Organic matter-rich rock physical modeling method and system considering multiple pore media

Abstract

The invention discloses an organic matter-rich rock physical modeling method and system considering multiple pore media, and the method comprises the steps: setting initial bulk moduli and shear moduli of different minerals, and obtaining the bulk modulus and shear modulus of a rock skeleton; obtaining a predicted organic carbon content based on a logging curve; the kerogen content is obtained by predicting the organic carbon content; the porosity of communicating holes in a rock skeleton is corrected through the content of kerogen; adding kerogen as a solid filler into the rock skeleton; different pore types are distinguished, and pores are added into the rock skeleton; bound water is added into the rock skeleton; adding a movable fluid into the rock skeleton; determining the bulk modulus and shear modulus of minerals in the rock skeleton; and the longitudinal and transverse wave speed prediction effect is optimized. Organic carbon is converted into kerogen, the kerogen is added into a rock skeleton to correct the content of dry communicating holes and bound water pores, and fluid is added by considering different pore conditions, so that the accuracy and reliability of a model speed prediction result are improved.

Description

Technical Field

[0001] The present invention relates to the field of geophysical technology, and in particular to a method and system for modeling organic-rich rock physics considering multi-porous media. Background Art

[0002] Rock physics modeling is an important method for conducting seismic rock physics research. Rock physics models can be used to predict shear waves, which helps to better identify sensitive parameters of reservoirs and reveal the relationship between reservoir elastic parameters and physical parameters. Organic-rich strata usually refer to strata where source rocks are developed, and the part of sediments rich in organic matter. The logging curve of this source rock section shows the characteristics of low velocity and low density, and the high-porosity sweet spot sandstone reservoir also has the characteristics of low velocity and low density, resulting in parameter aliasing between low-velocity high-organic mudstone and low-velocity high-porosity sweet spot sandstone. Therefore, it is necessary to establish a corresponding rock physics model for organic-rich rocks, and carry out relevant rock physics forward analysis, summarize the influence mechanism of rock physics elastic parameters of low-velocity source rocks and high-porosity sandstones, and guide the fine prediction of reservoirs.

[0003] In current research, there have been many studies on rock physics modeling, but there is a lack of research on organic-rich rock physics models that consider kerogen addition methods and are established using different addition methods for different pore fluid properties. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for physical modeling of organic-rich rocks taking into account multi-porous media.

[0005] The technical solution adopted by the present invention to solve the technical problem is to construct a physical modeling method of organic-rich rock considering multi-porosity media, comprising the following steps:

[0006] S1: Set the initial bulk modulus Ki and shear modulus μi of different minerals, and obtain the bulk modulus K of the rock skeleton m0 and shear modulus μ m0 ;

[0007] S2: Get predicted organic carbon content TOC based on well logging curves log ;

[0008] S3: Predicting the organic carbon content TOC by log Get the kerogen content Kerogen;

[0009] S4: Correcting the porosity of the connected pores in the rock skeleton by using the kerogen content;

[0010] S5: Kerogen is added as solid filler to the rock skeleton;

[0011] S6: Differentiate between different pore types and add pores to the rock framework;

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

[0013] S8: Adding movable fluid to the rock skeleton;

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

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

[0016] In some embodiments, 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 skeleton is obtained using the Voigt-Reuss-Hill model. m0 and shear modulus μ 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] Among them, TOC log To predict the organic carbon content, A, B, and C are the parameters of multivariate 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 predicted organic carbon content TOC log Get kerogen content:

[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 the threshold value T k , and obtain the value of A in formula (1);

[0023]

[0024] In formula (2), ρ i is the density of each component, ρm0 is the rock density, V i is the volume fraction of each component.

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

[0026]

[0028] Among them, the total porosity is ф 0 , the total porosity after correction is ф ` 0 、Porosity of movable fluid is ф 可动流体 , the corrected movable fluid porosity is ф` movable fluid.

[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 mineral aspect ratio ɑ k , calculate the bulk modulus K of the rock skeleton after adding kerogen sat-k and shear modulus μ sat-k .

[0032] In some embodiments, in step S6, irreducible water pores, movable fluid mud pores and movable fluid sand pores are added to the rock skeleton to obtain a multi-porosity rock skeleton, wherein the irreducible water pores, the movable fluid mud pores and the movable fluid sand pores are set with initial different pore aspect ratios to obtain a bulk modulus K of the multi-porosity rock skeleton. sat-XW and shear modulus μ sat-XW .

