A method and apparatus for standardizing elastic logging parameters in carbonate reservoirs

Abstract

This invention discloses a method and apparatus for standardizing elastic parameters of well logging in carbonate reservoirs. The invention establishes a sample library of elastic parameters for both reservoirs and non-reservoir areas based on their elastic parameter curves. Data from this sample library is used as input, and high-quality reservoir interpretation parameters from well logging are used as training constraints to train a neural network. The training output is then analyzed via cross-plotting to establish boundary standards for elastic parameters between reservoirs and non-reservoir areas. Well logging impedance power spectrum is then combined with seismic wave spectrum as a hard constraint, and the boundary standards for elastic parameters between reservoirs and non-reservoir areas are introduced to calculate and optimize a deconvolution operator. Finally, the optimized deconvolution operator is used to standardize the elastic parameters of well logging in carbonate reservoirs. This invention not only standardizes well logging curves but also improves reservoir identifiability. At the data foundation level, it clarifies the characteristics of sensitive reservoir parameters and indirectly improves the resolution of seismic data inversion and prediction.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and more specifically to a method and apparatus for standardizing elastic logging parameters in carbonate reservoirs. Background Technology

[0002] In the exploration and development of carbonate oil and gas reservoirs, quantitative prediction and evaluation of geophysical reservoirs is a crucial step, and the standardized analysis of reservoir elastic sensitive parameters is key and fundamental to this prediction and evaluation. Conventional logging curve standardization is based on the assumption of homogeneous formations and minimal lateral variation. However, in large-scale stable marine and clastic formations, where the formation thickness is significant and the lithology is stably distributed, the lateral characteristics can be approximated as homogeneous formations and standardized based on normalization under the constraint of the variation gradient of standard well logging curves. Therefore, conventional logging curve standardization methods are mostly applied to large-scale stable marine and clastic formations.

[0003] Currently, the standardization method for logging curves in carbonate reservoirs still relies on conventional standardization methods in some areas. However, due to the strong heterogeneity of the formation, especially the lateral and spatial distribution of the reservoir, it is impossible to determine the standard formation and standard well during the standardization process. Even if a certain standard layer and standard well can be identified in a local area, there are still differences in the distribution of reservoir sensitive parameters between different wells after standardization. This makes the elastic parameter characteristics of reservoir logging evaluation unable to meet the requirements of seismic prediction, which brings great difficulties to subsequent seismic prediction. Summary of the Invention

[0004] To address the problem that existing conventional logging curve standardization methods, especially elastic parameter curve standardization methods, are not applicable to carbonate reservoirs, this invention proposes a standardization method and device for elastic parameters in well logging of carbonate reservoirs. This solves the problem that the evaluation standards for basic elastic parameters in pre-stack inversion of carbonate reservoirs are not uniform, making it impossible to perform elastic parameter inversion and quantitative reservoir evaluation.

[0005] To achieve the above-mentioned objectives, the technical solution of the present invention is as follows:

[0006] A method for standardizing elastic logging parameters in carbonate reservoirs includes the following steps:

[0007] Establish a sample library of elastic parameters for carbonate reservoirs and non-reservoirs;

[0008] Establish a boundary standard for elastic parameters between carbonate reservoirs and non-reservoirs;

[0009] A combined well logging and seismic method was adopted, which combines the well logging impedance power spectrum as a hard constraint condition with the seismic wave spectrum, and introduces the boundary standard of reservoir and non-reservoir elastic parameters to calculate and optimize the deconvolution operator.

[0010] Standardization of logging elastic parameters for hydrochloric acid reservoirs is achieved based on a preferred deconvolution operator.

[0011] Preferably, in this invention, the establishment of a sample library of elastic parameters for carbonate reservoirs and non-reservoirs includes: calculating elastic parameter curves for carbonate reservoirs and non-reservoirs based on core, logging, and well logging lithology interpretation results, and establishing well logging sample libraries of elastic parameters for carbonate reservoirs and non-reservoirs based on depth points.

