Rock reservoir brittleness prediction method, device, equipment and medium

Abstract

The embodiment of the application relates to the technical field of seismic exploration, and discloses a brittle prediction method, device, equipment and medium for a rock reservoir, the prediction method comprising the following steps: based on well logging data of a target rock reservoir, a target layer section of the target rock reservoir is constructed according to rock components and rock physical characteristic parameters, so as to construct a rock physical model for representing the relationship between the volume modulus, shear modulus, rock density and the content of main brittle minerals of the rock; a model of a brittle indicator factor is constructed according to the rock physical model and the change of the content of the main brittle minerals, the brittle indicator factor being used for representing the relative brittle degree of the rock mass; and the brittle indicator factor is converted into actual brittleness for representing the actual brittle degree of the rock mass. The brittle prediction method disclosed by the application fully utilizes the rock physical characteristic analysis of the target rock reservoir to analyze the influence of the brittle minerals on the brittle prediction, and effectively improves the rationality and accuracy of the rock brittle prediction.

Description

Technical Field

[0001] This application relates to the field of seismic exploration technology, and in particular to a method, apparatus, equipment and medium for predicting the brittleness of rock reservoirs. Background Technology

[0002] With the deepening of unconventional oil and gas exploration and development, China's unconventional oil and gas industry has shifted from being dominated by marine shale gas to focusing on multiple types of unconventional oil and gas, including marine shale gas, continental shale oil and gas, transitional marine-continental shale oil and gas, and deep coalbed methane. Since fracturing is the primary method for unconventional oil and gas development, the more brittle the unconventional oil and gas, the easier it is to fracture and the more likely it is to form complex fracture networks.

[0003] In terms of brittleness prediction, generally speaking, rocks with higher brittleness are more prone to fracture. Therefore, seismic characterization of natural fractures and rock brittleness is crucial for the exploration and development of shale gas reservoirs. Numerous brittleness indicator factors have been proposed for unconventional hydrocarbon brittleness characterization in specific strata of different basins. However, current unconventional hydrocarbon brittleness characterization methods often fail to fully utilize the physical characteristics of reservoir rocks. Consequently, it is difficult to systematically analyze the influence of brittle minerals on brittleness prediction, leading to a strong subjectivity in the construction of brittleness indicator factors, which in turn affects the rationality and accuracy of three-dimensional brittleness prediction. Summary of the Invention

[0004] The purpose of this application is to provide at least one method, apparatus, equipment and medium for predicting the brittleness of rock reservoirs, which can fully utilize the rock physical characteristics of the target rock reservoir to analyze the influence of brittle minerals on brittleness prediction, and effectively improve the rationality and accuracy of rock brittleness prediction.

[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a method for predicting the brittleness of rock reservoirs, comprising:

[0006] Based on well logging data of the target rock reservoir, a rock physical model is constructed for the target section of the target rock reservoir according to rock composition and rock physical characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least major brittle minerals. The rock physical model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the major brittle minerals.

[0007] A model of brittleness indicator factors is constructed based on the rock physics model and the changes in the content of the main brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass.

[0008] A mapping relationship between the brittleness indicator factor and the actual brittleness index is constructed to convert the brittleness indicator factor into the actual brittleness index, which is used to characterize the actual brittleness degree of the rock mass.

[0009] In some embodiments, based on well logging data of the target rock reservoir, a rock physical model is constructed for the target interval of the target rock reservoir according to rock composition and rock physical characteristic parameters, including:

[0010] The logging data of the target rock reservoir is divided into upper target layer data and lower target layer data;

[0011] Based on the rock composition and rock physical characteristics, the rock physical model is constructed for the upper segment of the target layer and the lower segment of the target layer, respectively. The target layer includes the upper segment and the lower segment of the target layer.

[0012] In some embodiments, constructing a rock physical model for a target segment of a target rock reservoir based on rock composition and rock physical characteristic parameters includes:

[0013] The rock physical characteristic parameters are calculated based on the rock composition of the target layer, wherein the rock composition also includes porosity and saturation; the rock physical characteristic parameters include at least: the equivalent elastic modulus of the mixed minerals, the equivalent elastic modulus of the fluid portion in the rock pores, and the elastic modulus of the dry rock skeleton.

[0014] The bulk modulus and the shear modulus are calculated based on the rock physical characteristic parameters.

[0015] The rock density was calculated based on multiple minerals in the target stratigraphic section.

[0016] In some embodiments, a model for brittleness indicator factors is constructed based on the rock physics model and the changes in the content of the main brittle minerals, including:

[0017] By changing the content of the main brittle minerals in the rock physical model, elastic parameters corresponding to different contents of the main brittle minerals are obtained; the elastic parameters are determined based on the bulk modulus, shear modulus and density of the rock.

[0018] The brittleness characterization parameters are determined based on the changes in elastic parameters corresponding to different contents of the main brittle minerals. The brittleness characterization parameters are elastic parameters that are sensitive to changes in the content of the main brittle minerals.

[0019] A model of the brittleness indicator factor is constructed based on the brittleness characterization parameters.

[0020] In some embodiments, the content of the main brittle minerals in the rock physics model is changed to obtain elastic parameters corresponding to different contents of the main brittle minerals; including:

[0021] By substituting the different contents of the main brittle minerals into the rock physics model, the corresponding bulk modulus, shear modulus and density are obtained;

[0022] Based on the bulk modulus, the shear modulus, and the density, the elastic parameters corresponding to different contents of the main brittle minerals are calculated.

[0023] In some embodiments, brittleness characterization parameters are determined based on the changes in elastic parameters corresponding to different contents of the main brittle minerals, including:

[0024] The brittleness sensitivity of each elastic parameter is obtained by analyzing the variation law of each elastic parameter; the brittleness sensitivity is the amount of change of the elastic parameter obtained by the change of the content of the main brittle mineral;

[0025] The brittle sensitivity of each elastic parameter is arranged in descending order, and the first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities at the top of the list are determined as brittle characterization parameters.

[0026] In some embodiments, constructing a model for the brittleness indicator factor based on the brittleness characterization parameters includes:

[0027] The main brittle minerals corresponding to the brittleness characterization parameters are classified according to their different contents;

[0028] Linear fitting is performed on the data points of the brittleness characterization parameters corresponding to different contents of the main brittle minerals to obtain coordinate rotation parameters, which include coordinate rotation axes and rotation parameters;

[0029] The model of the brittleness indicator factor is obtained based on the coordinate rotation parameters.

