Model parameter determination method and device in theoretical rock physical modeling process

Abstract

The invention belongs to the field of oil and gas geophysical exploration, and relates to a method and a device for determining model parameters in a theoretical rock physics modeling process. The method includes the following steps that firstly, a theoretical rock physical model is determined based on geological deposition, lithology, construction, structure and other characteristics of a reservoir section; 2, based on a logging density curve or rock core actual measurement density data, the density of minerals and fluid is determined; step 3, determining shear moduli of minerals and fluids based on logging shear wave velocity or by taking rock characteristic factors as constraints; 4, based on the logging longitudinal wave velocity, determining volume moduli of the minerals and the fluid; and 5, determining other characteristic parameters in the rock physical model. According to the method, on the basis of logging, rock core and other data, a gradual rock physical model parameter determination method is adopted, rock physical model parameters capable of representing reservoir section rock physical characteristics are obtained, a systematic rock physical model parameter determination process is provided, and a basis is provided for reservoir prediction based on a rock physical model.

Description

Technical Field

[0001] The present invention belongs to the field of oil and gas geophysical exploration. Specifically, it relates to a method and device for determining model parameters in the process of theoretical rock physics modeling. Background Art

[0002] The reservoir prediction technology based on rock physics model inversion is widely used in seismic exploration. This technology is based on the pre-stack elastic parameter inversion results and combines with the rock physics model suitable for the reservoir in the work area, which can realize the quantitative characterization of the physical properties of the underground reservoir medium and plays an important role in reservoir geophysical modeling and oil and gas geophysical exploration.

[0003] However, the reservoir physical property parameter inversion technology based on the rock physics model is affected by the rock physics model. In the inversion process, a rock physics model that can characterize the properties of the underground reservoir is usually established based on geological, logging and other information and applied to seismic inversion. Due to the complex situation of underground media, the model parameters involved in the constructed rock physics model are often complex, usually including mineral and fluid elastic parameters, parameters characterizing rock pores and fractures. For anisotropic theoretical rock physics modeling, the rock physics model parameters will be more complex. The rock physics model parameters have a great influence on rock physics inversion, and there are differences in the inversion results using different rock physics model parameters. Therefore, determining relatively accurate rock physics parameters is an important research content for reservoir prediction based on rock physics inversion.

[0004] At present, the determination of rock physics model parameters mainly relies on the experience of technicians and the elastic characteristics of minerals, and manually adjusts mineral and fluid elastic moduli, fracture and pore related influencing factors, etc. in combination with core test results and logging data. The parameter test and optimization process is cumbersome, and the results have strong uncertainty. The rock physics model parameters determined by different personnel may also vary greatly. Summary of the Invention

[0005] In view of the problems such as the difficulty in determining model parameters and the cumbersome parameter test and optimization process in the process of theoretical rock physics modeling, the present invention proposes a method for determining model parameters in the process of theoretical rock physics modeling. The invention establishes a relatively systematic process for determining theoretical rock physics model parameters. Based on data such as logging and core, a method of gradually determining rock physics model parameters is adopted to obtain rock physics model parameters that can characterize the rock physics characteristics of the reservoir section, providing a basis for reservoir prediction based on the rock physics model.

[0006] To achieve the above object, the present invention provides a method for determining model parameters in the process of theoretical rock physics modeling, including the following steps:

[0007] First step, based on the geological deposition, lithology, structure and structural characteristics of the reservoir section, determine the theoretical rock physics model;

[0008] In the second step, based on the logging density curve or the measured density data of the core, determine the densities of minerals and fluids;

[0009] In the third step, based on the logging shear wave velocity or constrained by the rock property factor, determine the shear moduli of minerals and fluids;

[0010] In the fourth step, based on the logging compressional wave velocity, determine the bulk moduli of minerals and fluids;

[0011] In the fifth step, determine other characteristic parameters in the rock physics model.

