Permeability reservoir quality identification method, device, equipment and storage medium

Abstract

The present invention discloses a method, apparatus, device, and storage medium for identifying the quality of a permeable reservoir. The method comprises determining the square of the amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data based on first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir; performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; inputting the target square of the amplitude difference into a pre-constructed reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; and determining the identification result of the quality of the current reservoir based on the difference between the predicted value and a preset threshold. The present invention achieves semi-quantitative estimation of reservoir permeability, avoids decoupling complex nonlinear relationships during quantitative inversion of permeability, simplifies the permeability prediction process, and improves the efficiency of quality identification of permeable reservoirs.

Description

Technical Field

[0001] The present invention belongs to the field of exploration technology, and in particular relates to a method, device, equipment and storage medium for identifying the quality of a permeable reservoir. Background Art

[0002] Permeability describes the ability of rock to allow fluid to pass through under a certain pressure differential. It is also a key parameter for evaluating reservoir quality in petroleum engineering. It plays an important role in improving oil recovery, oil and gas development, reservoir assessment management and oil and gas development. Accurately estimating reservoir permeability will be beneficial to reservoir evaluation and thus reduce production costs.

[0003] Methods for obtaining reservoir permeability can be categorized into three types: direct core measurement, interpretation of well logging data, and prediction from seismic data. Direct measurement involves directly measuring various physical parameters based on the definition of permeability and is currently one of the more accurate methods for obtaining reservoir permeability. While permeability obtained through core measurement is highly accurate, these experiments are expensive and time-consuming. Furthermore, core measurement is limited by the coring range and can only characterize the permeability distribution over a very small area. Permeability obtained through interpretation of well logging data is less accurate than that obtained from core data and can provide continuous vertical permeability information. However, this method is more expensive, and the well only provides a single-hole view, lacking lateral continuity.

[0004] In order to obtain the permeability estimation between wells, the permeability prediction method using seismic data is the most commonly used. The following methods are commonly used to predict permeability using seismic data:

[0005] The first approach is the theoretical model approach. This approach is based on rock physics models and a series of nonlinear equations established using core data. This approach can rationally explain the influence of reservoir permeability on formation parameters based on rock physics theory. The key idea is to obtain formation parameters through inversion of seismic data and, based on the relationship between formation parameters and permeability, obtain permeability estimates. However, the problem with this approach is that the relationship under core conditions differs from that in conventional seismic frequency bands, and the accuracy of formation parameters obtained through seismic inversion may not necessarily meet prediction requirements.

[0006] The second approach uses machine learning to establish relationships between permeability and various seismic parameters or attributes, replacing relationships based on rock physics theory to obtain permeability predictions. This approach has been the most widely studied and, thanks to recent breakthroughs in machine learning, has great application prospects. However, it is data-driven, theoretically imperfect, prone to overfitting, and requires extensive well logging data to achieve effective network training and prediction results.

[0007] Therefore, the permeability prediction process in the prior art is relatively complicated, and the efficiency of identifying the quality of permeable reservoirs is low. Summary of the Invention

[0008] The main purpose of the present invention is to provide a method, device, equipment and storage medium for identifying the quality of a permeable reservoir, so as to solve the problems in the prior art of the complicated permeability prediction process and the low efficiency of permeable reservoir quality identification.

[0009] In response to the above problems, the present invention provides a method for identifying the quality of a permeable reservoir, comprising:

[0010] Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0011] Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0012] Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir;

[0013] The identification result of the quality of the current reservoir is determined according to the difference between the predicted value and a preset threshold.

[0014] Furthermore, in the above-mentioned permeability reservoir quality identification method, determining the identification result of the current reservoir quality according to the difference between the predicted value and a preset threshold value includes:

[0015] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0016] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0017] Furthermore, in the above-mentioned method for identifying the quality of permeable reservoirs, the function corresponding to the reservoir permeability model is:

[0018]

[0019] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0020] Furthermore, in the above-mentioned permeability reservoir quality identification method, determining, based on first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir, the square of the amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data includes:

[0021] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0022] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0023] The present invention also provides a permeability reservoir quality identification device, comprising:

[0024] a determination module, configured to determine, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0025] a transform module, configured to perform a time-frequency domain transform on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0026] A prediction module, configured to input the square of the target amplitude difference into a pre-built reservoir permeability model for prediction, thereby obtaining a predicted value of the permeability of the current reservoir;

[0027] The identification module is used to determine the identification result of the quality of the current reservoir according to the size of the predicted value and a preset threshold.

