Waveform indication reservoir prediction method and device and storage medium

Abstract

The application discloses a reservoir prediction method and device for waveform indication inversion result and a storage medium. The method comprises the following steps: acquiring element logging parameters of drilled wells in a region; a mathematical fitting conversion model of each element and geological sweet spot evaluation parameters is established through mathematical fitting, wherein the geological sweet spot evaluation parameters comprise total organic carbon, porosity and gas content; a seismic waveform indication simulation is carried out based on the element logging parameters to obtain an element inversion body; the element inversion body is converted into a related physical parameter body through the mathematical fitting conversion model; and the type of a reservoir is predicted according to the related physical parameter body. The application is used for solving the technical problem of the accuracy of reservoir prediction in a work area where well logging data is insufficient or rock elastic parameters are not obviously different, and can effectively improve the prediction accuracy and reliability of the reservoir.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of underground energy storage, and in particular to a reservoir prediction method, apparatus, equipment, and storage medium based on waveform-indicating inversion results. Background Technology

[0002] Currently, shale gas reservoir prediction methods mainly establish well-seismic relationships through well-seismic calibration. Based on well logging information, elastic parameters can be obtained, and sensitive parameters for sweet spot indices can be identified. These sensitive parameters are then used to establish a fitting relationship with the sweet spot indices. Based on the fitting relationship and the spatial distribution characteristics of the seismic wavefield, the corresponding sweet spot indices are inverted, leading to shale gas sweet spot prediction and comprehensive evaluation. This is known as the seismic waveform indication inversion method. Seismic waveform indication inversion is a high-precision seismic inversion method based on the patented algorithm "Shock Waveform Indication Markov Chain Monte Carlo Stochastic Simulation (SMCMC)," employing the concept of "phase-controlled stochastic simulation," effectively improving the accuracy and reliability of reservoir prediction. As an emerging geological exploration technology, the waveform indication inversion method based on elemental logging parameters can accurately collect elemental composition information of underground rocks by performing chemical elemental analysis on rock cuttings brought out during drilling, enriching the application scope of waveform indication inversion technology. Furthermore, it shows great potential in improving the accuracy and reliability of shale gas reservoir prediction methods.

[0003] However, this prediction method requires high-quality logging data to establish well-seismic relationships. The reliability of logging data from some parameter wells is poor due to wellbore conditions, failing to meet the requirements of seismic inversion. In some areas, with clearer geological understanding, logging projects have been cancelled to further save on single-well investment and achieve efficient development, making it difficult to accurately and effectively apply traditional reservoir evaluation and prediction methods to shale gas reservoirs. Furthermore, when using seismic inversion technology to predict shale gas reservoirs, it relies on detailed logging curves and seismic data, including shear wave velocity, p-wave velocity, and rock density. Elastic parameters are not inherent characteristics of rocks and lack clear physical meaning. Applying elastic parameters for quantitative reservoir interpretation introduces certain uncertainties. The Zoeppritz equation needs to be solved once for each change in pre-stack incident angle, resulting in a large computational load, long time period, high specialization, and significant technical difficulty. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method, apparatus, equipment, and storage medium for reservoir prediction based on waveform-indicating inversion results, which can at least solve the technical problem of low reservoir prediction accuracy in areas where well logging data is scarce or rock elastic parameters are not significantly different.

[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a reservoir prediction method based on waveform-indicating inversion results, comprising:

[0006] Obtain logging parameters for elements already drilled in the area;

[0007] A mathematical fitting transformation model is established between each element and the geological sweet spot evaluation parameters, including total organic carbon, porosity, and gas content, through mathematical fitting.

[0008] Based on the element logging parameters, a seismic waveform indication simulation is performed to obtain the element inversion volume;

[0009] The element inversion volume is converted into a relevant physical property parameter volume using the mathematical fitting transformation model.

[0010] The reservoir type is predicted based on the relevant physical property parameters.

[0011] At least one embodiment of this application also provides a reservoir prediction apparatus for waveform-indicating inversion results, comprising:

[0012] The acquisition module is used to acquire logging parameters of drilled elements in the area;

[0013] The model building module is used to establish a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting. The geological sweet spot evaluation parameters include total organic carbon, porosity, and gas content.

[0014] The inversion module is used to perform seismic waveform indication simulation based on the element logging parameters to obtain the element inversion body;

[0015] The conversion module is used to convert the element inversion volume into a relevant physical property parameter volume through the mathematical fitting conversion model;

[0016] The prediction module is used to predict the reservoir type based on the relevant physical property parameters.

[0017] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described reservoir prediction method for waveform indication inversion results.

[0018] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described reservoir prediction method for waveform indication inversion results.

