Natural gas hydrate saturation identification method and device

Abstract

The invention discloses a natural gas hydrate saturation identification method and device. According to the single layer or multiple layers of hydrates and the existence condition of free gas, a stratum theoretical model is constructed, wherein the stratum theoretical model comprises a model without free gas below the single layer of hydrates, a model containing free gas below the single layer of hydrates, a model without free gas below the multiple layers of hydrates and a model containing free gas below the multiple layers of hydrates; determining a model AVO attribute value of each stratum theoretical model, and determining a sensitive attribute in the model AVO attribute value according to the relevance between the model AVO attribute value and the hydrate saturation; determining change characteristics of sensitive attribute values of the sensitive attributes under different hydrate saturation values in each stratum theoretical model; acquiring an actually measured sensitive attribute value of the to-be-identified region; and matching the change characteristics with the actually measured sensitive attribute values, and determining the hydrate saturation of the to-be-identified region according to a matching result. According to the technical scheme of the embodiment of the invention, the hydrate saturation identification accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and particularly relates to a method and device for identifying the saturation of natural gas hydrates. Background Art

[0002] Natural gas hydrate (referred to as "hydrate" for short) is an ice-like solid crystalline substance formed by water and gas molecules under low temperature and high pressure. It is a clean alternative energy source with great potential. Estimating the hydrate saturation can provide a strong basis for mastering the distribution and estimating the reserves.

[0003] Currently, the commonly used methods for estimating hydrate saturation mainly include resistivity method, seismic velocity method, and rock physics model method. In the resistivity method, due to different empirical coefficients in the Archie equation for different formation conditions, the predicted hydrate saturation will vary with the different values of the empirical coefficients; the accuracy of the hydrate saturation predicted by the seismic velocity method is low and the error is large, and the lithology, porosity, and effective pressure of sediments will change under different geological backgrounds, resulting in changes in the linear relationship between velocity and saturation, making it difficult to use uniformly; for the rock physics model method, different lithology combinations and porosity and other parameters need to be changed for different geological backgrounds, and using different rock physics models will result in large differences in the estimated saturation, making it impossible to use uniformly.

[0004] In summary, due to the deficiencies of the existing methods for estimating hydrate saturation, a more accurate method for identifying hydrate saturation is needed. Summary of the Invention

[0005] The present invention provides a method and device for identifying the saturation of natural gas hydrates to more accurately identify the hydrate saturation of the formation.

[0006] According to one aspect of the present invention, a method for identifying the saturation of natural gas hydrates is provided, including: constructing a formation theoretical model according to the presence of single-layer or multi-layer hydrates and free gas, the formation theoretical model including a model without free gas under single-layer hydrate, a model with free gas under single-layer hydrate, a model without free gas under multi-layer hydrates, and a model with free gas under multi-layer hydrates; determining the model AVO attribute values of each of the formation theoretical models, and determining the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation; determining the change characteristics of the sensitive attribute values of the sensitive attributes at different hydrate saturation values in each of the formation theoretical models; obtaining the measured sensitive attribute values of the sensitive attributes in the measured AVO attribute values of the area to be identified; matching the change characteristics of each of the formation theoretical models with the measured sensitive attribute values, and determining the hydrate saturation of the area to be identified according to the matching result.

[0007] Optionally, the model of free gas underlying the multi-layer hydrate includes the following sub-models: a first sub-model, where the formation between the hydrate layers of the first sub-model is a thick layer and the free gas saturation below is higher than a preset value; a second sub-model, where the formation between the hydrate layers of the second sub-model is a thick layer and the free gas saturation below is lower than the preset value; a third sub-model, where the formation between the hydrate layers of the third sub-model is a thin layer and the free gas saturation below is higher than the preset value; a fourth sub-model, where the formation between the hydrate layers of the fourth sub-model is a thin layer and the free gas saturation below is lower than the preset value.

[0008] Optionally, determining the model AVO attribute values of each of the formation theoretical models includes: setting the formation parameters of each of the formation theoretical models, where the formation parameters include P-wave velocity, S-wave velocity, density, hydrate layer thickness, and saturation; calculating the incident reflectivity, gradient, P-wave impedance reflectivity, S-wave impedance reflectivity, and fluid factor based on the formation parameters, and generating the AVO attribute profile and AVO attribute value table of each of the formation theoretical models.

[0009] Optionally, calculating the incident reflectivity, gradient, P-wave impedance reflectivity, S-wave impedance reflectivity, and fluid factor based on the formation parameters includes: calculating the incident reflectivity and gradient through the following formula:

[0010]

[0011] where P is the incident reflectivity, G is the gradient, Vp, Vs, and ρ are the average P-wave velocity, average S-wave velocity, and the densities above and below the interface respectively; ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media; θ is the incident angle, σ and Δσ are the average value of the Poisson's ratio and the difference in Poisson's ratio between the two media respectively; R(θ) represents the reflection amplitude value at an incident angle of θ, and A0 is a coefficient;

[0012] Calculating the P-wave impedance reflectivity and the S-wave impedance reflectivity through the following formula:

[0013]

[0014] where R P , R S are the P-wave impedance reflectivity and the S-wave impedance reflectivity respectively, Vp, Vs, and ρ are the average P-wave velocity, average S-wave velocity, and the densities above and below the interface respectively. ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media, IP, IS, ΔIP, and ΔIS are the P-wave impedance, S-wave impedance, the difference in P-wave impedance between the two media, and the difference in S-wave impedance between the two media respectively;

[0015] The fluid factor is calculated by the following formula:

[0016]

[0017] where F is the fluid factor, Vp and Vs are the average P-wave velocity and S-wave velocity respectively. ΔVp and ΔVs are the differences in P-wave velocity and S-wave velocity between two media, and β is a coefficient.

