Method and device for jointly predicting hydrate saturation based on double parameters and equivalent medium

Abstract

The present invention discloses a method and device for jointly predicting hydrate saturation based on dual parameters and an equivalent medium. The method includes obtaining deep resistivity and acoustic travel time data of a target hydrate reservoir area; using the deep resistivity and acoustic travel time data as input parameters of a dual-parameter model to obtain a predicted value of hydrate saturation from the dual-parameter model; calculating a predicted value of hydrate saturation from the equivalent medium model using the equivalent medium model; and performing weighted averaging on the predicted value of hydrate saturation from the dual-parameter model and the predicted value of hydrate saturation from the equivalent medium model to obtain a final predicted value of hydrate saturation. By combining the dual-parameter model with the equivalent medium model, the present invention can more comprehensively capture the physical characteristics of the hydrate reservoir, thereby improving the prediction accuracy of hydrate saturation. This combination utilizes the complementary advantages of the two models. The simplicity of the dual-parameter model and the physical basis of the equivalent medium model make the prediction results more reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrate geophysical exploration, and particularly relates to a method and device for jointly predicting hydrate saturation based on dual parameters and an equivalent medium. Background Art

[0002] In the prior art, the prediction of hydrate saturation mainly relies on a single parameter such as resistivity or acoustic travel time. These methods often cannot comprehensively reflect the occurrence state of hydrates. Although the equivalent medium model takes into account the microscopic morphology of hydrates, it is computationally complex in practical applications and difficult to quickly predict hydrate saturation. Summary of the Invention

[0003] In order to solve the problems of insufficient accuracy, high computational complexity, and failure to comprehensively consider the occurrence state of hydrates in the existing hydrate saturation prediction methods, the present invention provides a method and device for jointly predicting hydrate saturation based on dual parameters and an equivalent medium to improve the accuracy and efficiency of prediction.

[0004] To achieve the above object, the technical solution of the present invention is as follows:

[0005] In a first aspect, the present invention provides a method for jointly predicting hydrate saturation based on dual parameters and an equivalent medium, including:

[0006] Obtain deep resistivity and acoustic travel time data of the target hydrate reservoir area;

[0007] Use the deep resistivity and acoustic travel time data as input parameters of the dual-parameter model to obtain the predicted value of hydrate saturation by the dual-parameter model;

[0008] Use the equivalent medium model to calculate the predicted value of hydrate saturation by the equivalent medium model;

[0009] Perform weighted averaging on the predicted value of hydrate saturation by the dual-parameter model and the predicted value of hydrate saturation by the equivalent medium model to obtain the final predicted value of hydrate saturation.

[0010] Optionally, the dual-parameter model is:

[0011]

[0012] is the predicted value of hydrate saturation by the dual-parameter model, and are the baseline values of deep resistivity and acoustic travel time respectively, a and b are model coefficients; rt is deep resistivity and ac is acoustic travel time.

[0013] Optionally, the calculation method for using the equivalent medium model to calculate the predicted value of hydrate saturation by the equivalent medium model is:

[0014] Pore fluid bulk modulus is: ;

[0015] wherein is the water saturation; and are the bulk moduli of water and hydrate respectively;

[0016] Predicted value of hydrate saturation of equivalent medium model is:

[0017] Optionally, the calculation method for weighted averaging the predicted value of hydrate saturation of the dual-parameter model and the predicted value of hydrate saturation of the equivalent medium model to obtain the final predicted value of hydrate saturation is:

[0018]

[0019] is the finally predicted hydrate saturation, and are the weight coefficients.

[0020] Optionally, and have values of 0.32 and 0.68 respectively.

[0021] In a second aspect, the present invention provides a device for predicting hydrate saturation based on the joint prediction of dual-parameters and equivalent medium, comprising:

[0022] A data acquisition module, configured to acquire deep resistivity and acoustic travel time data of a target hydrate reservoir area;

[0023] A dual-parameter model module, configured to use the deep resistivity and acoustic travel time data as input parameters of the dual-parameter model to obtain a predicted value of hydrate saturation of the dual-parameter model;

[0024] An equivalent medium model module, configured to calculate a predicted value of hydrate saturation of the equivalent medium model by using the equivalent medium model;

[0025] A weighted averaging module, configured to perform weighted averaging on the predicted value of hydrate saturation of the dual-parameter model and the predicted value of hydrate saturation of the equivalent medium model to obtain a final predicted value of hydrate saturation.