[0012] Preferably, in this invention, establishing the boundary standard for elastic parameters between carbonate reservoirs and non-reservoirs includes: based on a sample library of elastic parameters of carbonate reservoirs and non-reservoirs, using the P-wave velocity, S-wave velocity, and density logging parameters of reservoirs and non-reservoirs as inputs to a BP neural network, using the interpretation parameters of high-quality reservoirs as constraints for the neural network, outputting training data of P-wave velocity, S-wave velocity, and density of reservoirs and non-reservoirs at corresponding depths, using the output of the trained neural network as the basic sample library for establishing the boundary standard for elastic parameters between reservoirs and non-reservoirs, and finally establishing the boundary standard for elastic parameters between reservoirs and non-reservoirs through cross-plot analysis.

[0013] Preferably, in this invention, establishing the boundary standard for elastic parameters between carbonate reservoirs and non-reservoir areas specifically includes the following steps:

[0014] S21. Determine the length of the target well section for analysis;

[0015] S22. Based on the core, logging and well logging lithology interpretation results, extract the reservoir and non-reservoir logging curves in the target well section respectively, and analyze the maximum and minimum values ​​of the logging curves in the target well section;

[0016] S23. Using the maximum and minimum values ​​of the extracted logging curves as input to the neural network, and adding the interpretation results of high-quality reservoirs as constraints, the logging elastic parameters of reservoirs and non-reservoirs are selected through neural network training.

[0017] S24. Using cross-plot analysis, the logging elastic parameters of the selected reservoirs and non-reservoirs are compared and analyzed to establish the boundary standards for elastic parameters of high-quality reservoirs and non-reservoirs.

[0018] Preferably, in this invention, a combined well-logging and seismic method is adopted, combining the well-logging impedance power spectrum as a hard constraint condition with the seismic wave spectrum. Simultaneously, a boundary standard for elastic parameters between reservoirs and non-reservoirs is introduced to calculate and optimize the deconvolution operator, ultimately achieving standardization of well-logging elastic parameters in carbonate reservoirs based on the elastic parameter boundary standard constraint. Specifically, this includes the following steps:

[0019] Step S31. Extract the seismic signal spectrum based on the well-side tunnels in the study area;

[0020] Step S32. Extract the logging impedance power spectrum of the entire well section based on the well data of the study area;

[0021] Step S33. Combine the seismic signal spectrum with the logging impedance power spectrum of the entire well section to perform deconvolution operator calculation and obtain the first deconvolution operator;

[0022] Step S34. Based on the logging section trained by the neural network, extract the logging impedance power spectrum of the training well section and combine it with the seismic signal spectrum of the well side channel to perform deconvolution operator calculation to obtain the second deconvolution operator;

[0023] Step S35. Using the logging impedance power spectrum of the high-quality reservoir section interpreted by well logging as a constraint condition, select the logging impedance power spectrum of the high-quality reservoir section of different wells and combine it with the seismic signal spectrum of the well side channel to calculate the deconvolution operator again to obtain the third deconvolution operator.

[0024] Step S36. Use different deconvolution operators to deconvolve the logging curves in the study area, obtain the deconvolution results of logging curves with different deconvolution operators, and perform cross-section analysis of the logging curves after deconvolution based on the elastic parameter boundary standard between reservoir and non-reservoir, and select the optimal result that meets the boundary standard to achieve the optimization of deconvolution operators.

[0025] Based on the same inventive concept, this invention also proposes a standardization device for elastic logging parameters in carbonate reservoirs. The device is used to implement the aforementioned standardization method for elastic logging parameters, including:

[0026] The module for establishing a sample library of elastic parameters for carbonate reservoirs and non-reservoirs calculates elastic parameter curves for carbonate reservoirs and non-reservoirs based on core, logging, and well logging lithology interpretation results, and establishes a sample library of elastic parameters for well logging carbonate reservoirs and non-reservoirs based on depth points.