[0018] Optionally, determining the sensitive attribute in the model AVO attribute value according to the correlation between the model AVO attribute value and the hydrate saturation includes:

[0019] By analyzing the change trend of each AVO attribute value in the AVO attribute value table with respect to the hydrate saturation, calculating the linear correlation coefficient between each AVO attribute value and the hydrate saturation; determining the target AVO attribute value whose linear correlation coefficient is greater than a preset threshold, and taking the category to which it belongs as the sensitive attribute.

[0020] Optionally, matching the change characteristics of each formation theoretical model with the measured sensitive attribute value, and determining the hydrate saturation of the area to be identified according to the matching result includes: calculating the absolute error or standardized Euclidean distance between the measured sensitive attribute value and the sensitive attribute value of each formation theoretical model; screening the formation theoretical models with error values less than a preset threshold, if there are at least two candidate formation theoretical models, taking the hydrate saturation corresponding to the candidate formation theoretical model with the smallest absolute error or the shortest Euclidean distance as the hydrate saturation of the area to be identified; when the error values or Euclidean distances of the candidate formation theoretical models are all greater than the preset threshold, dynamically adjusting the formation parameters of the theoretical model and rematching.

[0021] Optionally, the method further includes:

[0022] Performing noise filtering processing on the measured sensitive attribute value, and using wavelet transform or Kalman filter algorithm to eliminate environmental interference; if the matching error between the sensitive attribute value of the formation theoretical model and the measured sensitive attribute value exceeds the preset tolerance range, iteratively optimizing the formation parameters through genetic algorithm until the error converges within the tolerance range.

[0023] According to another aspect of the present invention, there is provided a device for identifying natural gas hydrate saturation, including:

[0024] A formation theoretical model construction unit for constructing a formation theoretical model according to the presence of single-layer or multi-layer hydrates and free gas, the formation theoretical model including a model without free gas below a single-layer hydrate, a model with free gas below a single-layer hydrate, a model without free gas below multi-layer hydrates, and a model with free gas below multi-layer hydrates;

[0025] A sensitive attribute determination unit, configured to determine the model AVO attribute values of each of the formation theoretical models, and determine the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation;

[0026] A variation characteristic determination unit, configured to determine the variation characteristics of the sensitive attribute values of the sensitive attribute at different hydrate saturation values in each of the formation theoretical models;

[0027] A matching unit, configured to obtain the measured sensitive attribute value of the sensitive attribute in the measured AVO attribute value of the area to be identified; match the variation characteristics of each of the formation theoretical models with the measured sensitive attribute value, and determine the hydrate saturation of the area to be identified according to the matching result

[0028] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0029] At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for identifying natural gas hydrate saturation according to any embodiment of the present invention.

[0030] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a method for identifying natural gas hydrate saturation according to any embodiment of the present invention when executed.

[0031] The technical solution of the embodiment of the present invention conducts forward simulation analysis around the AVO response characteristics of hydrate reservoirs under different formation models, optimizes sensitive attributes, and comprehensively discusses the AVO responses and variation characteristics of sensitive attributes of hydrate reservoirs at different hydrate and free gas saturations; uses the measured AVO response characteristics and sensitive attribute values to match the AVO response characteristics and sensitive attribute values of different formation theoretical models in the forward simulation, and determines the hydrate saturation according to the matching result, improving the identification accuracy of the hydrate saturation.

[0032] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of a method for identifying the saturation of natural gas hydrate provided in Embodiment 1 of the present invention;

[0035] Figure 2 It is a flowchart of a method for determining the AVO attribute value of a model provided in Embodiment 2 of the present invention;

[0036] Figure 3 It is an AVO attribute profile diagram of a model without free gas under a single-layer natural gas hydrate with a saturation of 20% provided in Embodiment 2 of the present invention;

[0037] Figure 4 It is an AVO attribute profile diagram of a model with a single-layer natural gas hydrate saturation of 20% and a free gas saturation of 10% provided in Embodiment 2 of the present invention;

[0038] Figure 5 It is an AVO attribute profile diagram of a model with multiple layers of natural gas hydrate and no free gas below provided in Embodiment 2 of the present invention;

[0039] Figure 6 It is an AVO attribute profile diagram of a model with multiple layers of natural gas hydrate, thin interlayers, and high free gas saturation below provided in Embodiment 2 of the present invention;

[0040] Figure 7 It is an AVO attribute profile diagram of a model with multiple layers of natural gas hydrate, thin interlayers, and low free gas saturation below provided in Embodiment 2 of the present invention;

[0041] Figure 8 It is an AVO attribute profile diagram of a model with multiple layers of natural gas hydrate, thick interlayers, and high free gas saturation below provided in Embodiment 2 of the present invention;

[0042] Figure 9 It is an AVO attribute profile diagram of a model with multiple layers of natural gas hydrate, thick interlayers, and low free gas saturation below provided in Embodiment 2 of the present invention;

[0043] Figure 10 It is a flowchart of a method for determining sensitive attributes provided in Embodiment 3 of the present invention;

[0044] Figure 11 It is a trend diagram of AVO attribute changes of a model with a single-layer natural gas hydrate and no free gas below applicable to Embodiment 3 of the present invention;