[0028] Furthermore, in the above-mentioned permeability reservoir quality identification device, the identification module is specifically used to:

[0029] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0030] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0031] Furthermore, in the above-mentioned permeability reservoir quality identification device, the function corresponding to the reservoir permeability model is:

[0032]

[0033] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0034] Furthermore, in the above-mentioned permeability reservoir quality identification device, the determination module is specifically used to:

[0035] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0036] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0037] The present invention also provides a permeability reservoir quality identification device, comprising a memory and a processor;

[0038] The memory stores a computer program, which, when executed by the processor, implements the steps of the permeability reservoir quality identification method as described above.

[0039] The present invention also provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the permeability reservoir quality identification method as described in any one of the above items.

[0040] The present invention also provides a permeability reservoir quality identification device, comprising a memory and a processor;

[0041] The memory stores a computer program, which, when executed by the processor, implements the following steps:

[0042] Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0043] Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0044] Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir;

[0045] The identification result of the quality of the current reservoir is determined according to the difference between the predicted value and a preset threshold.

[0046] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0047] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0048] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0049] Furthermore, the function corresponding to the reservoir permeability model is:

[0050]

[0051] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0052] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0053] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0054] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0055] The present invention also provides a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0056] Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0057] Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0058] Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir;

[0059] The identification result of the quality of the current reservoir is determined according to the difference between the predicted value and a preset threshold.

[0060] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0061] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0062] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0063] Furthermore, the function corresponding to the reservoir permeability model is:

[0064]

[0065] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0066] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0067] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0068] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0069] Compared with the prior art, one or more embodiments of the above solutions may have the following advantages or beneficial effects:

[0070] The present invention provides a method, apparatus, device, and storage medium for identifying the quality of a permeable reservoir. Based on first reflection seismic data from a first angle of the current reservoir and second reflection seismic data from a second angle of the current reservoir, the method determines the squared amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data. The method then performs a time-frequency domain transformation on the squared amplitude difference to obtain a target squared amplitude difference in the time-frequency domain. The target squared amplitude difference is input into a pre-constructed reservoir permeability model for prediction to obtain a predicted value for the permeability of the current reservoir. The method then determines the quality identification result of the current reservoir based on the difference between the predicted value and a preset threshold. Based on the relationship between layer permeability and angular reflection coefficient, the method employs a semi-quantitative estimation approach for reservoir permeability based on theoretically based seismic attributes. This approach avoids the need to decouple complex nonlinear relationships during quantitative permeability inversion. While quantitative permeability inversion results cannot be obtained, the obtained reservoir permeability characterization attributes can still achieve the purpose of predicting and evaluating reservoir quality, simplifying the permeability prediction process and improving the efficiency of permeability reservoir quality identification.

[0071] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0073] Figure 1 is a flow chart of an embodiment of a method for identifying the quality of a permeable reservoir according to the present invention;

[0074] Figure 2 A comparison diagram of the fracture porosity prediction scheme and the prediction scheme of this application;

[0075] Figure 3 Schematic diagram of the structure of an embodiment of a permeable reservoir quality identification device of the present invention;

[0076] Figure 4 It is a schematic structural diagram of an embodiment of a permeability reservoir quality identification device of the present invention. DETAILED DESCRIPTION

[0077] The following describes the embodiments of the present invention in detail with reference to the accompanying drawings and examples, so that the present invention can fully understand how to apply technical means to solve technical problems and achieve technical effects, and thus implement the invention accordingly. It should be noted that, as long as no conflict exists, the various embodiments of the present invention and the various features of the embodiments can be combined with each other, and the resulting technical solutions are all within the scope of protection of the present invention.