[0012] Optionally, in the first step, the geology of the reservoir section is determined as clastic rock, carbonate rock or volcanic rock according to lithology;

[0013] The theoretical rock physics models include, but are not limited to, layered models, spherical pore models, inclusion models, contact models, fracture models, empirical formulas, and combined rock physics models.

[0014] Optionally, the rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1 - 0.45.

[0015] Optionally, in the fifth step, the other characteristic parameters include, but are not limited to, fracture density and pore aspect ratio.

[0016] Optionally, it further includes a sixth step. Based on all the determined parameters, compare the forward curve of the rock physics model with the measured curve, analyze the adaptability of the model parameters, and make fine adjustments to all the model parameters to improve the coincidence degree between the forward curve and the measured curve.

[0017] Optionally, it further includes a seventh step. Promote the established rock physics model and the determined model parameters to other wells in the work area and analyze the applicability.

[0018] Optionally, the methods for determining each parameter include the following steps:

[0019] Based on the given parameter ranges of minerals and fluids that conform to actual conditions, use an adaptive method to find the optimal parameter value for each point, then take the average value of the reservoir section as the parameter reference value of minerals and fluids, and make fine adjustments based on this until the forward parameter curve basically coincides with the measured parameters.

[0020] The second aspect of the present invention provides a device for determining model parameters in the process of theoretical rock physics modeling, including:

[0021] A theoretical rock physics model determination module, configured to determine a theoretical rock physics model based on the geological deposition, lithology, structure, and structural characteristics of the reservoir section;

[0022] A mineral and fluid density determination module for determining the density of minerals and fluids based on well logging density curves or measured core density data;

[0023] A mineral and fluid shear modulus determination module for determining the shear modulus of minerals and fluids based on well logging shear wave velocity or constrained by rock property factors;

[0024] A mineral and fluid bulk modulus determination module for determining the bulk modulus of minerals and fluids based on well logging compressional wave velocity;

[0025] An other characteristic parameter determination module for determining other characteristic parameters in the rock physics model.

[0026] The third aspect of the present invention provides an electronic device, which includes:

[0027] A memory storing executable instructions;

[0028] A processor that runs the executable instructions in the memory to implement the model parameter determination method in the theoretical rock physics modeling process described above.

[0029] The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the model parameter determination method in the theoretical rock physics modeling process.

[0030] Based on well logging, core and other data, the present invention adopts a step-by-step method for determining rock physics model parameters to obtain rock physics model parameters that can characterize the rock physics properties of the reservoir section. This method provides a systematic process for determining rock physics model parameters, optimizes the parameter testing process, and provides a basis for reservoir prediction based on the rock physics model.

[0031] Other features and advantages of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0033] Figure 1 Shows a technical flow chart for determining rock physics model parameters.

[0034] Figure 2 Shows a diagram of well logging data and core data according to an embodiment of the present invention.

[0035] Figure 3Shows the forward density curve of the rock physics model, the measured well logging density curve, and the measured core density situation map according to an embodiment of the present invention.

[0036] Figure 4 Shows the comparison diagram of the forward P-wave velocity, S-wave velocity, density after calibration of the rock physics model parameters according to an embodiment of the present invention and the measured P-wave velocity and density.

[0037] Figure 5 Shows the effect diagram of applying the rock physics model and the determined model parameters according to an embodiment of the present invention to other wells in the work area. Detailed implementation mode

[0038] 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 by the embodiments set forth herein.

[0039] To achieve the above object, the present invention provides a method for determining model parameters in the process of theoretical rock physics modeling, including the following steps:

[0040] The first step is to determine the theoretical rock physics model based on the geological sedimentation, lithology, structure and structural characteristics of the reservoir section;

[0041] The second step is to determine the densities of minerals and fluids based on the well logging density curve or the measured core density data;

[0042] The third step is to determine the shear moduli of minerals and fluids based on the well logging S-wave velocity or constrained by the rock property factor;

[0043] The fourth step is to determine the bulk moduli of minerals and fluids based on the well logging P-wave velocity;

[0044] The fifth step is to determine other characteristic parameters in the rock physics model.