[0045] Figure 12 It is an actual station position map of a single-layer natural gas hydrate applicable to the third embodiment of the present invention and without free gas below;

[0046] Figure 13 It is a schematic structural diagram of a natural gas hydrate saturation identification device provided by the fourth embodiment of the present invention;

[0047] Figure 14 It is a schematic structural diagram of an electronic device for implementing a method for identifying the saturation of natural gas hydrate in an embodiment of the present invention. Detailed implementation manners

[0048] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0050] Embodiment 1

[0051] Figure 1 It is a flowchart of a method for identifying the saturation of natural gas hydrate provided by the first embodiment of the present invention. This embodiment is applicable to the situation of identifying the saturation of natural gas hydrate in a formation. This method can be executed by a natural gas hydrate saturation identification device, which can be implemented in the form of hardware and / or software, and the natural gas hydrate saturation identification device can be configured in an electronic device. As Figure 1 shown, the method includes:

[0052] S110. Construct a formation theoretical model based on the presence of single-layer or multi-layer hydrates and free gas. The formation theoretical model includes a model without free gas below a single-layer hydrate, a model with free gas below a single-layer hydrate, a model without free gas below multi-layer hydrates, and a model with free gas below multi-layer hydrates.

[0053] The formation theoretical model is used to study the saturation of natural gas hydrates. It is constructed based on different hydrate distribution states (single-layer or multi-layer) and the presence of free gas, including four models: no free gas below a single-layer hydrate, free gas below a single-layer hydrate, no free gas below multi-layer hydrates, and free gas below multi-layer hydrates. It is constructed according to the single-layer or multi-layer distribution of hydrates and the presence or absence of free gas to simulate different actual formation situations.

[0054] The model with free gas below a single-layer hydrate adds a free gas layer on the basis of a single-layer hydrate and sets different free gas saturations to explore how the change in its saturation affects the AVO attributes together with the hydrates when free gas exists, which is more in line with some actual formation situations; the model without free gas below multi-layer hydrates contains multi-layer hydrate layers with different parameters for each layer. This model simulates the multi-layer hydrate reservoir in reality, studies the comprehensive influence of the multi-layer structure on the AVO attributes, and analyzes the interaction between different hydrate layers; the model with free gas below multi-layer hydrates combines the factors of multi-layer hydrates and free gas.

[0055] In the embodiment of the present invention, the model with free gas below multi-layer hydrates includes the following sub-models:

[0056] The first sub-model, where the formation between the hydrate layers of the first sub-model is thick and the free gas saturation below is higher than the preset value;

[0057] The second sub-model, where the formation between the hydrate layers of the second sub-model is thick and the free gas saturation below is lower than the preset value;

[0058] The third sub-model, where the formation between the hydrate layers of the third sub-model is thin and the free gas saturation below is higher than the preset value;

[0059] The fourth sub-model, where the formation between the hydrate layers of the fourth sub-model is thin and the free gas saturation below is lower than the preset value.

[0060] Among them, the model with free gas below multi-layer hydrates includes the following four situations: the formation between the hydrate layers is thick and the free gas saturation below is high, the formation between the hydrate layers is thick and the free gas saturation below is low, the formation between the hydrate layers is thin and the free gas saturation below is high, and the formation between the hydrate layers is thin and the free gas saturation below is low.

[0061] S120. Determine the model AVO attribute values of each theoretical formation model, and determine the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation.

[0062] For different theoretical formation models, calculate the AVO attribute values by combining the formation parameters of each model. In the model of single-layer natural gas hydrate without free gas below, according to the given various formation parameters, calculate the AVO attribute values at different hydrate saturations. Compare and analyze the AVO attribute values and hydrate saturation data of different models, and observe the trend of the attribute values changing with the hydrate saturation, so as to determine the sensitive attributes. The sensitive attribute refers to the attribute in the AVO attribute values that is closely related to the hydrate saturation and can significantly reflect the change of the hydrate saturation. Compare the changes of the AVO attribute values of different models at different hydrate saturations. For example, in the model of single-layer natural gas hydrate without free gas below, as the hydrate saturation increases from 20% to 70%, the amplitude value of a certain AVO attribute increases from 0.00239 to 0.058, showing an obvious change trend. Although the other AVO attributes also change, the change range and the degree of correlation with the hydrate saturation are not as good as this AVO attribute; comprehensively analyzing the results of various models, it is found that the amplitude value of this AVO attribute in the AVO response characteristics is more sensitive to the hydrate saturation, so as to determine the category of this AVO attribute as a sensitive attribute.

[0063] S130. Determine the change characteristics of the sensitive attribute values of the sensitive attributes at different hydrate saturation values in each theoretical formation model.

[0064] When studying the hydrate saturation, determining the change characteristics of the sensitive attributes at different hydrate saturation values in different theoretical formation models helps to accurately grasp the characteristics of the hydrate reservoir and improve the accuracy of saturation prediction.

[0065] Exemplarily, take the four theoretical formation models mentioned above as examples respectively:

[0066] Model of single-layer hydrate without free gas below: In this model, as the hydrate saturation increases, the sensitive attribute shows an upward trend. Without the interference of free gas, the increase in hydrate saturation will significantly cause the AVO attribute value of the sensitive attribute to increase.