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

[0034] In some embodiments, in step S8, the bulk modulus K of the added movable fluid to the multi-porous rock skeleton after adding irreducible water is calculated using the Gassmann model. 可动流体 , shear modulus μ可动流体 , calculate the bulk modulus K of the multi-porous rock skeleton after adding movable fluid sat and shear modulus μ sat .

[0035] In some embodiments, in step S9, the initial kerogen aspect ratio ɑ is fixed k 、Bound water pore aspect ratioɑ 束缚水 、Aspect ratio of movable fluid mud poresɑ 泥 and movable fluid sand pore aspect ratio ɑ 砂 , constantly adjusting the bulk modulus K of different minerals i , shear modulus μ i , the relationship between rock modulus and velocity is used to calculate the P-wave velocity and S-wave velocity, so that the error between the P-wave velocity and S-wave velocity is less than the initial prediction error threshold T v1 , determine the appropriate bulk modulus and shear modulus of 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 shear wave velocity.

[0037] The present invention also provides an organic-rich rock physics modeling system considering multi-porous media, comprising: a memory and a processor;

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

[0039] The processor is used to implement the organic-rich rock physics modeling system considering multi-porous media as described in any of the above embodiments when executing one or more programs stored in the memory.

[0040] The implementation of the present invention has the following beneficial effects: the method for physical modeling of 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 interconnected pores and bound water pores, and adds fluids considering different porosity conditions, thereby improving the accuracy and reliability of the model velocity prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order 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 certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work. In the drawings:

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

[0043] Figure 2 is a flow chart of a method for physical modeling of organic-rich rock considering multi-porosity media in some embodiments of the present invention;

[0044] Figure 3 is a schematic diagram of an organic-rich rock physics model considering multi-porosity media in some embodiments of the present invention;

[0045] Figure 4 is a graph showing the variation of the content of each component with depth in some embodiments of the present invention;

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

[0047] Figure 6 is a comparison diagram of the calculated longitudinal and transverse wave velocities and the actual longitudinal and transverse wave velocities in some embodiments of the present invention;

[0048] Figure 7 It is a histogram of the distribution of longitudinal and shear wave velocity errors in some embodiments of the present invention. DETAILED DESCRIPTION

[0049] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific embodiments of the present invention are now described in detail with reference to the accompanying drawings. In the following description, it should be understood that the directions or positional relationships indicated by "front", "back", "up", "down", "left", "right", "longitudinal", "horizontal", "vertical", "horizontal", "top", "bottom", "inside", "outside", "head", "tail", etc. are based on the directions or positional relationships shown in the accompanying drawings, are constructed and operated in a specific direction, and are only for the convenience of describing the present technical solution, rather than indicating that the device or element referred to must have a specific direction, and therefore cannot be understood as a limitation to the present invention.

[0050] It should also be noted that, unless otherwise clearly specified and limited, the terms such as "installed", "connected", "connected", "fixed", "set" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. When an element is referred to as being "on" or "under" another element, the element can be "directly" or "indirectly" located on the other element, or there may be one or more intermediate elements. The terms "first", "second", "third", etc. are only for the convenience of describing the present technical solution, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first", "second", "third", etc. can explicitly or implicitly include one or more of the features. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0051] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0052] Rock physics modeling is an important method for conducting seismic rock physics research. Rock physics models can be used to predict shear waves, which helps to better identify sensitive parameters of reservoirs and reveal the relationship between reservoir elastic parameters and physical parameters. Organic-rich strata usually refer to strata where source rocks are developed, and the part of sediments rich in organic matter. The logging curve of this source rock section shows the characteristics of low velocity and low density, and the high-porosity sweet spot sandstone reservoir also has the characteristics of low velocity and low density, resulting in parameter aliasing between low-velocity high-organic mudstone and low-velocity high-porosity sweet spot sandstone. Therefore, it is necessary to establish a corresponding rock physics model for organic-rich rocks, and carry out relevant rock physics forward analysis, summarize the influence mechanism of rock physics elastic parameters of low-velocity source rocks and high-porosity sandstones, and guide the fine prediction of reservoirs.

