Organic-rich rock physics modeling method and system considering multiple-porosity media

Abstract

The application discloses a kind of organic-rich rock physics modeling method and system considering multiple-pore medium, method includes setting initial different mineral bulk modulus and shear modulus, and obtaining the bulk modulus and shear modulus of rock skeleton;Predicted organic carbon content is obtained based on logging curve;Obtain kerogen content through predicted organic carbon content;Through kerogen content, the porosity of connected pore in rock skeleton is corrected;Kerogen is added to rock skeleton as solid filler;Different pore types are added to rock skeleton;Add bound water to rock skeleton;Add movable fluid to rock skeleton;Determine the bulk modulus and shear modulus of mineral in rock skeleton;Optimize the prediction effect of P and S wave velocity.It converts organic carbon into kerogen and adds it to rock skeleton to correct the content of dry connected pore and bound water pore, considers different pore conditions to add fluid, so as to improve the accuracy and reliability of model velocity prediction result.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical technology, and particularly relates to a method and system for modeling the physical properties of rock rich in organic matter considering multiple pore media. BACKGROUND

[0002] Rock physical modeling is an important method for seismic rock physical research. Through rock physical modeling, the prediction of shear waves can be realized, which helps to better identify sensitive parameters of the reservoir and reveal the relationship between the elastic parameters of the reservoir and the physical parameters. The rock rich in organic matter refers to the part of the formation where the source rock is developed and the organic matter is rich in the sediment. The logging curve of this source rock section shows the characteristics of low speed and low density, and the high-porosity sweet spot sandstone reservoir also has the characteristics of low speed and low density, resulting in the parameter aliasing phenomenon between the low-speed high-organic matter mudstone and the low-speed high-porosity sweet spot sandstone. Therefore, it is necessary to establish a corresponding rock physical model for the rock rich in organic matter and carry out related rock physical forward analysis to summarize the rock physical elastic parameter influencing mechanism of the low-speed source rock and the high-porosity sandstone, and guide the fine prediction of the reservoir.

[0003] In the current research, there are many studies on rock physical modeling, but there is a lack of research on the rock physical modeling of rock rich in organic matter considering the addition method of kerogen and the addition method of different pore fluid properties. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method and system for modeling the physical properties of rock rich in organic matter considering multiple pore media.

[0005] The technical solution adopted by the present application to solve the technical problem is: constructing a method for modeling the physical properties of rock rich in organic matter considering multiple pore media, comprising the following steps:

[0006] S1: setting initial different mineral volume modulus Ki and shear modulus μi, and obtaining the volume modulus K of the rock skeleton m0 and the shear modulus μ m0 ;

[0007] S2: obtaining the predicted organic carbon content TOC based on the logging curve log ;

[0008] S3: obtaining the kerogen content Kerogen through the predicted organic carbon content TOC log ;

[0009] S4: correcting the porosity of the connected pores in the rock skeleton through the kerogen content Kerogen

[0010] S5: adding kerogen as a solid filler to the rock skeleton

[0011] S6: distinguishing different pore types and adding pores to the rock framework;

[0012] S7: adding bound water to the rock framework;

[0013] S8: adding movable fluid to the rock framework;

[0014] S9: determining the bulk modulus K and shear modulus μ of the minerals in the rock framework; i i ;

[0015] S10: optimizing the prediction effect of P and S wave velocities.

[0016] In some embodiments, in step S1, initial bulk modulus Ki and shear modulus μi of different minerals are set, and the Voigt-Reuss-Hill model is used to obtain the bulk modulus K and shear modulus μ of the rock framework. m0 m0 .

[0017] In some embodiments, in step S2, the predicted organic carbon content is calculated according to the following formula (0):

[0018] TOC log = (A*GR + B*log RT + C*DT) / ρ (0)

[0019] wherein TOC log is the predicted organic carbon content, A, B, and C are parameters of multiple fitting, GR is the natural gamma curve, RT is the resistivity curve, DT is the acoustic time difference curve, and ρ is the density curve obtained from the logging curve.

[0020] In some embodiments, in step S3, based on formula (1), the kerogen content Kerogen is obtained by using the predicted organic carbon content TOC log .

[0021]

[0022] wherein in formula (1): Kerogen is the kerogen content; ρ m0 is the rock density; ρ k is the density of kerogen; the fitting coefficient A in formula (1) is continuously adjusted so that the error between the density calculated by formula (2) and the actual density logging is less than a threshold T k , to obtain the value of A in formula (1);

[0023]

[0024] wherein in formula (2): ρ i is the density of each component, ρ​​m0 V is the density of the rock. i This represents the volume fraction of each component.