[0067] Model of free gas below single-layer hydrate: When the free gas saturation is low, as the hydrate saturation increases, the AVO attribute value of the sensitive attribute first decreases and then increases. In the case of high free gas saturation, during the process of increasing hydrate saturation, the AVO attribute value of the sensitive attribute also shows a trend of first decreasing and then increasing, but the change amplitude is different from that in the case of low free gas saturation, indicating that the presence of free gas changes the variation law of the AVO attribute value of the sensitive attribute with hydrate saturation, and this influence also varies with different free gas saturations.

[0068] Model of no free gas below multi-layer hydrates: Taking the model with multi-layer natural gas hydrates and no free gas below as an example, the AVO attribute values of the sensitive attributes at the bottoms of different natural gas hydrate layers are different. Generally, the AVO attribute values of the sensitive attributes show a gradually increasing trend, reflecting that in the formation structure with multi-layer hydrates and no free gas, as the depth increases, the AVO attribute values of the sensitive attributes may increase, which is related to the parameter differences of different hydrate layers and the interaction between layers.

[0069] Model of free gas below multi-layer hydrates: In the case of thin interlayers and high free gas saturation, the variation trend of the AVO attribute value of the sensitive attribute is relatively complex. In the model of free gas below multi-layer hydrates, the variation of the AVO attribute value of the sensitive attribute is jointly affected by the interlayer thickness and free gas saturation, and the variation characteristics are relatively complex, showing different variation trends under different formation conditions.

[0070] S140. Obtain the measured sensitive attribute value of the sensitive attribute in the measured AVO attribute value of the area to be identified; match the variation characteristics of each formation theoretical model with the measured sensitive attribute value, and determine the hydrate saturation of the area to be identified according to the matching result.

[0071] In the area to be identified, obtain the AVO attribute value through the actual formation parameters of the area to be identified, and extract the corresponding measured sensitive attribute value in combination with the determined sensitive attribute. Compare the variation characteristics of the sensitive attribute in different formation theoretical models at different hydrate saturations with the measured sensitive attribute value of the area to be identified. Match the measured sensitive attribute value with the variation characteristics of the sensitive attribute value of the formation theoretical model. According to the matching result, when the measured sensitive attribute value is closest to the sensitive attribute value at a specific hydrate saturation in a certain formation theoretical model, determine the hydrate saturation corresponding to this theoretical model as the hydrate saturation of the area to be identified. In this way, the formation theoretical model and measured data can be effectively utilized to accurately predict the hydrate saturation of the area to be identified.

[0072] Embodiment 2

[0073] Figure 2It is a flowchart of a method for determining AVO attribute values provided in the second embodiment of the present invention. As Figure 2 shown, the method includes:

[0074] S210. Set the formation parameters of the theoretical model for each formation. The formation parameters include P-wave velocity, S-wave velocity, density, hydrate layer thickness, and saturation.

[0075] Different combinations of values of the formation parameters can simulate various formation conditions. The P-wave velocities of different formations vary significantly, and the change in S-wave velocity is correlated with the change in P-wave velocity. The two jointly reflect the elastic characteristics of the formation; the change in density affects the energy distribution and reflection and refraction during the propagation of seismic waves; the hydrate layer thickness and saturation are related to natural gas hydrates, and their changes directly affect the physical properties of the formation. By studying the AVO attributes under different combinations of thickness and saturation, a quantitative relationship with hydrate saturation can be established, thereby achieving accurate prediction of hydrate saturation.

[0076] S220. Calculate the incident reflectivity, gradient, P-wave impedance reflectivity, S-wave impedance reflectivity, and fluid factor based on the formation parameters, and generate the AVO attribute profile and AVO attribute value table for each formation theoretical model.

[0077] In the embodiment of the present invention, S220 specifically includes:

[0078] Calculate the incident reflectivity and gradient through the following formula:

[0079]

[0080] where P is the incident reflectivity, G is the gradient, Vp, Vs, and ρ are the average P-wave velocity, S-wave velocity, and density above and below the interface respectively; ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media; θ is the incident angle, σ and Δσ are the average value of Poisson's ratio and the difference in Poisson's ratio between the two media respectively; R(θ) represents the reflection amplitude value at an incident angle of θ, and A0 is a coefficient.

[0081] Calculate the P-wave impedance reflectivity and S-wave impedance reflectivity through the following formula:

[0082]

[0083]

[0084] where R P , R SThey are the P-wave impedance reflectivity and the S-wave impedance reflectivity respectively. Vp, Vs, and ρ are the average P-wave velocity, the average S-wave velocity, and the densities above and below the interface respectively. ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media. IP, IS, ΔIP, and ΔIS are the P-wave impedance, S-wave impedance, the difference in P-wave impedance between the two media, and the difference in S-wave impedance between the two media respectively.

[0085] The fluid factor is calculated by the following formula:

[0086]

[0087] Where F is the fluid factor, Vp and Vs are the average P-wave velocity and the average S-wave velocity respectively. ΔVp and ΔVs are the differences in P-wave velocity and S-wave velocity between the two media, and β is a coefficient.

[0088] Based on the above calculation method, the AVO attribute values of each theoretical formation model can be calculated. Exemplarily, the AVO attributes of each theoretical formation model and the corresponding table are as follows:

[0089] According to the formation parameters of the single-layer natural gas hydrate model with no free gas below (Table 1) and the forward modeling using the formula in Example 2, the corresponding AVO attribute profile (such as Figure 3 the AVO attribute profile of the single-layer natural gas hydrate model with no free gas below at a saturation of 20% as shown) and the AVO attribute values (Table 2):

[0090]

[0091] Table 1: Formation parameters of the single-layer natural gas hydrate model with no free gas below.

