Seismic inversion evaluation method and device for reservoir physical property parameters

Abstract

The invention relates to the field of oil and gas geophysical exploration, and particularly discloses a reservoir physical property parameter seismic inversion evaluation method and device, and the method comprises the steps: building a rock physical model based on the geological features of a work area reservoir; based on the rock physical model, sequentially carrying out inversion test on seismic traces beside a modeling well, a non-modeling well and all wells in a work area to determine parameters of the rock physical model and an inversion algorithm; utilizing the rock physical model and an inversion algorithm to perform seismic inversion on the physical property parameters of the work area reservoir to obtain an inversion result; and extracting a work area well connection profile and reservoir section plane attributes from the inversion result, and then performing analysis. According to the reservoir physical property parameter seismic inversion evaluation method, on the basis of problems needing to be noticed in the reservoir physical property parameter seismic inversion process of the rock physical model and the quality control mode, the trial and error processes in the technical inversion process can be reduced, the working efficiency of reservoir physical property inversion is improved, and the technical practicability is improved.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas geophysical exploration, and in particular to a method and device for seismic inversion evaluation of reservoir physical property parameters. Background Art

[0002] Rock physics bridges the gap between reservoirs and seismic response. Rock physics modeling establishes the relationship between reservoir elastic parameters and seismic response. By using lithology and fluid substitution analysis to study the impact of lithology, physical properties, and fluid changes on seismic response, we quantitatively describe the relationship between oil and gas and seismic response, thereby demonstrating the feasibility of inversion and optimizing fluid-sensitive parameters.

[0003] Seismic inversion of reservoir physical parameters based on prestack elastic parameters is a primary method for characterizing subsurface reservoir physical properties. It primarily involves data-driven and model-driven approaches. Data-driven methods require extensive training with diverse data types, which imposes certain limitations. Model-driven methods primarily characterize differences in subsurface elasticity and physical properties based on rock physics models, which are constructed with strong theoretical support. However, the accuracy of reservoir physical parameter inversion based on rock physics models is affected by the accuracy of well logging data, prior wells, and rock physics models. Inversion results vary between different rock physics models, necessitating careful well data quality control, modeling well selection, and rock physics model parameter optimization. Currently, most methods for evaluating seismic inversion results rely on direct modification of the rock physics model for full-scale calculations, generating a data volume for subsequent analysis. This process is time-consuming and labor-intensive, lacking a systematic process for interim quality control and result evaluation.

[0004] Based on this technical background, the present invention studies a method and device for seismic inversion evaluation of reservoir physical property parameters. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and device for seismic inversion evaluation of reservoir physical property parameters. This method is based on the issues that need to be paid attention to and the quality control methods during the seismic inversion of reservoir physical property parameters of the rock physics model. It can reduce the trial and error process in the technical inversion process, improve the work efficiency of reservoir physical property inversion, and enhance the practicality of the technology.

[0006] In order to achieve the above-mentioned object, the first aspect of the present invention provides a method for seismic inversion evaluation of reservoir physical property parameters, comprising:

[0007] Establish a rock physics model based on the reservoir geological characteristics of the work area;

[0008] Based on the rock physics model, inversion tests are performed on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithm;

[0009] Using the determined rock physics model parameters and inversion algorithm, seismic inversion is performed on the reservoir physical property parameters of the work area to obtain an inversion result;

[0010] The well-connected profile and reservoir section plane attributes of the work area are extracted from the inversion results and then analyzed and evaluated.

[0011] A second aspect of the present invention provides a reservoir property parameter seismic inversion evaluation device, comprising:

[0012] Model building module, used to build rock physics models based on the geological characteristics of the reservoir in the work area;

[0013] An inversion algorithm determination module is used to perform inversion tests on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area based on the rock physics model to determine the rock physics model parameters and inversion algorithm;

[0014] A physical property parameter inversion module is used to perform seismic inversion on the physical property parameters of the reservoir in the work area using the rock physics model parameters and the inversion algorithm to obtain an inversion result;

[0015] The analysis module is used to extract the well-connected profile of the work area and the plane attributes of the reservoir section from the inversion results and then perform analysis.

[0016] A third aspect of the present invention provides an electronic device, comprising:

[0017] a memory storing executable instructions;

[0018] A processor runs the executable instructions in the memory to implement the seismic inversion evaluation method for reservoir physical property parameters described in the first aspect.

[0019] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the reservoir physical property parameter seismic inversion evaluation method described in the first aspect.

[0020] The beneficial effects of the present invention include:

[0021] (1) The reservoir physical property parameter seismic inversion evaluation method proposed in the present invention is based on the issues that need to be paid attention to and the quality control methods during the seismic inversion of reservoir physical property parameters based on the rock physics model. It can reduce the trial and error process in the technical inversion process, improve the work efficiency of reservoir physical property inversion, and enhance the practicality of the technology.