[0025] In some embodiments, in step S4, the porosity of the interconnected pores in the rock skeleton is corrected by combining formulas (1), (3), (4), (5), (6), and (7):

[0026]

[0027] `

[0028] Wherein, the total porosity is ф0, the corrected total porosity is ф0, and the movable fluid porosity is ф 可动流体 ,

[0029] `

[0030] The corrected movable fluid porosity is ф 可动流体 .

[0031] In some embodiments, in step S5, according to Kuster- The model adds the bulk modulus K of kerogen to the rock skeleton. k Shear modulus μ k Density ρ k and the ratio of mineral length to width α k The bulk modulus K of the rock skeleton after adding kerogen was calculated. sat-k and shear modulus μ sat-k .

[0032] In some embodiments, in step S6, bound water pores, movable fluid argillaceous pores, and movable fluid sandy pores are added to the rock skeleton to obtain a multi-porous rock skeleton. The bound water pores, the movable fluid argillaceous pores, and the movable fluid sandy pores are initially set with different pore aspect ratios to obtain the bulk modulus K of the multi-porous rock skeleton. sat-XW and shear modulus μ sat-XW .

[0033] In some embodiments, in step S7, the bulk modulus K of bound water is added to the multiporous rock skeleton using a patch saturation model. 束缚水 Shear modulus μ 束缚水 Using the sphere radius b, frequency f, permeability p, and saturation s, the bulk modulus K of the multi-porous rock skeleton after adding bound water is calculated. sat-p and shear modulus μ sat-p .

[0034] In some embodiments, in step S8, the bulk modulus K of the added mobile fluid in the multi-porous rock skeleton after the addition of bound water is calculated using the Gassmann model.可动流体 Shear modulus μ 可动流体 The bulk modulus K of the multiporous rock skeleton after the addition of movable fluid was calculated. sat and shear modulus μ sat .

[0035] In some embodiments, in step S9, the initial kerogen aspect ratio α is fixed. k The ratio of the pore length to the width of the bound water is α. 束缚水 The aspect ratio of movable fluid mud pores is α. 泥 The aspect ratio of movable fluid sand pores α 砂 Continuously adjust the bulk modulus K of different minerals i Shear modulus μ i The P-wave velocity and S-wave velocity are calculated using the relationship between rock modulus and velocity, ensuring that the error between the P-wave velocity and S-wave velocity is less than the initial prediction error threshold T. v1 To determine the appropriate bulk modulus and shear modulus for different minerals in the study area.

[0036] In some embodiments, in step S10, an intelligent optimization algorithm is used to optimize the prediction effects of longitudinal wave velocity and transverse wave velocity.

[0037] The present invention also provides a physical modeling system for organic-rich rocks that takes into account multi-porous media, including: a memory and a processor;

[0038] The memory is used to store computer programs;

[0039] The processor is configured to implement the physical modeling system for organic-rich rocks that takes into account multi-porous media, as described in any of the above embodiments, when executing one or more programs stored in memory.

[0040] The present invention has the following beneficial effects: the physical modeling method for organic-rich rocks that considers multi-porous media converts organic carbon into kerogen and adds it to the rock skeleton to correct the content of dry connected pores and bound water pores, and adds fluids considering different pore conditions, thereby improving the accuracy and reliability of the model velocity prediction results. Attached Figure Description

[0041] To more clearly illustrate the technical solution of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort. In the drawings:

[0042] Figure 1 This is a flowchart of organic carbon prediction and kerogen calculation in some embodiments of the present invention;

[0043] Figure 2 This is a flowchart of a physical modeling method for organic-rich rocks that considers multi-porous media in some embodiments of the present invention.

[0044] Figure 3 This is a schematic diagram of a physical model of an organic-rich rock considering a multi-porous medium in some embodiments of the present invention.

[0045] Figure 4 These are graphs showing the variation of component content with depth in some embodiments of the present invention;

[0046] Figure 5 This is a diagram showing the ratio of kerogen to fluid in some embodiments of the present invention;

[0047] Figure 6 This is a comparison diagram of calculated P-wave and S-wave velocities and actual P-wave and S-wave velocities in some embodiments of the present invention;

[0048] Figure 7 This is a bar chart showing the longitudinal and transverse wave velocity error distribution in some embodiments of the present invention. Detailed Implementation

[0049] To provide a clearer understanding of the technical features, objectives, and effects of this invention, specific embodiments are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the orientations or positional relationships indicated by terms such as "front," "rear," "upper," "lower," "left," "right," "longitudinal," "horizontal," "vertical," "horizontal," "top," "bottom," "inner," "outer," "head," and "tail" are based on the orientations or positional relationships shown in the accompanying drawings, and are constructed and operated in a specific orientation. They are only for the convenience of describing this technical solution and do not indicate that the device or element referred to must have a specific orientation; therefore, they should not be construed as limitations on this invention.