[0092]

[0093]

[0094] Table 2: AVO attribute values of the single-layer natural gas hydrate model with no free gas below.

[0095] According to the formation parameters of the single-layer natural gas hydrate model with low or high free gas saturation below (Table 3 and Table 4) and the forward modeling using the formula in Example 2, the corresponding AVO attribute profile (such as Figure 4 the AVO attribute profile of the single-layer natural gas hydrate model with a saturation of 20% and a free gas saturation of 10% as shown) and the AVO attribute values (Table 5 and Table 6):

[0096]

[0097] Table 3: Formation parameters of the single-layer natural gas hydrate model with low free gas saturation below.

[0098]

[0099] Table 4: Formation parameters when there is a single layer of natural gas hydrate and the free gas saturation below is high.

[0100]

[0101]

[0102] Table 5: AVO attribute values when there is a single layer of natural gas hydrate and the free gas saturation is low.

[0103] Saturation P G P*G Rp Rs F 20 -0.158418 -0.04461 0.00706703 -0.319152 -0.114455 -0.192808 30 -0.166575 -0.01897 0.00315993 -0.33057 -0.149125 -0.181137 50 -0.194405 0.021 -0.00408251 -0.386934 -0.205227 -0.168396 70 -0.2216 0.063 -0.0139608 -0.443163 -0.285613 -0.156907

[0104] Table 6: AVO attribute values when there is a single layer of natural gas hydrate and the free gas saturation is high.

[0105] According to the formation parameters of the multi-layer natural gas hydrate model with no free gas below (Table 7) and the forward modeling using the formula in Example 2, the corresponding AVO attribute profiles (such as the AVO attribute profile diagram of the multi-layer natural gas hydrate model with no free gas below shown in Figure 5 ) and AVO attribute values (Table 8) are obtained:

[0106]

[0107] Table 7: Formation parameters of the multi-layer natural gas hydrate model with no free gas below.

[0108]

[0109] Table 8: AVO attribute values of the multi-layer natural gas hydrate model with no free gas below.

[0110] According to the formation parameters of the multi-layer natural gas hydrate model with free gas below and the forward modeling using the formula in Example 2, the corresponding AVO attribute profiles and AVO attribute values are obtained, including the following four cases:

[0111] 1. According to the formation parameters of the model with thin interlayers and high free gas saturation (Table 9) and the forward modeling using the formula in Example 2, the corresponding AVO attribute profiles (such as the AVO attribute profile diagram of the multi-layer natural gas hydrate model with thin interlayers and high free gas saturation below shown in Figure 6 ) and AVO attribute values (Table 10) are obtained:

[0112]

[0113] Table 9: Formation parameters of the multi-layer natural gas hydrate (with thin interlayers) model with free gas below.

[0114]

[0115] Table 10: AVO attribute values of multi-layer natural gas hydrates (thin interlayer) with high free gas saturation below.

[0116] 2. According to the formation parameters of the model with a thin interlayer and low free gas saturation (the same as Table 9 above) and forward modeling using the formula in Example 2, the corresponding AVO attribute profile (such as the AVO attribute profile diagram of the multi-layer natural gas hydrates model with a thin interlayer and low free gas saturation below shown in Figure 7 ) and AVO attribute values (Table 11) are obtained.

[0117]

[0118] Table 11: AVO attribute values of multi-layer natural gas hydrates (thin interlayer) with low free gas saturation below.

[0119] 3. According to the formation parameters of the model with a thick interlayer and high free gas saturation (Table 12) and forward modeling using the formula in Example 2, the corresponding AVO attribute profile (such as the AVO attribute profile diagram of the multi-layer natural gas hydrates model with a thick interlayer and high free gas saturation below shown in Figure 8 ) and AVO attribute values

[0120] (Table 13).

[0121]

[0122] Table 12: Formation parameters of the multi-layer natural gas hydrates (thick interlayer) model with free gas below.

[0123]

[0124] Table 13: AVO attribute values of multi-layer natural gas hydrates (thick interlayer) with high free gas saturation below.

[0125] 4. According to the formation parameters of the model with a thick interlayer and low free gas saturation (the same as Table 12 above) and forward modeling using the formula in Example 2, the corresponding AVO attribute profile (such as the AVO attribute profile diagram of the multi-layer natural gas hydrates model with a thick interlayer and low free gas saturation below shown in Figure 9 ) and AVO attribute values (Table 14):

[0126]

[0127] Table 14: AVO attribute values of multi-layer natural gas hydrates (thick interlayer) with low free gas saturation below.

[0128] In summary, the theoretical formation model covers a variety of complex geological scenarios and restores the true formation conditions under different geological conditions. In the model without free gas below a single-layer hydrate, the precise setting of each layer's parameters simulates a simple and pure hydrate reservoir environment; while the model with free gas below multiple-layer hydrates takes into account various factors such as the number of hydrate layers, the thickness of the interlayer, and the free gas saturation, and can restore the complex actual formation structure; the AVO attribute values under different models can be calculated using the constructed formation model, which reflects the propagation characteristics and reflection characteristics of seismic waves under different formation conditions. By analyzing the AVO response characteristics of different models, the influence laws of hydrate saturation, free gas, and formation structure on seismic waves can be deeply understood. Through the systematic analysis of the AVO attribute values of different formation models, the sensitive attributes closely related to hydrate saturation are determined, and the measured attribute values are compared with the attribute change characteristics in each model to find the most matching model, improving the accuracy and reliability of identification.