[0022] (2) The seismic inversion evaluation method for reservoir physical property parameters proposed in the present invention determines the rock physical model and inversion algorithm through the modeling wells in the work area, and inverts the seismic traces of non-modeling wells and all wells in the work area and analyzes the inversion effect, thereby ensuring the effectiveness of the inversion algorithm, optimizing the intermediate process and result evaluation process, and improving efficiency.

[0023] (3) The seismic inversion evaluation method for reservoir physical property parameters proposed in the present invention achieves precise control of data and parameters by performing well data quality control, modeling well selection, and rock physics model parameter optimization during the inversion process.

[0024] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings.

[0026] Figure 1 This is a flow chart of the seismic inversion evaluation method for reservoir physical property parameters proposed in the present invention.

[0027] Figure 2 This is a flow chart of a specific implementation of the reservoir physical property parameter seismic inversion evaluation method proposed in the present invention.

[0028] Figure 3 This is a comparison diagram of the measured elasticity and physical property curves of the modeling well and the elastic curve of the rock physics forward modeling in a specific embodiment of the seismic inversion evaluation method for reservoir physical property parameters proposed by the present invention.

[0029] Figure 4 This is a schematic diagram of predicted physical property parameters obtained by inverting the forward elastic parameter curve of the modeling well using the EMGM algorithm in a specific embodiment of the seismic inversion evaluation method for reservoir physical property parameters proposed in the present invention.

[0030] Figure 5 This is a schematic diagram of predicted physical property parameters obtained by inverting the measured elastic parameter curve of the modeling well using the EMGM algorithm in a specific embodiment of the seismic inversion evaluation method for reservoir physical property parameters proposed in the present invention.

[0031] Figure 6 This is a schematic diagram of the logging physical property prediction results of two non-modeling wells in a specific implementation of the seismic inversion evaluation method for reservoir physical property parameters proposed by the present invention.

[0032] Figure 7 This is a schematic diagram of the physical property prediction results of two non-modeling wellside seismic traces in a specific implementation of the reservoir physical property parameter seismic inversion evaluation method proposed in the present invention. DETAILED DESCRIPTION

[0033] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0034] The present invention provides a reservoir property parameter seismic inversion evaluation method, such as Figure 1 Shown, including:

[0035] Establish a rock physics model based on the reservoir geological characteristics of the work area;

[0036] Based on the rock physics model, inversion tests are carried out on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithm;

[0037] Using the determined rock physics model parameters and inversion algorithm, seismic inversion is performed on the reservoir physical properties of the work area to obtain inversion results;

[0038] The well-connected profiles and reservoir section plane attributes of the work area are extracted from the inversion results and then analyzed.

[0039] The method proposed in the present invention is based on the issues that need to be paid attention to and the quality control methods during the seismic inversion of reservoir physical property parameters based on the rock physics model. It can reduce the trial and error process in the technical inversion process, improve the work efficiency of reservoir physical property inversion, and enhance the practicality of the technology.

[0040] According to the present invention, establishing a rock physics model based on the geological characteristics of the reservoir in the work area includes:

[0041] Select rock physics models based on the geological, structural, and structural characteristics of the reservoir in the work area;

[0042] The parameters of the rock physics model are determined based on the well logging physical property curves and elastic curves of the modeling wells in the work area.

[0043] According to the present invention, the selected rock physics model is selected according to the actual situation of the reservoir in the work area, including but not limited to the following models: Xu-White model, Xu-Payne model, Wood model, Patchy model, Brie formula, Hudson model, and Schoenberg linear sliding model.

[0044] According to the present invention, based on the rock physics model, inversion tests are performed on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area in sequence to determine the rock physics model parameters and inversion algorithm, including:

[0045] Determine the modeling wells in the work area: Modeling wells should be selected from measured logging curves that meet the input and output of the rock physics model, have no outliers, and have obvious reservoir response characteristics;

[0046] The model parameters of the rock physics model are determined based on the well logging physical property curves and elastic curves of the modeling wells in the work area: forward modeling is performed using the selected model parameters, and the well logging physical property curves are input to obtain the forward modeled elastic curve. If the forward modeled elastic curve is basically consistent with the measured elastic curve, the final model parameters are these model parameters; if the forward modeled elastic curve is not consistent with the measured elastic curve, the model parameters are modified until the two are basically consistent;

[0047] Based on the rock physics model, inversion tests are conducted on the modeling wells in the work area to determine the inversion algorithm;

[0048] Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and the inversion results were analyzed.