[0026] In a third aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0027] A memory for storing computer programs;

[0028] A processor, when executing the program stored in the memory, implements the steps of the method for jointly predicting hydrate saturation based on dual parameters and equivalent medium as described in any one of the above.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] By combining the dual-parameter model (deep resistivity and acoustic travel time) with the equivalent medium model, the present invention can more comprehensively capture the physical properties of hydrate reservoirs, thereby improving the prediction accuracy of hydrate saturation. This combination utilizes the complementary advantages of the two models. The simplicity of the dual-parameter model and the physical basis of the equivalent medium model make the prediction results more reliable. Description of the Drawings

[0031] Figure 1 It is a flowchart of the method for jointly predicting hydrate saturation based on dual parameters and equivalent medium provided by an embodiment of the present application;

[0032] Figure 2 It is a comparison chart of hydrate saturation prediction results calculated by different methods;

[0033] Figure 3 It is a schematic diagram of the composition of the device for jointly predicting hydrate saturation based on dual parameters and equivalent medium provided by an embodiment of the present application;

[0034] Figure 4 It is a schematic diagram of the composition of an electronic device provided by an embodiment of the present application;

[0035] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Detailed Embodiments

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0038] Example 1:

[0039] Figure 1 This is a schematic flow chart of a method for jointly predicting hydrate saturation based on dual parameters and equivalent medium provided by an embodiment of the present application. As Figure 1 shown, the method for jointly predicting hydrate saturation based on dual parameters and equivalent medium provided by an embodiment of the present application may specifically include the following steps:

[0040] Step 110: Obtain deep resistivity and acoustic travel time data of the target hydrate reservoir area;

[0041] Step 120: Use the deep resistivity and acoustic travel time data as input parameters of the dual-parameter model to obtain the predicted value of hydrate saturation by the dual-parameter model;

[0042] In this step, by combining two different logging parameters, deep resistivity and acoustic travel time, to predict hydrate saturation, these two parameters respectively reflect the influence of hydrates on resistivity and acoustic wave propagation velocity. Through the established dual-parameter model, these two logging parameters, deep resistivity and acoustic travel time, are associated with hydrate saturation, thereby improving the prediction accuracy, and solving the problem of poor prediction accuracy existing in the existing prediction of hydrate saturation mainly relying on a single parameter such as resistivity or acoustic travel time. At the same time, since only two logging parameters are used, the calculation process is relatively simple and can quickly predict hydrate saturation.

[0043] Step 130: Use the equivalent medium model to calculate the predicted value of hydrate saturation by the equivalent medium model;

[0044] Step 140: Perform weighted averaging on the predicted value of hydrate saturation by the dual-parameter model and the predicted value of hydrate saturation by the equivalent medium model to obtain the final predicted value of hydrate saturation.

[0045] Since the dual-parameter model only uses two parameters, it may not be able to fully reflect the complex physical properties of the hydrate reservoir. Especially in the case of uneven hydrate distribution or complex reservoir structure, the prediction accuracy may be insufficient; in addition, since the accuracy of the dual-parameter model depends on the baseline values of deep resistivity and acoustic travel time, if the baseline values are inaccurate, the prediction results may deviate. For this reason, this method combines the dual-parameter model with the equivalent medium model, which can more comprehensively capture the physical properties of the hydrate reservoir, thereby improving the prediction accuracy of hydrate saturation. This combination utilizes the complementary advantages of the two models, the simplicity of the dual-parameter model and the physical basis of the equivalent medium model, making the prediction results more reliable.

[0046] It should be understood that although Figure 1The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least some of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in rotation with at least some of the steps or stages in other steps.

[0047] Specifically, the dual-parameter model is as follows:

[0048] is the predicted value of hydrate saturation of the dual-parameter model, and are the baseline values of deep resistivity and acoustic travel time respectively, a and b are model coefficients; rt is deep resistivity, and ac is acoustic travel time.

