A method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions.

Abstract

This invention belongs to the field of natural gas hydrate geological exploration and well logging evaluation technology, and relates to a method for predicting the effective resistivity and inverting hydrate saturation of hydrate-bearing sediments based on a logistic function. The method includes: acquiring basic petrophysical data of the target reservoir or sample to be tested; establishing a forward model of effective resistivity based on a logistic function; determining the fixed parameters in the forward model and the fitting parameters to be inverted based on the geological characteristics or experimental conditions of the target; constructing an objective function, optimizing the fitting parameters using an optimization algorithm to obtain the optimal model parameters, and establishing a univariate response equation of effective resistivity with respect to hydrate saturation; and calculating the effective resistivity or hydrate saturation of unmeasured points based on the determined response equation. This invention significantly improves the accuracy and reliability of describing the resistivity response characteristics of complex hydrate reservoirs, laying the foundation for refined evaluation of reservoir saturation and efficient resource exploration and development.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration and well logging evaluation technology of natural gas hydrates, specifically involving a method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on a logic function. Background Technology

[0002] Among existing geophysical exploration techniques, electrical methods are crucial for estimating hydrate saturation and evaluating reservoir characteristics due to their sensitivity to differences in formation conductivity. However, in practical applications, existing resistivity evaluation models still face challenges in their applicability to fine-grained clayey sedimentary reservoirs. This is primarily because the reservoir geological environment is complex, with high clay content, and hydrates exhibit diverse microscopic occurrences within sediment pores, displaying various microstructures such as pore-filling, particle-encapsulated, and cemented forms. Different occurrence forms alter the internal conductive pathways of the sediment, resulting in a complex nonlinear relationship between resistivity response and hydrate saturation.

[0003] The Archie formula and its derivative models, which are currently widely used, are mainly based on pure sandstone. Their empirical parameters often fail to simultaneously account for the conductivity effect of argillaceous materials and the complex changes in pore structure. While various physical models based on equivalent medium theory or geometric assumptions consider multiphase mixing, their model assumptions are usually quite idealized and often fail to uniformly describe the nonlinear resistivity response caused by the changes in conductive channels due to the differences in the microscopic distribution of conductive components such as water and argillaceous materials and high-resistivity components such as hydrates and gases in the pore space under different hydrate occurrence forms.

[0004] Therefore, when using existing models to interpret actual data, saturation calculation errors are prone to occur, affecting the accuracy of reservoir evaluation. To address these issues, a novel prediction method is needed that can comprehensively consider the influence of clay content and flexibly characterize the nonlinear resistivity response under different occurrence morphologies. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions. This method constructs a forward model of effective resistivity based on logistic functions, utilizing the nonlinear evolution characteristics of these functions to characterize the nonlinear influence of different hydrate microstructures and clay content on reservoir resistivity. Based on this, an optimization algorithm is used to invert and optimize key parameters in the model, thereby achieving accurate prediction of the effective resistivity of hydrate-bearing sediment layers and reliable inversion of hydrate saturation under complex reservoir conditions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for predicting the effective resistivity and inverting hydrate saturation of hydrate-bearing sediments based on logistic functions includes the following steps:

[0008] (1) Obtain basic rock physics data of the target reservoir or the sample to be tested;

[0009] (2) Establish an effective resistivity forward model based on the form of a logic function;

[0010] (3) Determine the fixed parameters in the effective resistivity forward model and the fitting parameters to be inverted based on the geological characteristics or experimental conditions of the target to be analyzed;

[0011] (4) Construct the objective function, use the optimization algorithm to optimize the above fitting parameters, obtain the optimal model parameters, and establish the univariate response equation of effective resistivity with respect to hydrate saturation based on this;

[0012] (5) Calculate the effective resistivity or hydrate saturation at the unmeasured points based on the determined response equation.