[0049] Preferably, performing inversion testing on the modeling wells in the work area based on the rock physics model to determine the inversion algorithm includes:

[0050] Input the elastic parameter curve of the forward modeling of the modeling wells in the work area into the rock physics model. Perform inversion testing on the modeling wells in the work area based on the rock physics model to obtain the inversion curve. If the inversion curve matches the measured curve, determine the inversion algorithm. If the two do not match, change the inversion algorithm and repeat the above process until they match.

[0051] Based on the rock physics model parameters and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and analyzed. The inversion results include:

[0052] Based on the rock physics model parameters and the inversion algorithm, the non-modeling wells in the work area are inverted to obtain an inversion curve, and the correlation coefficient between the inversion curve and the measured curve of the non-modeling wells in the work area is calculated, or the sum of squares of the relative errors of the inversion curve relative to the measured curve of the non-modeling wells in the work area is statistically analyzed. If the correlation coefficient or the sum of squares of the relative errors meet the set conditions, the inversion effect is reliable. If the correlation coefficient or the sum of squares of the relative errors do not meet the set conditions, the anomalies of the measured curve are corrected, or the measured curve is standardized, or the differences in reservoir characteristics and the non-modeling wells in the work area are corrected, and the above process is repeated until the inversion effect is reliable. If the inversion result is still unreliable, the rock physics model parameters of the modeling well are modified and the above process is repeated.

[0053] Based on the rock physics model and inversion algorithm, the inversion results of all the seismic traces near the wells are inverted to obtain the inversion results. It is judged whether the inversion results are consistent with the low-frequency trends of the actual test values of the seismic traces near the wells. If the two are consistent, the inversion effect is reliable. If the two are inconsistent, the elastic parameters of all the seismic traces near the wells are corrected and the above process is repeated until the inversion effect is reliable.

[0054] In the present invention, nonlinear inverse problems such as inversion of complex rock physics models can be divided into two categories: deterministic inversion and stochastic inversion. Deterministic inversion includes Newton's method and its derivative algorithms, and stochastic inversion includes probabilistic inversion methods under the Bayesian framework, such as the Bayesian classification algorithm, the EMGM algorithm, etc.

[0055] In the present invention, an inversion algorithm is determined for the modeling wells in the work area through a rock physics model, and seismic traces of non-modeling wells and all wells in the work area are inverted and the inversion effects are analyzed, thereby ensuring the effectiveness of the inversion algorithm, optimizing the intermediate process and result evaluation process, and improving efficiency.

[0056] According to the present invention, the reservoir physical property parameters of the work area are the seismic pre-stack elastic parameters of the reservoir in the work area;

[0057] The seismic pre-stack elastic parameters of the reservoir in the work area include P-wave velocity, S-wave velocity and density.

[0058] In the present invention, in order to ensure the accuracy of the elastic parameter inversion results obtained from seismic data, quality control of the elastic parameter inversion results needs to be carried out before performing seismic inversion of reservoir physical property parameters to ensure the reliability of the physical property parameter inversion input data.

[0059] Preferably, the analysis of the work area well-connected profile and reservoir section plane attributes extracted from the inversion results includes:

[0060] Extract the well-connected profile of the work area from the inversion results, conduct well-seismic comparison analysis and analyze the vertical variation law of the reservoir;

[0061] Extracting reservoir section plane attributes from inversion results to analyze plane distribution;

[0062] The vertical variation and planar distribution of the reservoir are consistent with geological laws.

[0063] In the present invention, accurate control of data and parameters is achieved by performing well data quality control, modeling well selection, and rock physics model parameter optimization during the inversion process.

[0064] The present invention will be described in more detail below through examples.

[0065] Example 1:

[0066] like Figure 2 As shown, this embodiment provides a flow chart of a method for seismic inversion evaluation of reservoir physical property parameters;

[0067] This example uses a carbonate rock block in western China as an example to enhance the beaded reflection characteristics of carbonate fracture-vuggy reservoirs using the method provided by the present invention. The specific implementation steps are as follows:

[0068] Step 1: Establish a rock physics model that conforms to the reservoir geological characteristics of the work area:

[0069] There are many types of theoretical rock physics models, and different combinations of rock physics models are selected for different types of reservoirs. For example, the Xu-White model is mostly used for clastic reservoirs, and the Xu-Payne model is mostly used for carbonate reservoirs; fluid mixing can be analyzed using the Wood model for homogeneous media, the Patchy model for patch saturation, and the Brie formula; the Hudson model and the Schoenberg linear sliding model, which incorporate the effects of fracture anisotropy, are also used;

[0070] The reservoir section in this example is a clastic reservoir, and the Xu-White model is used for rock physics modeling. The specific formula is:

[0071] ① The rock matrix modeling adopts the Voigt-Reuss-Hill model:

[0072]

[0073] Among them, f i is the volume component of the i-th medium, M i is the elastic modulus (bulk modulus or shear modulus) of the i-th medium;