[0053] Castagna et al. (1985) proposed a linear relationship between S-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 S-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 the approximate equation of dry rock modulus into the Xu-White model; Bai Junyu (2012) modified the Xu-White model by using a variable aspect ratio instead of a fixed aspect ratio; Sohail et al. (2019) compared the performance of the empirical formula method and the rock physics model method in S-wave velocity prediction and believed that the modified Xu-White model is currently the most effective choice. In the current physical modeling of organic-rich rocks, kerogen is mainly equivalent to both matrix minerals and pore fluids based on its characteristics. Based on the Xu-White model, the Gassmann fluid replacement equation, Voigt-Reuss-Hill average theory and Wood formula were introduced to establish the physical modeling of organic-rich rocks mainly composed of shale and coal seams. However, the rationality of kerogen calculation was not considered in detail. When considering bound water, due to the complex existence of bound water, the division of different fluid pores after kerogen was added to the rock pores and the addition method of different fluids were not considered in detail.

[0054] In current research, there have been many studies on rock physics modeling, but there are fewer studies on organic-rich rock physics models that consider kerogen addition methods and different addition methods for different pore fluid properties. Therefore, how to establish organic carbon rock physics modeling that considers multiple porous media is a problem that needs to be solved at present.

[0055] In this regard, the present invention provides an organic-rich rock physics modeling method that takes into account multi-porous media, which 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 porosity conditions, thereby improving the accuracy and reliability of the model velocity prediction results.

[0056] Combination Figures 1 to 3 As shown, the present invention shows a method for physical modeling of organic-rich rock considering multi-porosity media, wherein: Figure 1 Organic carbon prediction and kerogen calculation flow chart, Figure 2 is a flow chart of the method for modeling organic-rich rock physics considering multi-porosity media. Figure 3 It is a schematic diagram of an organic-rich rock physics model considering multiple porous media, which can more intuitively demonstrate the organic-rich rock physics modeling method considering multiple porous media of the present invention.

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

[0058] S1: Set the initial bulk modulus (Ki) and shear modulus (μi) of different minerals, and obtain the bulk modulus of the rock skeleton (K m0 ) and shear modulus (μ m0 ); In step S1, the 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 example, the mineral content varies with depth as follows Figure 4 As shown in the figure, the blue part is the content of calcite, the red part is the content of quartz, the yellow part is the content of mudstone, the green part is the content of bound water, the brown part is the content of kerogen, and the cyan part is the content of movable fluid. The content of minerals constituting the rock skeleton (V i ), density (ρ i ), porosity (ф 0 ), permeability (p), and longitudinal and transverse wave velocities (vp, vs) and other logging data. According to the mineral composition of the rock, the initial quartz bulk modulus: 50Gpa, shear modulus: 30Gpa, clay bulk modulus: 25Gpa, shear modulus: 15Gpa, calcite bulk modulus: 40Gpa, shear modulus: 20Gpa, the Voigt-Reuss-Hill model is used to calculate the bulk modulus of the rock skeleton (K m0 ) and shear modulus (μ m0 ).

[0059] S2: Get the predicted organic carbon content (TOC) based on the well logging curve 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 the organic carbon content, A, B, and C are the parameters of multivariate 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.

[0062] In this embodiment, the organic carbon content (TOC) is predicted based on the well logging curve. log). The well logging curve is fitted with the TOC laboratory data to select the curves that are sensitive to TOC changes in the well logging curve. In this example, the curves that are sensitive to organic carbon include the natural gamma curve (GR), density curve (DEN), acoustic time difference curve (DT) and resistivity curve (RT). The predicted organic carbon content (TOC) is calculated using the above selected well logging curves according to the ΔlogR method or the well logging curve multivariate fitting method. log ). Preferably, the predicted organic carbon content (TOC) can be calculated according to the multivariate fitting method given by formula (0). log );

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

[0064] In this example, the multivariate fitting parameters are A = -0.0052, B = 34.6173, and C = 0.2557. The predicted organic carbon content (TOC log ) predicts the kerogen content. In this embodiment, the kerogen content varies with depth as follows: Figure 5 As shown. Understandably, the well logging organic carbon content (TOC log ) is predicted using TOC laboratory data and other curves. The predicted organic carbon content can also be called logging organic carbon content (TOC log ), the predicted organic carbon content (TOC log ) and logging organic carbon content (TOC log ) are the same physical quantities.