[0050] It should also be noted that, unless otherwise explicitly specified and limited, terms such as "installation," "connection," "linking," "fixing," and "setting" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. When an component is referred to as being "on" or "below" another component, the component can be located "directly" or "indirectly" on the other component, or there may be one or more intermediary components. The terms "first," "second," "third," etc., are only for the convenience of describing this technical solution and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances.

[0051] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0052] Rock physics modeling is an important method for seismic rock physics research. It enables the prediction of shear waves, helps to better identify reservoir sensitive parameters, and reveals the relationship between reservoir elastic parameters and physical properties. Organic-rich strata typically refer to strata with developed source rocks, where the sediments are rich in organic matter. Well logging curves for these source rock sections show low velocity and low density characteristics. High-porosity sweet spot sandstone reservoirs also exhibit low velocity and low density characteristics, leading to parameter overlap between low-velocity, high-organic-matter mudstone and low-velocity, high-porosity sweet spot sandstone. Therefore, it is necessary to establish corresponding rock physics models for organic-rich rocks and conduct relevant forward rock physics analysis to summarize the influence mechanism of rock physics elastic parameters between low-velocity source rocks and high-porosity sandstone, guiding precise reservoir prediction.

[0053] Castagna et al. (1985) proposed a linear relationship between shear wave velocity and p-wave velocity in clastic silicate rocks with high water content; Vernik et al. (2018) proposed a hybrid technique based on the Greenberg-Castagna method to improve the prediction model of shear wave velocity and incorporate the influence of kerogen. Xu and White (1996) proposed the relationship between velocity and porosity and clay content in argillaceous sandstone and developed the Xu-White model; Keys et al. (2002) introduced an approximate equation for dry rock modulus into the Xu-White model; Bai Junyu (2012) modified the Xu-White model by replacing the fixed aspect ratio with a variable aspect ratio; Sohail et al. (2019) compared the performance of empirical formula methods and rock physics model methods in shear wave velocity prediction and concluded that the modified Xu-White model is the most effective choice at present. In current rock physics modeling of organic-rich rocks, the main approach is to simultaneously equate kerogen to matrix minerals and pore fluids based on its characteristics. Based on the Xu-White model, this study introduces the Gassmann fluid substitution equation, Voigt-Reuss-Hill average theory, and Wood's formula to establish a physical model of organic-rich rocks, primarily shale and coal seams. However, it does not fully consider the rationality of kerogen calculations, the complex existence of bound water, the classification of pores for different fluids after adding kerogen to the rock pores, or the methods for adding different fluids.

[0054] While there has been considerable research on rock physics modeling, there is a lack of research on organic-rich rock physics models that consider kerogen addition methods and different addition methods for different pore fluid properties. Therefore, how to establish an organic carbon rock physics model that considers multiple porous media is a problem that needs to be solved.

[0055] To address this, the present invention provides a physical modeling method for organic-rich rocks that considers multi-porous media. This method converts organic carbon into kerogen and adds it to the rock skeleton to correct the content of dry interconnected pores and bound water pores. It also adds fluids considering different pore conditions, thereby improving the accuracy and reliability of the model's velocity prediction results.

[0056] Combination Figures 1 to 3 As shown, this invention illustrates a physical modeling method for organic-rich rocks that considers multi-porous media, wherein, Figure 1 Flowchart for organic carbon prediction and kerogen calculation Figure 2 This is a flowchart of a physical modeling method for organic-rich rocks that considers multi-porous media. Figure 3 This is a schematic diagram of a physical model of organic-rich rocks considering multiple porous media, which can more intuitively demonstrate the physical modeling method of organic-rich rocks considering multiple porous media of the present invention.

[0057] Combination Figures 1 to 3 As shown, in some embodiments, the physical modeling method for organic-rich rocks considering multi-porous media includes the following steps:

[0058] S1: Set the initial bulk modulus (Ki) and shear modulus (μi) for different minerals, and obtain the bulk modulus (Ki) of the rock skeleton. m0 ) and shear modulus (μ m0 In step S1, initial bulk modulus (Ki) and shear modulus (μi) of different minerals are set, and the bulk modulus (Ki) of the rock skeleton is obtained using the Voigt-Reuss-Hill model. m0 ) and shear modulus (μ m0 In this embodiment, the mineral content varies with depth as follows: Figure 4 As shown, the blue portion represents the calcite content, the red portion represents the quartz content, the yellow portion represents the mudstone content, the green portion represents the bound water content, the brown portion represents the kerogen content, and the cyan portion represents the mobile fluid content. This leads to the determination of the mineral content (V) of the rock's framework minerals. i ), density (ρ) i Well logging data including porosity (ф0), permeability (p), and P-wave and S-wave velocities (vp, vs) were used. Based on the mineral composition of the rock, given initial bulk modulus of quartz: 50 GPa, shear modulus: 30 GPa, bulk modulus of clay: 25 GPa, shear modulus: 15 GPa, bulk modulus of calcite: 40 GPa, shear modulus: 20 GPa, the bulk modulus of the rock skeleton (Ki) was calculated using the Voigt-Reuss-Hill model. m0 ) and shear modulus (μ m0 ).