[0074] ② Dry rock skeleton modeling uses the differential equivalent medium (DEM) model:

[0075] Equivalent bulk modulus and equivalent shear modulus and The coupled differential equations are (Berryman, 1992):

[0076]

[0077]

[0078] The initial condition is and where K1 and μ1 are the bulk modulus and shear modulus of the initial main phase material, K2 and μ2 are the bulk modulus and shear modulus of the gradually added inclusions (phase 2), and y is the content of phase 2;

[0079] ③Wood’s formula (Wood, 1955):

[0080] Mixed fluid model to calculate fluid modulus:

[0081]

[0082] ρ is the average density, defined as

[0083]

[0084] Among them, K R is the mixed fluid modulus, f i , K i , ρ i are the volume component, bulk modulus, and density of each component respectively;

[0085] ④Gassmann model:

[0086] Based on the calculated dry rock modulus and mixed fluid modulus, the saturated rock modulus can be calculated using the Biot-Gassmann equation. The Biot-Gassmann equation is as follows:

[0087]

[0088] μ sat =μ dry

[0089] Among them, K sat 、μ sat is the effective bulk modulus of saturated rock, K dry 、μ dry is the effective bulk modulus of the dry rock skeleton, K0 is the bulk modulus of the minerals that make up the rock, and K fl is the effective bulk modulus of the pore fluid, φ is the porosity;

[0090] According to the above formula, the rock physics relationship of the embodiment is a complex nonlinear relationship, which can be expressed as: d = f RPM (m)

[0091] Where d represents the reservoir elastic parameters, such as P-wave velocity, S-wave velocity, and density, which can be expressed as d = [V p , V s ,den], m represents the parameters of reservoir physical properties, oil and gas properties, such as mud content, porosity, and water saturation, which can be expressed as m = [V sh ,φ,S w ], f RPM Represents the constructed theoretical rock physics model relationship;

[0092] Step 2: Determine the parameters of the rock physics model based on well logging physical properties and fluid and elastic curves;

[0093] Wells involved in rock physics modeling within the work area must be standardized and optimized for modeling. Priority should be given to wells with more complete rock physics logging curves, higher logging quality, and logging parameter distribution that matches the actual geological conditions.

[0094] For rock physics modeling of the same reservoir section, the rock physics parameter determination criteria are: the elastic parameters (P-wave velocity, S-wave velocity, density) of the rock physics forward modeling have the highest correlation coefficient with the actual well logging curve or the smallest sum of square errors;

[0095] It is generally believed that different logging wells have differences in logging sampling environment and sampling equipment, and the rock physics modeling of a single well is generally extended to seismic inversion;

[0096] In this embodiment, the measured curves used in rock physics modeling and the forward logging curves are compared with the measured curves. Figure 3 As shown, Figure 3 There is no measured water saturation curve for the modeling wells. Since the saturation has very little effect on the elastic parameters, the water saturation of the reservoir section is set to 1. Figure 3 The modeling wells in the experiment have measured P-wave velocity, measured S-wave velocity and measured density. The measured P-wave velocity and density are used for quality control to determine the parameters of the rock physics model. The correlation coefficient between the forward-modeled P-wave velocity and density and the measured values is used to judge the quality of the rock physics model parameters. Figure 3 From left to right in the figure are P-wave velocity (Vp), S-wave velocity (Vs), density (den), mineral content, porosity, and TOC. The forward modeled P-wave and S-wave densities are dashed lines, while the measured ones are solid lines. The correlation coefficient between the forward modeled Vp and the measured Vp is 0.9015, the correlation coefficient between the forward modeled Vs and the measured Vs is 0.9193, and the correlation coefficient between the forward modeled den and the measured den is 0.9376. The predicted curve is consistent with the measured curve, so the third step can be carried out.

[0097] It should be noted that the meanings of the symbols in the figure of this embodiment are as follows: Vp is the longitudinal wave velocity, Vs is the shear wave velocity, den is the density, PHI is the porosity, VQU is the quartz content, VCLY is the argillaceous content, VCA is the calcite content, TOC is the organic matter content. In the figure of this embodiment, the units of Vp and Vs are m / s, and the unit of den is g / cm 3 , the units of mineral parameters, porosity, and TOC are decimals;

[0098] Step 3: Based on the rock physics model determined in the second step, perform logging rock physics inversion analysis of the modeling wells;

[0099] Based on the rock physics model, an inversion algorithm is selected to conduct an inversion test on the modeling well. This includes two aspects: 1. The input is the forward elastic parameter curve, and the degree of fit between the inversion curve and the measured curve is compared. If the degree of fit is high, it means that this inversion algorithm is applicable and can be extended to seismic inversion. If there is a large deviation, other applicable inversion algorithms need to be selected; 2. The input is the measured elastic parameter curve. Based on the established rock physics model and inversion algorithm, the degree of fit between the inversion curve and the measured curve is compared. The correlation coefficient or the sum of squared relative errors can be used to judge the quality of the results;

[0100] Nonlinear inversion problems such as inversion of complex rock physics models can be divided into two categories: deterministic inversion and stochastic inversion. Deterministic inversion includes Newton's method and its derivative algorithms, while stochastic inversion includes probabilistic inversion methods under the Bayesian framework, such as the Bayesian classification algorithm and the EMGM algorithm.