[0030] In some embodiments, the rotation parameters include rotation angle and translation parameters, and the coordinate rotation axis is calculated using the following formula:

[0031] Y = tan(θ_BI)*X + b_BI);

[0032] In the formula, θ_BI is the rotation angle, b_BI is the translation parameter, and X is the X-axis;

[0033] The model for obtaining the brittleness indicator factor based on the coordinate rotation parameters includes:

[0034] The brittleness indicator factor is constructed using the following formula:

[0035] Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI);

[0036] Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI;

[0037] In the formula, Rotate_X_BI is the X-coordinate parameter after coordinate rotation, Rotate_Y_BI is the Y-coordinate parameter after coordinate rotation, and represents the brittleness indicator factor BI_RPM; E and Poisson are both the brittleness characterization parameters.

[0038] In some embodiments, constructing the mapping relationship between the brittleness indicator factor and the actual brittleness index includes:

[0039] The mapping relationship between the brittleness indicator factor and the actual brittleness index is established using the following formula:

[0040] BI_map = k * BI_RPM + b;

[0041] In the formula, BI_map is the actual fragility index after mapping, BI_RPM is the fragility indicator factor, and k and b are mapping parameters.

[0042] In some embodiments, it also includes:

[0043] The original seismic data of the rock reservoir to be tested are acquired and seismic inversion is performed to obtain the inversion data volume of the rock reservoir to be tested. The inversion data volume includes the P-wave velocity volume, the S-wave velocity volume and the density volume. The rock reservoir to be tested and the target rock reservoir belong to the same study area.

[0044] The brittleness indicator factor of the rock reservoir to be tested is obtained by calculating based on the model of the inversion data volume and the brittleness indicator factor.

[0045] Based on the mapping relationship, the brittleness indicator factor of the rock reservoir to be tested is transformed to obtain the actual brittleness index of the rock reservoir to be tested.

[0046] At least one embodiment of this application also provides a device for predicting the brittleness of rock reservoirs, comprising:

[0047] The first construction module is used to construct a rock physics model based on well logging data of the target rock reservoir, according to rock composition and rock physics characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least major brittle minerals. The rock physics model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the major brittle minerals.

[0048] The second construction module is used to construct a model of brittleness indicator factors based on the rock physics model and according to the changes in the content of the main brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass.

[0049] The third construction module is used to construct the mapping relationship between the brittleness indicator factor and the actual brittleness index, so as to convert the brittleness indicator factor into the actual brittleness index, which is used to characterize the actual brittleness degree of the rock mass.

[0050] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for predicting the brittleness of rock reservoirs.

[0051] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting the brittleness of rock reservoirs.

[0052] The rock reservoir brittleness prediction method, apparatus, equipment, and medium provided in the embodiments of this application fully utilize the rock physical characteristics of the target rock reservoir to construct a rock physical model suitable for the characteristics of the target rock reservoir in the study area for the target layer of the target rock reservoir, and determine the brittleness indicator factor by the change of the content of the main brittle minerals to analyze the influence of brittle minerals on brittleness prediction. Furthermore, the brittleness indicator factor is mapped to the actual brittleness index of the target rock reservoir through a mapping relationship, thereby realizing the quantitative prediction of the brittleness of the target rock reservoir and effectively improving the rationality and accuracy of rock brittleness prediction. Attached Figure Description

[0053] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0054] Figure 1 This is a flowchart of a method for predicting the brittleness of rock reservoirs provided in one embodiment of this application. Figure 1 ;

[0055] Figure 2 This is a schematic diagram of the logging curves of key wells in the study area provided in one embodiment of this application;

[0056] Figure 3 This is a flowchart of a method for predicting the brittleness of rock reservoirs provided in one embodiment of this application. Figure 2 ;

[0057] Figure 4 This is a schematic diagram of the modeling results of the overall rock physics model of the target rock reservoir provided in one embodiment of this application;

[0058] Figure 5 This is a flowchart of a method for predicting the brittleness of rock reservoirs provided in one embodiment of this application. Figure 3 ;

[0059] Figure 6 This is a cross-plot of AC-porosity in the upper segment of the target layer of a logging curve provided in one embodiment of this application;

[0060] Figure 7 This is a cross-plot of AC-porosity in the lower segment of the target layer of a logging curve provided in one embodiment of this application;

[0061] Figure 8 This is a schematic diagram of the rock physics modeling result of the target layer upper segment data provided in one embodiment of this application;

[0062] Figure 9 This is a schematic diagram of the response of elastic parameters to changes in calcite content in the upper segment of the target layer, provided in one embodiment of this application.

[0063] Figure 10 This is a petrographic cross-section of E-Poisson's calcite at different contents in the upper part of the target layer, provided in one embodiment of this application;

[0064] Figure 11 This is a petrographic cross-section of E-Poisson's calcite at different contents in the lower segment of the target layer, provided in one embodiment of this application;

[0065] Figure 12 This is a petrophysical cross-plot of Poisson's ratio and brittleness indicator for the upper segment of the target layer, provided in one embodiment of this application;

[0066] Figure 13 This is a petrophysical cross-plot of Poisson's ratio and brittleness indicator for the lower segment of the target layer provided in one embodiment of this application;

[0067] Figure 14 This is a comparison diagram of the brittleness prediction results of the upper segment of the target layer provided in one embodiment of this application;

[0068] Figure 15 This is a schematic diagram of a rock reservoir brittleness prediction device provided in one embodiment of this application;

[0069] Figure 16 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0071] This application proposes a method for predicting the brittleness of rock reservoirs. The implementation details of the brittleness prediction method for rock reservoirs in this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution.

[0072] Example 1:

[0073] The brittleness prediction method for rock reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 1 As shown, it includes:

[0074] Step S110: Based on the logging data of the target rock reservoir, construct a rock physical model for the target section of the target rock reservoir according to the rock composition and rock physical characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least the main brittle minerals. The rock physical model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the main brittle minerals.

[0075] Well logging is the measurement of physical parameters of formation rocks. In oil drilling, well logging is mandatory after reaching the designed well depth to obtain various petroleum geological and engineering data, serving as the primary data for well completion and oilfield development. Well logging utilizes the electrochemical, electrical, acoustic, and radioactive properties of rock formations to measure geophysical parameters, falling under the category of applied geophysics. Well logging data is an indispensable and crucial resource in geological data analysis, accurately describing the rock physical characteristics at the well location. Based on well logging data, rock physical analysis can be performed on the target rock reservoir characteristics of the study area, analyzing the relationship between elastic parameters and lithology, porosity, etc. Based on this rock physical analysis, a rock physical model suitable for the target rock reservoir characteristics of the study area can be constructed. Well logging data includes well logging curves, such as... Figure 2 As shown, the logging curves of a key well in a certain study area are illustrated as an example.

[0076] Rock components include various minerals, porosity, and saturation. Minerals are the basic building blocks of rocks, and their chemical composition determines the properties of the rock. Common minerals include quartz, clay, feldspar, calcite, and dolomite. Different combinations and contents of minerals can give rocks different structures and characteristics. Quartz, calcite, and dolomite can also be brittle minerals, and the brittle minerals that have the greatest impact on the brittleness of rocks can be considered the main brittle minerals.