[0065] S3: The predicted organic carbon content (TOC log ) to obtain the kerogen content (Kerogen); in step S3, based on formula (1), the predicted organic carbon content (TOC log )Get kerogen content (Kerogen):

[0066]

[0067] Wherein, in formula (1): Kerogen is the kerogen content; ρ m0 is the rock density (density logging curve); ρ k is the density of kerogen (usually a constant value). In this example, the density of kerogen is 1.3 g cm -3 ; Constantly adjust the fitting coefficient A in formula (1) so that the error between the density calculated by formula (2) and the actual density logging is less than the threshold T k , and obtain the value of A in formula (1);

[0068]

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

[0070] Specifically, if Figure 1 The flowchart of calculating kerogen content using predicted organic carbon content data is shown in Figure 1. According to the predicted organic carbon content, the coefficient A is first given as 1.2, and the size of 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 value T. k =0.1 to obtain the value of A in the formula (in this example, A=1.2), 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, among which the clay content accounts for the vast majority of the rock components, and the fluids filled in the pores are divided into bound water and movable fluids, among which the movable fluids can include water, oil and gas.

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

[0072]

[0074] Among them, the total porosity is ф 0 , the total porosity after correction is ф ` 0 、Porosity of movable fluid is ф 可动流体 , the corrected movable fluid porosity is ф` movable fluid.

[0083] Specifically, the porosity of movable fluid in the rock is corrected using the calculated kerogen content. The content of each component in the original logging curve is calculated using the mineral content of each rock skeleton interpreted by the logging (V` i ), total porosity (ф 0 ), corrected total porosity (ф` 0 ), bound water porosity (ф 束缚水), movable fluid porosity (ф 可动流体 ) Corrected movable fluid porosity (ф` 可动流体 ) and the kerogen content (Kerogen) calculated by formula (1), the movable fluid porosity is corrected based on formula (3), formula (4), formula (5), formula (6) and formula (7), where the sum of the contents of each component is 1, that is, the content of each mineral skeleton (V i ), bound water porosity (ф 束缚水 ), corrected movable fluid porosity (ф ` 可动流体 ) and kerogen content (Kerogen) is 1, that is, the content of each mineral skeleton (V i ), bound water porosity (ф 束缚水 ), corrected movable fluid porosity (ф ` 可动流体 ) and kerogen content (Kerogen) is 1, that is, in this example, the corrected movable fluid porosity is obtained by subtracting the irreducible water porosity from the total logging porosity and then subtracting the calculated kerogen content. After porosity correction, the sum of mineral components, kerogen content and porosity is 1, and the sum of the corrected movable fluid porosity and the irreducible water porosity is the corrected total porosity. In this embodiment, the proportion of each pore changes with depth as shown in Figure 5 , where the green part is the proportion of movable fluid in the original total porosity, the red part is the proportion of bound water in the original total porosity, and the cyan part is the proportion of kerogen content in the original total porosity. The original total porosity is the sum of movable fluid, bound water and kerogen contents.

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

[0089] S6: Differentiate different pore types and add pores to the rock skeleton; in step S6, add bound water pores, movable fluid mud pores and movable fluid sand pores to the rock skeleton to obtain a multi-pore rock skeleton, and the bound water pores, the movable fluid mud pores and the movable fluid sand pores are set with initial different pore aspect ratios, including the bound water pore aspect ratio (ɑ 束缚水 ), movable fluid mud pore aspect ratio (ɑ 泥 ) and the movable fluid sand pore aspect ratio (ɑ sand), the bulk modulus of the multi-porous rock skeleton (K sat-XW ) and shear modulus (μ sat-XW ). In this embodiment, the traditional Xu-White model: adding muddy pores and sandy pores to the rock skeleton and adding relevant parameters of the pores. However, in this example, the improved Xu-White model is used: adding three types of pores to the rock skeleton, including bound water pores, movable fluid muddy pores and movable fluid sandy pores, and setting different pore aspect ratios (ɑ 束缚水 =0.02,ɑ 泥 =0.2,ɑ 砂 =0.4), and the bulk modulus of the multi-porous rock skeleton (K sat-XW ) and shear modulus (μ sat-XW ), where the relationship is obtained by formula (0).