[0101] In this embodiment, the EMGM algorithm is preferably used for rock physics model inversion. Figure 4 Inversion results of elastic parameter curves for forward modeling of input modeling wells; Figure 5 The inversion results of the elastic parameter curve measured in the input modeling well are used. Figure 4 The inversion results of the elastic parameter curves in the forward modeling are basically consistent with the measured curves, indicating that the selected EMGM algorithm is capable of inverting the rock physical model in the second step of modeling; Figure 5 When the input is the measured elastic parameter curve of the modeling well, the inversion result is also consistent with the measured curve, indicating that the inversion can be performed within the logging error range;

[0102] Step 4: Based on the rock physics model determined in the second step and the inversion algorithm determined in the third step, conduct logging inversion analysis of non-modeling wells in the work area;

[0103] The determined rock physics model is extended to other non-modeling wells for inversion. The degree of agreement between the logging inversion results and the measured results is analyzed using two criteria: 1. Calculate the correlation coefficient between the inversion curve and the measured curve. A high correlation coefficient indicates that the inversion result is reliable. 2. Calculate the sum of squared relative errors between the inversion curve and the measured curve. A small sum of squared relative errors indicates that the inversion result is reliable. If the inversion results of non-modeling wells deviate significantly, it is necessary to check whether the logging curves are abnormal, whether standardization correction has been performed, and whether the reservoir characteristics are significantly different from those of the modeling wells.

[0104] In this embodiment, based on the model and inversion algorithm established in steps 2 and 3, the logging inversion of non-modeled wells is extended and applied. Figure 6 (a) Figure 6(b) The logging inversion results of two non-modeling wells A and B. The correlation coefficients between the inverted physical property parameters of well A and the measured ones are 0.7251, 0.9499, 0.9262, and 0.2783, respectively. The correlation coefficients between the inverted physical property parameters of well B and the measured ones are 0.5145, 0.9324, 0.8505, and 0.2156, respectively. The logging inversion results of the two non-modeling wells are consistent with the measured results, indicating that the established rock physics model is more suitable for this reservoir.

[0105] Step 5: Extract all the seismic traces near the wells and analyze the inversion results of all the seismic traces near the wells based on the rock physics model determined in the second step and the inversion algorithm determined in the third step.

[0106] When extracting seismic traces from wellbore, it is important to note that to ensure the accuracy of the elastic parameter inversion results obtained from seismic data, quality control of the elastic parameter inversion results must be performed before performing seismic inversion of reservoir physical property parameters to ensure the reliability of the input data for physical property inversion.

[0107] In this example, the inversion effect of the bypass channel of wells A and B is shown. Figure 7 As shown in (a) and (b), it can be seen that when the input wellside elastic parameter results are consistent with the low-frequency trend of the measured results, the wellside seismic trace physical property inversion results are consistent with the low-frequency trend of the measured physical property curve, indicating that it can be used for seismic reservoir physical property parameter inversion;

[0108] Step 6: Apply the determined rock physics model and inversion algorithm to perform seismic inversion of reservoir physical parameters based on the three pre-stack seismic elastic parameters (P-wave velocity, S-wave velocity, and density);

[0109] Step 7: Extract the well-to-well profile of the work area for comparative analysis and analysis of vertical reservoir variation, and extract the reservoir section plane attributes for analysis of plane distribution;

[0110] Seismic inversion of reservoir physical properties is conducted, and two methods are used to evaluate the results: 1. Extracting well-connected sections in the work area for well-seismic comparison and analysis of vertical reservoir variations; 2. Extracting reservoir section properties for planar distribution analysis. It is important to note that reservoir response characteristics, both in vertical and planar distribution, must conform to geological patterns.