[0077] In this embodiment, the entire target rock reservoir can be used as the target stratigraphic segment for rock physics modeling. In other embodiments, to address situations where the rock physics relationships of the target stratigraphic system are complex and a single rock physics model is insufficient to accurately characterize the rock physics features of the target rock reservoir, the target rock reservoir can be segmented into smaller layers, and then a separate rock physics model can be constructed for each segment. This involves segmenting the target rock reservoir and establishing the relationship between the bulk modulus, shear modulus, rock density, and the content of major brittle minerals for each segment.

[0078] The rock physical characteristics parameters vary with the content of mineral components. These parameters include at least: the equivalent elastic modulus of the mixed minerals, the equivalent elastic modulus of the fluid portion in the rock pores, and the elastic modulus of the dry rock skeleton.

[0079] Step S120: Construct a model of brittleness indicator factors based on the rock physics model and the changes in the content of major brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass.

[0080] Specifically, rock physics models can characterize the relationship between the bulk modulus, shear modulus, rock density and the content of major brittle minerals. Therefore, a brittleness indicator factor related to the change of the content of major brittle minerals can be constructed by changing the content of major brittle minerals. This brittleness indicator factor is used to characterize the relative brittleness of the rock mass. The brittleness indicator factor can be constructed by comprehensively evaluating the change characteristics of elastic parameters when brittle minerals change. The brittleness indicator factor can characterize the relative strength of brittleness with high precision, but it differs from the actual brittleness of the target rock reservoir.

[0081] Step S130: Construct a mapping relationship between brittleness indicator factors and actual brittleness index to convert brittleness indicator factors into actual brittleness index, which is used to characterize the actual brittleness of rock mass.

[0082] Specifically, since the brittleness indicator factor can accurately characterize the relative strength of brittleness, but differs from the actual brittleness of the target rock reservoir, the brittleness indicator factor is converted into the actual brittleness index. This mapping relationship transforms the brittleness indicator factor into a value that can characterize the actual brittleness of the rock mass. The mapped brittleness result can effectively characterize the true brittleness of the target rock reservoir.

[0083] The actual brittleness index is an important parameter for evaluating shale and tight sandstone reservoirs. Accurate calculation of the actual brittleness index provides a basis for optimizing reservoir stimulation parameters and selecting the "sweet spot." A higher brittleness index indicates better brittleness, stronger fracture-forming ability, and a greater ease of forming complex fracture networks, resulting in more ideal stimulation effects. Conversely, a lower brittleness index indicates poorer brittleness, more pronounced plasticity, and a greater ease of forming simpler fracture morphologies, which reduces the effectiveness of fracturing stimulation.

[0084] In one embodiment, the actual brittleness index of the target rock reservoir can be characterized by a mineralogical method, specifically the proportion of the main brittle mineral content to the total mineral content. For example, under laboratory conditions, the mineral composition of the target rock reservoir can be obtained through X-ray diffraction data, and the rock brittleness index can be further quantitatively calculated. In addition, the elemental content measured by X-ray fluorescence logging can also be used to calculate the brittleness index.

[0085] The brittleness prediction method for rock reservoirs provided in this embodiment fully utilizes the petrophysical characteristics of the target rock reservoir to construct a petrophysical model suitable for the characteristics of the target rock reservoir in the study area for the target stratigraphic segment. It also determines the brittleness indicator factor by analyzing the changes in the content of major brittle minerals to analyze the influence of brittle minerals on brittleness prediction. Furthermore, it maps the brittleness indicator factor to the actual brittleness index of the target rock reservoir through a mapping relationship, thereby realizing the quantitative prediction of the brittleness of the target rock reservoir and effectively improving the rationality and accuracy of rock brittleness prediction.

[0086] Example 2:

[0087] The brittleness prediction method for rock reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 3 As shown, it includes:

[0088] Step S210: Based on the logging data of the target rock reservoir, construct a rock physical model for the target section of the target rock reservoir according to the rock composition and rock physical characteristic parameters.

[0089] like Figure 4As shown, an exemplary schematic diagram illustrates the modeling results of the overall rock physics model of the target rock reservoir. The rock physics model is constructed based on the logging data of the entire target rock reservoir. The modeled P-wave and S-wave velocities are in high agreement with the measured values, indicating that the constructed rock physics model and model parameters are reasonable and can accurately characterize the rock physics features of the target rock reservoir.

[0090] In one embodiment, step 210 may specifically include:

[0091] Rock physical characteristic parameters are calculated based on the rock composition of the target layer. The rock composition also includes porosity and saturation.

[0092] The bulk modulus and shear modulus were calculated based on rock physical characteristic parameters;

[0093] The rock density was calculated based on multiple minerals in the target stratigraphic section.

[0094] Specifically, taking the calculation of rock physical characteristic parameters for rock components in target layer data as an example, the specific process is as follows:

[0095] (1) The equivalent elastic modulus of the mixed minerals was calculated using the Voigt-Reuss-Hil average:

[0096]

[0097] In the formula, K0 and μ0 are the equivalent bulk modulus and equivalent shear modulus of the mixed mineral, respectively. Exemplarily, the mixed mineral is composed of multiple minerals, including quartz, argillaceous material, calcite, carbonate rocks, clay, etc. For example, the mixed mineral is shown to include quartz and argillaceous material.

[0098] K V =V qu *K qu +V sh *K sh ;

[0099]

[0100] μ V =V qu *μ qu +V sh *μ sh ;

[0101]

[0102] In the formula, K qu and μ qu These are the bulk modulus and shear modulus of quartz, K. sh and μ shThese are the bulk modulus and shear modulus of clay, V, respectively. qu and V sh These represent the normalized percentages of quartz and clay content, respectively, based on matrix volume percentage.

[0103] (2) Calculate the elastic modulus of the dry rock skeleton. Using a differential equivalent medium model (DEM), all pores are included to calculate the bulk modulus and shear modulus of the dry rock skeleton:

[0104]

[0105] The initial conditions are and Where K1 and μ1 are the bulk modulus and shear modulus of the initial main phase material, typically the equivalent bulk modulus K0 and equivalent shear modulus μ0 of the mixed minerals, and K2 and μ2 are the bulk modulus and shear modulus of the gradually added pores (phase 2), where y is the content of phase 2, and for fluid inclusions and empty inclusions, y is equal to the porosity φ. eff2 and Q eff2 The shape factor is obtained by the final calculation using the above formula. and The bulk modulus K, respectively, serves as the framework of dry rock. dry With shear modulus μ dry This is the elastic modulus of the dry rock skeleton.