[0090] S7: Adding bound water to the rock skeleton; In step S7, the bulk modulus (K 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 multi-porous rock skeleton after adding bound water. sat-p ) and shear modulus (μ sat-p In this embodiment, the patchy saturation model is used to saturate the multi-porous rock skeleton (K sat-XW , μ sat-XW ) Bulk modulus of bound water (K 束缚水 ), shear modulus (μ 束缚水=0), sphere radius (b=0.1), frequency (f=5000), permeability (p) and saturation (s), where the permeability and saturation of the example are obtained from the well logging curve, the frequency is the well logging frequency, and the bulk modulus (K) of the multi-porous rock skeleton after adding bound water is calculated. sat-p ) and shear modulus (μ sat-p ).

[0091] S8: Adding movable fluid to the rock skeleton; In step S8, the bulk modulus (K 可动流体 ), shear modulus (μ 可动流体 = 0), calculate the bulk modulus (K) of the multi-porous rock skeleton after adding the movable fluid sat ) and shear modulus (μ sat In this embodiment, after the irreducible water is added to the rock skeleton (or the multi-porous rock skeleton), the bulk modulus (K 可动流体 ), shear modulus (μ 可动流体 =0), the equivalent model of saturated fluid can be obtained. The bulk modulus of the movable fluid in this example is obtained by calculating the bulk modulus and saturation of the fluid filling the movable fluid pores. The bulk modulus (K sat ) and shear modulus (μ sat ).

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

[0093] In this example, the bulk modulus (K i ) and shear modulus (μ i ). In this example, the process of adjusting and optimizing the bulk modulus and shear modulus of rock skeleton minerals is as follows: Figure 2, first fix the initial kerogen aspect ratio (ɑ k ), bound water pore aspect ratio (ɑ 束缚水 ), movable fluid mud pore aspect ratio (ɑ 泥 ) and the movable fluid sand pore aspect ratio (ɑ 砂 ), where according to the above-mentioned properties of the filling and pores, in this example, the kerogen aspect ratio ɑ k =0.02, bound water pore aspect ratio ɑ 束缚水 =0.02, movable fluid mud pore aspect ratio ɑ 泥 =0.2, movable fluid sand pore aspect ratio ɑ 砂 =0.4. Bring it into the modeling process to calculate the speed error value under this condition. If the speed error value is greater than the specified threshold value T v1 , returns the adjustment of different mineral bulk moduli (K i ), shear modulus (μ i ), until the speed error value is less than the specified threshold value T v1 In this example, the error threshold T v1 =0.2, stop this cycle, and determine the appropriate bulk modulus and shear modulus of different minerals in the study area. In this example, the input bulk modulus and shear modulus are shown in Table 1:

[0094] Table 1 Input parameters related to each framework mineral in the example

[0095]

[0096]

[0097] S10: Optimizing the prediction effect of longitudinal and transverse wave velocities. In step S10, the prediction effect of longitudinal and transverse wave velocities is optimized by using an intelligent optimization algorithm.

[0098] In this embodiment, an intelligent optimization algorithm is used (the specific algorithm can be selected according to actual needs. In this embodiment, a particle swarm optimization algorithm is used, which is not limited here) to optimize the prediction effect of the P-wave and S-wave velocities. In which, the particle swarm algorithm is used to set the maximum number of iterations (I) of the algorithm and optimize the kerogen aspect ratio (ɑ) in step S5. k ), the bound water pore aspect ratio (ɑ) in step S6 束缚水 ), movable fluid mud pore aspect ratio (ɑ 泥 ) and the movable fluid sand pore aspect ratio (ɑ sand), and the optimal range of kerogen aspect ratio is specified to be ɑ kmax —ɑ kmin , the optimal range of bound water pore aspect ratio is ɑ 束缚水max —ɑ 束缚水min , the optimal range of movable fluid mud pore aspect ratio is ɑ 泥max —ɑ泥min , the optimal range of movable fluid mud pore aspect ratio is ɑ 砂max —ɑ 砂min , the optimized pore aspect ratio is brought into the above steps S5 and S6, and steps S5 to S9 are looped to calculate the total shear wave velocity.