[0111] Example 2:

[0112] like Figure 1 As shown, this embodiment provides a method for seismic inversion evaluation of reservoir physical property parameters, including:

[0113] Establish a rock physics model based on the reservoir geological characteristics of the work area;

[0114] Based on the rock physics model, inversion tests are carried out on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithm;

[0115] The rock physics model and inversion algorithm are used to perform seismic inversion on the reservoir physical parameters in the work area to obtain the inversion results;

[0116] Extract the well section and reservoir section plane attributes of the work area from the inversion results and then analyze them;

[0117] The rock physics model established based on the reservoir geological characteristics of the work area includes:

[0118] Select rock physics models based on the reservoir geology, structure, and structural characteristics of the work area;

[0119] Determine the parameters of the rock physics model based on the well logging physical property curves and elastic curves of the work area;

[0120] The rock physics model is selected from at least one of the Xu-White model, the Xu-Payne model, the Wood model, the Patchy model, the Brie formula, the Hudson model, and the Schoenberg linear sliding model;

[0121] Based on the rock physics model, inversion tests are conducted on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithms, including:

[0122] Determine the modeling wells in the work area: Modeling wells should be selected from measured logging curves that meet the input and output of the rock physics model, have no outliers, and have obvious reservoir response characteristics;

[0123] The model parameters of the rock physics model are determined based on the well logging physical property curves and elastic curves of the modeling wells in the work area: forward modeling is performed using the selected model parameters, and the well logging physical property curves are input to obtain the forward modeled elastic curve. If the forward modeled elastic curve is basically consistent with the measured elastic curve, the final model parameters are these model parameters; if the forward modeled elastic curve is not consistent with the measured elastic curve, the model parameters are modified until the two are basically consistent;

[0124] Based on the rock physics model, inversion tests are conducted on the modeling wells in the work area to determine the inversion algorithm;

[0125] Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and the inversion results were analyzed.

[0126] Based on the rock physics model, the inversion algorithm is determined by performing inversion tests on the modeling wells in the work area, including:

[0127] Input the forward-modeled or measured elastic parameter curve into the rock physics model to obtain the measured curve. Based on the rock physics model, perform inversion testing on the modeling wells in the work area to obtain the inversion curve. If the inversion curve matches the measured curve, determine the inversion algorithm. If the two do not match, change the inversion algorithm and repeat the above process until they match.

[0128] Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and analyzed. The inversion results include:

[0129] Based on the rock physics model and inversion algorithm, the non-modeling wells in the work area are inverted to obtain an inversion curve, and the correlation coefficient between the inversion curve and the measured curve of the non-modeling wells in the work area is calculated, or the sum of squares of the relative errors of the inversion curve relative to the measured curve of the non-modeling wells in the work area is statistically analyzed. If the correlation coefficient or the sum of squares of the relative errors meet the set conditions, the inversion effect is reliable. If the correlation coefficient or the sum of squares of the relative errors do not meet the set conditions, the anomalies of the measured curve are corrected, or the measured curve is standardized, or the differences in reservoir characteristics and the non-modeling wells in the work area are corrected, and the above process is repeated until the inversion effect is reliable. If the inversion result is still unreliable, the rock physics model parameters of the modeling well are modified and the above process is repeated.

[0130] Based on the rock physics model and inversion algorithm, all the seismic traces near the wells are inverted to obtain inversion results. It is judged whether the inversion results are consistent with the low-frequency trends of the actual test values of all the seismic traces near the wells. If the two are consistent, the inversion effect is reliable. If the two are inconsistent, the elastic parameters of all the seismic traces near the wells are corrected and the above process is repeated until the inversion effect is reliable.

[0131] The reservoir physical property parameters in the work area are the seismic pre-stack elastic parameters of the reservoir in the work area;

[0132] The seismic pre-stack elastic parameters of the reservoir in the work area include P-wave velocity, S-wave velocity and density;

[0133] The analysis of the well-connected sections and reservoir section plane attributes extracted from the inversion results includes:

[0134] Extract the well-connected profile of the work area from the inversion results, conduct well-seismic comparison analysis and analyze the vertical variation law of the reservoir;

[0135] Extracting reservoir section plane attributes from inversion results to analyze plane distribution;

[0136] The vertical variation and planar distribution of the reservoir are consistent with geological laws.

[0137] Example 3:

[0138] This embodiment provides a reservoir property parameter seismic inversion evaluation device, comprising:

[0139] Model building module, used to build rock physics models based on the geological characteristics of the reservoir in the work area;

[0140] An inversion algorithm determination module is used to perform inversion tests on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area based on the rock physics model to determine the rock physics model parameters and inversion algorithm;

[0141] A physical property parameter inversion module is used to perform seismic inversion on the physical property parameters of the reservoir in the work area using the rock physics model parameters and the inversion algorithm to obtain an inversion result;

[0142] An analysis module is used to extract the well-connected profile and reservoir section plane attributes of the work area from the inversion results and then analyze them.