[0129] Example 3

[0130] Figure 10 is a flowchart of a method for determining sensitive attributes provided in Example 3 of the present invention, as Figure 10 shown, the method includes:

[0131] S1010. By analyzing the variation trend of each AVO attribute value in the AVO attribute value table with respect to the hydrate saturation, calculate the linear correlation coefficient between each AVO attribute value and the hydrate saturation.

[0132] S1020. Determine the target AVO attribute values whose linear correlation coefficients are greater than a preset threshold, and use the category to which they belong as the sensitive attribute.

[0133] The AVO attribute value table covers the values of various AVO attributes under different theoretical formation models at different hydrate saturations. Exemplarily, in the model without free gas below a single-layer natural gas hydrate, observe the variation of attribute values such as the normal incidence reflectivity (P), gradient (G), P-wave impedance reflectivity (Rp), S-wave impedance reflectivity (Rs), and fluid factor (F) with the hydrate saturation from 20% to 70%. As the saturation increases, the P value changes from -0.020741 to -0.086504, showing a gradually decreasing trend; the G value increases from 0.00239 to 0.058, showing an upward trend.

[0134] To more precisely measure the degree of association between each AVO attribute value and the hydrate saturation, the linear correlation coefficient is calculated through the correlation coefficient formula, and it is calculated for each AVO attribute in each model to obtain a series of correlation coefficients. Exemplarily, a threshold is preset in advance, the calculated linear correlation coefficient is compared with the preset threshold, and the attribute category corresponding to the AVO attribute value with a linear correlation coefficient greater than the preset threshold is determined as the sensitive attribute. After calculating and comparing the AVO attribute values of each model, it is found that for the gradient G attribute in multiple models, its linear correlation coefficient is greater than the preset threshold. Therefore, the G attribute is determined as the sensitive attribute, and the change trend of the G attribute value is used to identify the hydrate saturation, improving the accuracy and reliability.

[0135] In the embodiment of the present invention, matching the change characteristics of each of the formation theoretical models with the measured sensitive attribute values, and determining the hydrate saturation of the to-be-identified area according to the matching result includes:

[0136] Calculating the absolute error or the standardized Euclidean distance between the measured sensitive attribute value and the sensitive attribute values of each of the formation theoretical models;

[0137] Screening the formation theoretical models with error values less than the preset threshold. If there are at least two candidate formation theoretical models, taking the hydrate saturation corresponding to the candidate formation theoretical model with the smallest absolute error or the shortest Euclidean distance as the hydrate saturation of the to-be-identified area;

[0138] When the error values or Euclidean distances of the candidate formation theoretical models are all greater than the preset threshold, dynamically adjust the formation parameters of the theoretical model and rematch.

[0139] Analyze the AVO response characteristics of the theoretical model and the change trend of the sensitive attribute value, and compare them with the actual AVO response characteristics and sensitive attribute values of the existing logging data. When the two G attribute amplitude values are close, the hydrate saturation corresponding to the sensitive attribute value of the theoretical model is the predicted hydrate saturation, and verify the predicted hydrate saturation with the actual hydrate saturation.

[0140] After obtaining the measured sensitive attribute value (such as the G attribute value) of the area to be recognized, calculate the absolute error or standardized Euclidean distance between the sensitive attribute values (also the G attribute value) of the theoretical models of each formation. The absolute error is directly calculated as the absolute value of the difference between the measured value and the theoretical value, reflecting the absolute deviation degree between the two. The standardized Euclidean distance takes into account the distribution characteristics of the data. By standardizing the data, the influence of different attribute dimensions and variances is eliminated, and the similarity degree between data points is measured more accurately. For the area to be recognized, its measured G attribute value is 0.01, and the G attribute value of a certain formation theoretical model is 0.012, and the absolute error is |0.01 - 0.012| = 0.002. If the standardized Euclidean distance is considered, it is also necessary to calculate in combination with the mean and standard deviation of the data.

[0141] Set a preset threshold, compare the calculated absolute error or standardized Euclidean distance with the preset threshold, and select the formation theoretical models with error values less than the preset threshold as candidate models. If there are at least two candidate formation theoretical models, at this time, select the candidate model with the smallest absolute error or the shortest standardized Euclidean distance, and use the hydrate saturation corresponding to this model as the hydrate saturation of the area to be recognized.

[0142] When the error value or standardized Euclidean distance of the formation theoretical model is greater than the preset threshold, it is necessary to dynamically adjust the formation parameters of the theoretical model, recalculate the sensitive attribute value after adjustment, and perform model matching again. If the saturation setting of a hydrate-bearing layer is unreasonable, resulting in a large difference from the measured value, adjust the saturation and recalculate the relevant attribute values until a model with an error value less than the preset threshold is found.

[0143] Analyze the AVO response characteristics of the theoretical model and the change trend of the sensitive attribute value, and compare them with the actual AVO response characteristics and sensitive attribute values of the existing logging data. When the two G attribute amplitude values are close, the hydrate saturation corresponding to the sensitive attribute value of the theoretical model is the predicted hydrate saturation, and verify the predicted hydrate saturation with the actual hydrate saturation, and the results are in good agreement.