[0013] In step (1), the initial absolute porosity of the target reservoir or the sample to be tested is obtained. φ 0 pore water resistivity R w And so on, as well as a series of corresponding hydrate saturations. S h Compared with the measured effective resistivity R t The data may be derived from laboratory sand-filling experiments, core tests, numerical simulations, or in-situ geophysical logging.

[0014] In step (2), it is necessary to clarify the relationship between the volume fraction of sandstone skeleton, mudstone, gas, water, and hydrate and their corresponding content or saturation.

[0015] The volume fractions of each component in hydrate sediments should satisfy certain relationships:

[0016] (1);

[0017] In the formula, n g , n s , n w , n h , n sh These represent the volume fractions of gas, sandstone skeleton, water, hydrates, and clay, respectively.

[0018] The rock matrix is ​​defined as consisting of a sandstone framework and argillaceous material. This is based on the initial absolute porosity. φ 0 With mud content V sh Sandstone skeleton volume fraction n s With clay volume fraction n sh Calculate according to equations (2) and (3) respectively:

[0019] (2);

[0020] (3);

[0021] In the formula, φ 0 The initial absolute porosity, V sh The mud content is 0~1.

[0022] The pore spaces outside the rock matrix are occupied by gas, water, and hydrates, with the following saturation relationships:

[0023] (4);

[0024] In the formula, S g , S w , S h These represent the saturation levels of gases, water, and hydrates, respectively.

[0025] The model proposed in this invention is divided into two cases: excess gas and excess water. In the case of excess gas, the water saturation level is... S w =0, gas saturation S g =1- S h Excessive water has the opposite effect.

[0026] The volume fractions of gas, water, and hydrate can be expressed as follows:

[0027] (5);

[0028] (6);

[0029] (7).

[0030] Dimensionless Kersten To evaluate the effective resistivity of hydrate-bearing sediments:

[0031] (8);

[0032] In the formula, R The effective resistivity is expressed in Ω·m. R dry The effective resistivity of the dry sediment, in Ω·m. R sat ρ is the effective resistivity when the wet phase is saturated, in Ω·m.

[0033] in addition, Kersten The number varies with changes in hydrate saturation and pore structure parameters. Therefore, a logical model is used to represent the effective resistivity of hydrate-bearing sediments, i.e., equation (9):

[0034] (9);

[0035] In the formula, the parameter U and L They represent K e The upper and lower asymptotes take values ​​of 1 and 0, respectively; S This is the critical hydrate saturation level at which electrical conduction changes. α To transform the intensity, β The turning point is symmetrical.

[0036] Combining equations (8) and (9), a general expression for the effective resistivity forward model based on logical functions can be established:

[0037] (10);

[0038] In the formula, R The effective resistivity is expressed in Ω·m. R dry The effective resistivity of the dry sediment is given in Ω·m. R sat The effective resistivity when the wet phase is saturated is expressed in Ω·m. S The critical hydrate saturation level at which electrical conduction changes, ranging from 0 to 1; α To transform the intensity, β To ensure the left-right symmetry of the transition point, the fit is determined. S h The hydrate saturation of the reservoir is 0~1.

[0039] For hydrate-bearing sediments, the effective resistivity of dry sediments and wetted phases should be treated according to different hydrate formation conditions and corresponding occurrence morphologies, and a distribution model should be used to characterize the effective resistivity of hydrate-bearing sediment layers.

[0040] ① Under conditions of excess gas, hydrates are predominantly granular, with gas occupying the pore centers. Therefore, the effective resistivity of dry sediments containing only gas and a sandstone framework can be expressed as:

[0041] (11);

[0042] The contributions of hydrates, water, and clay to conductivity are considered as a single phase (wetting phase), and the effective resistivity of the wetting phase is... R wet Represented as:

[0043] (12);

[0044] Then, the effective resistivity under wet phase saturation conditions is calculated:

[0045] (13);

[0046] In the formula, n g , n s , n w , n h , n sh These represent the volume fractions of gas, sandstone skeleton, water, hydrates, and clay, respectively. R dry The effective resistivity of the dry sediment is given in Ω·m. R g , R s , R w , R h , R sh The effective resistivity (Ω·m) of gas, sandstone skeleton, water, hydrate, and clay under reservoir conditions are respectively. R wet The effective resistivity of the wetting phase is given in Ω·m. R sat ρ is the effective resistivity when the wet phase is saturated, in Ω·m.