[0143] The rock physics model established based on the reservoir geological characteristics of the work area includes:

[0144] Select rock physics models based on the reservoir geology, structure, and structural characteristics of the work area;

[0145] Determine the parameters of the rock physics model based on the well logging physical property curves and elastic curves of the work area;

[0146] The rock physics model is selected from at least one of the Xu-White model, the Xu-Payne model, the Wood model, the Patchy model, the Brie formula, the Hudson model, and the Schoenberg linear sliding model;

[0147] Based on the rock physics model, inversion tests are conducted on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithms, including:

[0148] Determine the modeling wells in the work area: Modeling wells should be selected from measured logging curves that meet the input and output of the rock physics model, have no outliers, and have obvious reservoir response characteristics;

[0149] The model parameters of the rock physics model are determined based on the well logging physical property curves and elastic curves of the modeling wells in the work area: forward modeling is performed using the selected model parameters, and the well logging physical property curves are input to obtain the forward modeled elastic curve. If the forward modeled elastic curve is basically consistent with the measured elastic curve, the final model parameters are these model parameters; if the forward modeled elastic curve is not consistent with the measured elastic curve, the model parameters are modified until the two are basically consistent;

[0150] Based on the rock physics model, inversion tests are conducted on the modeling wells in the work area to determine the inversion algorithm;

[0151] Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and the inversion results were analyzed.

[0152] Based on the rock physics model, the inversion algorithm is determined by performing inversion tests on the modeling wells in the work area, including:

[0153] Input the forward-modeled or measured elastic parameter curve into the rock physics model to obtain the measured curve. Based on the rock physics model, perform inversion testing on the modeling wells in the work area to obtain the inversion curve. If the inversion curve matches the measured curve, determine the inversion algorithm. If the two do not match, change the inversion algorithm and repeat the above process until they match.

[0154] Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area were inverted and analyzed. The inversion results include:

[0155] Based on the rock physics model and inversion algorithm, the non-modeling wells in the work area are inverted to obtain an inversion curve, and the correlation coefficient between the inversion curve and the measured curve of the non-modeling wells in the work area is calculated, or the sum of squares of the relative errors of the inversion curve relative to the measured curve of the non-modeling wells in the work area is statistically analyzed. If the correlation coefficient or the sum of squares of the relative errors meet the set conditions, the inversion effect is reliable. If the correlation coefficient or the sum of squares of the relative errors do not meet the set conditions, the anomalies of the measured curve are corrected, or the measured curve is standardized, or the differences in reservoir characteristics and the non-modeling wells in the work area are corrected, and the above process is repeated until the inversion effect is reliable. If the inversion result is still unreliable, the rock physics model parameters of the modeling well are modified and the above process is repeated.

[0156] Based on the rock physics model and inversion algorithm, all the seismic traces near the wells are inverted to obtain inversion results. It is judged whether the inversion results are consistent with the low-frequency trends of the actual test values of all the seismic traces near the wells. If the two are consistent, the inversion effect is reliable. If the two are inconsistent, the elastic parameters of all the seismic traces near the wells are corrected and the above process is repeated until the inversion effect is reliable.

[0157] The reservoir physical property parameters in the work area are the seismic pre-stack elastic parameters of the reservoir in the work area;

[0158] The seismic pre-stack elastic parameters of the reservoir in the work area include P-wave velocity, S-wave velocity and density;

[0159] The analysis of the well-connected sections and reservoir section plane attributes extracted from the inversion results includes:

[0160] Extract the well-connected profile of the work area from the inversion results, conduct well-seismic comparison analysis and analyze the vertical variation law of the reservoir;

[0161] Extracting reservoir section plane attributes from inversion results to analyze plane distribution;

[0162] The vertical variation and planar distribution of the reservoir are consistent with geological laws.

[0163] Example 4:

[0164] An embodiment of the present invention provides an electronic device including a memory and a processor.

[0165] a memory storing executable instructions;

[0166] The processor runs the executable instructions in the memory to implement the seismic inversion evaluation method for reservoir physical property parameters.

[0167] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0168] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0169] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of the present invention.

[0170] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0171] Embodiment 5:

[0172] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, a method for seismic inversion evaluation of reservoir physical property parameters is implemented.

[0173] The computer-readable storage medium according to an embodiment of the present invention stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the various embodiments of the present invention.

[0174] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0175] The reservoir property parameter seismic inversion evaluation method proposed in the embodiment of the present invention, based on the issues that need to be paid attention to and the quality control methods during the seismic inversion of reservoir property parameters of the rock physics model, can reduce the trial and error process in the technical inversion process, improve the work efficiency of reservoir property inversion, and enhance the practicality of the technology.

[0176] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A seismic inversion evaluation method for reservoir physical property parameters, characterized in that: include: Establish a rock physics model based on the reservoir geological characteristics of the work area; Based on the rock physics model, inversion tests are performed on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithm; Using the rock physics model parameters and the inversion algorithm, seismic inversion is performed on the reservoir physical property parameters of the work area to obtain an inversion result; The well-connected profile and reservoir section plane attributes of the work area are extracted from the inversion results and then analyzed and evaluated.