[0045] In the present invention, the lithology of the reservoir section may be clastic rock, carbonate rock, or volcanic rock, and the reservoir characteristics vary greatly. Considering different reservoir rock physics models, a suitable theoretical rock physics model is selected to determine the framework.

[0046] The P-wave velocity and S-wave velocity in the elastic parameters are related to density. When determining the elastic parameters of rock components, first determine the densities of mineral and fluid parameters, and then determine other elastic moduli based on this.

[0047] Optionally, in the first step, the geological conditions of the reservoir section are determined as clastic rock, carbonate rock or volcanic rock according to the lithology;

[0048] The theoretical rock physics model includes, but is not limited to, 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.

[0049] Optionally, the rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1 - 0.45.

[0050] In the present invention, if there is no shear wave logging data in the work area, then the rock property factor is used as a constraint to determine (such as Poisson's ratio).

[0051] In the present invention, the shear wave velocity is only related to the shear modulus and density. The shear modulus of minerals and fluids is corrected by logging shear waves. The correction method is the same as the density correction method. If there is no shear wave data in the work area logging, it will affect the determination of rock physics model parameters to a certain extent. A factor that can characterize the rock characteristics can be used for constraint. Commonly used is Poisson's ratio (the range is generally 0.1 - 0.45, and the larger the value, the stronger the deformation ability when stressed).

[0052] Optionally, in the fifth step, the other characteristic parameters include, but are not limited to, fracture density and pore aspect ratio.

[0053] In the present invention, other parameters characterizing the characteristics of fractures, pores, etc. in the rock physics model will affect the forward modeling results of elastic parameters. First, a large step size is used to determine the preferred range, and then fine-tuning is carried out to make the matching degree higher. The correction method is the same as the density correction method.

[0054] Optionally, it further includes a sixth step of comparing the forward modeling curve of the rock physics model with the measured curve based on all the determined parameters, analyzing the adaptability of the model parameters, and comprehensively fine-tuning all the model parameters to improve the matching degree between the forward modeling curve and the measured curve.

[0055] Optionally, it further includes a seventh step of promoting the established rock physics model and the determined model parameters to other wells in the work area and analyzing the applicability.

[0056] Optionally, the method for determining each parameter includes the following steps:

[0057] Based on the given parameter range of minerals and fluids that conform to actual conditions, the optimal parameter value of each point is obtained through an adaptive method, and then the average value of the reservoir section is taken as the parameter reference value of minerals and fluids, and fine-tuning is carried out based on this until the forward modeling curve of the parameters basically coincides with the measured parameters.

[0058] The second aspect of the present invention provides a device for determining model parameters in the process of theoretical rock physics modeling, including:

[0059] A theoretical rock physics model determination module, configured to determine a theoretical rock physics model based on the geological deposition, lithology, structure, and structural characteristics of the reservoir section;

[0060] A density determination module for minerals and fluids, configured to determine the densities of minerals and fluids based on logging density curves or measured core density data;

[0061] A shear modulus determination module for minerals and fluids, configured to determine the shear moduli of minerals and fluids based on logging shear wave velocities or constrained by rock characteristic factors;

[0062] A bulk modulus determination module for minerals and fluids, configured to determine the bulk moduli of minerals and fluids based on logging P-wave velocities;

[0063] An other characteristic parameter determination module, configured to determine other characteristic parameters in the rock physics model.

[0064] A third aspect of the present invention provides an electronic device, which includes:

[0065] A memory storing executable instructions;

[0066] A processor, which runs the executable instructions in the memory to implement the model parameter determination method in the theoretical rock physics modeling process as described above.

[0067] A fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the model parameter determination method in the theoretical rock physics modeling process as described above.

