants.

[0028] Preferably, the expression of the petrographic coefficient is:

[0029]

[0030] Where R is the lithofacies coefficient; Δt AC is the logging acoustic time difference, Δt ma is the time difference of skeleton sound wave, p ma is the skeleton density value, ρ DEN is the logging density value.

[0031] Preferably, the expression of the pore structure parameter is:

[0032]

[0033] Among them, F is the pore structure parameter, R is the lithofacies coefficient, K is the permeability, φ is the porosity, and A is a constant.

[0034] In a second aspect, the present application provides a device for constructing pore structure parameters of a low-porosity and low-permeability reservoir, comprising:

[0035] A formation test data acquisition module, used to acquire formation test data;

[0036] A porosity logging interpretation model building module, used to build a porosity logging interpretation model based on the formation test data;

[0037] A permeability model building module, used to build a permeability model according to the formation test data;

[0038] A lithofacies coefficient establishment module, used to establish lithofacies coefficients according to the formation test data;

[0039] The pore structure parameter forming module is used to form pore structure parameters according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

[0040] In a third aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method for constructing pore structure parameters of a low-porosity and low-permeability reservoir as described in any embodiment of the first aspect are implemented.

[0041] The above technical solution provided by the embodiment of the present application has the following advantages compared with the prior art:

[0042] A method, device and storage medium for constructing pore structure parameters of low-porosity and low-permeability reservoirs provided in the present application. A method, device and storage medium for constructing pore structure parameters of low-porosity and low-permeability reservoirs provided in the present application carry out rock physical analysis on the basis of experimental analysis and formation test data, and use artificial intelligence algorithms to innovatively construct pore structure parameters based on lithofacies identification to characterize the effectiveness of low-porosity and low-permeability reservoirs, which solves the problems of strong heterogeneity of low-porosity and low-permeability reservoirs in clastic rocks and difficulty in identifying and predicting effective reservoirs, and provides important parameters for predicting the distribution of effective reservoirs in low-porosity and low-permeability reservoirs in clastic rocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 It is a schematic flow chart of a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided by an embodiment of the present invention;

[0046] Figure 2 It is a schematic structural diagram of a device for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided by an embodiment of the present invention;

[0047] Figure 3 It is a structural schematic diagram of an electronic device provided by the present invention;

[0048] Figure 4 It is a structural schematic diagram of a non-transitory computer-readable storage medium provided by the present invention;

[0049] Figure 5 It is a schematic diagram of a technical process of reconstructing multiple sensitive parameter curves based on lithofacies discrimination in a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided by an embodiment of the present invention;

[0050] Figure 6 It is a schematic diagram of a pore structure parameter calibration chart in a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided in an embodiment of the present invention;

[0051] Figure 7 It is a schematic diagram of the analysis of the liquid production and pore structure parameters of a pilot oil test in a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0053] Figure 1 A schematic flow chart of a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir provided in an embodiment of the present application.

[0054] The present application provides a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir, the method comprising the steps of:

[0055] S1: Obtain formation test data;

[0056] In the embodiment of the present application, the step of obtaining formation test data includes the following steps:

[0057] Obtain rock samples;

[0058] Obtaining measured data of the rock sampling;

[0059] Get well logging data.

[0060] Specifically, rock samples are taken from the formation, and then the rock samples are measured to obtain measured data, and the logging data of the formation is obtained.

[0061] S2: constructing a porosity logging interpretation model based on the formation test data;

[0062] Specifically, the core porosity and conventional logging data are regressed by using the cross plot analysis method, and the logging curve with a high correlation coefficient is selected to establish a porosity logging interpretation model. The expression of the porosity logging interpretation model is:

[0063]

[0064] Among them, φ is the porosity, AC is the logging acoustic time difference value, DEN is the logging density value, CNL is the logging neutron porosity value, and a, b, c, and d are constants.

[0065] S3: constructing a permeability model according to the formation test data;

[0066] In an embodiment of the present application, constructing a permeability model according to the formation test data comprises the following steps:

[0067] Constructing a low-porosity and low-permeability dual-precision permeability model based on the formation test data;

[0068] An ultra-low porosity and low permeability dual-precision permeability model is constructed based on the formation test data.