2. The evaluation method according to claim 1, wherein: The rock physics model established based on the reservoir geological characteristics of the work area includes: The rock physics model is selected based on the reservoir geology, structure and structural characteristics of the work area.

3. The evaluation method according to claim 2, wherein: The rock physics model is selected from at least one of a layered model, a spherical pore model, an inclusion model, a contact model, a fracture model, an empirical formula, and a combined rock physics model.

4. The evaluation method according to claim 1, wherein: Based on the rock physics model, inversion tests are performed on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area to determine the rock physics model parameters and inversion algorithm, including: Determine the modeling wells in the work area: Modeling wells should be selected from measured logging curves that meet the input and output of the rock physics model, have no outliers, and have obvious reservoir response characteristics; The model parameters of the rock physics model are determined based on the well logging physical property curves and elastic curves of the modeling wells in the work area: forward modeling is performed using the selected model parameters, and the well logging physical property curves are input to obtain the forward modeled elastic curve. If the forward modeled elastic curve is basically consistent with the measured elastic curve, the final model parameters are these model parameters; if the forward modeled elastic curve is not consistent with the measured elastic curve, the model parameters are modified until the two are basically consistent; Performing inversion testing on the modeling wells in the work area based on the rock physics model to determine the inversion algorithm; Based on the rock physics model and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area are inverted and the inversion effects are analyzed.

5. The evaluation method according to claim 4, wherein: Based on the rock physics model, inversion testing is performed on the modeling wells in the work area to determine the rock physics model parameters and inversion algorithm, including: Inputting the elastic parameter curve of the forward modeling of the modeling wells in the work area into the rock physics model, performing an inversion test on the modeling wells in the work area based on the rock physics model to obtain an inversion curve, if the inversion curve is consistent with the measured curve, determining the inversion algorithm, if the two do not match, changing the inversion algorithm and repeating the above process until the two match; Based on the rock physics model parameters and inversion algorithm, the seismic traces of non-modeling wells and all wells in the work area are inverted and the inversion results are analyzed, including: Based on the rock physics model parameters and the inversion algorithm, the non-modeling wells in the work area are inverted to obtain an inversion curve, and the correlation coefficient between the inversion curve and the measured curve of the non-modeling wells in the work area is calculated, or the sum of squares of the relative errors of the inversion curve relative to the measured curve of the non-modeling wells in the work area is calculated; if the correlation coefficient or the sum of squares of the relative errors meet the set conditions, the inversion effect is reliable; if the correlation coefficient or the sum of squares of the relative errors do not meet the set conditions, the abnormality of the measured curve is corrected, or the measured curve is standardized, or the difference between the reservoir characteristics and the non-modeling wells in the work area is corrected, and the above process is repeated until the inversion effect is reliable; if the inversion result is still unreliable, the rock physics model parameters of the modeling well are modified, and the above process is repeated; Based on the rock physics model and inversion algorithm, all well-side seismic traces are inverted to obtain inversion results, and it is determined whether the inversion results are consistent with the low-frequency trends of the actual test values of all well-side seismic traces. If the two are consistent, the inversion effect is reliable. If the two are not consistent, the elastic parameters of all well-side seismic traces are corrected and the above process is repeated until the inversion effect is reliable.

6. The prediction method according to claim 1, characterized in that The reservoir physical property parameters of the work area are seismic pre-stack elastic parameters of the reservoir in the work area; The seismic pre-stack elastic parameters of the reservoir in the work area include longitudinal wave velocity, shear wave velocity and density.

7. The prediction method according to claim 1, wherein: The analysis of the work area well section and reservoir section plane attributes extracted from the inversion results includes: Extract the well-connected profile of the work area from the inversion results, conduct well-seismic comparison analysis and reservoir vertical change law analysis; Extracting reservoir section plane attributes from the inversion results to analyze plane distribution; The vertical variation and planar distribution of the reservoir are consistent with geological laws.

8. A reservoir physical parameter seismic inversion evaluation device, characterized in that: include: Model building module, used to build a rock physics model based on the geological characteristics of the reservoir in the work area; An inversion algorithm determination module is used to perform inversion tests on the modeling wells, non-modeling wells, and seismic traces near all wells in the work area based on the rock physics model to determine the rock physics model parameters and inversion algorithm; A physical property parameter inversion module, used to perform seismic inversion on the physical property parameters of the reservoir in the work area using the rock physics model parameters and the inversion algorithm to obtain an inversion result; The analysis module is used to extract the well-connected profile of the work area and the plane attributes of the reservoir section from the inversion results and then perform analysis.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor runs the executable instructions in the memory to implement the seismic inversion evaluation method for reservoir physical property parameters according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for seismic inversion evaluation of reservoir physical property parameters according to any one of claims 1 to 7 is implemented.

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