[0144] Exemplarily, Figure 11 is a graph of the AVO attribute change trend of a single-layer natural gas hydrate model without free gas below applicable to the third embodiment of the present invention. Figure 12It is an actual station position map of a single-layer natural gas hydrate applicable to the third embodiment of the present invention, and there is no free gas below. Table 15 below shows the AVO attribute values of a single layer with low saturation and no free gas below. Through comparison, the AVO response characteristics show that P, Rp, Rs, and F are all negative anomalies, and G is a positive anomaly, which is consistent with the conclusion in the theoretical model. Moreover, the amplitude value of G is closest to the amplitude value of G when the saturation of natural gas hydrate in the theoretical model is 21%, and is significantly lower than the amplitude values of G at other natural gas hydrate saturations. It is predicted that the saturation of natural gas hydrate at this station is 21%, which is consistent with the actual drilling results.

[0145] In the embodiment of the present invention, the method may further include:

[0146] Perform noise filtering processing on the measured sensitive attribute values, and use wavelet transform or Kalman filter algorithm to eliminate environmental interference;

[0147] If the matching error between the sensitive attribute values of the formation theoretical model and the measured sensitive attribute values exceeds the preset tolerance range, optimize the formation parameters through genetic algorithm iteration until the error converges within the tolerance range.

[0148] When wavelet transform is used to process the measured sensitive attribute values, according to the difference in frequency between noise and useful signals, the noise is separated from the signal. For the measured sensitive attribute values containing noise, wavelet transform can effectively remove high-frequency noise without affecting the main characteristics of the signal. In actual hydrate detection, the noise generated by environmental factors will cause large fluctuations in the measured sensitive attribute values. The Kalman filter algorithm can smooth these fluctuations to obtain more stable and accurate measured sensitive attribute values. When genetic algorithm is used to optimize the formation parameters, first encode the formation parameters, and then select the initial population according to the fitness function to select individuals with high fitness; then perform crossover operation to exchange part of the gene fragments of individuals to generate new individuals; finally perform mutation operation to randomly change the values of part of the genes to increase the diversity of the population. Through continuous iteration, the formation parameters are continuously optimized, and the error between the sensitive attribute values of the model and the measured sensitive attribute values gradually converges within the tolerance range, and the error between the sensitive attribute values and the measured values continuously decreases, improving the accuracy of the model in predicting the saturation of hydrate.

[0149] In summary, the solutions of the embodiments of the present invention carry out forward simulation analysis around the AVO response characteristics of hydrate reservoirs under different formation models, optimize sensitive attributes, and comprehensively discuss the AVO responses and the variation characteristics of sensitive attributes of hydrate reservoirs at different hydrate and free gas saturations. By comparing the measured AVO response characteristics and sensitive attribute values with the AVO response characteristics and sensitive attribute values of different formation theoretical models obtained by forward simulation, when the two values are close, the hydrate saturation corresponding to the sensitive attribute value of the theoretical model is the predicted hydrate saturation, and this value is verified with the measured hydrate saturation, and the results are in good agreement.

[0150] Embodiment 4

[0151] Figure 13 FIG. is a schematic structural diagram of a device for identifying hydrate saturation provided in Embodiment 4 of the present invention. As Figure 13 shown, the device includes:

[0152] A formation theoretical model construction unit 1310, configured to construct a formation theoretical model according to the presence of single-layer or multi-layer hydrates and free gas, where the formation theoretical model includes a model without free gas under a single-layer hydrate, a model with free gas under a single-layer hydrate, a model without free gas under multi-layer hydrates, and a model with free gas under multi-layer hydrates;

[0153] A sensitive attribute determination unit 1320, configured to determine the model AVO attribute values of each of the formation theoretical models, and determine the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation;

[0154] A variation characteristic determination unit 1330, configured to determine the variation characteristics of the sensitive attribute values of the sensitive attributes at different hydrate saturation values in each of the formation theoretical models;

[0155] A matching unit 1340, configured to obtain the measured sensitive attribute value of the sensitive attribute in the measured AVO attribute values of the area to be identified; match the variation characteristics of each of the formation theoretical models with the measured sensitive attribute value, and determine the hydrate saturation of the area to be identified according to the matching result.

[0156] A device for identifying hydrate saturation provided by an embodiment of the present invention can execute a method for identifying hydrate saturation provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0157] Embodiment 5

[0158] Figure 14FIG. 0 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0159] As Figure 14 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0160] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0161] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for identifying the saturation of natural gas hydrates.

[0162] In some embodiments, a method for identifying natural gas hydrate saturation can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for identifying natural gas hydrate saturation described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute a method for identifying natural gas hydrate saturation by any other suitable means (e.g., by means of firmware).

[0163] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0164] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0165] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0166] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0167] The systems and techniques described herein can be implemented in a computing system that includes a back-end component (e.g., as a data server), or a computing system that includes a middleware component (e.g., an application server), or a computing system that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0168] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0169] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0170] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for identifying the saturation of natural gas hydrates, characterized in that, Including: Construct a formation theoretical model according to the single layer or multiple layers of hydrates and the presence of free gas. The formation theoretical model includes a model without free gas below a single layer of hydrates, a model with free gas below a single layer of hydrates, a model without free gas below multiple layers of hydrates, and a model with free gas below multiple layers of hydrates; Determine the model AVO attribute values of each of the formation theoretical models, and determine the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation; Determine the variation characteristics of the sensitive attribute values of the sensitive attributes at different hydrate saturation values in each of the formation theoretical models; Obtain the measured sensitive attribute values of the sensitive attributes in the measured AVO attribute values of the area to be identified; Match the variation characteristics of each of the formation theoretical models with the measured sensitive attribute values, and determine the hydrate saturation of the area to be identified according to the matching results.