[0059] S2: Predicted Total Organic Carbon (TOC) content based on well logging curves log In step S2, the predicted organic carbon content (TOC) is calculated according to the following formula (0). log ):

[0060] TOC log =(A*GR+B*log RT+C*DT) / ρ (0)

[0061] Among them, TOC log To predict organic carbon content, A, B, and C are parameters for multivariate fitting, GR is the natural gamma curve, RT is the resistivity curve, DT is the sonic transit time curve, and ρ is the density curve obtained from the well logging curve.

[0062] In this embodiment, the total organic carbon (TOC) content is predicted based on well logging curves. logBy fitting well logging curves with TOC laboratory data, the curves most sensitive to TOC changes are selected. In this example, the curves sensitive to organic carbon include the natural gamma ray curve (GR), density curve (DEN), sonic transit time curve (DT), and resistivity curve (RT). Using the selected well logging curves, the predicted organic carbon content (TOC) is calculated according to the ΔlogR method or multivariate fitting method of well logging curves. log Preferably, the predicted total organic carbon (TOC) content can be calculated using the multivariate fitting method given in formula (0). log );

[0063] TOC log =(A*GR+B*log RT+C*DT) / ρ (0)

[0064] In this example, the parameters for the multivariate fit are A = -0.0052, B = 34.6173, and C = 0.2557. The predicted total organic carbon (TOC) content will be calculated. log To predict kerogen content, in this embodiment, the variation of kerogen with depth is as follows: Figure 5 As shown. Understandably, the total organic carbon (TOC) content in the well logging here... log The organic carbon content is predicted using TOC laboratory data and other curves. This predicted organic carbon content can also be called well logging organic carbon content (TOC). log This predicts the total organic carbon (TOC) content. log ) and the total organic carbon content (TOC) in well logging log () are the same physical quantity.

[0065] S3: Predicting Total Organic Carbon (TOC) log In step S3, the kerogen content is obtained; based on formula (1), the total organic carbon content (TOC) is predicted. log Obtain Kerogen content:

[0066]

[0067] In formula (1): Kerogen is the kerogen content; ρ m0 Rock density (density logging curve); ρ k The density of kerogen (usually a constant value) is 1.3 g·cm³. -3 ; continuously adjust the fitting coefficient A in formula (1) so that the error between the density calculated using formula (2) and the actual density logging is less than the threshold T. k Thus, the value of A in formula (1) is obtained;

[0068]

[0069] In formula (2): Tk is the difference between the actual logging curve density and the rock density calculated based on the kerogen content, and ρ i For the density of each component, ρ m0 V is the density of the rock. i This represents the volume fraction of each component.

[0070] Specifically, such as Figure 1 The flowchart in the example uses predicted organic carbon content data to calculate kerogen content. Based on the predicted organic carbon content, a coefficient A = 1.2 is first given. The fitting coefficient A in the formula is continuously adjusted so that the error between the density calculated by formula (2) and the actual density is less than the threshold T. k =0.1 to obtain the value of A in the formula (A=1.2 in this example), where the density is calculated by multiplying the volume content of each component by the density of each component and then adding them together. In this example, the minerals in the rock skeleton include quartz, clay and calcite, with clay accounting for the majority of the rock components. The fluids filling the pores are divided into bound water and mobile fluids, where the mobile fluids may contain water, oil and gas.

[0071] S4: The porosity of the interconnected pores in the rock skeleton is corrected by using the kerogen content; wherein, in step S4, the porosity of the interconnected pores in the rock skeleton is corrected by combining formulas (1), (3), (4), (5), (6), and (7):

[0072]

[0073] `

[0074] Wherein, the total porosity is ф0, the corrected total porosity is ф0, and the movable fluid porosity is ф 可动流体 ,

[0075] `

[0076] The corrected movable fluid porosity is ф 可动流体 .