[0019] The reservoir prediction method based on waveform indication inversion results provided in this application utilizes existing elemental logging data and a waveform indication inversion algorithm to accurately predict the characteristics and distribution of shale gas reservoirs. This method addresses the technical problem of low reservoir prediction accuracy in areas lacking logging data or with insignificant differences in rock elastic parameters. It effectively improves the prediction accuracy and reliability of reservoirs, enhances vertical and horizontal inversion resolution, strengthens the determinism of inversion results, shortens inversion calculation time, and reduces the ambiguity in shale gas geological sweet spot prediction, thus solving the difficult problem of shale gas geological sweet spot prediction. Furthermore, this prediction method is simple in principle, has no obvious inapplicability issues, and is easy to promote on a large scale.

[0020] In some optional embodiments, the step of establishing a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting includes:

[0021] Based on the different combinations of elements, the preferred combination of sensitive elements is determined;

[0022] A mathematical fitting transformation model is established between the sensitive element combination and the geological sweet spot evaluation parameters through mathematical fitting.

[0023] In this way, the established mathematical fitting transformation model can be used to obtain geological sweet spot evaluation parameters based on the element inversion body, so as to realize the prediction of shale gas reservoirs.

[0024] In some alternative embodiments, the geological sweet spot evaluation parameters include at least one of total organic carbon, porosity, gas content, and rock composition.

[0025] By using a variety of geological sweet spot evaluation parameters covering multiple aspects such as rock physical properties and hydrocarbon content, reservoir prediction can be achieved, ensuring the accuracy of reservoir prediction.

[0026] In some optional embodiments, the step of performing seismic waveform indication simulation based on the elemental logging parameters to obtain the elemental inversion volume includes:

[0027] Acquire seismic waveform data;

[0028] Establish a mapping relationship between the seismic waveform data and the element logging parameters;

[0029] Based on the mapping relationship between the seismic waveform data and the element logging parameters, the seismic waveform data is converted into an initial element curve sample set;

[0030] An initial model is established using the initial set of element curve samples.

[0031] The initial model is iteratively modified based on seismic waveform data within a Bayesian framework to obtain the element inversion model.

[0032] Multi-parameter simulation is achieved by mapping the seismic waveform data with element logging parameters, which improves the vertical and horizontal inversion resolution, enhances the determinism of the inversion results, shortens the inversion calculation time, and reduces the ambiguity of shale gas geological sweet spot prediction, thus solving the problem of shale gas geological sweet spot prediction.

[0033] In some optional embodiments, the step of predicting the reservoir type based on the relevant physical property parameters includes:

[0034] Obtain shale gas reservoir logging evaluation criteria;

[0035] The predicted type of the reservoir is determined based on the comparison results between the relevant physical property parameters and the shale gas logging evaluation criteria.

[0036] The predicted reservoir type can be determined by comparing the results of shale gas reservoir logging evaluation standards with relevant physical property parameters, which is simple and convenient.

[0037] In some optional embodiments, the step of obtaining the element logging parameters of the drilled area includes:

[0038] Obtain elemental logging data of cuttings and core samples from drilled wells in the area;

[0039] The elemental logging parameters are determined based on the elemental logging data from the cuttings and core.

[0040] By utilizing existing elemental logging data, reservoir prediction can be achieved, solving the problem that conventional pre-stack and post-stack methods cannot be used for inversion, and providing a new technical method for seismic inversion and energy storage prediction and evaluation. Attached Figure Description

[0041] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0042] Figure 1 This is a flowchart of a reservoir prediction method based on waveform indication inversion results provided in one embodiment of this application;

[0043] Figure 2 This is a flowchart of a reservoir prediction method based on waveform indication inversion results provided in another embodiment of this application;

[0044] Figure 3 This is a flowchart of another embodiment of the present application for conducting seismic waveform indication simulation to obtain an element inversion volume;

[0045] Figure 4 This is a common structure diagram of extracting sample well set curves using wavelet transform, provided in another embodiment of this application;

[0046] Figure 5 This is a graph of logging data provided in another embodiment of this application;

[0047] Figure 6 This is another embodiment of the present application providing a histogram of data curves before and after removing outliers;

[0048] Figure 7 This is a schematic diagram showing the data before and after standardization processing provided in another embodiment of this application;

[0049] Figure 8 This is a schematic diagram showing the data before and after normalization processing provided in one embodiment of this application;

[0050] Figure 9 This is an element indication inversion result diagram provided in another embodiment of this application;

[0051] Figure 10 This is a schematic diagram of the TOC calculation results provided in another embodiment of this application;

[0052] Figure 11 This is a schematic diagram of the POR calculation results provided in another embodiment of this application;

[0053] Figure 12 This is a schematic diagram of the GASZ calculation results provided in another embodiment of this application;