[0047] ② Under conditions of excess water, the hydrate morphology is predominantly pore-filling, with the gas surrounded by a hydrate shell. Therefore, the contributions of the gas and hydrate are considered as a single phase (the non-wetting phase), and the effective resistivity of the non-wetting phase is... R nwet Represented as:

[0048] (14);

[0049] Effective resistivity of dry sediments:

[0050] (15);

[0051] Effective resistivity under wet-phase saturation conditions:

[0052] (16);

[0053] In the formula, n g , n s , n w , n h , n sh These represent the volume fractions of gas, sandstone skeleton, water, hydrates, and clay, respectively. R nwet The effective resistivity of the unwetting phase is given in Ω·m. R sat The effective resistivity when the wet phase is saturated is expressed in Ω·m. R dry The effective resistivity of the dry sediment is given in Ω·m. R g , R s , R w , R h , R sh The effective resistivity (Ω·m) represents that of gas, sandstone skeleton, water, hydrate, and clay under reservoir conditions.

[0054] Based on the above-established resistivity of dry sediments and wet saturated resistivity for different occurrence forms, combined with the general expression of the effective resistivity model based on the logical function form of equation (10), the effective resistivity of hydrate-bearing sediment layers can be calculated.

[0055] In step (3), it is necessary to identify the known and unknown quantities in the effective resistivity forward model based on the geological characteristics and test conditions of the target to be analyzed.

[0056] First, determine the formation conditions (excess gas / excess water) and occurrence morphology (granular encapsulated type / pore-filled type) of the target hydrate-bearing sediment layer, and then analyze and determine the effective resistivity forward model. R sat and R dryAll potential physical parameters, including environmental factors (such as clay content, gas saturation, etc.) and component physical properties (such as clay resistivity, skeleton resistivity, hydrate resistivity, etc.). If a parameter has been determined through geological data analysis or experimental testing, the relevant parameter is set to a fixed value; if a parameter is unknown or difficult to measure directly, it is set as an unknown quantity to be inverted.

[0057] Finally, the shape of the fitting curve of the forward model of effective resistivity will be characterized. α , β and inflection point saturation S Together with the aforementioned screened unknown physical parameters, they form the final set of parameters to be fitted.

[0058] In step (4), a goodness-of-fit R is constructed. 2 The objective function is to maximize or minimize the sum of squared residuals (RSS). The criteria for constructing the objective function include, but are not limited to, goodness of fit R. 2 Maximize and minimize the sum of squared residuals (RSS); use optimization algorithms to perform joint iterative optimization until the convergence condition is met, thereby obtaining the optimal combination of model fitting parameters for the dataset.

[0059] (17);

[0060] (18);

[0061] In the formula, N For data length, For the actual measured effective resistivity R t The average value, R t，i Let be the measured effective resistivity of the i-th data point.

[0062] Based on the optimal model parameter combination obtained above, and combining equations (2), (3), (4), (5), (6), (7), and (10) with the formulas for the corresponding hydrate occurrence morphology (if it is an excess gas condition and a particle-encapsulated occurrence, then it is equations (11), (12), and (13); if it is an excess water condition and a pore-filled occurrence, then it is equations (14), (15), and (16)), a univariate response equation for the effective resistivity determined by the sample with respect to the hydrate saturation can be established.

[0063] In step (5), the response equation established in step (4) can be used to input the hydrate saturation to predict its effective resistivity response, or the resistivity logging data of unmeasured points can be input to calculate the corresponding hydrate saturation, thereby realizing the quantitative evaluation of the hydrate reservoir.