[0078] In recent years, my country's oil and gas exploration has gradually shifted its focus to unconventional oil and gas exploration areas, such as low porosity and low permeability. Unlike conventional oil and gas reservoirs, these new oil and gas reservoirs have complex accumulation conditions and strong heterogeneity. Their production capacity is significantly affected by reservoir permeability, and the investment risk is high. Therefore, it is necessary to provide a high-precision method for predicting the permeability of oil and gas reservoirs.

[0079] Research has shown that when a reservoir's permeability changes, its elastic properties also change, leading to changes in the amplitude of the acquired seismic data. Therefore, in oil and gas seismic exploration, seismic amplitude information is often used to directly or indirectly invert permeability parameters representing reservoir permeability.

[0080] In order to obtain the permeability estimation between wells, the permeability prediction method using seismic data is the most commonly used. The following methods are commonly used to predict permeability using seismic data:

[0081] The first approach is the theoretical model approach. This approach, based on rock physics models and a series of nonlinear equations established using core data, can rationally explain the impact of reservoir permeability on formation parameters based on rock physics theory. The main idea is to obtain formation parameters through seismic data inversion and, based on the relationship between formation parameters and permeability, obtain permeability estimates. For example, the sensitivity relationship between a class of elastic parameters in seismic attribute parameters and permeability can be exploited, with elastic parameter data that is more sensitive to permeability being prioritized. Permeability data can then be reconstructed using statistical relationships or rock physics panels, converting the elastic parameters obtained from seismic inversion into permeability data. Due to the large number of elastic parameters, the conventional approach of reconstructing permeability data based on empirical screening of sensitive elastic parameters is inefficient, and due to the physical limitations of the elastic parameters themselves, their sensitivity to permeability is limited. The problem with this approach is that the relationships under core conditions differ from those in conventional seismic frequency bands, and the accuracy of formation parameters obtained through seismic inversion may not necessarily meet prediction requirements.

[0082] The second approach uses machine learning to establish relationships between permeability and various seismic parameters or attributes, replacing relationships based on rock physics theory to obtain permeability predictions. This approach has been the most widely studied and, thanks to recent breakthroughs in machine learning, has great application prospects. However, it is data-driven, theoretically imperfect, prone to overfitting, and requires extensive well logging data to achieve effective network training and prediction results.

[0083] Therefore, the permeability prediction process in the prior art is relatively complicated, and the efficiency of identifying the quality of permeable reservoirs is low.

[0084] In order to solve the above technical problems, the present invention provides the following embodiments:

[0085] Example 1

[0086] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a method for identifying the quality of a permeable reservoir.

[0087] Figure 1 FIG. 1 is a flow chart of an embodiment of a method for identifying the quality of a permeable reservoir according to the present invention, as shown in FIG. Figure 1 As shown, the quality identification method of the permeable reservoir in this embodiment may specifically include the following steps:

[0088] 100. Determine, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0089] In a specific implementation process, first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir can be obtained from the seismic data, and the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data can be determined based on the first reflection seismic data at the first angle of the current reservoir and the second reflection seismic data at the second angle of the current reservoir.

[0090] Specifically, the first reflected seismic data and the second reflected seismic data can be subtracted and squared to obtain the square of the reflected seismic data difference; the obtained square of the reflected seismic data difference can be subjected to sub-wavelet removal to obtain the square of the amplitude difference between the first reflection coefficient corresponding to the first reflected seismic data and the second reflection coefficient corresponding to the second reflected seismic data.

[0091] It should be noted that, in this embodiment, the first reflected seismic data can be subjected to wavelet removal to obtain a first reflection coefficient corresponding to the first reflected seismic data, and the second reflected seismic data can be subjected to wavelet removal to obtain a second reflection coefficient corresponding to the second reflected seismic data, and then subtracted and squared to obtain the square of the amplitude difference between the first reflection coefficient corresponding to the first reflected seismic data and the second reflection coefficient corresponding to the second reflected seismic data.