2. The method according to claim 1, wherein The model with free gas below multiple layers of hydrates includes the following sub-models: The first sub-model, where the formation between the hydrate layers of the first sub-model is a thick layer and the free gas saturation below is higher than the preset value; The second sub-model, where the formation between the hydrate layers of the second sub-model is a thick layer and the free gas saturation below is lower than the preset value; The third sub-model, where the formation between the hydrate layers of the third sub-model is a thin layer and the free gas saturation below is higher than the preset value; The fourth sub-model, where the formation between the hydrate layers of the fourth sub-model is a thin layer and the free gas saturation below is lower than the preset value.

3. The method according to claim 1, wherein The determining the model AVO attribute values of each of the formation theoretical models includes: Set the formation parameters of each of the formation theoretical models. The formation parameters include P-wave velocity, S-wave velocity, density, hydrate layer thickness, and saturation; Based on the formation parameters, calculate the incident reflectivity, gradient, P-wave impedance reflectivity, S-wave impedance reflectivity, and fluid factor, and generate the AVO attribute profile and AVO attribute value table of each of the formation theoretical models.

4. The method according to claim 3, wherein The calculating the incident reflectivity, gradient, P-wave impedance reflectivity, S-wave impedance reflectivity, and fluid factor based on the formation parameters includes: Calculate the incident reflectivity and gradient through the following formula: where P is the incident reflectivity, G is the gradient, Vp, Vs, and ρ are the average P-wave velocity, S-wave velocity, and density above and below the interface respectively; ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media; θ is the incident angle, σ and Δσ are the average value of the Poisson's ratio and the difference in Poisson's ratio between the two media respectively; R(θ) represents the reflection amplitude value at an incident angle of θ, and A0 is a coefficient; Calculate the P-wave impedance reflectivity and the S-wave impedance reflectivity through the following formula: wherein, R P , R S are respectively the P-wave impedance reflectivity and the S-wave impedance reflectivity, Vp, Vs, and ρ are respectively the average P-wave velocity, the average S-wave velocity, and the densities above and below the interface. ΔVp, ΔVs, and Δρ are the differences in P-wave velocity, S-wave velocity, and density between the two media, and IP, IS, ΔIP, and ΔIP are respectively the P-wave impedance, the S-wave impedance, the difference in P-wave impedance between the two media, and the difference in S-wave impedance between the two media; Calculate the fluid factor through the following formula: where F is the fluid factor, Vp and Vs are the average P-wave velocity and S-wave velocity respectively. ΔVp and ΔVs are the differences in P-wave velocity and S-wave velocity between the two media, and β is a coefficient.

5. The method according to claim 3, wherein The determining the sensitive attributes in the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation includes: By analyzing the variation trend of each AVO attribute value in the AVO attribute value table with respect to the hydrate saturation, calculate the linear correlation coefficient between each AVO attribute value and the hydrate saturation; Determine the target AVO attribute values with the linear correlation coefficient greater than a preset threshold, and take the category to which they belong as the sensitive attribute.

6. The method according to claim 1, wherein The matching of the variation characteristics of each formation theoretical model with the measured sensitive attribute values and determining the hydrate saturation of the area to be identified according to the matching result includes: Calculate the absolute error or standardized Euclidean distance between the measured sensitive attribute values and the sensitive attribute values of each formation theoretical model; Screen the formation theoretical models with error values less than the preset threshold. If there are at least two candidate formation theoretical models, take the hydrate saturation corresponding to the candidate formation theoretical model with the smallest absolute error or the shortest Euclidean distance as the hydrate saturation of the area to be identified; When the error values or Euclidean distances of the candidate formation theoretical models are all greater than the preset threshold, dynamically adjust the formation parameters of the theoretical model and rematch.

7. The method according to claim 6, characterized in that, The method further includes: Perform noise filtering on the measured sensitive attribute values, and use wavelet transform or Kalman filtering algorithm to eliminate environmental interference; If the matching error between the sensitive attribute value of the formation theoretical model and the measured sensitive attribute value exceeds the preset tolerance range, iteratively optimize the formation parameters through a genetic algorithm until the error converges within the tolerance range.

8. A natural gas hydrate saturation identification device, characterized in that, It includes: A formation theoretical model construction unit for constructing a formation theoretical model according to the presence of single-layer or multi-layer hydrates and free gas. The formation theoretical model includes a model without free gas below a single-layer hydrate, a model with free gas below a single-layer hydrate, a model without free gas below multi-layer hydrates, and a model with free gas below multi-layer hydrates; A sensitive attribute determination unit for determining the model AVO attribute values of each formation theoretical model and determining the sensitive attributes among the model AVO attribute values according to the correlation between the model AVO attribute values and the hydrate saturation; A variation characteristic determination unit for determining the variation characteristics of the sensitive attribute values of the sensitive attribute at different hydrate saturation values in each formation theoretical model; A matching unit for obtaining the measured sensitive attribute values of the sensitive attribute in the measured AVO attribute values of the area to be identified; Match the variation characteristics of each formation theoretical model with the measured sensitive attribute values, and determine the hydrate saturation of the area to be identified according to the matching result.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a method for identifying natural gas hydrate saturation according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to implement a method for identifying natural gas hydrate saturation according to any one of claims 1-7 when executed.

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