[0077] Specifically, the calculated kerogen content is used to correct for the porosity of mobile fluids in the rock. (Original)

[0078] `

[0079] The content of each component in the well logging curve is the mineral content of each rock skeleton interpreted from the well logging (V). i Total porosity

[0080] `

[0081] (ф0), Corrected total porosity (ф0), Bound water porosity (ф)束缚水 ), movable fluid porosity (ф)

[0082] `

[0083] 可动流体 Corrected movable fluid porosity (ф) 可动流体 The kerogen content calculated by formula (1) is used to correct the mobile fluid porosity based on formulas (3), (4), (5), (6), and (7), where the sum of the contents of each component is 1, i.e., the content of each mineral skeleton (V). i ),

[0084] `

[0085] Bound water porosity (ф) 束缚水 Corrected movable fluid porosity (ф) 可动流体 The sum of the content of ) and kerogen is 1, which is the content of each mineral skeleton (V) i ), bound water porosity (ф) 束缚水 ),school

[0086] `

[0087] Positively, the porosity of the movable fluid (ф) 可动流体 The corrected mobile fluid porosity is obtained by summing the mineral composition, kerogen content, and porosity to 1. In this example, the corrected mobile fluid porosity is the total logging porosity minus the bound water porosity, and then minus the calculated kerogen content. After porosity correction, the sum of the mineral composition, kerogen content, and porosity is 1, and the sum of the corrected mobile fluid porosity and bound water porosity is the corrected total porosity. In this example, the proportion of each pore type varies with depth as follows: Figure 5 The green portion represents the proportion of movable fluid to the original total porosity, the red portion represents the proportion of bound water to the original total porosity, and the cyan portion represents the proportion of kerogen content to the original total porosity. The original total porosity is the sum of movable fluid, bound water, and kerogen content.

[0088] S5: Add kerogen as a solid filler to the rock skeleton; in step S5, according to Kuster- The (KT) model adds the bulk modulus of kerogen (K) to the rock skeleton. k ), shear modulus (μ) k ), density (ρ) k ) and mineral aspect ratio (α) k The bulk modulus (K) of the rock skeleton after adding kerogen was calculated. sat-k ) and shear modulus (μ sat-k In this embodiment, according to Kuster- (KT) model towards rock skeleton (K m0 μm0 The bulk modulus (K) of adding kerogen k =4.9GPa), shear modulus (μ k =1.1GPa), density (ρ) k =1.3 g·cm⁻³) and the initial kerogen aspect ratio (α) k =0.02), the bulk modulus (K) of the rock skeleton after adding kerogen was calculated. sat-k ) and shear modulus (μ sat-k ).

[0089] S6: Differentiate between different pore types and add pores to the rock skeleton; In step S6, bound water pores, movable fluid argillaceous pores, and movable fluid sandy pores are added to the rock skeleton to obtain a multi-porous rock skeleton. The bound water pores, the movable fluid argillaceous pores, and the movable fluid sandy pores are initially set with different aspect ratios, including the aspect ratio of bound water pores (α). 束缚水 ), the aspect ratio of movable fluid mud pores (α) 泥 The volume modulus (K) of the multi-porous rock framework was obtained by measuring the aspect ratio of the movable fluid sand pores (αsand) and the pore volume ratio (αsand). sat-XW ) and shear modulus (μ sat-XW In this embodiment, the traditional Xu-White model adds argillaceous and sandy pores to the rock framework and adds relevant pore parameters. However, in this example, an improved Xu-White model is used: three types of pores are added to the rock framework, including bound water pores, movable fluid argillaceous pores, and movable fluid sandy pores, and different pore aspect ratios (α) are set for different pore types. 束缚水 =0.02, a 泥 =0.2, a 砂 =0.4), to obtain the bulk modulus (K) of the multiporous rock skeleton at this time. sat-XW ) and shear modulus (μ sat-XW The relationship between them is obtained from formula (0).

[0090] S7: Add bound water to the rock skeleton; in step S7, the bulk modulus (K) of bound water is added to the multiporous rock skeleton using a patch saturation model. 束缚水 ), shear modulus (μ) 束缚水 =0), sphere radius (b), frequency (f), permeability (p), and saturation (s), and calculate the bulk modulus (K) of the multiporous rock skeleton after adding bound water. sat-p ) and shear modulus (μ sat-p In this embodiment, the patchy saturation model is used to model the multiporous rock framework (K). sat-XW μ sat-XW Adding the bulk modulus (K) of bound water 束缚水), shear modulus (μ) 束缚水 =0), sphere radius (b=0.1), frequency (f=5000), permeability (p), and saturation (s). The permeability and saturation of the example were obtained from well logging curves, and the frequency was the well logging frequency. The bulk modulus (K) of the multiporous rock skeleton after adding bound water was calculated. sat-p ) and shear modulus (μ sat-p ).