[0106] (3) Calculate the equivalent elastic modulus of the fluid component in the rock pores. The pore fluids are mixed using the Wood equation, and the bulk modulus K of the mixed fluid is calculated. f :

[0107]

[0108] In the formula, K o K g and K w These are the bulk moduli of oil, gas, and water, respectively, S o S g and S w These are the saturation levels of oil, gas, and water, respectively, and S... o +S g +S w =1.

[0109] The bulk density ρ of the mixed fluid f The expression is as follows:

[0110] ρ f =S w ρ w +S o ρ o +S g ρ g ;

[0111] In the formula, ρ o ρ g and ρ w These are the volume densities of oil, gas, and water, respectively.

[0112] Furthermore, when the rock porosity is small, such as around 10% or less, and the fluid is not uniform in pore size, a gas-liquid mixing method can be established to calculate the bulk modulus of the two-phase mixed fluid, with the following formula:

[0113] K f =S w K w +(1-S w )K g ;

[0114] Subsequently, the above formula was modified to establish a new formula:

[0115]

[0116] In the formula, e is an empirical coefficient, which is generally 2-5.

[0117] (4) Based on the calculated elastic modulus of the dry rock skeleton, the Biot-Gassmann equation is used to add a fluid mixture into the pore space to calculate the elastic modulus of the saturated rock, i.e., the bulk modulus and shear modulus of the rock. Under the low-frequency assumption, the Biot-Gassmann equation is expressed as:

[0118]

[0119] μ sat =μ dry ;

[0120] In the formula, K sat μ sat These are the bulk modulus and shear modulus of saturated rock, K. dry μ dry K0 is the bulk modulus and shear modulus of the dry rock framework, K0 is the equivalent bulk modulus of the mixed minerals, and K0 is the equivalent bulk modulus of the mixed minerals. f It is the effective bulk modulus of the porous fluid, and φ is the porosity.

[0121] (5) The rock density is calculated based on multiple minerals. The rock density is obtained by weighted averaging of the minerals, and the expression is as follows:

[0122]

[0123] Among them, f i ρ i These represent the volume fraction and density of each mineral.

[0124] Step S220: Change the content of the main brittle minerals in the rock physical model to obtain the elastic parameters corresponding to different contents of the main brittle minerals. The elastic parameters are determined based on the bulk modulus, shear modulus and density of the rock.

[0125] Specifically, based on rock physics model f RPM The different contents of the main brittle minerals are used as inputs and substituted into the rock physics model f. RPM This yields the corresponding bulk modulus, shear modulus, and density. Specifically, the content of the main brittle minerals, V, is changed. brittle_mineral Forward modeling yields elastic parameters corresponding to different contents of the main brittle minerals, expressed as follows:

[0126] {K,μ,ρ}=f RPM (V brittle_mineral );

[0127] Where K is the bulk modulus, μ is the shear modulus, and ρ is the density.

[0128] After obtaining the bulk modulus K, shear modulus μ, and density ρ, the elastic parameters corresponding to different contents of the main brittle minerals can be calculated based on the bulk modulus, shear modulus μ, and density. The elastic parameters include longitudinal wave velocity, transverse wave velocity, Young's modulus, Poisson's ratio, Lamé constant, etc.

[0129] Step S230: Determine the brittleness characterization parameters based on the changes in elastic parameters corresponding to different contents of the main brittle minerals. The brittleness characterization parameters are elastic parameters that are sensitive to changes in the content of the main brittle minerals.

[0130] Specifically, step S230 may include:

[0131] Step S231: Analyze the variation law of each elastic parameter to obtain the brittleness sensitivity of each elastic parameter; the brittleness sensitivity is the change of elastic parameter obtained with the change of the content of the main brittle minerals;

[0132] In this step, the sensitivity of different elastic parameters to changes in the content of major brittle minerals in the target layer can be qualitatively analyzed by examining the curve characteristics and cross-plot characteristics of the elastic parameters. Specifically, the sensitivity of the elastic parameters can be quantitatively analyzed by examining the changes in the content of major brittle minerals. The formula for calculating the sensitivity is as follows:

[0133]

[0134] In the formula, A brittle_mineral_diff The sensitivity of elastic parameters to changes in the content of the main brittle minerals. The average elastic parameters are the initial brittle mineral content of the target layer. The average value of elastic parameters after the change in the content of brittle minerals in the target layer.

[0135] Step S232: Arrange the brittle sensitivity of each elastic parameter in descending order, and determine the first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities at the top of the list as brittle characterization parameters.

[0136] According to the sensitivity calculation formula in step S231, the sensitivity of the corresponding elastic parameters for the target layer under the condition of the change of the content of the main brittle minerals can be obtained. The brittle sensitivity of each elastic parameter is arranged in descending order. The first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities that are ranked first are determined as brittle characterization parameters. In this embodiment, two more sensitive elastic parameters are preferred as brittle characterization parameters, which lays the foundation for the construction of high-precision brittleness indicator factors.

[0137] For example, Young's modulus E and Poisson's ratio are used as parameters characterizing brittleness. Young's modulus E is the first elastic parameter; Poisson's ratio is the second elastic parameter. A petrophysical cross-plot of Poisson's ratio-Young's modulus under the variation of the main brittle mineral components is constructed based on the petrophysical model corresponding to the target stratigraphic data.

[0138] Step S240: Construct a model of brittleness indicator factors based on brittleness characterization parameters.

[0139] In this step, a model of the brittleness indicator can be constructed by adaptive coordinate rotation based on the brittleness characterization parameters.

[0140] Specifically, in one embodiment, step S240 may include the following steps:

[0141] Step S241: Divide the main brittle minerals corresponding to the brittleness characterization parameters according to their different contents;

[0142] Step S242: Perform linear fitting on the data points of the brittleness characterization parameters corresponding to different contents of the main brittle minerals to obtain the coordinate rotation parameters, which include the coordinate rotation axis and rotation parameters.

[0143] Step S243: Obtain the model of the brittleness indicator factor based on the coordinate rotation parameters.

[0144] Specifically, the coordinate rotation parameters for constructing the brittleness indicator can be directly extracted based on this rock physics intersection map. There are two specific extraction methods:

[0145] Method 1) Based on the average actual brittle content of the target layer, select data points corresponding to the contents of the main brittle minerals in the petrophysical cross-section and perform E-Poisson linear fitting. The linear fitting formula is as follows:

[0146] Poisson=tan(θ_BI)*E+b_BI;

[0147] The coordinate rotation parameters are determined from the linear fitting line, namely the rotation angle θ_BI and the translation parameter b_BI.

[0148] Method 2) Based on the actual brittle content range of the target layer, the contents of the main brittle minerals are divided, and the data points of the brittle characterization parameters corresponding to each content of the main brittle minerals are linearly fitted to determine the rotation angle θ_BI and the translation parameter b_BI.