[0092] 101. Perform a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0093] After obtaining the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data, the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data can be transformed into the time-frequency domain to obtain the target square of the amplitude difference in the time-frequency domain.

[0094] 102. Input the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the current reservoir permeability;

[0095] In a specific implementation process, the function corresponding to the pre-built reservoir permeability model can be expressed by formula (1):

[0096]

[0097] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θi ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0098] Specifically, the process of constructing the function of the reservoir permeability model can refer to the following method:

[0099] (1) Based on the dual-porosity and dual-permeability model and Boit theory, determine the function of the reflection coefficient at the reflecting interface that varies with angle and includes permeability;

[0100] In its implementation, elastic wave theory is based on a homogeneous, single-phase, isotropic elastic medium. Compared to elastic medium theory, two-phase medium theory more accurately depicts the true nature of subsurface strata. Furthermore, rock physics experiments have found that the attenuation caused by fluids is difficult to explain using single-phase medium theories, highlighting the necessity and importance of two-phase medium theory. Furthermore, based on two-phase medium theory, researchers can more precisely describe the fluid or permeability properties in porous media, gaining more information about geological parameters, such as the influence of reservoir permeability and viscosity on elastic parameters and reflection coefficients, guiding more refined exploration and development.

[0101] The dual-porosity and dual-permeability model in a two-phase medium has two pore volumes: matrix and fracture. The matrix pores are the main oil storage space, while the fractures are the main flow channels. Both the matrix and the fractures have pore volumes and permeabilities.

[0102] This example uses the dual-porosity, dual-permeability model and the equivalent Biot theory to derive an expression for the seismic reflection coefficient at different incidence angles, which includes reservoir permeability parameters. Because angle-domain seismic data is the convolution of the angle wavelet and the angle reflection coefficient, this expression establishes a connection between the reservoir's permeability characteristics and the angle-domain seismic data, providing a foundation for subsequent attribute construction and reservoir permeability prediction based on seismic data.

[0103] It should be noted that the detailed implementation process of this step can be referred to the existing related technologies and will not be described in detail here.

[0104] (2) extracting a parameter representing permeability based on a function of any two reflection coefficients, and generating a permeability function corresponding to the permeability parameter;

[0105] Specifically, based on conventional classification and summarization of similar terms, the function of any two reflection coefficients can be simplified and derived to extract the parameters representing the permeability and generate the permeability function corresponding to the permeability parameters. The permeability function can be expressed by formula (2):

[0106]

[0107] Among them, C(θi ) represents a second parameter that is independent of frequency but varies with angle and is a constant, i=1 or 2.

[0108] From the derived formula (2), it can be seen that the angular reflection coefficient is related to frequency, angle-related factors, medium density, and permeability. Rewriting formula (2) as an expression for permeability parameters yields a reflection coefficient-related expression for permeability, i.e., properties related to medium characteristics (density and permeability). First, square both sides of formula (2) and then differentiate them with respect to frequency, yielding formula (3):

[0109]

[0110] It can be seen that the frequency-independent parameter C(θ i ) is eliminated, and then formula (3) is rewritten as formula (4):

[0111]

[0112] Then rewrite equation (4) into equation (1) and remember the permeability function corresponding to the reservoir permeability model.

[0113] (3) Based on the permeability function, construct the reservoir permeability model.

[0114] In a specific implementation process, the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data can be input into a pre-constructed reservoir permeability model for calculation to obtain a predicted value of the permeability of the current reservoir.

[0115] 103. Determine the identification result of the quality of the current reservoir based on the difference between the predicted value and a preset threshold.

[0116] In a specific implementation process, the quality of the reservoir can be calibrated using existing logging data in the current reservoir to obtain a preset threshold, and then the identification result of the quality of the current reservoir is determined based on the size of the predicted value and the preset threshold.

[0117] Specifically, if the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirements; if the predicted value is less than or equal to the preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirements.

[0118] It should be noted that, in this embodiment, the reservoirs meeting the preset quality requirements may be classified into quality grades according to the specific difference between the predicted value and the preset threshold value, so as to more clearly obtain the quality of the current reservoir.