[0091] S8: Add movable fluid to the rock skeleton; in step S8, the bulk modulus (K) of the added movable fluid to the multiporous rock skeleton after adding bound water is calculated using the Gassmann model. 可动流体 ), shear modulus (μ) 可动流体 =0), calculate the bulk modulus (K) of the multiporous rock skeleton after adding movable fluid. sat ) and shear modulus (μ sat In this embodiment, after adding bound water to the rock skeleton (or multi-porous rock skeleton), the bulk modulus (K) of the movable fluid is added to the movable fluid pores in the rock skeleton using the Gassmann model. 可动流体 ), shear modulus (μ) 可动流体 =0), which yields an equivalent model of saturated fluid. In this example, the bulk modulus of the movable fluid is calculated from the bulk modulus of the fluid filling the pores and the saturation. The bulk modulus (K) of the rock skeleton after adding the movable fluid is then calculated. sat ) and shear modulus (μ sat ).

[0092] S9: Determine the bulk modulus (K) of minerals in the rock framework. i ) and shear modulus (μ i In step S9, the initial kerogen aspect ratio (α) is fixed. k ), the aspect ratio of bound water pores (ɑ) 束缚水 ), the aspect ratio of movable fluid mud pores (α) 泥 ) and the aspect ratio of movable fluid sand pores (α) 砂 ), continuously adjusting the bulk modulus (K) of different minerals i ), shear modulus (μ) i The P-wave velocity and S-wave velocity are calculated using the relationship between rock modulus and velocity, ensuring that the error between the P-wave velocity and S-wave velocity is less than the initial prediction error threshold T. v1 To determine the appropriate bulk modulus and shear modulus for different minerals in the study area.

[0093] In this embodiment, the bulk modulus (K) of minerals in the rock framework is determined. i ) and shear modulus (μ iIn this example, the process for adjusting and optimizing the bulk modulus and shear modulus of the rock skeleton minerals is as follows: Figure 2 First, fix the initial kerogen aspect ratio (α). k ), the aspect ratio of bound water pores (ɑ) 束缚水 ), the aspect ratio of movable fluid mud pores (α) 泥 ) and the aspect ratio of movable fluid sand pores (α) 砂 Based on the properties of the filler and pores described above, in this example, the aspect ratio of the kerogen is α. k =0.02, the aspect ratio of bound water pores α 束缚水 =0.02, movable fluid mud porosity aspect ratio α 泥 =0.2, movable fluid sand pore length-to-width ratio α 砂 =0.4. Substitute this value into the modeling process to calculate the velocity error value under this condition. If the velocity error value is greater than the specified threshold T... v1 Return to adjust the bulk modulus (K) of different minerals i ), shear modulus (μ) i This continues until the speed error value is less than the specified threshold T. v1 In this example, the error threshold T v1 =0.2, stop this loop, and determine the appropriate bulk modulus and shear modulus for different minerals in the study area. The bulk modulus and shear modulus input in this example are shown in Table 1:

[0094] Table 1 shows the relevant input parameters for each skeleton mineral in the example.

[0095]

[0096]

[0097] S10: Optimize the prediction effect of P-wave and S-wave velocities. In step S10, an intelligent optimization algorithm is used to optimize the prediction effect of P-wave and S-wave velocities.

[0098] In this embodiment, an intelligent optimization algorithm (the specific algorithm can be selected according to actual needs; in this embodiment, a particle swarm optimization algorithm is used, and it is not limited here) is used to optimize the prediction effect of P-wave and S-wave velocities. Specifically, the particle swarm optimization algorithm is used, with a maximum number of iterations (I) set, and the kerogen aspect ratio (α) in step S5 is optimized. k The aspect ratio of the bound water pores in step S6 (α) 束缚水 ), the aspect ratio of movable fluid mud pores (α) 泥 The pore length-to-width ratio of movable fluid sand (α sand) is specified, and the optimal range for the kerogen length-to-width ratio is defined as α. kmax —ɑ kmin The optimal range for the aspect ratio of bound water pores is α. 束缚水max —ɑ束缚水min The optimal range for the aspect ratio of movable fluid mud pores is α. 泥max —ɑ 泥min The optimal range for the aspect ratio of movable fluid mud pores is α. 砂max —ɑ 砂min The optimized aperture aspect ratio is then substituted into steps S5 and S6 above, and steps S5 to S9 are repeated to calculate the total transverse wave velocity.

[0099] The error between the calculated P-wave and S-wave velocities and the actual P-wave and S-wave velocities is less than the threshold value (T). v2 If the error between the P-wave velocity and the actual P-wave velocity is greater than the threshold value (T), v2 Return to the optimization and iterate to the maximum number of times; if the error between the P-wave velocity and the actual P-wave velocity is less than the error threshold (T) v2 If the iteration stops and the final calculated bulk modulus (K) is output, then the iteration stops. sat0 ) and shear modulus (μ sat0 ).