[0027] The module for establishing the boundary standards of elastic parameters between carbonate reservoirs and non-reservoirs establishes a basic sample library of boundary standards between reservoirs and non-reservoirs based on the neural network training results of elastic parameters of reservoirs and non-reservoirs, and establishes the boundary standards of elastic parameters between reservoirs and non-reservoirs through cross-analysis.

[0028] The deconvolution operator calculation and optimization module adopts a well-seismic combined approach, using the well logging impedance power spectrum as a hard constraint condition, and introducing the reservoir and non-reservoir boundary standard. It combines the seismic wave spectrum with the well logging spectrum to calculate and optimize the deconvolution operator.

[0029] The logging impedance power spectrum is combined with the seismic wave spectrum as a hard constraint condition, and the boundary standard of reservoir and non-reservoir elastic parameters is introduced to calculate and optimize the deconvolution operator.

[0030] The elastic parameter standardization module, based on the optimized deconvolution operator, ultimately achieves the standardization of elastic parameters in carbonate reservoir logging.

[0031] A computer device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, characterized in that, when the processor executes the computer program, it implements the steps of the above-described well logging elastic parameter standardization method.

[0032] A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when executed in a computer processor, implements the steps of the above-described well logging elastic parameter standardization method.

[0033] The beneficial effects of this invention are:

[0034] (1) This invention achieves effective standardization of logging elastic parameter curves in heterogeneous carbonate reservoirs. By introducing the deconvolution algorithm, not only is the logging curve standardized, but the reservoir's identifiability is also improved. At the data foundation level, the characteristics of the reservoir's sensitive parameters are clarified, and the resolution of seismic data inversion prediction is indirectly improved.

[0035] (2) Based on conventional logging data and seismic data, this invention realizes the standardization of reservoir logging curves, which can provide an effective standardization method for reservoir characteristic curves for carbonate reservoirs or reservoirs with strong heterogeneity, thereby supporting geophysical prediction and evaluation. Attached Figure Description

[0036] The foregoing and hereinafter detailed description of the invention becomes clearer when read in conjunction with the following drawings, in which:

[0037] Figure 1 This is a flowchart of the method of the present invention;

[0038] Figure 2 This is a schematic diagram of the device structure of the present invention;

[0039] Figure 3 This is a schematic diagram illustrating the establishment of the reservoir elastic parameter limit standard of the present invention;

[0040] Figure 4This is a schematic diagram of the deconvolution operator calculation of the present invention;

[0041] Figure 5 The image shows the logging curves after processing using the conventional standardized method (the depth range of the logging section in the image is 3400.00-2720m).

[0042] Figure 6 This is a comparison chart of logging curves after deconvolution standardization processing according to the present invention (the depth range of the logging section in the chart is 3400.00-2720m). Detailed Implementation

[0043] To enable those skilled in the art to better understand the technical solutions of this invention, specific embodiments will be used to further illustrate the technical solutions for achieving the objectives of this invention. It should be noted that the technical solutions claimed by this invention include, but are not limited to, the following embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort should fall within the scope of protection of this invention.

[0044] Conventional logging curve standardization is based on the assumptions of homogeneous formations and minimal lateral variation. In large, stable marine and clastic formations, where the formation thickness is significant and the lithology is stably distributed, their lateral characteristics can be approximated as homogeneous formations. Therefore, conventional logging curve standardization methods are primarily applied to large, stable marine and clastic formations.

[0045] Currently, the standardization methods for logging curves in carbonate reservoirs still rely on conventional standardization methods in some areas, failing to consider the significant impact of lateral and spatial heterogeneity variations in formations or reservoirs. This results in the elastic parameters of reservoir logging evaluation failing to meet the requirements of seismic prediction, thus limiting the effectiveness and reliability of quantitative seismic prediction. Common logging curve standardization methods, such as the overlay method, mean-variance method, histogram translation method, and trend surface analysis method, all rely on standard layers having essentially the same mean, variance, or variation pattern. However, they cannot meet the standardization requirements when effective standard layers cannot be determined or when considering lateral heterogeneity variations in formations.