[0054] Figure 13 This is a single-well reservoir classification and evaluation map provided in another embodiment of this application;

[0055] Figure 14 This is a target layer reservoir classification plan view provided in another embodiment of this application;

[0056] Figure 15 This is a schematic diagram of a reservoir prediction device for waveform-indicating inversion results provided in another embodiment of this application;

[0057] Figure 16 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0059] This invention proposes a reservoir prediction method based on waveform indication inversion results. The implementation details of the reservoir prediction method based on waveform indication inversion results in this embodiment are described below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0060] Example 1:

[0061] The specific process of the reservoir prediction method based on the waveform indication inversion results in this embodiment can be as follows: Figure 1 As shown, it includes:

[0062] Step 110: Obtain the logging parameters of the elements already drilled in the area.

[0063] Elemental logging parameters mainly involve the content, distribution, and ratios of various elements in underground rocks. These parameters are of great significance for lithological identification, stratigraphic division, and reservoir evaluation.

[0064] In some cases, logging parameters for elements of drilled wells in the region are obtained by querying relevant databases.

[0065] In some cases, the elemental logging parameters of wells in the area are determined by analyzing various logging data collected in real time at the drilling site, including elemental logging data of drilled cuttings and core samples.

[0066] In some cases, the elemental logging parameters of the drilled wells in the area include elemental logging curves; further, elemental logging curves refer to X-ray fluorescence (XRF) elemental logging curves, which perform elemental analysis on cuttings samples and display the analysis results in the form of curves, which reflect the changes in the content of different elements in the formation.

[0067] Step 120: Establish a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting.

[0068] Specifically, based on different permutations and combinations of elements, multiple element combinations are obtained. Sensitive elements are selected for the geological sweet spot evaluation parameters that need to be transformed. A mathematical model of the sensitive elements or combinations of sensitive elements and evaluation parameters is established through mathematical fitting.

[0069] Among them, geological sweet spot evaluation parameters are a series of key indicators used to assess oil and gas enrichment areas (i.e., geological sweet spots). These parameters typically cover multiple aspects such as rock physical properties, hydrocarbon potential, and geochemical characteristics. For each geological sweet spot evaluation parameter, a mathematical model is established using mathematical fitting to correlate sensitive elements or combinations of sensitive elements with that evaluation parameter.

[0070] Step 130: Perform seismic waveform indication simulation based on the element logging parameters to obtain the element inversion body.

[0071] Specifically, the elemental logging parameters of the drilled wells in the region include elemental logging curves. Based on the elemental logging curves, seismic waveform indication simulations are carried out to obtain a series of high-precision elemental inversion bodies. That is, using elemental curve data as input, combined with seismic waveform data, a mapping relationship between seismic waveforms and reservoir parameters (specifically elemental content) is established. Using the established mapping relationship, multi-parameter phase control simulations are performed to generate high-precision elemental inversion bodies containing multiple seismic waveform parameters. The series of high-precision elemental inversion bodies generated reflects the distribution characteristics of elements in the reservoir.

[0072] Step 140: Convert the element inversion volume into a relevant physical property parameter volume using the mathematical fitting transformation model.

[0073] Specifically, the obtained element inversion volume is substituted into the mathematical fitting and transformation model of each element and the geological sweet spot evaluation parameters to obtain the relevant physical property parameter volume, wherein the physical property parameter volume is the geological sweet spot evaluation parameter, which may include total organic carbon, porosity and gas content, etc.

[0074] Step 150: Predict the reservoir based on the relevant physical property parameters.

[0075] Specifically, by using relevant object parameters, namely geological sweet spot evaluation parameters, including total organic carbon, porosity, and gas content, the geological sweet spot of shale gas can be evaluated relatively effectively, thereby enabling the prediction of shale gas reservoirs.

[0076] In this embodiment, existing elemental logging data is used to accurately predict the characteristics and distribution of shale gas reservoirs through a waveform indication inversion algorithm. This addresses the technical problem of reservoir prediction accuracy in areas with insufficient logging data or insignificant differences in rock elastic parameters. It effectively improves the prediction accuracy and reliability of reservoirs, enhances the vertical and horizontal inversion resolution, strengthens the determinism of inversion results, shortens the inversion calculation time, and reduces the ambiguity of shale gas geological sweet spots, thus solving the difficult problem of shale gas geological sweet spot prediction. Furthermore, this prediction method is simple in principle, has no obvious inapplicability issues, and is easy to promote on a large scale.

[0077] Example 2:

[0078] Based on the above embodiments, the step of obtaining elemental logging parameters of drilled wells in the area includes:

[0079] Obtain elemental logging data of cuttings and core samples from drilled wells in the area;

[0080] The elemental logging parameters are determined based on the elemental logging data from the cuttings and core.