[0100] Specifically, since pores exhibit different properties at different depths, and their aspect ratio changes with formation variations, an intelligent optimization algorithm is used to optimize the pore aspect ratio to account for the impact of complex pore structures on the predicted velocity. The process for adjusting and optimizing the aspect ratio in this example is as follows: Figure 2 Using the particle swarm optimization algorithm, the mineral bulk modulus (K0) of the rock framework is determined. i ) and shear modulus (μ i After that, given the initial aspect ratio, it is substituted into the modeling process to calculate the speed error value under this condition. If the calculated speed error value is greater than the threshold (T) v2 =0.1), then return to the first step of the loop to re-optimize each aspect ratio, and specify the optimization range of the kerogen aspect ratio as α. kmax =0.01—ɑ kmin =0.3, the optimal range for the aspect ratio of bound water pores is α. 束缚水max =0.01—ɑ 束缚水min =0.3, the optimal range for the aspect ratio of movable fluid mud pores is α. 泥max =0.1—ɑ 泥min =0.5, the optimal range for the aspect ratio of movable fluid mud pores is α. 砂max =0.1—ɑ 砂min =0.9. If the threshold is not reached, return to optimization and iterate to the maximum number of iterations, and set the maximum number of iterations of the algorithm (I=100). Then, bring the optimized porosity aspect ratio back into steps S5 and S6, and loop through steps S5 to S9, so that the error between the calculated P-wave and S-wave velocities and the actual P-wave and S-wave velocities is less than the specified threshold T. v2 In this example, the error threshold Tv2 =0.1, stop the iteration loop and output the final calculated bulk modulus (K). sat0 ) and shear modulus (μ sat0 The predicted velocity is calculated, and the error between the predicted and actual velocities is obtained. Here, the P-wave and S-wave velocities include both P-wave and S-wave velocities.

[0101] like Figure 6 As shown, the first column shows the curves of predicted P-wave velocity and actual P-wave velocity as a function of depth, the second column shows the curves of predicted S-wave velocity and actual S-wave velocity as a function of depth, where the red line represents the predicted velocity and the black line represents the actual velocity, the third column shows the error between the predicted P-wave velocity and the actual P-wave velocity, and the fourth column shows the error between the predicted S-wave velocity and the actual S-wave velocity, which is obtained by subtracting the predicted velocity from the actual velocity and then dividing by the actual velocity. Figure 7 To predict the probability histogram of P-wave and S-wave velocity errors, from Figure 6 and Figure 7 It can be seen that the physical modeling method for organic-rich rocks considering multi-porous media established in this invention has successfully predicted the P-wave and S-wave velocities, with a prediction error range of approximately ±0.05 and a trend towards a normal distribution. Therefore, the physical modeling method for organic-rich rocks considering multi-porous media in this application can improve the accuracy and reliability of the model's velocity prediction results.

[0102] Understandably, the physical modeling method for organic-rich rocks that considers multi-porous media in this application includes methods for predicting organic carbon and calculating kerogen, using intelligent optimization algorithms (including particle swarm optimization) to simultaneously consider three aspect ratios during the modeling process, and a physical model construction process for organic-rich rocks, which improves the overall feasibility of physical modeling of organic-rich rocks that considers pore parameters, pore types, and filling fluids.

[0103] This application also provides a physical modeling system for organic-rich rocks that considers multi-porous media, including: a memory and a processor;

[0104] This memory is used to store computer programs;

[0105] The processor is used to implement the physical modeling system for organic-rich rocks that takes into account multi-porous media, as described in the above embodiments, when executing one or more programs stored in memory.

[0106] The memory can be a computer-readable signal medium or a computer-readable storage device, or any combination thereof. Memory can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of memory may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. The processor includes, but is not limited to, a microprocessor, a microcontroller, a digital signal processor, a microcomputer, a central processing unit, a field-programmable gate array, a programmable logic device, a state device, a logic circuit, an analog circuit, a digital circuit, and / or any device that operates signals (analog and / or digital) based on operating instructions.