[0046] Therefore, for carbonate reservoirs with strong heterogeneity, there is an urgent need to establish an effective method for standardizing reservoir logging curves, especially elastic parameter curves. This will help advance the exploration and development technology of this type of oil and gas reservoir and further improve the relevant theories and methods of pre-stack elastic parameter inversion technology for carbonate reservoirs.

[0047] Based on this, embodiments of the present invention propose a method and apparatus for standardizing elastic parameters of carbonate reservoir logging. The method of the present invention is based on logging and seismic data, uses the boundary standard of elastic parameters between carbonate reservoirs and non-reservoirs as a constraint, and adopts the deconvolution algorithm to standardize the elastic parameters of carbonate logging, while also improving the identifiability of the reservoir. At the data foundation level, it clarifies the characteristics of the sensitive parameters of the reservoir, and indirectly improves the resolution of seismic data inversion prediction.

[0048] To facilitate understanding of the technical solution of this invention, this invention first introduces and explains the standardization method of elastic parameters for logging in carbonate reservoirs.

[0049] This embodiment discloses a method for standardizing elastic parameters in well logging of carbonate reservoirs, as detailed in the appendix of the instruction manual. Figure 1 The method mainly includes the following steps:

[0050] Step S1. Establish a sample library of elastic parameters for carbonate reservoirs and non-reservoirs.

[0051] In this embodiment, the method for establishing the elastic parameter sample library is as follows: elastic parameter curves of carbonate reservoirs and non-reservoirs are calculated based on the core, logging and well logging lithology interpretation results, respectively, and two elastic parameter sample libraries are established for carbonate logging reservoirs and non-reservoirs based on depth points.

[0052] Step S2. Establish the boundary standard for elastic parameters between carbonate reservoirs and non-reservoir areas.

[0053] In this embodiment, the method for establishing the elastic parameter limit standard is roughly as follows:

[0054] Based on the elastic parameter sample library of carbonate reservoirs and non-reservoirs established in step S1, the P-wave velocity, S-wave velocity, and density logging parameters of reservoirs and non-reservoirs are used as inputs to a BP neural network. Then, the interpretation parameters of high-quality reservoirs are used as constraints for training the neural network. Finally, the training data of P-wave velocity, S-wave velocity, and density of reservoirs and non-reservoirs at the corresponding depths are output. The output data after neural network training is used as the basic sample library for establishing the boundary standard between reservoirs and non-reservoirs. Based on the data in the basic sample database, the elastic parameter boundary standard between reservoirs and non-reservoirs is finally established through cross-plot analysis.

[0055] Furthermore, the method is specifically as follows:

[0056] Step S21. Determine the length L of the analysis window (the target well section to be standardized). w The unit is the number of depth points, and the window length is generally 3-5 depth points.

[0057] Step S22. Based on the core, logging and well logging lithology interpretation results, extract reservoir and non-reservoir logging curves in the target well section respectively, and analyze the maximum and minimum values ​​within the window.

[0058] Step S23. Use the maximum and minimum values ​​of the extracted logging curves as input to the BP neural network, and add the interpretation results of high-quality reservoirs as constraints of the neural network. Optimize the logging elastic parameters of reservoirs and non-reservoir areas through neural network training.

[0059] Step S24. Using cross-plot analysis, compare and analyze the elastic parameters of the selected reservoir and non-reservoir, and establish boundary standards for P-wave velocity, S-wave velocity, and density of high-quality reservoir and non-reservoir, referring to the appendix of the instruction manual. Figure 3 As shown, the boundary between the P-wave and S-wave velocity ratios for this reservoir and non-reservoir is 1.92.

[0060] In this embodiment, it should be noted that P-wave velocity, S-wave velocity, and density are the basic data of elastic parameters and the original data of array logging. These data are the basic data for analyzing the standardization of elastic parameter curves.