[0069] Specifically, based on the measured core data, in order to obtain a better porosity-permeability relationship, a dual-precision permeability model of low porosity and low permeability and ultra-low porosity and low permeability was established.

[0070] In the embodiment of the present application, the expression of the low porosity and low permeability dual-precision permeability model is:

[0071]

[0072] Among them, K is permeability, φ is porosity, φ<10%, and m, n, e are constants.

[0073] In the embodiment of the present application, the expression of the ultra-low porosity and low permeability dual-precision permeability model is:

[0074]

[0075] Where K is the permeability, φ is the porosity, 10%≤φ<15%, and e, x, and y are constants.

[0076] S4: Establishing lithofacies coefficients according to the formation test data;

[0077] Specifically, rock physical analysis is carried out based on the analysis of test data, and artificial intelligence is used to identify sensitive parameters of lithology. The composition, particle size, sorting and other conditions of clastic reservoirs play an important role in the porosity and permeability of sandstone, especially clay minerals have a direct impact on the physical properties, oil content and electrical properties of sandstone reservoirs. Therefore, the present invention fully considers the problem of lithology purity in the establishment of the pore structure parameter model. By carrying out rock physical analysis and using the lithology discrimination technology based on Bayesian criterion classification, the discrimination results are quantitatively displayed with a chaotic matrix, and the two AC and DEN logging curves that mainly reflect the lithology are determined to form a lithology coefficient. With the help of the core apparent density measured by the analysis of the test data, the accuracy of the skeleton density derived by the limit method of the logging data is confirmed, and the skeleton acoustic wave velocity value is obtained by analogy. In the embodiment of the present application, the expression of the lithology coefficient is:

[0078]

[0079] Where R is the lithofacies coefficient; Δt AC is the logging acoustic time difference, Δt ma is the time difference of skeleton sound wave, p ma is the skeleton density value, ρ DEN is the logging density value.

[0080] S5: forming pore structure parameters according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

[0081] Specifically, multiple sensitive parameter curves are reconstructed based on lithofacies discrimination, a model algorithm is established, and a pore structure parameter curve is formed. Porosity, permeability, and lithology coefficient are used as characteristic physical parameters reflecting the low-porosity and low-permeability reservoir of clastic rocks, the expected estimation range and initial value are determined, and the maximum expectation algorithm (EM algorithm) is used to reconstruct the curve to form pore structure parameters based on lithofacies identification. In the embodiment of the present application, the expression of the pore structure parameter is:

[0082]

[0083] Among them, F is the pore structure parameter, R is the lithofacies coefficient, K is the permeability, φ is the porosity, and A is a constant.

[0084] Specifically, the pore structure standard chart is formed by integrating rock thin sections and analytical test data, and the high, medium and low pore structure parameters are calibrated. In practical applications, it is necessary to use core thin sections, analytical test data and other data to calibrate the pore structure parameter F to obtain an accurate threshold value. In order to facilitate the production of the calibration chart, the pore structure parameter formula can be taken logarithmically on both sides to establish a linear equation, that is:

[0085]

[0086] After the deformation of the formula, in the double logarithmic intersection diagram, the sample points with the same F are on the same straight line with a slope of -1, and the other sample points with different F are on other parallel lines. Based on the core thin section and core porosity and permeability data as the classification basis, the sample points are projected on the double logarithmic intersection diagram to make a pore structure parameter calibration plate to divide the high, medium and low reservoir characteristics.

[0087] The daily production of oil test (high-yield industrial liquid production, industrial liquid production, and low liquid production) is used as the standard to verify the accuracy of the pore structure parameter chart according to the actual work area. Experimental analysis and formation test data have found that the migration of oil and gas in the conductive layer has obvious heterogeneity. The long-distance migration calculation of oil and gas based on mass balance also shows that the migration route of oil and gas is mainly along the direction with the best permeability and the smallest capillary resistance in the conductive layer, rather than uniform migration in the entire conductive layer. Combined with the oil test data, the characterization effect of pore structure parameters on the reservoir drainage capacity can be reflected indirectly. The daily liquid production of a single meter of clastic reservoir and the pore structure parameters of this section are combined for analysis to verify the accuracy of the pore structure parameter chart. Since the pore structure parameters only reflect the reservoir physical conditions of the original formation, the liquid production data of the original formation should be selected.