[0068] Embodiment 1

[0069] This embodiment provides a model parameter determination method in the theoretical rock physics modeling process as shown in Figure 1 and includes the following steps:

[0070] First step, determine a theoretical rock physics model based on the geological sedimentation, lithology, structure and texture characteristics of the reservoir section;

[0071] Second step, determine the densities of minerals and fluids based on logging density curves or measured core density data;

[0072] Third step, determine the shear moduli of minerals and fluids based on logging shear wave velocities or constrained by rock characteristic factors;

[0073] Fourth step, determine the bulk moduli of minerals and fluids based on logging P-wave velocities;

[0074] Fifth step, determine other characteristic parameters in the rock physics model.

[0075] According to the embodiment of the present invention, in the first step, the geological formation of the reservoir section is determined as clastic rock, carbonate rock or volcanic rock according to the lithology;

[0076] The theoretical rock physics models include, but are not limited to, layered models, spherical pore models, inclusion models, contact models, fracture models, empirical formulas, and combined rock physics models.

[0077] According to an embodiment of the present invention, the rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1 - 0.45.

[0078] According to an embodiment of the present invention, in the fifth step, the other characteristic parameters include, but are not limited to, fracture density and pore aspect ratio.

[0079] According to an embodiment of the present invention, a sixth step is further included. Based on all the determined parameters, a forward curve of the rock physics model is compared with the measured curve, the adaptability of the model parameters is analyzed, and all the model parameters are comprehensively fine-tuned to improve the coincidence degree between the forward curve and the measured curve.

[0080] According to an embodiment of the present invention, a seventh step is further included. The established rock physics model and the determined model parameters are extended to other wells in the work area to analyze the applicability.

[0081] According to an embodiment of the present invention, the method for determining each parameter includes the following steps:

[0082] Based on the given parameter range of minerals and fluids that conform to actual conditions, the optimal parameter value of each point is obtained through an adaptive method, and then the average value of the reservoir section is taken as the parameter reference value of the minerals and fluids, and fine-tuning is performed based on this until the forward curve of the parameters basically coincides with the measured parameters.

[0083] The method of this embodiment is based on data such as logging and cores, and adopts a step-by-step method for determining rock physics model parameters to obtain rock physics model parameters that can characterize the rock physics properties of the reservoir section. This method provides a systematic process for determining rock physics model parameters, optimizes the parameter testing process, and provides a basis for reservoir prediction based on the rock physics model.

[0084] Embodiment 2

[0085] This embodiment provides a device for determining model parameters in the process of theoretical rock physics modeling, including:

[0086] A theoretical rock physics model determination module, configured to determine a theoretical rock physics model based on the geological deposition, lithology, structure, and structural characteristics of the reservoir section;

[0087] A density determination module for minerals and fluids, configured to determine the density of minerals and fluids based on logging density curves or measured density data of cores;

[0088] A shear modulus determination module for minerals and fluids, based on logging shear wave velocity or constrained by a rock property factor, to determine the shear modulus of minerals and fluids;

[0089] A bulk modulus determination module for minerals and fluids, configured to determine the bulk modulus of minerals and fluids based on the logging compressional wave velocity;

[0090] An other characteristic parameter determination module, configured to determine other characteristic parameters in the rock physics model.

[0091] In some embodiments, in the first step, the reservoir section geology is determined as clastic rock, carbonate rock or volcanic rock according to the lithology.

[0092] In some embodiments, the theoretical rock physics model includes 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.

[0093] In some embodiments, the rock property is the Poisson's ratio, and the range of the Poisson's ratio is 0.1 - 0.45.

[0094] In some embodiments, in the fifth step, the other characteristic parameters include, but are not limited to, fracture density and pore aspect ratio.

[0095] In some embodiments, a sixth step is further included. Based on all the determined parameters, a forward curve of the rock physics model is compared with the measured curve, the adaptability of the model parameters is analyzed, and all the model parameters are comprehensively fine-tuned to improve the coincidence degree between the forward curve and the measured curve.