[0061] In this embodiment, it should also be noted that the present invention is based on the training of a sample library of elastic parameters of reservoirs and non-reservoirs, thereby clarifying the boundary criteria and variation characteristics of reservoirs and non-reservoirs. The sample training can achieve the training purpose using a commonly used BP neural network. This process is existing technology and will not be elaborated on here.

[0062] Step S3. Using a combined well logging and seismic method, the well logging impedance power spectrum is combined with the seismic wave spectrum as a hard constraint condition. At the same time, the boundary standard of reservoir and non-reservoir elastic parameters is introduced to calculate and optimize the deconvolution operator.

[0063] In this embodiment, the calculation and optimization process of the deconvolution operator is as follows:

[0064] Step X. First, by establishing the boundary criteria for elastic parameters between reservoirs and non-reservoirs, wells within the study area that meet the criteria are selected as input wells for the deconvolution operator calculation. Then, the deconvolution operation is performed on the selected wells that meet the criteria according to the following steps.

[0065] Step S31. Extract the seismic signal spectrum based on the well-side tunnels in the study area.

[0066] Step S32. Extract the logging impedance power spectrum of the entire well section based on the data (velocity curve and density curve) of the selected well.

[0067] Step S33. Combine the seismic signal spectrum with the logging impedance power spectrum of the entire well section to perform deconvolution operator calculation, thereby obtaining the first deconvolution operator.

[0068] Step S34. Based on the logging section (target layer) trained by the neural network, extract the logging impedance power spectrum of the training well section and combine it with the seismic signal spectrum of the wellbore side path to perform a second deconvolution operator calculation, obtaining the second deconvolution operator (refer to the appendix of the instruction manual). Figure 4 (as shown);

[0069] Step S35. Using the logging impedance power spectrum of the high-quality reservoir section interpreted by well logging as a constraint condition, select the logging impedance power spectrum of the high-quality reservoir section of different wells and combine it with the well-side seismic signal spectrum, and perform the deconvolution operator calculation again to obtain the third deconvolution operator.

[0070] Step S36. Based on the three different deconvolution operators obtained from the above three deconvolution operations, deconvolve the logging curves (P-wave, S-wave, and density) in the study area respectively to obtain the logging curve deconvolution results corresponding to different deconvolution operators. Then, based on the elastic parameter boundary standard between reservoir and non-reservoir, perform cross-section analysis of the logging curves after deconvolution, and finally select the optimal result that meets the boundary standard to achieve the optimization of the deconvolution operator.

[0071] In this embodiment, the calculation process of the aforementioned three deconvolution operators is the same, except that different methods of deconvolution operator optimization are performed. This invention is based on the calculation process of a combined well-seismic deconvolution operator constrained by logging impedance power spectrum, which is essentially a well-controlled deconvolution operator calculation. The three deconvolution operations are essentially the same process, but the well sections from which the extracted logging impedance power spectra are obtained are different. To better highlight the reservoir, different well section methods are used to extract the logging impedance power spectra for deconvolution operator calculation and optimization.

[0072] In this embodiment, the process of calculating the deconvolution operator is as follows:

[0073] Any seismic signal x(t) consisting of an effective signal s(t) and noise n(t),

[0074] x(t)=s(t)+n(t) Equation (1);

[0075] Its frequency corresponds to the spectrum as follows

[0076] X(f) = S(f) + N(f) Equation (2);

[0077] The purity of a signal is generally defined as...

[0078]

[0079] According to the law of conservation of energy, we have

[0080]

[0081] Where R is the signal-to-noise ratio of the seismic signal, and

[0082]

[0083] Therefore, the purity spectrum p(f) of the signal is

[0084]

[0085] Where r(f) is the spectrum of signal-to-noise ratio.