[0064] Compared with existing methods, traditional resistivity models based on the Archie formula or its derivatives have two main limitations when dealing with reservoirs with high clay content, complex pore structures, or diverse hydrate occurrence morphologies: First, these models typically do not explicitly consider the conductivity contribution of clay and the fundamental impact of different microscopic occurrence morphologies on the conductivity path, leading to unclear physical mechanisms; second, their mathematical form struggles to describe the abrupt nonlinear resistivity changes accompanying morphological shifts, resulting in poor applicability under complex conditions such as cemented hydrates. This invention introduces the Kersten number to comprehensively characterize pore structure and fluid distribution, and constructs a unified model centered on logistic functions. This model not only separately characterizes the conductivity behavior of pore-filled and particle-encapsulated hydrates but also demonstrates excellent predictive ability for "cemented" hydrates, which are not initially considered in the design. This method has clear physical meaning, relatively simple parameter acquisition, and significantly improves the accuracy and reliability of describing the resistivity response characteristics of complex hydrate reservoirs, laying a solid foundation for refined reservoir saturation evaluation and efficient resource exploration and development. Attached Figure Description

[0065] The accompanying drawings, which form part of this invention, are used to provide a further understanding of this application. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0066] Figure 1 Schematic diagram of hydrate occurrence morphology under different hydrate formation conditions;

[0067] Figure 2 This is a comparison graph showing the fitting of different electrical response models in Example 1;

[0068] Figure 3 This is a comparison chart of fitting different electrical response models in Example 2. Detailed Implementation

[0069] The present invention will be further described below. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0070] Example 1

[0071] A method for predicting the effective resistivity and inverting hydrate saturation of hydrate-bearing sediments based on logistic functions includes the following steps:

[0072] (1) Obtain basic rock physics data of the target reservoir or the sample to be tested;

[0073] Hydrates are formed under saturated water conditions (pore-filled type), and the gas saturation is considered to be at this level. S g=0, several groups of hydrate-bearing sand-filled samples were obtained and subjected to rock physics experiments. The basic data obtained included: pore water resistivity. R w The porosity is 0.4 Ω·m. φ 0 =0.3848; A set of hydrate saturation values ​​were measured ( S h The measured effective resistivity values ​​are 0.218, 0.427, 0.6198, and 0.8217, respectively. R t The values ​​are 3.464, 3.634, 4.876, and 6.763 Ω·m, respectively.

[0074] (2) Establish an effective resistivity forward model based on the form of a logic function;

[0075] Given that the hydrate-filled sand sample is under saturated water conditions, it is assumed that the hydrate exists in a pore-filled state. Combining equations (10), (14), (15), and (16) in the above invention, an effective resistivity forward model based on a logic function can be established, namely equation (19).

[0076] (19);

[0077] (3) Determine the fixed parameters in the effective resistivity forward model and the fitting parameters to be inverted based on the geological characteristics or experimental conditions of the target to be analyzed;

[0078] Based on the experimental conditions of Example 1, fixed parameters were determined: since the hydrate is formed under saturated water conditions, it can be assumed that there is no gas in the sample, and the gas saturation is... S g =0; Additionally, no clay was added during sand filling, therefore clay content is not considered. V sh =0. Therefore, the remaining unknown quantity ( α , β , S , R s , R h Let be the fitting parameters to be inverted.

[0079] (4) Construct a goodness-of-fit R-value. 2 The objective function is maximized. An optimization algorithm is used to find the optimal fitting parameters to obtain the optimal model parameters, and a model is established based on these parameters. R about S h The univariate response equation;

[0080] The goodness of fit R between the fitted effective resistivity value and the original data 2 Maximizing the desired value is the objective, and a genetic algorithm is used for parameter fitting. The fitted parameters are... α , β , S The values ​​are 6.0607, 0.3594, and 0.5982 respectively. R s , R h The values ​​are 11.2451 and 10.0000 Ω·m, respectively.