[0049] In this way, through the dual-parameter model constructed above, the two parameters of deep resistivity and acoustic travel time can be associated with hydrate saturation, thereby improving the prediction accuracy. At the same time, the calculation process is relatively simple and can quickly predict hydrate saturation.

[0050] Specifically, the calculation method for calculating the predicted value of hydrate saturation of the equivalent medium model using the equivalent medium model is as follows:

[0051] The bulk modulus of pore fluid is:

[0052] Wherein, is the water saturation; and are the bulk moduli of water and hydrate respectively;

[0053] The predicted value of hydrate saturation of the equivalent medium model is:

[0054] In this way, through the above calculation method, the equivalent medium model can better describe the microscopic distribution and physical properties of hydrates in the reservoir and can comprehensively capture the physical properties of hydrate reservoirs.

[0055] Specifically, the calculation method for weighted averaging the predicted value of hydrate saturation of the dual-parameter model and the predicted value of hydrate saturation of the equivalent medium model to obtain the final predicted value of hydrate saturation is as follows:

[0056]

[0057] is the hydrate saturation of the final prediction, and are the weight coefficients, which are 0.32 and 0.68 respectively.

[0058] The following further verifies and explains this method by combining an application scenario example:

[0059] Taking well logging as an example ( Figure 2 ), the red dashed line in the figure is the predicted calculation result of the hydrate saturation obtained by using the dual-parameter model, the black dashed line is the predicted calculation result of the hydrate saturation obtained by using the equivalent medium model, the blue line is the predicted calculation result of the hydrate saturation obtained by using this method, and the green dots are the measured data of the hydrate saturation. It can be seen that there are large differences between the calculated results of the hydrate saturation of the dual-parameter and equivalent medium models and the measured data, while the combined prediction result obtained by using this method is in good agreement with the measured result. And through fitting, and are 0.32 and 0.68 respectively, and this parameter can be applied to the calculation of the hydrate saturation of other wells near the work area.

[0060] In summary, this method is based on rock physics theory, considering the influence of hydrates on resistivity and acoustic wave propagation velocity, as well as the microscopic distribution of hydrates in the reservoir. The theoretical basis is scientific and reasonable. By combining the dual-parameter model (deep resistivity and acoustic travel time) with the equivalent medium model, this method can capture the physical properties of hydrate reservoirs more comprehensively, thereby improving the prediction accuracy of hydrate saturation. This combination utilizes the complementary advantages of the two models. The simplicity of the dual-parameter model and the physical basis of the equivalent medium model make the prediction results more reliable.

[0061] Embodiment 2:

[0062] Referring to Figure 3 shown, the hydrate saturation prediction device 300 provided in this embodiment, mainly includes:

[0063] A data acquisition module 310, configured to acquire deep resistivity and acoustic travel time data of a target hydrate reservoir area;

[0064] A dual-parameter model module 320, configured to use the deep resistivity and acoustic travel time data as input parameters of the dual-parameter model to obtain a predicted value of the hydrate saturation of the dual-parameter model;

[0065] An equivalent medium model module 330, configured to use the equivalent medium model to calculate and obtain a predicted value of the hydrate saturation of the equivalent medium model;

[0066] A weighted average module 340 is configured to perform a weighted average on the predicted value of hydrate saturation from the dual-parameter model and the predicted value of hydrate saturation from the equivalent medium model to obtain a final predicted value of hydrate saturation.

[0067] Specifically, the dual-parameter model is:

[0068]

[0069] is the predicted value of hydrate saturation from the dual-parameter model, and are the baseline values of deep resistivity and acoustic travel time respectively, and a and b are model coefficients.

[0070] Specifically, the calculation method for calculating the predicted value of hydrate saturation from the equivalent medium model using the equivalent medium model is:

[0071] The bulk modulus of pore fluid is:

[0072] where is the water saturation; and are the bulk moduli of water and hydrate respectively;

[0073] The predicted value of hydrate saturation from the equivalent medium model is:

[0074] Specifically, the calculation method for performing a weighted average on the predicted value of hydrate saturation from the dual-parameter model and the predicted value of hydrate saturation from the equivalent medium model to obtain a final predicted value of hydrate saturation is:

[0075]

[0076] is the finally predicted hydrate saturation, and are the weight coefficients, which are 0.32 and 0.68 respectively.