[0086] After deconvolution of the signal, its frequency domain expression is:

[0087] Y(f)=X(f)H(f) Equation (7);

[0088] Where H(f) is the deconvolution operator, and the purity of the signal after deconvolution is as follows:

[0089]

[0090] In other words, deconvolution does not change the purity of the signal, which is the theoretical basis for spectral constraints on the signal before deconvolution.

[0091] To obtain the spectral-constrained deconvolution operator, for a known pulse signal δ(t), design a filter β(t) for the desired output α(t), i.e.

[0092] α(t)=δ(t)*β(t) Equation (9);

[0093] If α(f) is the spectrum of the desired output α(t) after deconvolution, then a filter b(t) can be designed to perform pulse deconvolution on the desired output α(t), i.e.

[0094] δ(t)=α(t)*b(t) Equation (10);

[0095] When the desired output is a sharp pulse signal, the pulse deconvolution operator b(t) is a spectral constraint-based deconvolution operator.

[0096] In the process of obtaining the deconvolution operator under well control conditions, considering the matching degree between well logging information and seismic information, the deconvolution operator is obtained by constraining the characteristics of the well logging power spectrum. The signal purity formula under the constraint condition is as follows:

[0097]

[0098] Where p(f) is the purity spectrum of the signal, X cor (f) is the spectrum of cross-correlation between seismic data and well data, A cor1 (f) represents the autocorrelation of well data within a certain time window (depth), A cor2(f) represents the autocorrelation of seismic data within a certain time window.

[0099] When calculating the deconvolution operator, the selected reservoir and non-reservoir logging curves are used as the basis to calculate the logging power spectrum. The power spectrum is then substituted into the above constraint formula (11). The formula designs the filter for the desired output, and the deconvolution operator can then be obtained.

[0100] Step S4. Based on the deconvolution operator selected in step S3, standardize the logging elastic parameter curves of hydrochloric acid reservoirs.

[0101] In this embodiment, the standardization of the elastic parameter curve specifically involves: deconvolving the logging curves within the study area using the deconvolution operator selected in step S3, thereby obtaining a new curve, which is the standardized reservoir elastic parameter curve. The standardized elastic parameters, specifically the P-wave / S-wave velocity ratio and Poisson's ratio, show significant differences in the favorable reservoir section (3448-3510m), while also highlighting the reservoir characteristics of other sections (see appendix to the specification). Figure 5 and Figure 6 ).

[0102] The method of this invention achieves effective standardization of logging elastic parameter curves in heterogeneous carbonate reservoirs. (See attached specification) Figure 5 and Figure 6 The comparison features are those after standardization using different methods. After deconvolution standardization, there are obvious reservoir elastic parameter characteristics. Figure 6 ).

[0103] The goal of the deconvolution method introduced in this invention is to standardize the elastic parameter response characteristics of different wells in carbonate reservoirs. The result can improve the sensitivity of well logging elastic parameters between reservoirs and non-reservoirs. Based on the processed well logging elastic parameter curves, subsequent seismic inversion calculation and processing can be carried out, which can indirectly improve the resolution of inversion results that are blurred due to lack of standardization.

[0104] Furthermore, based on the same inventive concept, an embodiment of the present invention provides a standardization device for elastic parameters of carbonate reservoir logging. This device is used to implement the formation pressure prediction method described above, as described in the following embodiments. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated. See the appendix to the specification. Figure 2 Specifically, the device may include: an elastic parameter sample library establishment module 201, an elastic parameter limit standard establishment module 202, a deconvolution operator calculation and optimization module 203, and an elastic parameter curveization module 204. The structure will be described in detail below.

[0105] The elastic parameter sample library establishment module 201 calculates the elastic parameter curves of carbonate reservoirs and non-reservoirs based on the core, logging and well logging lithology interpretation results, and establishes a well logging carbonate reservoir and non-reservoir elastic parameter sample library based on depth points.