[0081] Specifically, various logging data transmitted in real time from the drilling site are collected, namely, the elemental logging data of drilled cuttings and core samples. The logging data includes, but is not limited to, data on well depth, drilling time, elements, total hydrocarbons, logging curves, and geochemical analysis experiments. The elemental logging parameters are determined based on the logging data.

[0082] In some cases, preliminary quality audits are required on the collected logging data to ensure the validity and completeness of the data.

[0083] In some cases, the collected logging data require outlier removal preprocessing, including methods such as data smoothing and filtering. Furthermore, the preprocessing can employ methods with proven effectiveness, such as multi-point averaging or the five-point bell method, for data smoothing.

[0084] In some embodiments, the step of obtaining element logging parameters further includes:

[0085] The elemental logging parameters are standardized according to the standardized data conversion relationship of the elemental logging instrument.

[0086] Specifically, the analytical results are compared with those of standard samples from different analytical instruments, and a standard data conversion relationship is established for the on-site elemental logging instruments. The elemental data is then standardized and normalized to remove the influence of different dimensions and make the data comparable.

[0087] Example 3:

[0088] Based on the above embodiments, the step of establishing a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting includes:

[0089] Based on the different combinations of elements, the preferred combination of sensitive elements is determined;

[0090] A mathematical fitting transformation model is established between the sensitive element combination and the geological sweet spot evaluation parameters through mathematical fitting.

[0091] Specifically, multiple elements can form various element combinations, with different numbers of elements in each combination and different selected elements. As needed, preferred sensitive elements or sensitive element combinations are determined, and through mathematical fitting analysis, a mathematical fitting transformation model between the preferred sensitive elements or sensitive element combinations and the geological sweet spot evaluation parameters is determined.

[0092] In some alternative embodiments, the geological sweet spot evaluation parameters include at least one of total organic carbon, porosity, gas content, and rock composition.

[0093] Therefore, for each geological sweet spot evaluation parameter, a mathematical fitting transformation model between the sensitive element or combination of sensitive elements and each geological sweet spot evaluation parameter needs to be established. The geological sweet spot evaluation parameters include at least one of total organic carbon, porosity, gas content, and rock composition.

[0094] In some embodiments, the step of establishing a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting further includes:

[0095] The rock composition is determined based on the elemental logging parameters.

[0096] Specifically, rock composition information is determined through elemental logging parameters, and a mathematical fitting conversion model between elements and rock components is established through mathematical fitting. Specifically, rock composition information is obtained through XRF elemental logging, compared with logging and experimental analysis data, and a conversion model between elements and gas content, some logging parameters, and rock components is established through mathematical fitting to calculate geological sweet spot evaluation parameters, including total organic carbon, porosity, and gas content (NB / T 14017—2016).

[0097] Furthermore, the specific calculation process includes: element combination optimization, selecting sensitive elements and element combinations for the parameters to be transformed; and then conducting mathematical fitting analysis to establish a mathematical model between the elements or element combinations and the evaluation parameters.

[0098] The expression for the mathematical fitting transformation model of the sensitive element or combination of sensitive elements and the evaluation parameters is as follows:

[0099] TOC=β0+β1*X1+β2*X2+…+β k *X k (1)

[0100] Wherein, TOC is the reservoir geological sweet spot sensitivity parameter (i.e., evaluation parameter), β0, β1, β2…β k These are the regression coefficients; X1, X2…X k These represent the content of sensitive elements.

[0101] Example 4:

[0102] Based on the above embodiments, the step of performing seismic waveform indication simulation based on the element logging parameters to obtain the element inversion volume includes:

[0103] Acquire seismic waveform data;

[0104] Establish a mapping relationship between the seismic waveform data and the element logging parameters;

[0105] Based on the mapping relationship between the seismic waveform data and the element logging parameters, the seismic waveform data is converted into an initial element curve sample set;

[0106] An initial model is established using the initial set of element curve samples.

[0107] The initial model is iteratively modified based on seismic waveform data within a Bayesian framework to obtain the element inversion model.

[0108] In one example, the steps of performing seismic waveform indication simulation based on the element logging parameters to obtain the element inversion volume include:

[0109] Step 1: Acquire seismic waveform data, use seismic waveform feature vectors to describe waveform similarity, and globally select simulation samples to establish a sample set:

[0110] A=U∑V T (2)

[0111] In Equation (2), U is the seismic waveform feature data, which is an n×n orthogonal matrix; V is the well point attribute, which is an m×m orthogonal matrix; T is the conjugate transpose of V; Σ is an n×m non-negative real diagonal matrix, which represents the correlation between the seismic waveform attribute and the well point attribute. The mapping relationship between the seismic gather waveform and the well elastic parameter curve samples with different structural features is established by using singular value decomposition (SVD), and the initial element curve sample set of the same waveform group is established.