[0099] The error between the calculated P-wave velocity and the actual P-wave velocity 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 optimal search 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 ), the iteration stops and the final calculated bulk modulus (K sat0 ) and shear modulus (μ sat0 ).

[0100] Specifically, since pores have different properties at different depths, the pore aspect ratio changes with the change of the formation. When considering the complex pore structure affecting the results of the demonstration velocity prediction, the pore aspect ratio is optimized using an intelligent optimization algorithm. In this example, the process of adjusting and optimizing the aspect ratio is as follows: Figure 2 , using particle swarm optimization to determine the mineral bulk modulus (K i ) and shear modulus (μ i ), the initial aspect ratio is brought into the modeling process to calculate the speed error value at this time. 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 optimize the aspect ratios again, and set the kerogen aspect ratio optimization range to be ɑ kmax =0.01—ɑ kmin =0.3, the optimal range of bound water pore aspect ratio is ɑ 束缚水max =0.01—ɑ 束缚水min = 0.3, the optimal range of movable fluid mud pore aspect ratio is ɑ 泥max =0.1—ɑ 泥min = 0.5, the optimal range of movable fluid mud pore aspect ratio is ɑ 砂max =0.1—ɑ 砂min =0.9, if the threshold value is not reached, return to the optimization and iterate to the maximum number of times, and set the maximum number of iterations of the algorithm (I=100), so that the pore aspect ratio after optimization is brought into step S5 and step S6 again, and steps S5 to S9 are cycled to ensure that the error between the calculated P-wave velocity and the actual P-wave velocity is less than the specified threshold value T v2 In this example, the error threshold T v2 = 0.1, stop the iteration loop and output the last calculated bulk modulus (K sat0) and shear modulus (μ sat0 ), calculate the predicted velocity and obtain the error with the actual velocity. The longitudinal and transverse wave velocities here include longitudinal wave velocity and transverse wave velocity.

[0101] like Figure 6 As shown, the first column is the curve of the predicted P-wave velocity and the actual P-wave velocity changing with depth, the second column is the curve of the predicted S-wave velocity and the actual S-wave velocity changing with depth, where the red line is the predicted velocity and the black line is the actual velocity, the third column is the error between the predicted P-wave velocity and the actual P-wave velocity, and the fourth column is the error between the predicted S-wave velocity and the actual S-wave velocity, that is, the curve obtained by subtracting the predicted velocity from the actual velocity and dividing it by the actual velocity. Figure 7 To predict the error probability histogram of P- and S-wave velocities, Figure 6 and Figure 7 It can be seen that the physical modeling method of organic-rich rock considering multiple porous media established by the present invention successfully predicted the P-wave and S-wave velocities, and the prediction error range is approximately within plus or minus 0.05, and has a normal distribution trend. It can be seen that the physical modeling method of organic-rich rock considering multiple porous media of the present application can improve the accuracy and reliability of the model velocity prediction results.

[0102] Understandably, the method for physical modeling of organic-rich rocks considering multiple porous media of the present application includes organic carbon prediction and kerogen calculation methods, the use of intelligent optimization algorithms (including particle swarm algorithms) to simultaneously consider three aspect ratios during the modeling process, and the process of building a physical model of organic-rich rocks, thereby overall improving the feasibility of rock physical modeling that considers pore parameters, pore types, and filling fluids in organic-rich rocks.

[0103] The present application also provides an organic-rich rock physics modeling system considering multi-porous media, including: a memory and a processor;

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

[0105] The processor is used to implement the organic-rich rock physics modeling system considering multi-porous media of the above embodiment when executing one or more programs stored in the memory.

[0106] The memory may be a computer readable signal medium or a computer readable memory or any combination of the two. The memory may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of memory may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. 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 machine, 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 description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0108] It can be understood that the above embodiments only express the preferred implementation modes of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the patent scope of the present invention. It should be pointed out that, for ordinary technicians in this field, without departing from the concept of the present invention, the above technical features can be freely combined, and several deformations and improvements can be made, which all belong to the protection scope of the present invention. Therefore, all equivalent changes and modifications made to the scope of the claims of the present invention should belong to the scope covered by the claims of the present invention.