[0149] We can compare the Poisson's ratio range and Young's modulus range corresponding to different calcite contents to see if there is obvious overlap. For example, if the overlap of Young's modulus E is significantly smaller, it means that Young's modulus E varies more with calcite content and is more affected by porosity. Therefore, Young's modulus E can better characterize brittleness, and thus, a coordinate rotation axis can be constructed using Young's modulus E. The coordinate rotation axis is calculated using the following formula:

[0150] Y = tan(θ_BI)*X + b_BI);

[0151] In the formula, θ_BI is the rotation angle, b_BI is the translation parameter, and X is the X-axis.

[0152] Models for obtaining brittleness indicator factors based on coordinate rotation parameters include:

[0153] The brittleness indicator factor is constructed using the following formula:

[0154] Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI);

[0155] Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI;

[0156] In the formula, Rotate_X_BI is the X-coordinate parameter after coordinate rotation, Rotate_Y_BI is the Y-coordinate parameter after coordinate rotation, and represents the brittleness indicator factor BI_RPM; E represents the first elastic parameter; Poisson represents the second elastic parameter.

[0157] Step S250: Construct a mapping relationship between brittleness indicator factors and actual brittleness index to convert brittleness indicator factors into actual brittleness index, which is used to characterize the actual brittleness of rock mass.

[0158] In this step, since the brittleness indicator factor can accurately characterize the relative strength of brittleness, but differs from the actual brittleness of the target rock reservoir, the brittleness indicator factor is converted into an actual brittleness index. This mapping relationship transforms the brittleness indicator factor into a value that can characterize the actual brittleness of the rock mass. The mapped brittleness result can effectively characterize the true brittleness of the target rock reservoir. The actual brittleness index of the target rock reservoir can be characterized by mineralogy, specifically as the proportion of the main brittle mineral content to the total mineral content. For example, under laboratory conditions, the mineral composition of the target rock reservoir can be obtained through X-ray diffraction data, and the rock brittleness index can be further quantitatively calculated. Furthermore, elemental content measured by X-ray fluorescence logging can also be used to calculate the brittleness index.

[0159] Specifically, the following formula is used to establish the mapping relationship between the brittleness indicator factor and the actual brittleness index, mapping the brittleness indicator factor to the actual brittleness of the target layer;

[0160] BI_map = k * BI_RPM + b;

[0161] In the formula, BI_map is the actual brittleness index after mapping, BI_RPM is the brittleness indicator factor, and k and b are mapping parameters. The mapping parameters can be obtained through analysis of rock physics cross-reference diagrams.

[0162] Example 3:

[0163] The brittleness prediction method for rock reservoirs in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process can be as follows: Figure 5 As shown, it includes:

[0164] Step S310: Divide the logging data of the target rock reservoir into upper target layer data and lower target layer data.

[0165] Step S320: Based on the rock composition and rock physical characteristics, construct rock physical models for the upper segment of the target layer and the lower segment of the target layer, respectively. The target layer includes the upper segment and the lower segment of the target layer.

[0166] In this embodiment, since the rock physical relationships of the target rock reservoir are complex and a single rock physical model is difficult to accurately characterize the rock physical characteristics of the target rock reservoir, the target rock reservoir can be finely divided into sub-layers, and rock physical models can be constructed for each sub-layer.

[0167] Combination Figure 2 , Figures 6-7 As shown, logging data for the target rock reservoir can be divided into upper target layer data and lower target layer data; (Refer to...) Figure 6 and Figure 7 As shown, exemplary diagrams of the AC-porosity intersection of the upper and lower segments of the target layer are presented respectively. Figure 6 and Figure 7 It can be seen that there are significant differences in the rock physical characteristics between the upper and lower segments of the target layer. Therefore, it is necessary to construct rock physical models for the upper and lower segments of the target layer separately to achieve accurate characterization of the rock physical characteristics of the target rock reservoir.

[0168] AC refers to the sound wave transit time, measured in milliseconds per foot. A smaller sound wave transit time means that the sound wave takes less time to travel through the strata, indicating that the strata are relatively dense and hard. Conversely, a smaller transit time means that the strata are relatively loose.

[0169] like Figure 8 As shown, an exemplary schematic diagram illustrates the results of a rock physics modeling project based on data from the upper segment of the target layer. Figure 8 As can be seen, due to the presence of multiple pore types in the target rock reservoir, the rock physics model that does not consider pore types shows poor agreement in local sections, while the rock physics model that considers pore types performs significantly better than the model that does not. Combined with... Figure 2 and Figure 8 It can be seen that the rock physics model constructed from the upper segment data of the target layer can accurately characterize the rock physics features of the target rock reservoir. The P-wave velocity VP and S-wave velocity VS obtained by the model are in high agreement with the measured P-wave velocity VP and S-wave velocity VS. Therefore, it can be said that the rationality of the segmented rock physics model and model parameters is demonstrated, laying a solid foundation for the subsequent construction of brittleness indicator factors.

[0170] In one embodiment, step S320, constructing rock physical models for the upper segment data and the lower segment data of the target layer based on rock composition and rock physical characteristics, respectively, includes:

[0171] Rock physical characteristic parameters are calculated based on the rock composition of the target layer. The rock composition also includes porosity and saturation.

[0172] The bulk modulus and shear modulus were calculated based on rock physical characteristic parameters;

[0173] The rock density was calculated based on multiple minerals in the target stratigraphic section.

[0174] Specifically, taking the lower segment of the target layer as the target layer segment, and taking the calculation of rock physical characteristic parameters based on the rock components in the upper segment of the target layer as an example, the specific process is as follows:

[0175] (1) The equivalent elastic modulus of the mixed minerals was calculated using the Voigt-Reuss-Hil average:

[0176]

[0177] In the formula, K0 and μ0 are the equivalent bulk modulus and equivalent shear modulus of the mixed mineral, respectively. Exemplarily, the mixed mineral is composed of multiple minerals, including quartz, argillaceous material, calcite, carbonate rocks, clay, etc. For example, the mixed mineral is shown to include quartz and argillaceous material.

[0178] K V =V qu *K qu +V sh *K sh ;

[0179]

[0180] μ V =V qu *μ qu +V sh *μ sh ;

[0181]

[0182] In the formula, K qu and μ qu These are the bulk modulus and shear modulus of quartz, K. sh and μ sh These are the bulk modulus and shear modulus of clay, V, respectively. qu and V sh These represent the normalized percentages of quartz and clay content, respectively, based on matrix volume percentage.