[0112] Step 2: Perform multi-scale filtering on the element curves in the sample set established according to equation (3) in the wavelet domain, and select the low- and mid-frequency parts of the element curves to establish an initial model.

[0113]

[0114] Where l is the common structure correlation cutoff frequency, W is the element curve of the sample well set, and W' is the average value of the element curve of the sample well set. The wavelet function is used. The initial model constructed in this way makes full use of the high similarity between the low and medium frequency components of the sample wells, broadens the resolution of the model, and establishes a high-frequency initial model related to the seismic waveform.

[0115] Step 3: Under the constraints of the Bayesian framework in equation (4), the initial model established in the previous step is continuously modified according to the actual seismic waveform, so that the inversion results simultaneously conform to the mid-frequency seismic information and the structural characteristics of the well element curves, and finally the high-resolution waveform indication inversion results are obtained.

[0116]

[0117] In the formula, m represents the parametric model to be solved, d represents the seismic data; I represents prior information, σ represents the covariance of the seismic data, G represents the seismic wavelet matrix; n represents noise, Δm represents the perturbation of the model parameters, and σ represents the noise level. Δm This represents the variance of the perturbation of the model parameters.

[0118] By conducting seismic waveform indication simulation using elemental logging parameters, a high-precision elemental inversion volume is obtained. This method simultaneously improves the vertical and horizontal inversion resolution, enhances the determinism of the inversion results, shortens the inversion calculation time, and reduces the ambiguity of shale gas geological sweet spot prediction, thus solving the problem of shale gas geological sweet spot prediction.

[0119] Specifically, the obtained element inversion volume is converted into a relevant physical property parameter volume through the mathematical fitting transformation model; that is, the obtained element inversion volume is substituted into the mathematical fitting transformation model of each element and the geological sweet spot evaluation parameters to obtain the relevant physical property parameter volume.

[0120] The expression for determining the relevant physical property parameters based on the mathematical fitting transformation model is as follows:

[0121] S TOC =β0+β1*S X1 +β2*S X2 +…+β k *S Xk (5)

[0122] In equation (1), S TOC This is a reservoir physical property data volume, β0, β1, β2…β k S is the regression coefficient; X1 S X2 …S Xk The waveform indicators of the sensitive elements are the results of the inversion data volume.

[0123] Example 5:

[0124] Based on the above embodiments, the step of predicting the reservoir based on the relevant physical property parameters includes:

[0125] Obtain shale gas reservoir logging evaluation criteria;

[0126] The reservoir type is determined based on the comparison results between the relevant physical property parameters and the shale gas logging evaluation criteria.

[0127] Specifically, the goal is to obtain shale gas reservoir logging evaluation standards, use these standards to constrain physical properties and construct shale gas reservoir bodies, and then accurately identify and predict various types of shale gas reservoirs.

[0128] Example 6:

[0129] The reservoir prediction method based on waveform indication inversion results in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 2 As shown, it includes:

[0130] Step S1: Obtain the logging parameters of the elements that have been drilled in the area.

[0131] Specifically, the work involves collecting drilling cuttings and core element logging data from the work area, conducting a preliminary quality review of these data, and confirming their validity and completeness.

[0132] Step S2: Perform data standardization processing on the element data.

[0133] Specifically, outlier removal is achieved through sophisticated data smoothing and filtering methods. For data processing, methods such as multi-point averaging and the five-point bell method are tested and optimized for optimal results before data smoothing. Elemental data are analyzed against standard samples from different analytical instruments, and standardized data conversion relationships are established for the field elemental logging instruments. Data standardization is then performed. Table 1 shows the standardized conversion models for different instruments.

[0134] Table 1 Standardized Conversion Model for Different Instruments

[0135]

[0136]

[0137] Step S3: Establish the calculation model for key reservoir parameters.

[0138] Specifically, rock composition information is obtained through XRF elemental logging and compared with well logging and experimental analysis data. A mathematical fitting model is then used to establish a conversion model between elements and gas content, some well logging parameters, and rock components to calculate geological sweet spot evaluation parameters, including total organic carbon, porosity, and gas content (NB / T 14017—2016). The specific calculation process is as follows: First, element combination optimization is performed, identifying sensitive elements and element combinations for the parameters to be converted; then, mathematical fitting analysis is conducted to establish a mathematical model between the elements or element combinations and the evaluation parameters.

[0139] The expression for the mathematical fitting transformation model of the sensitive element or combination of sensitive elements and the evaluation parameters is as follows:

[0140] TOC=β0+β1*X1+β2*X2+…+β k *X k (1)

[0141] Wherein, TOC is the reservoir geological sweet spot sensitivity parameter (i.e., evaluation parameter), β0, β1, β2…β k These are the regression coefficients; X1, X2…X k These represent the content of sensitive elements.