[0119] The permeability reservoir quality identification method of this embodiment determines the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data based on first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir; performs a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; inputs the target square of the amplitude difference into a pre-constructed reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; and determines the identification result of the quality of the current reservoir based on the difference between the predicted value and a preset threshold. Based on the relationship between layer permeability and angular reflection coefficient, the present invention adopts a semi-quantitative estimation method of reservoir permeability based on seismic attributes with a theoretical basis, avoiding the decoupling of complex nonlinear relationships when quantitatively inverting permeability. Although quantitative permeability inversion results cannot be obtained, the obtained reservoir permeability characterization attributes can still achieve the purpose of predicting and evaluating reservoir quality, simplifying the permeability prediction process and improving the efficiency of permeability reservoir quality identification.

[0120] It should be noted that the method of the embodiment of the present invention can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present invention, and the multiple devices will interact with each other to complete the method.

[0121] Example 2

[0122] The feasibility of the technical solution of the present invention is illustrated by taking a certain research area in the Sichuan Basin.

[0123] In a specific implementation process, the fracture development zone is generally an important oil and gas storage space, and also an important oil and gas migration channel.

[0124] Predicting fracture porosity in fracture zones is crucial for determining reservoir development and designing well trajectories. Fracture porosity is the ratio or percentage of the space occupied by fractures to the total rock volume. Predicting fracture porosity can quantitatively predict the extent of fracture development, rock permeability, and the ability of fractures to store oil and gas as reservoirs.

[0125] Currently, the prediction of fracture porosity in fracture-developed zones has become the key to the efficient development of gas reservoirs in fracture-cavity carbonate and tight sandstone reservoirs.

[0126] In a specific implementation process, after predicting a certain study area in the Sichuan Basin through the fracture porosity prediction scheme and conducting actual mining, it was found that the fracture porosity of the reservoir was not strongly related to the permeability characteristics. The prediction of high-quality reservoirs based on fracture porosity often did not match the production characteristics. Therefore, the technical solution of the present invention was adopted. On the basis of the technical means of fracture porosity characterization, permeability characterization was used as the main means to predict the situation of high-quality reservoirs.

[0127] Figure 2 A comparison chart of the fracture porosity prediction scheme and the prediction scheme of this application.

[0128] from Figure 2 As can be seen in the upper part, the fracture porosity prediction scheme shows that the development of high-quality reservoirs is relatively stable and the lateral distribution is continuous.

[0129] However, from the prediction scheme of this application ( Figure 2 The lower part) shows that the development of high-quality reservoirs is discontinuous. Figure 2 It can be seen that the reservoir range predicted by fracture porosity is relatively wide, which cannot accurately depict the high-quality reservoir development area, and there are differences with production data.

[0130] Furthermore, a comparison of actual production data with the prediction results obtained by the proposed prediction scheme based on squared angle differences revealed that the proposed scheme's permeability more closely matched the production data, confirming the effectiveness and advancement of the proposed invention. This proposed prediction scheme successfully predicted high-quality reservoirs in the region and provided strong support for the deployment of 10 exploratory wells.

[0131] Example 3

[0132] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a permeability reservoir quality identification device.

[0133] Figure 3 FIG. 1 is a schematic structural diagram of an embodiment of a device for identifying the quality of a permeable reservoir according to the present invention, as shown in FIG. Figure 3 As shown, the permeability reservoir quality identification device of this embodiment may include a determination module 30 , a transformation module 31 , a prediction module 32 and an identification module 33 .

[0134] a determination module 30 for determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0135] In a specific implementation process, the determining module 30 is specifically configured to:

[0136] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0137] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0138] It should be noted that, in this embodiment, the determining module 30 may also be used to:

[0139] Performing wavelet removal on the first reflected seismic data to obtain a first reflection coefficient corresponding to the first reflected seismic data, and performing wavelet removal on the second reflected seismic data to obtain a second reflection coefficient corresponding to the second reflected seismic data;

[0140] A first reflection coefficient corresponding to the first reflection seismic data is subtracted from a second reflection coefficient corresponding to the second reflection seismic data and the result is squared to obtain the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data.