[0183] (3) Calculate the elastic modulus of the dry rock skeleton. Using a differential equivalent medium model (DEM), all pores are included to calculate the bulk modulus and shear modulus of the dry rock skeleton:

[0184]

[0185] The initial conditions are and Where K1 and μ1 are the bulk modulus and shear modulus of the initial main phase material, typically the equivalent bulk modulus K0 and equivalent shear modulus μ0 of the mixed minerals, and K2 and μ2 are the bulk modulus and shear modulus of the gradually added pores (phase 2), where y is the content of phase 2, and for fluid inclusions and empty inclusions, y is equal to the porosity φ. eff2 and Q eff2The shape factor is obtained by the final calculation using the above formula. and The bulk modulus K, respectively, serves as the framework of dry rock. dry With shear modulus μ dry This is the elastic modulus of the dry rock skeleton.

[0186] (3) Calculate the equivalent elastic modulus of the fluid component in the rock pores. The pore fluids are mixed using the Wood equation, and the bulk modulus K of the mixed fluid is calculated. f :

[0187]

[0188] In the formula, K o K g and K w These are the bulk moduli of oil, gas, and water, respectively, S o S g and S w These are the saturation levels of oil, gas, and water, respectively, and S... o +S g +S w =1.

[0189] The bulk density ρ of the mixed fluid f The expression is as follows:

[0190] ρ f =S w ρ w +S o ρ o +S g ρ g ;

[0191] In the formula, ρ o ρ g and ρ w These are the volume densities of oil, gas, and water, respectively.

[0192] Furthermore, when the rock porosity is small, such as around 10% or less, and the fluid is not uniform in pore size, a gas-liquid mixing method can be established to calculate the bulk modulus of the two-phase mixed fluid, with the following formula:

[0193] K f =S w K w +(1-S w )K g ;

[0194] Subsequently, the above formula was modified to establish a new formula:

[0195]

[0196] In the formula, e is an empirical coefficient, which is generally 2-5.

[0197] (4) Based on the calculated elastic modulus of the dry rock skeleton, the Biot-Gassmann equation is used to add a fluid mixture into the pore space to calculate the elastic modulus of the saturated rock, i.e., the bulk modulus and shear modulus of the rock. Under the low-frequency assumption, the Biot-Gassmann equation is expressed as:

[0198]

[0199] μ sat =μ dry ;

[0200] In the formula, K sat μ sat These are the bulk modulus and shear modulus of saturated rock, K. dry μ dry K0 is the bulk modulus and shear modulus of the dry rock framework, K0 is the equivalent bulk modulus of the mixed minerals, and K0 is the equivalent bulk modulus of the mixed minerals. f It is the effective bulk modulus of the porous fluid, and φ is the porosity.

[0201] (5) The rock density is calculated based on multiple minerals. The rock density is obtained by weighted averaging of the minerals, and the expression is as follows:

[0202]

[0203] Among them, f i ρ i These represent the volume fraction and density of each mineral.

[0204] Similarly, taking the lower segment of the target layer as the target layer segment, a rock physical model is constructed for the lower segment of the target layer according to the above process.

[0205] For example, taking calcite as the main brittle mineral in the study area, the brittleness indicator factor is constructed based on the rock physical models of the upper and lower sections of the target layer, respectively, since the rock physical models of the upper and lower sections of the target layer are different.

[0206] Step S330: Construct a model of brittleness indicator factors based on the rock physics model and the changes in the content of major brittle minerals.

[0207] In one embodiment, step S330 may specifically include:

[0208] Step S331: Change the content of the main brittle minerals in the rock physical model to obtain the elastic parameters corresponding to different contents of the main brittle minerals. The elastic parameters are determined based on the bulk modulus, shear modulus and density of the rock.

[0209] Specifically, based on rock physics model f RPM The different contents of the main brittle minerals are used as inputs and substituted into the rock physics model f. RPM This yields the corresponding bulk modulus, shear modulus, and density. Specifically, the content of the main brittle minerals, V, is changed. brittle_mineral Forward modeling yields elastic parameters corresponding to different contents of the main brittle minerals, expressed as follows:

[0210] {K,μ,ρ}=f RPM (V brittle_mineral );

[0211] Where K is the bulk modulus, μ is the shear modulus, and ρ is the density.

[0212] After obtaining the bulk modulus K, shear modulus μ, and density ρ, the elastic parameters corresponding to different contents of the main brittle minerals can be calculated based on the bulk modulus, shear modulus μ, and density. The elastic parameters include longitudinal wave velocity, transverse wave velocity, Young's modulus, Poisson's ratio, Lamé constant, etc.

[0213] Step S332: Determine the brittleness characterization parameters based on the changes in elastic parameters corresponding to different contents of the main brittle minerals. The brittleness characterization parameters are elastic parameters that are sensitive to changes in the content of the main brittle minerals.

[0214] Specifically, step S332 may include:

[0215] Step S3321: Analyze the variation law of each elastic parameter to obtain the brittleness sensitivity of each elastic parameter; the brittleness sensitivity is the change of elastic parameter obtained with the change of the content of the main brittle minerals;

[0216] In this step, the sensitivity of different elastic parameters to changes in the content of major brittle minerals in the target layer can be qualitatively analyzed by examining the curve characteristics and cross-plot characteristics of the elastic parameters. Specifically, the sensitivity of the elastic parameters can be quantitatively analyzed by examining the changes in the content of major brittle minerals. The formula for calculating the sensitivity is as follows:

[0217]

[0218] In the formula, A brittle_mineral_diff The sensitivity of elastic parameters to changes in the content of the main brittle minerals. The average elastic parameters are the initial brittle mineral content of the target layer. This represents the average elastic parameters after changes in the brittle mineral content of the target layer. The target layer consists of the upper and lower sections of the target layer.

[0219] Step S3322: Arrange the brittle sensitivity of each elastic parameter in descending order, and determine the first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities at the top of the list as brittle characterization parameters.

[0220] According to the sensitivity calculation formula in step S3321, the sensitivity of the corresponding elastic parameters for the target layer under the condition of the change of the content of the main brittle minerals can be obtained. The brittle sensitivity of each elastic parameter is arranged in descending order. The first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities that are ranked first are determined as brittle characterization parameters. In this embodiment, two more sensitive elastic parameters are preferred as brittle characterization parameters, which lays the foundation for the construction of high-precision brittleness indicator factors.

[0221] like Figure 9 As shown, this example demonstrates the response of elastic parameters to variations in calcite content in the upper segment of the target layer. For example, Young's modulus E and Poisson's ratio are used as brittleness characterization parameters. Young's modulus E is the first elastic parameter; Poisson's ratio is the second elastic parameter. A petrophysical cross-plot of Poisson's ratio and Young's modulus under variations in the main brittle mineral components is constructed based on the petrophysical model corresponding to the data from the upper segment of the target layer, as shown below. Figure 10 As shown, an exemplary petrographic cross-plot of Young's modulus E-Poisson's ratio is presented for different calcite contents in the upper segment of the target layer. Figure 11 As shown, an exemplary petrophysical cross-plot of Young's modulus E-Poisson's ratio is presented for different calcite contents in the lower segment of the target layer.