[0142] Step S4: Conduct seismic waveform indication simulation based on element curves to obtain a series of high-precision element inversion models.

[0143] Specifically, such as Figure 3 As shown, seismic waveform feature vectors are used to describe waveform similarity, and simulation samples are globally optimized to establish a sample set:

[0144] A=U∑V T (2)

[0145] In Equation (2), U is the seismic waveform feature data, which is an n×n orthogonal matrix; V is the well point attribute, which is an m×m orthogonal matrix; T is the conjugate transpose of V; Σ is an n×m non-negative real diagonal matrix, which represents the correlation between the seismic waveform attribute and the well point attribute. The mapping relationship between the seismic gather waveform and the well elastic parameter curve samples with different structural features is established by using singular value decomposition (SVD), and the initial element curve sample set of the same waveform group is established.

[0146] Secondly, the element curves in the sample set established according to equation (3) are subjected to multi-scale filtering in the wavelet domain, and the low-to-mid-frequency parts of the element curves are selected to establish an initial model.

[0147]

[0148] Where l is the common structure correlation cutoff frequency, W is the element curve of the sample well set, and W' is the average value of the element curve of the sample well set. This is a wavelet function. The initial model constructed from this fully utilizes the high similarity between the low and mid-frequency components of the sample wells, broadening the model's resolution and establishing a high-frequency initial model related to the seismic waveform, such as... Figure 4 As shown, Figure 4 This is a common structure diagram for extracting sample well set curves using wavelet transform.

[0149] Finally, under the constraints of the Bayesian framework in equation (4), the initial model established in the previous step is continuously modified according to the actual seismic waveform, so that the inversion results simultaneously conform to the mid-frequency seismic information and the structural characteristics of the well element curves, and finally high-resolution waveform indication inversion results are obtained.

[0150]

[0151] In the formula, m represents the parametric model to be solved, d represents the seismic data; I represents prior information, σ represents the covariance of the seismic data, G represents the seismic wavelet matrix; n represents noise, Δm represents the perturbation of the model parameters, and σ represents the noise level. Δm This represents the variance of the perturbation of the model parameters.

[0152] Step S5: Convert the material property sensitive parameter volume into the relevant material property parameter volume.

[0153] Specifically, by combining the mathematical model between the elements or element combinations obtained in S2 and the evaluation parameters, the element inversion results obtained in S3 are substituted into equation (1) to finally obtain the physical property data body of equation (5).

[0154] S TOC =β0+β1*S X1 +β2*S X2 +…+β k *S Xk (5)

[0155] In equation (5), S TOC This is a reservoir physical property data volume, β0, β1, β2…β k S is the regression coefficient; X1 S X2 …S Xk The waveform indicators of the sensitive elements are the results of the inversion data volume.

[0156] Step S6: Comprehensive reservoir prediction.

[0157] Specifically, the geological sweet spots of shale gas can be effectively evaluated by measuring gas content, organic carbon, and rock density. Shale gas reservoir bodies can be constructed by constraining physical property volumes using shale gas well logging evaluation standards, thereby accurately identifying various types of shale gas reservoirs. As shown in Table 2 below, Table 2 presents the shale gas well logging evaluation standards. Reservoir types are determined by comparing the relevant physical property parameters with the shale gas well logging evaluation standards.

[0158] Table 2 Shale Gas Reservoir Logging Evaluation Criteria

[0159] Evaluation parameters Class I Class II Class III Total organic carbon content, % ≥4 ≥2～＜4 <2 Porosity, % ≥5 ≥2～＜5 <2 <![CDATA[Gas content, m 3 .t -1 > ≥5 ≥2～＜5 <2

[0160] Furthermore, the identification results are compared and analyzed with the actual test results to verify the accuracy of shale gas reservoir prediction.

[0161] In this embodiment, existing elemental logging data is used to accurately predict the characteristics and distribution of shale gas reservoirs through a waveform indication inversion algorithm. This addresses the technical problem of reservoir prediction accuracy in areas with insufficient logging data or insignificant differences in rock elastic parameters. It effectively improves the prediction accuracy and reliability of reservoirs, enhances vertical and horizontal inversion resolution, strengthens the determinism of inversion results, shortens inversion calculation time, and reduces the ambiguity of shale gas geological sweet spots, thus solving the challenge of predicting shale gas geological sweet spots. After the multi-parameter simulation results are completed, they can be converted into various geologically significant geophysical parameters such as lithology and physical properties. Combining multiple parameters can effectively reduce the ambiguity of shale reservoir prediction in complex areas and improve prediction accuracy. Furthermore, this embodiment fills the gap in well logging data and solves the problem that conventional pre-stack and post-stack methods cannot be used for inversion, providing a new technical method for seismic inversion and reservoir prediction and evaluation.