[0096] In some embodiments, a seventh step is further included. The established rock physics model and the determined model parameters are extended to other wells in the work area, and the applicability is analyzed.

[0097] In some embodiments, the method for determining each parameter includes the following steps:

[0098] Based on the given parameter range of minerals and fluids that conforms to the actual conditions, the optimal parameter value of each point is obtained through an adaptive method, and then the average value of the reservoir section is taken as the parameter reference value of minerals and fluids, and fine-tuning is performed based on this until the forward parameter curve basically coincides with the measured parameters.

[0099] The device of this embodiment is based on data such as logging and core, and adopts a step-by-step method for determining rock physics model parameters to obtain rock physics model parameters that can characterize the rock physics properties of the reservoir section. This method provides a systematic process for determining rock physics model parameters, optimizes the parameter testing process, and provides a basis for reservoir prediction based on the rock physics model.

[0100] For other detailed descriptions and advantages of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0101] Embodiment 3

[0102] This embodiment provides an electronic device, including a memory and a processor.

[0103] The memory stores executable instructions.

[0104] The processor runs the executable instructions in the memory to implement the method for determining model parameters in the theoretical rock physics modeling process.

[0105] The method for determining model parameters in the theoretical rock physics modeling process includes the following steps:

[0106] In the first step, based on the geological sedimentation, lithology, structure, and texture characteristics of the reservoir section, determine the theoretical rock physics model.

[0107] In the second step, based on the logging density curve or the measured density data of the core, determine the densities of minerals and fluids.

[0108] In the third step, based on the logging shear wave velocity or constrained by the rock property factor, determine the shear moduli of minerals and fluids.

[0109] In the fourth step, based on the logging compressional wave velocity, determine the bulk moduli of minerals and fluids.

[0110] In the fifth step, determine other characteristic parameters in the rock physics model.

[0111] In some embodiments, in the first step, the geological reservoir section is determined as clastic rock, carbonate rock, or volcanic rock according to lithology.

[0112] In some embodiments, the theoretical rock physics model includes 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.

[0113] In some embodiments, the rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1 - 0.45.

[0114] In some embodiments, in the fifth step, the other characteristic parameters include but are not limited to fracture density and pore aspect ratio.

[0115] In some embodiments, there is also a sixth step. Based on all the determined parameters, compare the forward curve of the rock physics model with the measured curve, analyze the adaptability of the model parameters, and make fine-tuning of all the model parameters to improve the coincidence degree between the forward curve and the measured curve.

[0116] In some embodiments, there is also a seventh step. Promote the established rock physics model and the determined model parameters to other wells in the work area and analyze the applicability.

[0117] In some embodiments, the methods for determining each parameter include the following steps:

[0118] Based on the parameter ranges of minerals and fluids that meet the actual conditions, the optimal parameter value at each point is obtained through an adaptive method. Then, the average value of the reservoir section is taken as the reference value of the mineral and fluid parameters, and fine-tuning is carried out based on this until the forward parameter curve basically coincides with the measured parameters.

[0119] Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0120] The processor may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform the desired functions. In an embodiment of the present invention, the processor is used to run the computer-readable instructions stored in the memory.

[0121] According to the electronic device of this embodiment, based on data such as well logging and core, a step-by-step method for determining rock physics model parameters is adopted to obtain rock physics model parameters that can characterize the rock physics properties of the reservoir section. This method provides a systematic process for determining rock physics model parameters, optimizes the parameter testing process, and provides a basis for reservoir prediction based on the rock physics model.

[0122] For other detailed descriptions and advantages related to this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be elaborated herein.

[0123] Embodiment 4

[0124] This embodiment provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements a method for determining model parameters in the theoretical rock physics modeling process.