[0222] Step S333: Construct a model of brittleness indicator factors based on brittleness characterization parameters.

[0223] In this step, a model of the brittleness indicator can be constructed by adaptive coordinate rotation based on the brittleness characterization parameters.

[0224] Specifically, in one embodiment, the specific process of step S333 may include:

[0225] Step S3331: Divide the main brittle minerals corresponding to the brittleness characterization parameters according to their different contents;

[0226] Step S3332: Perform linear fitting on the data points of the brittleness characterization parameters corresponding to different contents of the main brittle minerals to obtain the coordinate rotation parameters, which include the coordinate rotation axis and rotation parameters.

[0227] Step S3333: Obtain the model of the brittleness indicator factor based on the coordinate rotation parameters.

[0228] like Figure 10 As shown, using the E-Poisson rock physics intersection map as the basis for adaptive coordinate rotation, the coordinate rotation parameters for constructing the brittleness indicator can be directly extracted from this rock physics intersection map. There are two specific extraction methods:

[0229] Method 1) Based on the average actual brittleness content of the target layer, in such cases... Figure 10 In the rock physics cross-sectional diagram shown, data points corresponding to the contents of major brittle minerals were selected for E-Poisson linear fitting. The linear fitting formula is as follows:

[0230] Poisson=tan(θ_BI)*E+b_BI;

[0231] like Figure 10 The black line in the middle represents the linear fit line between Young's modulus and Poisson's ratio. The coordinate rotation parameters, namely the rotation angle θ_BI and the translation parameter b_BI, are determined from this linear fit line.

[0232] Method 2) Based on the actual brittle content range of the target layer, the contents of the main brittle minerals are divided, and the data points of the brittle characterization parameters corresponding to each content of the main brittle minerals are linearly fitted to determine the rotation angle θ_BI and the translation parameter b_BI.

[0233] As Figure 10 The rock physics cross-sectional diagram shows a clear overlap of Poisson's ratio ranges corresponding to different calcite contents, while the overlap of Young's modulus E is significantly smaller. Therefore, Young's modulus E varies more with calcite content and is more affected by porosity. Thus, Young's modulus E can better characterize brittleness, and a coordinate rotation axis can be constructed using Young's modulus E. The coordinate rotation axis is constructed based on the selected parameters and calculated using the following formula:

[0234] Y = tan(θ_BI)*X + b_BI);

[0235] In the formula, θ_BI is the rotation angle, b_BI is the translation parameter, and X is the X-axis.

[0236] Models for obtaining brittleness indicator factors based on coordinate rotation parameters include:

[0237] The brittleness indicator factor is constructed using the following formula:

[0238] Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI);

[0239] Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI;

[0240] In the formula, Rotate_X_BI is the X-coordinate parameter after coordinate rotation, Rotate_Y_BI is the Y-coordinate parameter after coordinate rotation, representing the brittleness indicator factor BI_RPM; E represents the first elastic parameter; Poisson represents the second elastic parameter. For example... Figure 12 As shown, an exemplary petrophysical cross-plot of Poisson's ratio and brittleness indicator factors for the upper segment of the target layer is illustrated. Figure 13 As shown, an exemplary petrophysical cross-plot of Poisson's ratio and brittleness indicator factors for the lower segment of the target layer is illustrated.

[0241] Step S340: Construct the mapping relationship between the brittleness indicator factor and the actual brittleness index.

[0242] Specifically, the following formula is used to establish the mapping relationship between the brittleness indicator factor and the actual brittleness index, mapping the brittleness indicator factor to the actual brittleness of the target layer;

[0243] BI_map = k * BI_RPM + b;

[0244] In the formula, BI_map is the mapped actual brittleness index, BI_RPM is the brittleness indicator factor, and k and b are mapping parameters. These mapping parameters can be obtained through analysis of rock physics cross-reference diagrams. For example... Figure 14 As shown in the figure, an exemplary comparison of the brittleness prediction results of the upper segment of the target layer is presented. It can be seen that the result obtained after mapping the brittleness indicator factor (BI_RPM) (BI_RPM mapping) is highly correlated with the actual brittleness index (BI_mineral method) calculated by the mineral method, and the magnitudes are consistent. This indicates that the brittleness result after mapping (BI_RPM mapping) can effectively characterize the true brittleness of the target rock reservoir.

[0245] Step S350: Obtain the original seismic data of the rock reservoir to be tested and perform seismic inversion to obtain the inversion data volume of the rock reservoir to be tested. The inversion data volume includes the P-wave velocity volume, the S-wave velocity volume and the density volume; the rock reservoir to be tested and the target rock reservoir belong to the same study area.

[0246] The raw seismic data includes raw pre-stack gathers and post-stack seismic data. The target rock reservoirs are those within the same study area, and the target rock reservoirs are the wells to be developed in the study area. Seismic inversion is an important means of obtaining the physical parameters of the target rock reservoirs, and can effectively obtain elastic parameters such as P-wave and S-wave velocities and densities. Post-stack seismic inversion obtains high-fidelity post-stack seismic data based on amplitude-preserving seismic processing. Seismic records are synthesized from well logging data to achieve reasonable well-seismic calibration, estimate wavelets suitable for the waveform characteristics of the study area, and establish a low-frequency impedance model based on well logging data. Three-dimensional impedance inversion is achieved using methods such as sparse pulse inversion.

[0247] Pre-stack seismic inversion, based on amplitude-preserving processing of pre-stack migration gathers, first determines the angular stacking range through well logging AVO characteristic analysis, then performs angular volume stacking of the pre-stack gathers to obtain partial stacking data for near-, intermediate-, and far-migrated regions. Based on the seismic records synthesized from well logging data, the wavelets of each stacked data portion are estimated, and a low-frequency elastic impedance model is established based on well data to perform elastic impedance inversion of each stacked data portion.

[0248] Based on the elastic impedance inversion, the following relationship between elastic impedance and longitudinal wave velocity Vp, transverse wave velocity Vs, and density ρ is used for inversion:

[0249]

[0250] This allows us to obtain the inversion data volume of the rock reservoir to be tested, which includes the P-wave velocity volume, the S-wave velocity volume, and the density volume.

[0251] Step S360: Calculate the brittleness indicator factor of the rock reservoir to be tested based on the model of the inversion data volume and the brittleness indicator factor.

[0252] In one embodiment, the brittleness indicator factors of the rock reservoir under test are calculated based on the inversion data volume and the brittleness indicator factor model, including:

[0253] The brittleness characterization parameters are calculated based on the inversion data volume and the brittleness indicator factor model. The brittleness characterization parameters include the first elastic parameter and the second elastic parameter.