[0162] Example 7:

[0163] Another embodiment of this application relates to a reservoir prediction method based on waveform knowledge inversion results of element curves, applied to the Guizhou TZSX work area, and the specific details are as follows:

[0164] Data collection and verification for the TZSX work area involves collecting various data transmitted in real time from the drilling site, including but not limited to well depth, drilling time, elemental analysis, total hydrocarbons, logging curves, and geochemical analysis data. A preliminary quality review is conducted on this data to confirm its validity and completeness. Figure 5 As shown, Figure 5 To collect well logging data in the work area.

[0165] Data processing software is used to remove outliers by performing methods such as data smoothing and data filtering on the collected data; for example... Figure 6 As shown, Figure 6 This is a histogram of the data curves before and after removing outliers.

[0166] The analytical results of standard samples from different analytical instruments were compared, and a standardized data conversion relationship was established for the field elemental logging instruments. The elemental data were then standardized and normalized. For example... Figure 7 and Figure 8 As shown, where, Figure 7This is a schematic diagram showing the data before and after standardization. Figure 8 This is a schematic diagram showing the data before and after normalization.

[0167] Rock composition information was obtained by processing XRF elemental logging curves and compared with logging and experimental analysis data. A prediction model for key parameters such as elemental and organic carbon TOC (Equation 1), porosity POR (Equation 2), and gas content GASZ (Equation 3) was established by mathematical fitting.

[0168] TOC=1.53885+1.23445*K-0.75231*Al+0.044*Si R=0.86 (1)

[0169] POR=7.24853+0.57247*K-0.68611*Al-0.72002*Mg R=0.87 (2)

[0170] GASZ=0.57697+0.35384*K-0.2245*Al+0.0125*Si R=0.84 (3)

[0171] Software was used to conduct seismic waveform indication simulations based on elemental (K, Al, Si, Mg) curves, obtaining a series of high-precision elemental (K, Al, Si, Mg) seismic inversion data. For example... Figure 9 As shown, Figure 9 The inversion result diagram is used to indicate the elements.

[0172] TOC data is obtained by transforming the prediction model using equation (1). For example... Figure 10 As shown, Figure 10 This is the result of the TOC calculation.

[0173] POR data is obtained by transforming the prediction model using equation (1). For example... Figure 11 As shown, Figure 11 The result is the POR calculation result.

[0174] GASZ data are obtained through the prediction model transformation using equation (1). For example... Figure 12 As shown, Figure 12 The result is for GASZ calculation.

[0175] Finally, based on the shale gas reservoir logging evaluation standards (Table 3), the reservoir type evaluation and overall spatial distribution of single-well shale gas were carried out, such as... Figure 13 and Figure 14 As shown. Among them, Figure 13 This is a single-well reservoir classification and evaluation map. Figure 14 A plan view for classifying the target reservoir.

[0176] Table 3 Shale Gas Reservoir Logging Evaluation Criteria

[0177] Evaluation parameters Class I Class II Class III Total organic carbon content, % ≥4 ≥2～＜4 <2 Porosity, % ≥5 ≥2～＜5 <2 <![CDATA[Gas content, m 3 .t -1 > ≥5 ≥2～＜5 <2

[0178] The predicted results were compared and analyzed with the actual well logging and core test results (Table 4), which confirmed that the predicted results of this method are consistent with the actual results.

[0179] Table 4 Comparison of Predicted Results with Well Logging and Core Experiment Results

[0180]

[0181] Example 8:

[0182] Another embodiment of this application relates to a reservoir prediction device based on waveform indication inversion results. The implementation details of this embodiment's reservoir prediction based on waveform indication inversion results are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of the reservoir prediction based on waveform indication inversion results in this embodiment can be seen as follows: Figure 15 As shown, it includes an acquisition module 801, a model building module 802, an inversion module 803, a transformation module 804, and a prediction module 805.

[0183] The acquisition module 801 is used to acquire the logging parameters of elements that have been drilled in the area.

[0184] Model building module 802 is used to establish a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting.

[0185] The inversion module 803 is used to perform seismic waveform indication simulation based on the element logging parameters to obtain the element inversion body.

[0186] The conversion module 804 is used to convert the element inversion volume into a related physical property parameter volume through the mathematical fitting conversion model.

[0187] The prediction module 805 is used to predict the reservoir type based on the relevant physical property parameters.

[0188] In one embodiment, the model building module is further configured to determine a preferred combination of sensitive elements based on different combinations of the elements;

[0189] A mathematical fitting transformation model is established between the sensitive element combination and the geological sweet spot evaluation parameters through mathematical fitting.