[0088] In this embodiment, the X region of JZ Depression is selected as the research object for research.

[0089] The present invention provides a method for constructing pore structure parameters of a low-porosity and low-permeability reservoir, which specifically includes the following steps:

[0090] (1) Obtaining the logging interpretation model of core porosity and permeability based on the core measured data and logging data collected and sampled

[0091] The cross plot analysis method is used to perform regression analysis on the core porosity and conventional logging data, and the logging curve with high correlation coefficient is selected to establish the porosity logging interpretation model:

[0092]

[0093] Among them, φ is the porosity, AC is the logging acoustic time difference value, DEN is the logging density value, CNL is the logging neutron porosity value, and a, b, c, and d are constants.

[0094] Based on the measured core data, in order to obtain a better porosity-permeability relationship, a low-porosity-low-permeability and ultra-low-porosity-low-permeability dual-precision permeability model is established:

[0095] The expression of the low porosity and low permeability dual-precision permeability model is:

[0096]

[0097] Among them, K is permeability, φ is porosity, φ<10%, and m, n, e are constants.

[0098] In the embodiment of the present application, the expression of the ultra-low porosity and low permeability dual-precision permeability model is:

[0099]

[0100] Where K is the permeability, φ is the porosity, 10%≤φ<15%, and e, x, and y are constants.

[0101] (2) Carry out rock physical analysis based on analytical data and use artificial intelligence to identify lithofacies sensitive parameters

[0102] Through rock physics analysis, the two logging curves AC and DEN that mainly reflect the lithology are determined to form the lithology coefficient. With the help of the core apparent density measured by the analysis of the test data, the accuracy of the skeleton density derived by the limit method of the logging data is confirmed, and the skeleton acoustic wave velocity value is obtained by analogy. Establish the lithofacies coefficient:

[0103]

[0104] Where R is the lithofacies coefficient; Δt AC is the logging acoustic time difference, Δt ma is the time difference of skeleton sound wave, p ma is the skeleton density value, ρ DEN is the logging density value.

[0105] (3) Reconstruct multiple sensitive parameter curves based on lithofacies discrimination, establish a model algorithm, and form a pore structure parameter curve

[0106] The porosity, permeability and lithology coefficient are used as the physical parameters reflecting the characteristic properties of low-porosity and low-permeability reservoirs of clastic rocks. The expected estimation range and initial value are determined, and the maximum expectation algorithm (EM algorithm) is used to reconstruct the curve to form the pore structure parameters based on lithofacies identification:

[0107]

[0108] Among them, F is the pore structure parameter, R is the lithofacies coefficient, K is the permeability, φ is the porosity, and A is a constant.

[0109] (4) Comprehensive rock thin sections and analytical test data are used to form a standard pore structure chart and calibrate the high, medium and low pore structure parameters. Take the logarithm of both sides of the above pore structure parameter formula, where the definition is is r, that is:

[0110] logR=-log(r)+logF.

[0111] After the deformation of the formula, in the double logarithmic intersection diagram, the sample points with the same F are on the same straight line with a slope of -1, and the other sample points with different F are on other parallel lines. Based on the core thin section and core porosity data, the sample points are projected on the double logarithmic intersection diagram, and the pore structure relationship is divided according to the F value of the landing point: 0-2 is the low value area; 2-12 is the medium value area; and 12 or more is the high value area.

[0112] (5) The accuracy of the pore structure parameter chart is verified by taking the daily production of oil test (high-yield industrial liquid production, industrial liquid production, and low liquid production) as the standard. Combined with the original formation oil test data, the reservoir drainage capacity is used to reflect the accuracy of the pore structure parameter chart. The daily liquid production per meter of the clastic reservoir is combined with the pore structure parameters of the section for analysis. According to the oil test data of 10 wells in the X area, the reservoir with a liquid production of more than 2 cubic meters and a pore structure parameter greater than 12 is an effective reservoir in the low-porosity and low-permeability reservoir.