[0125] The method for determining model parameters in the theoretical rock physics modeling process includes the following steps:

[0126] First step, based on the geological sedimentation, lithology, structure, and texture characteristics of the reservoir section, determine the theoretical rock physics model;

[0127] Second step, based on the well logging density curve or the measured density data of the core, determine the densities of minerals and fluids;

[0128] Third step, based on the well logging shear wave velocity or constrained by the rock property factor, determine the shear moduli of minerals and fluids;

[0129] In the fourth step, based on the logging compressional wave velocity, determine the bulk moduli of minerals and fluids.

[0130] In the fifth step, determine other characteristic parameters in the rock physics model.

[0131] In some embodiments, in the first step, the geology of the reservoir section is determined as clastic rock, carbonate rock or volcanic rock according to lithology.

[0132] In some embodiments, the theoretical rock physics models include layered models, spherical pore models, inclusion models, contact models, fracture models, empirical formulas, and combined rock physics models.

[0133] In some embodiments, the rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1 - 0.45.

[0134] In some embodiments, in the fifth step, the other characteristic parameters include but are not limited to fracture density and pore aspect ratio.

[0135] In some embodiments, a sixth step is further included. Based on all the determined parameters, compare the forward curve of the rock physics model with the measured curve, analyze the adaptability of the model parameters, and make fine-tuning of all the model parameters to improve the coincidence degree between the forward curve and the measured curve.

[0136] In some embodiments, a seventh step is further included. Promote the established rock physics model and the determined model parameters to other wells in the work area and analyze the applicability.

[0137] In some embodiments, the methods for determining each parameter include the following steps:

[0138] Based on the given parameter ranges of minerals and fluids that conform to actual conditions, obtain the optimal parameter value for each point through an adaptive method, then take the average value of the reservoir section as the parameter reference value of minerals and fluids, and make fine-tuning based on this until the forward curve of the parameters basically coincides with the measured parameters.

[0139] According to the computer-readable storage medium of the embodiment of the present invention, non-temporary computer-readable instructions are stored thereon. When the non-temporary computer-readable instructions are run by a processor, all or part of the steps of the methods of the various embodiments of the present invention described above are executed.

[0140] The above-mentioned computer-readable storage media include but are not limited to: optical storage media (such as CD-ROM and DVD), magneto-optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built-in rewritable non-volatile memories (such as memory cards), and media with built-in ROM (such as ROM cartridges).

[0141] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included in the protection scope of the present invention.

[0142] According to the computer-readable storage medium of this embodiment, based on data such as logging and core, a step-by-step method for determining rock physics model parameters is adopted to obtain rock physics model parameters that can characterize the rock physics properties of the reservoir section. This method provides a systematic process for determining rock physics model parameters, optimizes the parameter testing process, and provides a basis for reservoir prediction based on the rock physics model.

[0143] For other detailed descriptions and advantages of this embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0144] Embodiment 5

[0145] In order to verify the effect of the model parameter determination method in the theoretical rock physics modeling process of the present invention, taking actual logging data as an example, the reservoir section studied is a clastic rock reservoir. A theoretical rock physics model for clastic rocks is established based on the Xu-White model framework, and the method provided by the present invention is used to determine the rock physics model parameters. Figure 2 The logging data of the embodiment is shown. As can be seen from the figure, the logging curves include compressional wave velocity, density, volume contents of various mineral components, fluid volume content, porosity, and measured core density. Based on the Xu-White model framework, a theoretical rock physics model applicable to the logging depth section is established, and rock physics model parameter calibration is carried out.

[0146] The first step is to determine the rock physics model.

[0147] Rock matrix model: Voigt-Reuss-Hill model; differential equivalent medium (DEM) model is used for dry rock skeleton modeling; Wood formula is used to calculate the mixed fluid modulus; Gassmann equation is used to calculate the saturated rock modulus.