[0141] A transformation module 31 is configured to perform a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0142] A prediction module 32 is configured to input the square of the target amplitude difference into a pre-built reservoir permeability model for prediction, thereby obtaining a predicted value of the permeability of the current reservoir;

[0143] In a specific implementation process, the function corresponding to the reservoir permeability model is:

[0144]

[0145] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0146] Specifically, the process of constructing the function of the reservoir permeability model can refer to the following method:

[0147] (1) Based on the dual-porosity and dual-permeability model and Boit theory, determine the function of the reflection coefficient at the reflecting interface that varies with angle and includes permeability;

[0148] In its implementation, elastic wave theory is based on a homogeneous, single-phase, isotropic elastic medium. Compared to elastic medium theory, two-phase medium theory more accurately depicts the true nature of subsurface strata. Furthermore, rock physics experiments have found that the attenuation caused by fluids is difficult to explain using single-phase medium theories, highlighting the necessity and importance of two-phase medium theory. Furthermore, based on two-phase medium theory, researchers can more precisely describe the fluid or permeability properties in porous media, gaining more information about geological parameters, such as the influence of reservoir permeability and viscosity on elastic parameters and reflection coefficients, guiding more refined exploration and development.

[0149] The dual-porosity and dual-permeability model in a two-phase medium has two pore volumes: matrix and fracture. The matrix pores are the main oil storage space, while the fractures are the main flow channels. Both the matrix and the fractures have pore volumes and permeabilities.

[0150] This example uses the dual-porosity, dual-permeability model and the equivalent Biot theory to derive an expression for the seismic reflection coefficient at different incidence angles, which includes reservoir permeability parameters. Because angle-domain seismic data is the convolution of the angle wavelet and the angle reflection coefficient, this expression establishes a connection between the reservoir's permeability characteristics and the angle-domain seismic data, providing a foundation for subsequent attribute construction and reservoir permeability prediction based on seismic data.

[0151] It should be noted that the detailed implementation process of this step can be referred to the existing related technologies and will not be described in detail here.

[0152] (2) extracting a parameter representing permeability based on a function of any two reflection coefficients, and generating a permeability function corresponding to the permeability parameter;

[0153] Specifically, based on conventional classification and summarization of similar terms, the function of any two reflection coefficients can be simplified and derived to extract the parameters representing the permeability, and generate a permeability function corresponding to the permeability parameters. The permeability function can be expressed by formula (2).

[0154] In a specific implementation process, the calculation formula (2) corresponding to the permeability function can refer to the above-mentioned related records and will not be repeated here.

[0155] From the derived formula (2), it can be seen that the angular reflection coefficient is related to frequency, angle-related factors, medium density, and permeability. Rewriting formula (2) as an expression for permeability parameters yields a reflection coefficient-related expression for permeability, i.e., properties related to medium characteristics (density and permeability). First, square both sides of formula (2) and then differentiate them with respect to frequency, yielding formula (3).

[0156] In a specific implementation process, the calculation formula (3) can refer to the above-mentioned related records and will not be repeated here.

[0157] By calculating formula (3), we can see that the frequency-independent parameter C(θ i ) is eliminated, and then formula (3) is rewritten into formula (4), and formula (4) is rewritten into formula (1). Remember the permeability function corresponding to the reservoir permeability model.

[0158] In a specific implementation process, the calculation formula (4) can refer to the above-mentioned related records and will not be repeated here.

[0159] (3) Based on the permeability function, construct the reservoir permeability model.

[0160] In a specific implementation process, the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data can be input into a pre-constructed reservoir permeability model for calculation to obtain a predicted value of the permeability of the current reservoir.

[0161] The identification module 33 is used to determine the identification result of the quality of the current reservoir according to the size of the predicted value and a preset threshold.