[0081] Based on the fitting parameters obtained in step (4) α , β , S , R s , R h By combining equations (2), (3), (4), (5), (6), (7), and (19), we can obtain... R about S h The univariate response equation is, i.e., equation (20);

[0082] (20).

[0083] Using equation (20), the effective resistivity R of each sample point in this embodiment was calculated to be 3.3652, 3.7429, 4.8314, and 6.7856 Ω·m, respectively, corresponding to goodness of fit R. 2 It is 0.9965.

[0084] The fitting results of resistivity response models such as the Archie formula, parallel equivalent circuit model, and hydroelectric similarity equivalent model are compared with those of other models. The results show that the effective resistivity model based on the logistic function form fits better than other models on this dataset.

[0085] (5) Calculate the effective resistivity or hydrate saturation at the unmeasured points based on the determined response equation.

[0086] Equation (20) can be used for two application scenarios: effective resistivity prediction and hydrate saturation inversion.

[0087] ① Effective resistivity prediction (known) S h ,beg R When the hydrate saturation at unmeasured points is... S h When known (e.g., from core analysis or other well logging interpretation results), the hydrate saturation at unmeasured points will be... Sh Substituting into equation (20), the corresponding predicted effective resistivity value R can be obtained;

[0088] If the unmeasured points are known S h Substituting 0.5 into equation (20), we can obtain the corresponding predicted effective resistivity value of 4.0313 Ω·m.

[0089] ② Hydrate saturation inversion (with known measured resistivity) R t Find the corresponding S h When measured / logging resistivity exists at unmeasured points R t When, consider equation (20) as being about S h function R = f ( S h ),make f ( S h )= R and in S h Within the range [0,1], the corresponding solution can be obtained using conventional numerical methods (such as iterative root-finding methods). S h This is the inversion value of hydrate saturation at that point.

[0090] If the measured resistivity of the unmeasured point is known R t It is 4.8 Ω·m, combined with equation (20). S h The solution is obtained using the binary search iterative method within the range [0,1]. S h ≈0.6438. S h Substituting 0.6438 into equation (20) for verification, we get: R ≈4.79998Ω·m, and R t The error of 4.8 Ω·m is approximately 1.8 × 10⁻⁶. -5 .

[0091] Example 2

[0092] A method for predicting the effective resistivity and inverting hydrate saturation of hydrate-bearing sediments based on logistic functions includes the following steps:

[0093] (1) Obtain basic rock physics data of the target reservoir or the sample to be tested;

[0094] A hydrate-bearing core model under saturated water conditions was constructed using COMSOL numerical simulation software. The basic data obtained included: porosity. φ 0 =0.4033; A set of hydrate saturation values ​​were measured. S h The corresponding measured effective resistivity values ​​are 0.109, 0.187, 0.296, 0.35, 0.484, 0.56, 0.626, and 0.739, respectively. R t The values ​​are 1.1110, 1.4777, 2.9910, 9.8020, 1238.8503, 1665.0017, 2014.6059, and 2893.9372 Ω·m, respectively.

[0095] Furthermore, hydrate formation causes changes in pore water temperature and salinity, which in turn affects pore water resistivity. Therefore, the pore water resistivity at the corresponding hydrate saturation level was recorded. R w The values ​​are 0.2130, 0.1987, 0.1779, 0.1673, 0.1399, 0.1237, 0.1093, and 0.0841 Ω·m, respectively.

[0096] (2) Establish an effective resistivity forward model based on the form of a logic function;

[0097] The constructed hydrate-bearing core model is known to be under saturated water conditions, and it is assumed that the hydrates exist in a pore-filling state. Similar to Example 1, combined with equations (10), (14), (15), and (16) in the above invention, an effective resistivity forward model based on a logical function can be established, namely equation (19).

[0098] (19).