[0148] ① The Voigt-Reuss-Hill model is used for rock matrix modeling:

[0149]

[0150] where f i is the volume fraction of the i-th medium, and M i is the elastic modulus (bulk modulus or shear modulus) of the i-th medium;

[0151] ② The differential equivalent medium (DEM) model is used for dry rock skeleton modeling:

[0152] Equivalent bulk modulus and equivalent shear modulus and The coupled differential equations are respectively (Berryman, 1992):

[0153]

[0154]

[0155] The initial conditions are and where K1 and μ1 are the bulk modulus and shear modulus of the initial matrix 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;

[0156] ③ Wood's formula (Wood, 1955):

[0157] The fluid modulus is calculated by the mixed fluid model:

[0158]

[0159] ρ is the average density, defined as

[0160]

[0161] where K R is the mixed fluid modulus, f i 、K i 、ρ i are the volume fractions, bulk moduli, and densities of the respective components;

[0162] ④ Gassmann equation:

[0163] 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:

[0164]

[0165] μ sat = μ dry ...

[0166] where K sat 、μ sat are the effective bulk modulus of the saturated rock, K dry 、μ dry are the effective bulk modulus of the dry rock skeleton, K0 is the bulk modulus of the minerals composing the rock, K fl is the effective bulk modulus of the pore fluid, and φ is the porosity.

[0167] In the second step, based on the logging density curve or the measured density data of the core, the densities of minerals and fluids are determined.

[0168] When there are measured density data of the core, it is considered that the core test data is more reliable than the density. In this embodiment, there are anomalies in the logging density curve, and the density parameters are corrected based on the core test data. Figure 3 The forward density curve of the rock physics model, the measured logging density curve, and the measured core density are shown. The forward curve coincides with the trend of the measured core density, indicating that the density parameters of minerals and fluids are relatively accurate.

[0169] The method for determining the mineral density can be to give the density ranges of minerals and fluids that meet the actual conditions, find the most optimal density value at each point through an adaptive method, then take the average value of the reservoir section as the reference value of the mineral and fluid density, and make fine-tuning based on this until the forward density curve basically coincides with the measured density.

[0170] It should be noted that the interpretations corresponding to the labels in the embodiment drawings 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 shale content, VCA is the calcite content, TOC is the organic matter content, SW is the water saturation. In the embodiment drawings, the units of Vp and Vs are m / s, the unit of den is g / cm3, the units of mineral parameters, porosity, TOC, and water saturation are in decimal system, model represents the forward of the rock physics model, real represents the logging curve value, and core is the measured core data. The labels are not case-sensitive.

[0171] In the third step, there is no shear wave logging data in the work area of the embodiment, so it is determined according to the rock characteristic factor as a constraint (such as Poisson's ratio).

[0172] The embodiment is a shale reservoir, and the Poisson's ratio of the shale rock is between 0.1 and 0.2. Based on this, the relationship between the shear modulus and the bulk modulus is constrained to determine the shear modulus.

[0173] In the fourth step, the bulk moduli of minerals and fluids are determined based on the logging longitudinal wave.

[0174] The longitudinal wave velocity is related to the shear wave velocity and density. First, determine the parameters related to the shear wave velocity and density, and then determine the remaining parameters related to the longitudinal wave velocity, which can reduce the mutual influence between parameters. The correction method is the same as the density correction method.

[0175] In the fifth step, the pore aspect ratio in the rock physics model is corrected.

[0176] Other parameters in the rock physics model that characterize the characteristics of fractures, pores, etc. will affect the forward results of elastic parameters. The method of first determining the preferred range with a large step size and then making fine-tuning to make the coincidence degree higher is adopted. The correction method is the same as the density correction method.

[0177] Step 6: Compare the forward curves of the rock physics model with the measured curves based on the determined rock physics parameters, analyze the adaptability of the model parameters, and make fine adjustments to all model parameters to improve the coincidence degree between the forward curves and the measured curves.

[0178] Figure 4 For the comparison between the forward P-wave velocity, S-wave velocity, and density after the calibration of the rock physics model parameters and the measured P-wave velocity and density, it can be seen that the coincidence degree is good.