[0107] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0108] It is understood that the above embodiments only illustrate preferred embodiments of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can freely combine the above technical features without departing from the concept of the present invention, and can also make several modifications and improvements, all of which fall within the protection scope of the present invention. Therefore, all equivalent transformations and modifications made with respect to the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A physical modeling method for organic-rich rocks considering multi-porous media, characterized in that, Includes the following steps: S1: Set the initial bulk modulus Ki and shear modulus μi for different minerals, and obtain the bulk modulus K of the rock skeleton. m0 and shear modulus μ m0 ; S2: Predicted Total Organic Carbon (TOC) based on well logging curves log In step S2, the predicted organic carbon content is calculated according to the following formula (0): TOC log =(A0*GR+B*log RT+C*DT) / ρ (0) Among them, TOC log To predict organic carbon content, A0, B, and C are parameters for multivariate fitting, GR is the natural gamma curve, RT is the resistivity curve, DT is the sonic transit time curve, and ρ is the density curve obtained from the well logging curve. S3: Based on the predicted organic carbon content (TOC) log Obtain the kerogen content; in step S3, based on formula (1), obtain the predicted organic carbon content (TOC). log Obtain Kerogen content: In formula (1): Kerogen is the kerogen content; ρ m0 Density of rock; ρ k Let A be the density of kerogen; continuously adjust the fitting coefficient A in formula (1) so that the formula can be used to achieve the desired result. The calculated value is related to the rock density ρ m0 The error is less than the threshold T k Thus, the value of A in formula (1) is obtained; In formula (2): ρ i For the density of each component, ρ m0 V is the density of the rock. i The volume fraction of each component; S4: Correct the porosity of interconnected pores in the rock skeleton by using the Kerogen content; S5: Add kerogen as a solid filler to the rock skeleton; S6: Differentiate between different pore types and add pores to the rock skeleton; S7: Add bound water to the rock skeleton; S8: Add movable fluid to the rock skeleton; S9: Determine the bulk modulus K of minerals in the rock framework. i and shear modulus μ i ; S10: Optimizes the prediction effect of P-wave and S-wave velocities.

2. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 1, characterized in that, In step S1, the initial bulk modulus Ki and shear modulus μi of different minerals are set, and the bulk modulus K of the rock framework is obtained using the Voigt-Reuss-Hill model. m0 and shear modulus μ m0 .

3. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 1, characterized in that, In step S4, by combining formulas (1), (3), (4), (5), (6), and (7), the porosity of the interconnected pores in the rock skeleton is corrected: Among them, the mineral content of each rock skeleton is V` i Total porosity is ф0, corrected total porosity is ф`0, and movable fluid porosity is ф 可动流体 The porosity of bound water is ф 束缚水 The corrected porosity of the movable fluid is ф` 可动流体 .

4. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 3, characterized in that, In step S5, according to The model adds the bulk modulus K of kerogen to the rock skeleton. k Shear modulus μ k Density ρ k and the ratio of mineral length to width α k The bulk modulus K of the rock skeleton after adding kerogen was calculated. sat-k and shear modulus μ sat-k .

5. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 4, characterized in that, In step S6, bound water pores, movable fluid argillaceous pores, and movable fluid sandy pores are added to the rock skeleton to obtain a multi-porous rock skeleton. The bound water pores, movable fluid argillaceous pores, and movable fluid sandy pores are initially set with different aspect ratios to obtain the bulk modulus K of the multi-porous rock skeleton. sat-XW and shear modulus μ sat-XW .

6. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 5, characterized in that, In step S7, the bulk modulus K of bound water is added to the multiporous rock skeleton using a patch saturation model. 束缚水 Shear modulus μ 束缚水 Using the sphere radius b, frequency f, permeability p, and saturation s, the bulk modulus K of the multi-porous rock skeleton after adding bound water is calculated. sat-p and shear modulus μ sat-p .

7. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 6, characterized in that, In step S8, the bulk modulus K of the added mobile fluid to the multi-porous rock skeleton after the addition of bound water is calculated using the Gassmann model. 可动流体 Shear modulus μ 可动流体 The bulk modulus K of the multiporous rock skeleton after the addition of movable fluid was calculated. sat and shear modulus μ sat .

8. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 7, characterized in that, In step S9, the initial kerogen aspect ratio α is fixed. k The ratio of the pore length to the width of the bound water is α. 束缚水 The aspect ratio of movable fluid mud pores is α. 泥 The aspect ratio of movable fluid sand pores α 砂 Continuously adjust the bulk modulus K of different minerals i Shear modulus μ i The P-wave velocity and S-wave velocity are calculated using the relationship between rock modulus and velocity, ensuring that the error between the P-wave velocity and S-wave velocity is less than the initial prediction error threshold T. v1 To determine the appropriate bulk modulus and shear modulus for different minerals in the study area.

9. The physical modeling method for organic-rich rocks considering multi-porous media according to claim 8, characterized in that, In step S10, an intelligent optimization algorithm is used to optimize the prediction effects of P-wave velocity and S-wave velocity.

10. A physical modeling system for organic-rich rocks considering multi-porous media, characterized in that, include: Memory and processor; The memory is used to store computer programs; The processor is configured to implement, when executing one or more programs stored in memory, the physical modeling system for organic-rich rocks that takes into account multi-porous media as described in any one of claims 1 to 9.

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