[0190] In one embodiment, the geological sweet spot evaluation parameters include at least one of total organic carbon, porosity, gas content, and rock composition.

[0191] In one embodiment, the inversion module is further configured to acquire seismic waveform data;

[0192] Establish a mapping relationship between the seismic waveform data and the element logging parameters;

[0193] Based on the mapping relationship between the seismic waveform data and the element logging parameters, the seismic waveform data is converted into an initial element curve sample set;

[0194] An initial model is established using the initial set of element curve samples.

[0195] The initial model is iteratively modified based on seismic waveform data within a Bayesian framework to obtain the element inversion model.

[0196] In one embodiment, the prediction module is also used to obtain shale gas reservoir logging evaluation criteria;

[0197] Based on the comparison results between the relevant physical property parameters and the shale gas well logging evaluation criteria, the predicted reservoir type is determined.

[0198] In one embodiment, the acquisition module is further configured to perform data standardization operations on the element logging parameters according to the standardized data conversion relationship of the element logging instrument.

[0199] In one embodiment, the acquisition module is further configured to acquire logging data of rock cuttings and core elements from drilled wells in the area;

[0200] The elemental logging parameters are determined based on the elemental logging data from the cuttings and core.

[0201] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0202] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0203] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0204] Example 9:

[0205] Another embodiment of this application relates to an electronic device, such as... Figure 16 As shown, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the above method steps.

[0206] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0207] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0208] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods in the above embodiments.

[0209] Example 10:

[0210] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the above-described method steps.

[0211] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0212] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A reservoir prediction method based on waveform-indicating inversion results, characterized in that, include: Obtain logging parameters for elements already drilled in the area; A mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters was established through mathematical fitting. Based on the element logging parameters, a seismic waveform indication simulation is performed to obtain the element inversion volume; The element inversion volume is converted into a relevant physical property parameter volume using the mathematical fitting transformation model. The reservoir type is predicted based on the relevant physical property parameters.

2. The reservoir prediction method based on waveform-indicating inversion results according to claim 1, characterized in that, The steps for establishing a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting include: Based on the different combinations of elements, the preferred combination of sensitive elements is determined; A mathematical fitting transformation model is established between the sensitive element combination and the geological sweet spot evaluation parameters through mathematical fitting.

3. The reservoir prediction method based on waveform-indicating inversion results according to claim 1, characterized in that, The geological sweet spot evaluation parameters include at least one of total organic carbon, porosity, gas content, and rock composition.

4. The reservoir prediction method based on waveform-indicating inversion results according to claim 1, characterized in that, The step of performing seismic waveform indication simulation based on the element logging parameters to obtain the element inversion volume includes: Acquire seismic waveform data; Establish a mapping relationship between the seismic waveform data and the element logging parameters; Based on the mapping relationship between the seismic waveform data and the element logging parameters, the seismic waveform data is converted into an initial element curve sample set; An initial model is established using the initial set of element curve samples. The initial model is iteratively modified based on seismic waveform data within a Bayesian framework to obtain the element inversion model.

5. The reservoir prediction method based on waveform-indicating inversion results according to claim 1, characterized in that, The step of predicting the reservoir type based on the relevant physical property parameters includes: Obtain shale gas reservoir logging evaluation criteria; The predicted type of the reservoir is determined based on the comparison results between the relevant physical property parameters and the shale gas logging evaluation criteria.

6. The reservoir prediction method based on waveform-indicating inversion results according to claim 1, characterized in that, The step of obtaining the elemental logging parameters of the drilled wells in the area also includes: The elemental logging parameters are standardized according to the standardized data conversion relationship of the elemental logging instrument.

7. The reservoir prediction method based on waveform-indicating inversion results according to claim 6, characterized in that, The steps for obtaining elemental logging parameters of drilled wells in the area include: Obtain elemental logging data of cuttings and core samples from drilled wells in the area; The elemental logging parameters are determined based on the elemental logging data from the cuttings and core.

8. A reservoir prediction device with waveform indication of inversion results, characterized in that, include: The acquisition module is used to acquire logging parameters of drilled elements in the area; The model building module is used to establish a mathematical fitting transformation model between each element and the geological sweet spot evaluation parameters through mathematical fitting. The geological sweet spot evaluation parameters include total organic carbon, porosity, and gas content. The inversion module is used to perform seismic waveform indication simulation based on the element logging parameters to obtain the element inversion body; The conversion module is used to convert the element inversion volume into a relevant physical property parameter volume through the mathematical fitting conversion model; The prediction module is used to predict the reservoir type based on the relevant physical property parameters.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the reservoir prediction method for waveform-indicating inversion results as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the reservoir prediction method with waveform indication inversion results as described in any one of claims 1 to 7.

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