[0179] Step 7: Promote the established rock physics model and the determined model parameters to other wells in the work area and analyze the applicability.

[0180] Figure 5 For the effect of promoting the rock physics model of the embodiment and the determined model parameters to other wells in the work area. There is no water saturation logging curve for this well. Since the water saturation has little effect on the elastic parameters of rock physics, the water saturation of this well is regarded as 100% for model forward calculation. It can be seen that the coincidence degree between the forward elastic tri-parameter curves of the promoted well and the measured curves is high. The correlation coefficients between the forward curves of Vp, Vs, and den and the actual logging trends are 0.9186, 0.9183, and 0.9220 respectively, indicating that the established rock physics model and the determined rock physics parameters are applicable in the same reservoir section of the work area and can be promoted and applied in the work area.

[0181] For other detailed descriptions of this exemplary embodiment, reference can be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0182] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for determining model parameters in a theoretical rock physics modeling process, characterized in that: The steps include: In the first step, a theoretical rock physics model is determined based on the geological sedimentary, lithological, structural and architectural characteristics of the reservoir section; The second step is to determine the density of minerals and fluids based on well logging density curves or core measured density data; The third step is to determine the shear modulus of minerals and fluids based on the well logging shear wave velocity or with rock property factors as constraints; The fourth step is to determine the bulk modulus of minerals and fluids based on the logging P-wave velocity; The fifth step is to determine other characteristic parameters in the rock physics model.

2. The method for determining model parameters in the theoretical rock physics modeling process according to claim 1, wherein: In the first step, the reservoir section geology is determined by lithology as clastic, carbonate, or volcanic; The theoretical rock physics model includes 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.

3. The method for determining model parameters in the theoretical rock physics modeling process according to claim 1, wherein: The rock property is Poisson's ratio, and the range of Poisson's ratio is 0.1-0.

45.

4. The method for determining model parameters in the theoretical rock physics modeling process according to claim 1, wherein: In the fifth step, the other characteristic parameters are crack density and / or pore aspect ratio.

5. The method for determining model parameters in the theoretical rock physics modeling process according to claim 1, wherein: It also includes a sixth step, which is to compare the forward curve of the rock physics model with the measured curve based on all the determined parameters, analyze the adaptability of the model parameters, and fine-tune all the model parameters to improve the consistency between the forward curve and the measured curve.

6. The method for determining model parameters in the theoretical rock physics modeling process according to claim 5, wherein: It also includes the seventh step, which is to extend the established rock physics model and the determined model parameters to other wells in the work area and analyze their applicability.

7. The method for determining model parameters in the theoretical rock physics modeling process according to any one of claims 1 to 6, wherein: The method for determining each parameter includes the following steps: Based on the given parameter range of minerals and fluids that meets actual conditions, the optimal parameter value of each point is obtained through adaptive method, and then the average value of the reservoir section is taken as the parameter reference value of the minerals and fluids. Based on this, fine-tuning is performed until the parameter forward curve basically matches the measured parameters.

8. A device for determining model parameters in a theoretical rock physics modeling process, characterized in that: include: A theoretical rock physics model determination module is used to determine a theoretical rock physics model based on geological sedimentation, lithology, structure and structural characteristics of the reservoir section; A mineral and fluid density determination module is used to determine the density of minerals and fluids based on well logging density curves or core measured density data; The shear modulus determination module for minerals and fluids determines the shear modulus of minerals and fluids based on well logging shear wave velocity or with rock property factors as constraints; A module for determining the bulk modulus of minerals and fluids, for determining the bulk modulus of minerals and fluids based on the well logging P-wave velocity; Other characteristic parameter determination module is used to determine other characteristic parameters in the rock physics model.

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 method for determining model parameters in the theoretical rock physics modeling process 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, which, when executed by a processor, implements the method for determining model parameters in the theoretical rock physics modeling process according to any one of claims 1 to 7.

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