[0106] The elastic parameter limit standard establishment module 202 establishes a basic sample library of the limit standard between reservoirs and non-reservoirs based on the neural network training results of the elastic parameters of reservoirs and non-reservoirs, and establishes the limit standard of elastic parameters between reservoirs and non-reservoirs through cross-analysis.

[0107] The deconvolution operator calculation and optimization module 203 adopts a combined well-seismic method, combining the well logging impedance power spectrum as a hard constraint condition with the seismic wave spectrum, and introducing the boundary standard of reservoir and non-reservoir elastic parameters to perform deconvolution operator calculation and optimization.

[0108] The elastic parameter curveization module 204 ultimately standardizes the elastic parameters of carbonate reservoir logging based on the selected deconvolution operator.

[0109] It should be noted that the systems, devices, models, or units described in the above embodiments can be implemented by computer chips or physical entities, or by products with certain functions. For ease of description, the above devices are described in this specification as various units based on their functions. Of course, in implementing this invention, the functions of each unit can be implemented in one or more software and / or hardware.

[0110] Furthermore, in this specification, adjectives such as first and second may only be used to distinguish an element or action, without necessarily implying any actual such relationship or order.

[0111] Furthermore, embodiments of the present invention also provide a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein when the processor executes the computer program, it implements the steps of any of the above methods.

[0112] Furthermore, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed in a computer processor, implements the steps of any of the above methods.

[0113] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for standardizing elastic logging parameters in carbonate reservoirs, characterized in that, Includes the following steps: Establish a sample library of elastic parameters for carbonate reservoirs and non-reservoirs; Establish a boundary standard for elastic parameters between carbonate reservoirs and non-reservoirs; A combined well logging and seismic method was adopted, which combines the well logging impedance power spectrum as a hard constraint condition with the seismic wave spectrum, and introduces the boundary standard of reservoir and non-reservoir elastic parameters to calculate and optimize the deconvolution operator. Standardization of logging elastic parameters for hydrochloric acid reservoirs is achieved based on a preferred deconvolution operator. The establishment of the boundary standard for elastic parameters between carbonate reservoirs and non-reservoirs includes: based on the elastic parameter sample library of carbonate reservoirs and non-reservoirs, using the P-wave velocity, S-wave velocity, and density logging parameters of reservoirs and non-reservoirs as input to a neural network, using the interpretation parameters of high-quality reservoirs as constraints of the neural network, outputting training data of P-wave velocity, S-wave velocity, and density of reservoirs and non-reservoirs at corresponding depths, using the output of the trained neural network as the basic sample library for establishing the boundary standard for elastic parameters between reservoirs and non-reservoirs, and finally establishing the boundary standard for elastic parameters between reservoirs and non-reservoirs through cross-plot analysis; The well logging and seismic testing method combines well logging impedance power spectrum as a hard constraint with seismic wave spectrum, and introduces reservoir and non-reservoir elastic parameter boundary standards to calculate and optimize deconvolution operators. This ultimately achieves standardization of carbonate reservoir well logging elastic parameters based on elastic parameter boundary standards. The specific steps include: Step S31. Extract the seismic signal spectrum based on the well-side tunnels in the study area; Step S32. Extract the logging impedance power spectrum of the entire well section based on the well data of the study area; Step S33. Combine the seismic signal spectrum with the logging impedance power spectrum of the entire well section to perform deconvolution operator calculation and obtain the first deconvolution operator; Step S34. Based on the logging section trained by the neural network, extract the logging impedance power spectrum of the training well section and combine it with the seismic signal spectrum of the well side channel to perform deconvolution operator calculation to obtain the second deconvolution operator; Step S35. Using the logging impedance power spectrum of the high-quality reservoir section interpreted by well logging as a constraint condition, select the logging impedance power spectrum of the high-quality reservoir section of different wells and combine it with the seismic signal spectrum of the well side channel to calculate the deconvolution operator again to obtain the third deconvolution operator. Step S36. Use different deconvolution operators to deconvolve the logging curves in the study area, obtain the deconvolution results of logging curves with different deconvolution operators, and perform cross-section analysis of the logging curves after deconvolution based on the elastic parameter boundary standard between reservoir and non-reservoir, and select the optimal result that meets the boundary standard to achieve the optimization of deconvolution operators.