[0162] Specifically, the identification module 33 is specifically used to:

[0163] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0164] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0165] The preset threshold value may be obtained by calibrating the quality of the reservoir using existing well logging data in the current reservoir, or may be obtained by other means, which is not specifically limited in this embodiment.

[0166] It should be noted that, in this embodiment, the reservoirs meeting the preset quality requirements may be classified into quality grades according to the specific difference between the predicted value and the preset threshold value, so as to more clearly obtain the quality of the current reservoir.

[0167] The permeability reservoir quality identification device of this embodiment determines the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data based on first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir; performs a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; inputs the target square of the amplitude difference into a pre-constructed reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; and determines the identification result of the quality of the current reservoir based on the difference between the predicted value and a preset threshold. Based on the relationship between layer permeability and angular reflection coefficient, the present invention adopts a semi-quantitative estimation method of reservoir permeability based on seismic attributes with a theoretical basis, avoiding the decoupling of complex nonlinear relationships when quantitatively inverting permeability. Although quantitative permeability inversion results cannot be obtained, the obtained reservoir permeability characterization attributes can still achieve the purpose of predicting and evaluating reservoir quality, simplifying the permeability prediction process and improving the efficiency of permeability reservoir quality identification.

[0168] Example 4

[0169] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a permeability reservoir quality identification device.

[0170] Figure 4 FIG. 1 is a schematic structural diagram of an embodiment of a permeable reservoir quality identification device according to the present invention, as shown in FIG. Figure 4 As shown, the device may include a processor 1010 and a memory 1020. As known to those skilled in the art, the device may also include an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0171] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0172] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0173] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0174] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WiFi, Bluetooth, etc.).

[0175] The bus 1050 comprises a pathway for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0176] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0177] The device for detecting clay distribution information in organic-rich shale provided by an embodiment of the present invention has a computer program stored in its memory. When the computer program is executed by a processor, the following steps are implemented:

[0178] Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0179] Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0180] Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir;

[0181] The identification result of the quality of the current reservoir is determined according to the difference between the predicted value and a preset threshold.

[0182] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0183] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0184] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0185] Furthermore, the function corresponding to the reservoir permeability model is:

[0186]

[0187] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0188] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0189] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0190] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0191] The permeability reservoir quality identification device of this embodiment determines the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data based on first reflection seismic data at a first angle of the current reservoir and second reflection seismic data at a second angle of the current reservoir; performs a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; inputs the target square of the amplitude difference into a pre-constructed reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; and determines the identification result of the quality of the current reservoir based on the difference between the predicted value and a preset threshold. Based on the relationship between layer permeability and angular reflection coefficient, the present invention adopts a semi-quantitative estimation method of reservoir permeability based on seismic attributes with a theoretical basis, avoiding the decoupling of complex nonlinear relationships when quantitatively inverting permeability. Although quantitative permeability inversion results cannot be obtained, the obtained reservoir permeability characterization attributes can still achieve the purpose of predicting and evaluating reservoir quality, simplifying the permeability prediction process and improving the efficiency of permeability reservoir quality identification.

[0192] Example 5

[0193] In order to solve the above technical problems existing in the prior art, an embodiment of the present invention provides a storage medium.

[0194] The storage medium provided by the embodiment of the present invention stores a computer program, and when the computer program is executed by a processor, the following steps are implemented.

[0195] Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data;

[0196] Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain;

[0197] Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir;

[0198] The identification result of the quality of the current reservoir is determined according to the difference between the predicted value and a preset threshold.

[0199] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0200] If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement;

[0201] If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

[0202] Furthermore, the function corresponding to the reservoir permeability model is:

[0203]

[0204] Where S represents permeability, ρ represents density, κ represents permeability, η represents viscosity coefficient, B(θ i ) represents the first parameter that is independent of frequency but changes with angle and is a constant, i = 1 or 2, R(θ i ) represents the reflection coefficient, i = 1 or 2, and ω represents the angular frequency.