[0113] In a second aspect, the present application provides a device for constructing pore structure parameters of a low-porosity and low-permeability reservoir, comprising:

[0114] The formation test data acquisition module 10 is used to acquire formation test data;

[0115] A porosity logging interpretation model building module 20 is used to build a porosity logging interpretation model according to the formation test data;

[0116] A permeability model building module 30, used to build a permeability model according to the formation test data;

[0117] A lithofacies coefficient establishing module 40, used to establish a lithofacies coefficient according to the formation test data;

[0118] The pore structure parameter forming module 50 is used to form pore structure parameters according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

[0119] The present application provides a low-porosity and low-permeability reservoir pore structure parameter construction device that can execute a low-porosity and low-permeability reservoir pore structure parameter construction method provided by the above steps.

[0120] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

[0121] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 100 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0122] like Figure 3 As shown, the electronic device 100 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 to a random access memory (RAM) 103. In the RAM 103, various programs and data required for the operation of the electronic device 100 are also stored. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0123] Typically, the following devices may be connected to the I / O interface 105: an input device 106 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the electronic device 100 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 100 having various devices is shown in the figure, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0124] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0125] Reference below Figure 4 , which shows a schematic diagram of the structure of a computer-readable storage medium suitable for implementing the embodiments of the present disclosure, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement the method for constructing pore structure parameters of low-porosity and low-permeability reservoirs as described in any of the above.

[0126] The present application provides a method, device and storage medium for constructing pore structure parameters of low-porosity and low-permeability reservoirs. Based on experimental analysis and formation test data, rock physical analysis is carried out, and artificial intelligence algorithms are used to innovatively construct pore structure parameters that characterize the effectiveness of low-porosity and low-permeability reservoirs based on lithofacies identification. The method solves the problems of strong heterogeneity in clastic low-porosity and low-permeability reservoirs and difficulty in identifying and predicting effective reservoirs, and provides important parameters for predicting the distribution of effective reservoirs in clastic low-porosity and low-permeability reservoirs.

[0127] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0128] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for constructing pore structure parameters of low-porosity and low-permeability reservoirs, characterized in that: The method comprises the steps of: Obtain formation test data; constructing a porosity logging interpretation model based on the formation test data; constructing a permeability model based on the formation test data; Establishing lithofacies coefficients based on the formation test data; The pore structure parameters are formed according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

2. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 1, characterized in that: The obtaining of formation test data comprises the following steps: Obtain rock samples; Obtaining measured data of the rock sampling; Get well logging data.

3. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 1, characterized in that: The expression of the porosity logging interpretation model is: Among them, φ is the porosity, AC is the logging acoustic time difference value, DEN is the logging density value, CNL is the logging neutron porosity value, and a, b, c, and d are constants.

4. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 1, characterized in that: The permeability model constructed according to the formation test data comprises the following steps: Constructing a low-porosity and low-permeability dual-precision permeability model based on the formation test data; An ultra-low porosity and low permeability dual-precision permeability model is constructed based on the formation test data.

5. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 4, characterized in that: The expression of the low porosity and low permeability dual-precision permeability model is: Among them, K is permeability, φ is porosity, φ<10%, and m, n, and e are constants.

6. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 4, characterized in that: The expression of the ultra-low porosity and low permeability dual-precision permeability model is: Where K is the permeability, φ is the porosity, 10%≤φ<15%, and e, x, and y are constants.

7. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 1, characterized in that: The expression of the lithofacies coefficient is: Where R is the lithofacies coefficient; Δt AC is the logging acoustic time difference, Δt ma is the time difference of skeleton sound wave, p ma is the skeleton density value, ρ DEN is the logging density value.

8. The method for constructing pore structure parameters of low-porosity and low-permeability reservoirs according to claim 1, characterized in that: The expression of the pore structure parameter is: Among them, F is the pore structure parameter, R is the lithofacies coefficient, K is the permeability, φ is the porosity, and A is a constant.

9. A device for constructing pore structure parameters of low-porosity and low-permeability reservoirs, characterized in that: include: A formation test data acquisition module, used to acquire formation test data; A porosity logging interpretation model building module is used to build a porosity logging interpretation model based on the formation test data; A permeability model building module, used to build a permeability model according to the formation test data; A lithofacies coefficient establishment module, used to establish lithofacies coefficients according to the formation test data; The pore structure parameter forming module is used to form pore structure parameters according to the porosity logging interpretation model, the permeability model and the lithofacies coefficient.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing pore structure parameters of a low-porosity and low-permeability reservoir as described in any one of claims 1 to 7 are implemented.

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