[0099] (3) Determine the fixed parameters in the model and the fitting parameters to be inverted based on the geological characteristics or experimental conditions of the target to be analyzed;

[0100] Based on the hydrate formation conditions of Example 2, fixed parameters were determined: since the hydrate is formed under saturated water conditions, it can be assumed that there is no gas in the model, and the gas saturation is... S g =0; Additionally, clay content was not considered during modeling, therefore clay content is not taken into account. V sh =0. Therefore, the remaining unknown quantity ( α , β , S , R s , R hLet be the fitting parameters to be inverted.

[0101] (4) Construct a goodness-of-fit R-value. 2 The objective function is maximized. An optimization algorithm is used to find the optimal fitting parameters to obtain the optimal model parameters, and a model is established based on these parameters. R about S h The univariate response equation;

[0102] The goodness of fit R between the fitted effective resistivity value and the original data 2 Maximizing the desired value is the objective, and a genetic algorithm is used for parameter fitting. The fitted parameters are... α , β , S The values ​​are 94.5809, 22.7853, and 0.4499, respectively. R s , R h The values ​​are 7508.0 and 7841.8 Ω·m, respectively.

[0103] For different hydrate saturation S h pore water resistivity R w Perform linear regression to obtain R w - S h Relationship;

[0104] (twenty one).

[0105] Based on the fitting parameters obtained in step (4) α , β , S , R s , R h By combining equations (2), (3), (4), (5), (6), (7), (19), and (21), we can obtain R about S h The univariate response equation is, i.e., equation (22);

[0106] (twenty two).

[0107] Using equation (22), the effective resistivity of each sample point in this embodiment is calculated. RThe values ​​are 117.8, 120.9, 125.8, 128.5, 1182.2, 1556.2, 1975.8, and 2973.1 Ω·m, respectively, corresponding to goodness-of-fit Rm. 2 It is 0.9912.

[0108] The fitting results of resistivity response models such as the Archie formula, parallel equivalent circuit model, and hydroelectric similarity equivalent model are compared with those of other models. The results show that the effective resistivity model based on the logistic function form fits the dataset significantly better than other models.

[0109] (5) Calculate the effective resistivity or hydrate saturation at the unmeasured points based on the determined response equation.

[0110] Equation (22) can be used for two application scenarios: effective resistivity prediction and hydrate saturation inversion.

[0111] ① Effective resistivity prediction (known) S h ,beg R When the hydrate saturation at unmeasured points is... S h When known (e.g., from core analysis or other well logging interpretation results), the hydrate saturation at unmeasured points will be... S h Substituting into equation (22), the corresponding predicted effective resistivity value R can be obtained.

[0112] If the unmeasured points are known S h Substituting 0.5 into equation (20), we can obtain the corresponding predicted effective resistivity value of 1255.45 Ω·m.

[0113] ② Hydrate saturation inversion (with known measured resistivity) R t Find the corresponding S h When measured / logging resistivity exists at unmeasured points R t When, consider equation (22) as being about S h function R = f ( S h ),make f ( S h )= R and in S h Within the range [0,1], the corresponding solution can be obtained using conventional numerical methods (such as iterative root-finding methods). S hThis is the inversion value of hydrate saturation at that point.

[0114] If the measured resistivity of the unmeasured point is known R t It is 1500 Ω·m, combined with equation (22). S h The solution is obtained using the binary search iterative method within the range [0,1]. S h ≈0.5498. S h Substituting 0.5498 into equation (22) for verification, we get: R ≈1499.98 Ω·m, and R t The error for 1500 Ω·m is approximately 1.4 × 10⁻⁶. -5 .