[0077] It should be noted that the device for predicting hydrate saturation based on the joint prediction of dual-parameters and equivalent medium provided in the embodiments of the present application can execute the method for predicting hydrate saturation based on the joint prediction of dual-parameters and equivalent medium provided in Embodiment 1, and has the corresponding functions and beneficial effects of executing the method.

[0078] Embodiment 3:

[0079] As Figure 4As shown in the figure, an embodiment of the present application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114. Among them, the processor 111, the communication interface 112, and the memory 113 complete mutual communication through the communication bus 114; the memory 113 is used to store a computer program; when the processor 111 executes the program stored on the memory 113, it implements the steps of the method for predicting hydrate saturation based on the combination of dual parameters and equivalent medium provided in Embodiment 1.

[0080] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for predicting hydrate saturation based on dual parameters and equivalent media, characterized in that: include: Acquire deep resistivity and acoustic transit time data of target hydrate reservoir areas; Using the deep resistivity and acoustic wave time difference data as input parameters of the dual-parameter model to obtain a predicted value of hydrate saturation of the dual-parameter model; The equivalent medium model is used to calculate the predicted value of hydrate saturation of the equivalent medium model; Taking a weighted average of the dual-parameter model hydrate saturation prediction value and the equivalent medium model hydrate saturation prediction value to obtain a final hydrate saturation prediction value; The two-parameter model is: is the predicted value of hydrate saturation of the dual-parameter model, and are the baseline values ​​of deep resistivity and acoustic transit time, respectively; a and b are model coefficients, rt is deep resistivity, and ac is acoustic transit time; The calculation method of using the equivalent medium model to calculate the predicted value of the hydrate saturation of the equivalent medium model is: Pore ​​fluid bulk modulus K f for: in, is water saturation; and are the bulk moduli of water and hydrate, respectively; Hydrate saturation prediction value of equivalent medium model for: 。 2. The dual-parameter and equivalent medium combined prediction method for hydrate saturation according to claim 1 is characterized in that: The weighted average of the dual-parameter model hydrate saturation prediction value and the equivalent medium model hydrate saturation prediction value is calculated as follows: is the final predicted hydrate saturation, and is the weight coefficient.

3. The dual-parameter and equivalent medium combined prediction method for hydrate saturation according to claim 2 is characterized in that: and The values ​​of are 0.32 and 0.68 respectively.

4. A device for predicting hydrate saturation based on dual parameters and equivalent medium, characterized in that: include: A data acquisition module, used to acquire deep resistivity and acoustic transit time data of the target hydrate reservoir area; A dual-parameter model module, used to use the deep resistivity and acoustic wave time difference data as input parameters of the dual-parameter model to obtain a dual-parameter model hydrate saturation prediction value; An equivalent medium model module is used to calculate the predicted value of hydrate saturation of the equivalent medium model using the equivalent medium model; A weighted average module, used to perform weighted average on the hydrate saturation prediction value of the dual-parameter model and the hydrate saturation prediction value of the equivalent medium model to obtain a final hydrate saturation prediction value; The two-parameter model is: is the predicted value of hydrate saturation of the dual-parameter model, and are the baseline values ​​of deep resistivity and acoustic time difference, respectively; a and b are model coefficients; rt is deep resistivity, ac is acoustic time difference; The calculation method of using the equivalent medium model to calculate the predicted value of the hydrate saturation of the equivalent medium model is: Pore ​​fluid bulk modulus K f for: in, is water saturation; and are the bulk moduli of water and hydrate, respectively; Hydrate saturation prediction value of equivalent medium model for: 。 5. The device for predicting hydrate saturation based on dual parameters and equivalent medium combination according to claim 4, characterized in that: The weighted average of the dual-parameter model hydrate saturation prediction value and the equivalent medium model hydrate saturation prediction value is calculated as follows: is the final predicted hydrate saturation, and is the weight coefficient.

6. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to implement the steps of the method for jointly predicting hydrate saturation based on dual parameters and equivalent media as described in any one of claims 1 to 3 when executing the program stored in the memory.

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