[0205] Furthermore, when the computer program is executed by the processor, the following steps may be implemented:

[0206] subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data;

[0207] Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

[0208] The storage medium of this embodiment determines the square of the amplitude difference between the first reflection coefficient corresponding to the first reflection seismic data and the second reflection coefficient corresponding to the second reflection seismic data based on the first reflection seismic data at a first angle of the current reservoir and the second reflection seismic data at a second angle of the current reservoir; performs a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; inputs the target square of the amplitude difference into a pre-constructed reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; and determines the quality identification result of the current reservoir based on the difference between the predicted value and a preset threshold. Based on the relationship between layer permeability and angular reflection coefficient, the present invention adopts a semi-quantitative estimation method of reservoir permeability based on seismic attributes with a theoretical basis, avoiding the decoupling of complex nonlinear relationships when quantitatively inverting permeability. Although quantitative permeability inversion results cannot be obtained, the obtained reservoir permeability characterization attributes can still achieve the purpose of predicting and evaluating reservoir quality, simplifying the permeability prediction process and improving the efficiency of permeability reservoir quality identification.

[0209] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0210] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0211] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0212] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0213] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0214] Furthermore, the functional units in various embodiments of the present invention may be integrated into a single processing module 32, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0215] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0216] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0217] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A method for identifying the quality of a permeable reservoir, characterized in that: include: Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data; Performing a time-frequency domain transformation on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; Inputting the square of the target amplitude difference into a pre-built reservoir permeability model for prediction to obtain a predicted value of the permeability of the current reservoir; Determining the identification result of the quality of the current reservoir according to the magnitude of the predicted value and a preset threshold; The function corresponding to the reservoir permeability model is: in, Indicates permeability, represents density, represents the permeability, represents the viscosity coefficient, It represents the first parameter that is independent of frequency but changes with angle and is a constant. , express The reflection coefficient under the condition, , Indicates the angular frequency.

2. The method for identifying the quality of a permeable reservoir according to claim 1, characterized in that: Determining the identification result of the quality of the current reservoir according to the difference between the predicted value and a preset threshold value includes: If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement; If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

3. The method for identifying the quality of a permeable reservoir according to claim 1, wherein: Determining, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data, comprising: subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data; Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

4. A permeability reservoir quality identification device, characterized in that: include: a determination module, configured to determine, based on first reflection seismic data at a first angle of a current reservoir and second reflection seismic data at a second angle of the current reservoir, a square of an amplitude difference between a first reflection coefficient corresponding to the first reflection seismic data and a second reflection coefficient corresponding to the second reflection seismic data; a transform module, configured to perform a time-frequency domain transform on the square of the amplitude difference to obtain a target square of the amplitude difference in the time-frequency domain; A prediction module, configured to input the square of the target amplitude difference into a pre-built reservoir permeability model for prediction, thereby obtaining a predicted value of the permeability of the current reservoir; An identification module, configured to determine an identification result of the quality of the current reservoir according to a difference between the predicted value and a preset threshold; The function corresponding to the reservoir permeability model is: in, Indicates permeability, represents density, represents the permeability, represents the viscosity coefficient, It represents the first parameter that is independent of frequency but changes with angle and is a constant. , express The reflection coefficient under the condition, , Indicates the angular frequency.

5. The permeability reservoir quality identification device according to claim 4, characterized in that: The identification module is specifically used to: If the predicted value is greater than a preset threshold, it is determined that the quality of the current reservoir meets the preset quality requirement; If the predicted value is less than or equal to a preset threshold, it is determined that the quality of the current reservoir does not meet the preset quality requirement.

6. The permeability reservoir quality identification device according to claim 4, characterized in that: The determining module is specifically configured to: subtracting the first reflected seismic data from the second reflected seismic data and squaring the result to obtain a square of a difference in the reflected seismic data; Wavelet removal is performed on the squared difference of the reflected seismic data to obtain the squared amplitude difference.

7. A permeability reservoir quality identification device, characterized in that: including memory and processor; The memory stores a computer program, which, when executed by the processor, implements the steps of the method for identifying the quality of a permeable reservoir according to any one of claims 1 to 3.

8. A 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 identifying the quality of a permeable reservoir according to any one of claims 1 to 3 are implemented.

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