Claims

1. A method for modeling organic-rich rock physics considering multi-porosity media, characterized in that: The following steps are involved: S1: Set the initial bulk modulus Ki and shear modulus μi of different minerals, and obtain the bulk modulus K of the rock skeleton m0 and shear modulus μ m0 ; S2: Get predicted organic carbon content TOC based on well logging curves log ; S3: Predicting the organic carbon content TOC by log Get the kerogen content Kerogen; S4: Correcting the porosity of the connected pores in the rock skeleton by using the kerogen content; S5: Kerogen is added as solid filler to the rock skeleton; S6: Differentiate between different pore types and add pores to the rock framework; S7: adding bound water to the rock skeleton; S8: Adding 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: Optimize the prediction effect of P- and S-wave velocities.

2. The method for physical modeling of organic-rich rock considering multi-porosity 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 skeleton is obtained using the Voigt-Reuss-Hill model. m0 and shear modulus μ m0 .

3. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 1, characterized in that: In step S2, the predicted organic carbon content is calculated according to the following formula (0): TOC log =(A*GR+B*log RT+C*DT) / ρ (0) Among them, TOC log To predict the organic carbon content, A, B, and C are the parameters of multivariate 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.

4. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 3, characterized in that: In step S3, based on formula (1), the predicted organic carbon content TOC log Get kerogen content: 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 the threshold value T k , and obtain the value of A in formula (1); In formula (2), ρ i is the density of each component, ρ m0 is the rock density, V i is the volume fraction of each component.

5. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 4, characterized in that: In step S4, the porosity of the connected pores in the rock skeleton is corrected by combining formula (1), formula (3), formula (4), formula (5), formula (6) and formula (7): Among them, the total porosity is ф0, and the corrected total porosity is ф ` 0. The porosity of the movable fluid is ф 可动流体 、The corrected movable fluid porosity is ф ` 可动流体 .

6. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 5, 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 mineral aspect ratio ɑ k , calculate the bulk modulus K of the rock skeleton after adding kerogen sat-k and shear modulus μ sat-k .

7. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 6, characterized in that: In step S6, irreducible water pores, movable fluid mud pores and movable fluid sand pores are added to the rock skeleton to obtain a multi-porosity rock skeleton, wherein the irreducible water pores, the movable fluid mud pores and the movable fluid sand pores are set with initial different pore aspect ratios to obtain the bulk modulus K of the multi-porosity rock skeleton. sat-XW and shear modulus μ sat-XW .

8. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 7, characterized in that: In step S7, the bulk modulus K of irreducible water is added to the multi-porous rock skeleton using the patch saturation model. 束缚水 , shear modulus μ 束缚水 , sphere radius b, frequency f, permeability p and saturation s, calculate the bulk modulus K of the multi-porous rock skeleton after adding bound water sat-p and shear modulus μ sat-p .

9. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 8, characterized in that: In step S8, the bulk modulus K of the movable fluid added to the multi-porous rock skeleton after adding bound water is calculated using the Gassmann model. 可动流体 , shear modulus μ 可动流体 , calculate the bulk modulus K of the multi-porous rock skeleton after adding movable fluid sat and shear modulus μ sat .

10. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 9, characterized in that: In step S9, the initial kerogen aspect ratio ɑ is fixed k 、Bound water pore aspect ratioɑ 束缚水 、Aspect ratio of movable fluid mud poresɑ 泥 and movable fluid sand pore aspect ratio ɑ 砂 , constantly adjusting the bulk modulus K of different minerals i , shear modulus μ i , the relationship between rock modulus and velocity is used to calculate the P-wave velocity and S-wave velocity, so that the error between the P-wave velocity and S-wave velocity is less than the initial prediction error threshold T v1 , determine the appropriate bulk modulus and shear modulus of different minerals in the study area.

11. The method for physical modeling of organic-rich rock considering multi-porosity media according to claim 10, characterized in that: In step S10, the prediction effects of longitudinal wave velocity and shear wave velocity are optimized by using an intelligent optimization algorithm.

12. A physical modeling system for organic-rich rock considering multi-porosity media, characterized in that: include: Memory and processor; The memory is used to store computer programs; The processor is used to implement the organic-rich rock physical modeling system considering multi-porosity media as described in any one of claims 1 to 11 when executing one or more programs stored in the memory.

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