[0115] While the specific embodiments of the present invention have been described above, they are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions, characterized in that, Includes the following steps: (1) Obtain basic rock physics data of the target reservoir or the sample to be tested; (2) Establish an effective resistivity forward model based on the form of a logic function; (3) Determine the fixed parameters in the effective resistivity forward model and the fitting parameters to be inverted based on the geological characteristics or experimental conditions of the target to be analyzed; (4) Construct the objective function, use the optimization algorithm to optimize the above fitting parameters, obtain the optimal model parameters, and establish the univariate response equation of effective resistivity with respect to hydrate saturation based on this; (5) Calculate the effective resistivity or hydrate saturation at the unmeasured points based on the determined response equation; The general expression for the effective resistivity forward model based on logistic functions is a calculation function for effective resistivity with respect to hydrate saturation, established by introducing the effective resistivity of dry sediments and the effective resistivity at wet phase saturation. The calculation formula is as follows: ； In the formula, R The effective resistivity is expressed in Ω·m. R dry The effective resistivity of the dry sediment is given in Ω·m. R sat The effective resistivity when the wet phase is saturated is expressed in Ω·m. S The critical hydrate saturation level at which electrical conduction changes, ranging from 0 to 1; α To transform the intensity, β To ensure the left-right symmetry of the transition point, the fit is determined. S h The hydrate saturation of the reservoir is 0~1.

2. The method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions as described in claim 1, characterized in that, The effective resistivity of dry sediments in the effective resistivity forward model based on logistic functions R dry and effective resistivity when wet phase is saturated R sat The calculations were performed using a distribution model based on the hydrate formation conditions and occurrence morphology.

3. The method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions, as described in claim 2, is characterized in that... Under conditions of excess gas Effective resistivity of dry sediments R dry Represented as: ； Effective resistivity of the wetting phase R wet Represented as: ； Effective resistivity in wet saturated state R sat Represented as: ； In the formula, n g , n s , n w , n h , n sh These represent the volume fractions of gas, sandstone skeleton, water, hydrates, and clay in the reservoir geological unit, respectively. R dry The effective resistivity of the dry sediment is given in Ω·m. R g , R s , R w , R h , R sh The effective resistivity (Ω·m) of gas, sandstone skeleton, water, hydrate, and clay under reservoir conditions are respectively. R wet The effective resistivity of the wetting phase is given in Ω·m. R sat ρ is the effective resistivity when the wet phase is saturated, in Ω·m.

4. The method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions, as described in claim 2, is characterized in that... Under conditions of excess water, Effective resistivity of the non-wetting phase R nwet Represented as: ； Effective resistivity of dry sediments R dry Represented as: ； Effective resistivity in wet saturated state R sat Represented as: ； In the formula, n g , n s , n w , n h , n sh These represent the volume fractions of gas, sandstone skeleton, water, hydrates, and clay in the reservoir geological unit, respectively. R nwet The effective resistivity of the unwetting phase is given in Ω·m. R sat The effective resistivity when the wet phase is saturated is expressed in Ω·m. R dry The effective resistivity of the dry sediment is given in Ω·m. R g , R s , R w , R h , R sh The effective resistivity (Ω·m) represents that of gas, sandstone skeleton, water, hydrate, and clay under reservoir conditions.

5. The method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions as described in claim 1, characterized in that, In step (3), the method for determining the fixed parameters in the effective resistivity forward model and the fitting parameters to be inverted is as follows: (3-1) Identify all potential component parameters in the effective resistivity forward model that determine the effective resistivity of dry sediments and the effective resistivity of wet saturated state. If a parameter has been determined through geological data analysis or experimental testing, set it as a fixed parameter; if a parameter is unknown, set it as the fitting parameter to be inverted. (3-2) Characterizing the nonlinear morphology of the forward model of effective resistivity α , β and inflection point saturation S Together with the unknown component parameters selected above, they form the final set of fitting parameters to be inverted.

6. The method for predicting the effective resistivity of hydrate-bearing sediments and inverting hydrate saturation based on logistic functions, as described in claim 1, is characterized in that... In step (4), the criteria for constructing the objective function include, but are not limited to, the goodness of fit R. 2 Maximize and minimize the sum of squared residuals (RSS); use an optimization algorithm to iteratively optimize the fitting parameters based on the objective function.

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