2. The method for standardizing elastic logging parameters in carbonate reservoirs according to claim 1, characterized in that, The establishment of the sample library of elastic parameters for carbonate reservoirs and non-reservoirs includes: calculating the elastic parameter curves of carbonate reservoirs and non-reservoirs based on the core, logging and well logging lithology interpretation results, respectively, and establishing sample libraries of elastic parameters for carbonate well logging reservoirs and non-reservoirs based on depth points.

3. The method for standardizing elastic logging parameters in carbonate reservoirs according to claim 1, characterized in that, The establishment of the boundary standard for elastic parameters between carbonate reservoirs and non-reservoirs specifically includes the following steps: Step S21. Determine the length of the target well section for analysis; Step S22. Based on the core, logging and well logging lithology interpretation results, extract the reservoir and non-reservoir logging curves in the target well section respectively, and analyze the maximum and minimum values ​​of the logging curves in the target well section; Step S23. Using the maximum and minimum values ​​of the extracted logging curves as input to the neural network, and adding the interpretation results of high-quality reservoirs as constraints, the logging elastic parameters of reservoirs and non-reservoirs are selected through neural network training. Step S24. Using cross-plot analysis, compare and analyze the logging elastic parameters of the selected reservoir and non-reservoir to establish the boundary standards for elastic parameters of high-quality reservoir and non-reservoir.

4. The method for standardizing elastic logging parameters in carbonate reservoirs according to claim 1, characterized in that, The neural network used to establish the boundary standard for elastic parameters between carbonate reservoirs and non-reservoir areas is a BP neural network.

5. The method for standardizing elastic logging parameters in carbonate reservoirs according to claim 1, characterized in that, Also includes: First, by establishing the boundary criteria for elastic parameters between reservoirs and non-reservoirs, wells within the study area that meet the criteria are selected as input wells for the deconvolution operator calculation. Then, the deconvolution operation is performed using the selected wells that meet the criteria.

6. The method for standardizing elastic logging parameters in carbonate reservoirs according to claim 1, characterized in that, When calculating the deconvolution operator, the optimal reservoir and non-reservoir logging curves are used as the basis. The logging power spectrum is calculated, and the deconvolution operator is obtained by substituting the logging power spectrum into the following constraint formula: Equation (11); in, This represents the purity spectrum of the signal. The spectrum of cross-correlation between seismic data and well data, For the autocorrelation of well data within a certain time window, This represents the autocorrelation of seismic data within a certain time window.

7. A standardization device for elastic parameters of well logging in carbonate reservoirs, characterized in that, The device is used to implement the well logging elastic parameter standardization method according to any one of claims 1-6, comprising: The elastic parameter sample library establishment module calculates the elastic parameter curves of carbonate reservoirs and non-reservoirs based on the core, logging and well logging lithology interpretation results, and establishes a well logging elastic parameter sample library of carbonate reservoirs and non-reservoirs based on depth points. The elastic parameter limit standard establishment module establishes a basic sample library of reservoir and non-reservoir limit standards based on the neural network training results of elastic parameters of reservoir and non-reservoir, and establishes the elastic parameter limit standards of reservoir and non-reservoir through cross-analysis. The deconvolution operator calculation and optimization module adopts a combined well-seismic approach, combining the well logging impedance power spectrum as a hard constraint condition with the seismic wave spectrum, and introducing the boundary standard of reservoir and non-reservoir elastic parameters to perform deconvolution operator calculation and optimization. The elastic parameter standardization module, based on the optimized deconvolution operator, ultimately achieves the standardization of elastic parameters in carbonate reservoir logging.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable in the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed in a computer processor, implements the steps of the method described in any one of claims 1-6.

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