[0254] Construct brittleness indicator factors based on brittleness characterization parameters.

[0255] Taking Young's modulus as the first elastic parameter and Poisson's ratio as the second elastic parameter as an example, Young's modulus and Poisson's ratio can be calculated based on the longitudinal wave velocity volume, the transverse wave velocity volume, and the density volume.

[0256] Specifically, the Young's modulus, which serves as the first elastic parameter E, and the Poisson's ratio, which serves as the second elastic parameter, are substituted into the model of the brittleness indicator factor, i.e., substituted into the following formula, to obtain the brittleness indicator factor of the rock reservoir to be tested:

[0257] Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI);

[0258] Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI;

[0259] In the formula, Rotale_X_BI is the X-coordinate parameter after coordinate rotation, Rotale_Y_BI is the Y-coordinate parameter after coordinate rotation, and represents the brittleness indicator factor BI_RPM, which is the brittleness indicator factor of the rock reservoir to be tested; E represents the first elastic parameter as a brittleness characterization parameter; Poisson represents the second elastic parameter as a brittleness characterization parameter.

[0260] Step S370: Based on the mapping relationship, the brittleness indicator factor of the rock reservoir to be tested is converted to obtain the actual brittleness index of the rock reservoir to be tested.

[0261] Based on the mapping relationship between the brittleness indicator factor and the actual brittleness index, the brittleness indicator factor of the rock reservoir to be tested is mapped to the actual brittleness of the rock reservoir to be tested;

[0262] BI_map = k * BI_RPM + b;

[0263] In the formula, BI_map is the mapped actual brittleness index, BI_RPM is the brittleness indicator factor, and k and b are mapping parameters. This allows for the acquisition of high-precision three-dimensional quantitative brittleness prediction results, improving the rationality and accuracy of brittleness prediction. In this embodiment, the original seismic data of the target area is fully utilized, combined with seismic inversion to achieve high-precision three-dimensional quantitative brittleness prediction, thus realizing the goal of high-precision quantitative brittleness prediction.

[0264] Example 4:

[0265] Another embodiment of this application relates to a device for predicting the brittleness of rock reservoirs. The implementation details of this embodiment's brittleness prediction device are described below. The following details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of this embodiment's brittleness prediction device can be seen as follows: Figure 15 As shown, it includes a building module 100, a second building module 200, and a third building module 300.

[0266] The first construction module 100 is used to construct a rock physics model based on well logging data of the target rock reservoir, according to rock composition and rock physics characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least the main brittle minerals. The rock physics model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the main brittle minerals.

[0267] The second construction module 200 is used to construct a model of brittleness indicator factors based on the rock physics model and the changes in the content of major brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass.

[0268] The third construction module 300 is used to construct the mapping relationship between brittleness indicator factors and actual brittleness index, so as to convert the brittleness indicator factors into actual brittleness index, which is used to characterize the actual brittleness of rock mass.

[0269] In one embodiment, the first construction module 100 is further configured to divide the logging data of the target rock reservoir into upper target layer data and lower target layer data;

[0270] Based on rock composition and rock physical characteristics, rock physical models are constructed for the upper and lower segments of the target layer, respectively. The target layer includes the upper and lower segments of the target layer.

[0271] In one embodiment, the first building module 100 is further configured to calculate rock physical characteristic parameters based on the rock composition of the target layer, wherein the rock composition further includes porosity and saturation; the rock physical characteristic parameters include at least: the equivalent elastic modulus of the mixed minerals, the equivalent elastic modulus of the fluid portion in the rock pores, and the elastic modulus of the dry rock skeleton.

[0272] The bulk modulus and shear modulus were calculated based on rock physical characteristic parameters;

[0273] The rock density was calculated based on multiple minerals in the target stratigraphic section.

[0274] In one embodiment, the second building module 200 is further configured to change the content of the main brittle minerals in the rock physical model to obtain elastic parameters corresponding to different contents of the main brittle minerals; the elastic parameters are determined based on the bulk modulus, shear modulus and density of the rock.

[0275] The brittleness characterization parameters are determined based on the changes in elastic parameters corresponding to different contents of the main brittle minerals. The brittleness characterization parameters are elastic parameters that are sensitive to changes in the content of the main brittle minerals.

[0276] Construct brittleness indicator factors based on brittleness characterization parameters.

[0277] In one embodiment, the second building module 200 is also used to input the different contents of the main brittle minerals into the rock physics model to obtain the corresponding bulk modulus, shear modulus and density;

[0278] Based on the bulk modulus, shear modulus, and density, the elastic parameters corresponding to different contents of the main brittle minerals are calculated.

[0279] In one embodiment, the second building module 200 is further configured to analyze the variation law of each elastic parameter to obtain the brittleness sensitivity of each elastic parameter; the brittleness sensitivity is the amount of change of the elastic parameter obtained by the change of the content of the main brittle minerals.

[0280] The brittle sensitivity of each elastic parameter is arranged in descending order, and the first and second elastic parameters corresponding to the two brittle sensitivities at the top of the list are determined as brittle characterization parameters.

[0281] In one embodiment, the second building module 200 is further configured to classify the main brittle minerals corresponding to the brittleness characterization parameters according to different contents;

[0282] Linear fitting was performed on the data points of the brittleness characterization parameters corresponding to different contents of the main brittle minerals to obtain the coordinate rotation parameters, which include the coordinate rotation axis and the rotation parameters.

[0283] A model for the brittleness indicator factor is obtained based on the coordinate rotation parameters.

[0284] In one embodiment, the rotation parameters include rotation angle and translation parameters, and the coordinate rotation axis is calculated using the following formula:

[0285] Y = tan(θ_BI)*X + b_BI);

[0286] In the formula, θ_BI is the rotation angle, b_BI is the translation parameter, and X is the X-axis;

[0287] The second building module 200 is also used to construct the brittleness indicator factor using the following formula:

[0288] Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI);

[0289] Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI;

[0290] In the formula, Rotate_X_BI is the X-coordinate parameter after coordinate rotation, Rotate_Y_BI is the Y-coordinate parameter after coordinate rotation, representing the brittleness indicator factor BI_RPM; E is the first elastic parameter; Poisson is the second elastic parameter.

[0291] In one embodiment, the third building module 300 is further configured to establish a mapping relationship between the brittleness indicator factor and the actual brittleness index using the following formula:

[0292] BI_map = k * BI_RPM + b;

[0293] In the formula, BI_map is the actual fragility index after mapping, BI_RPM is the fragility indicator factor, and k and b are mapping parameters.

[0294] In one embodiment, the brittleness prediction device for the rock reservoir further includes:

[0295] The inversion module is used to acquire the original seismic data of the rock reservoir to be measured and perform seismic inversion to obtain the inversion data volume of the rock reservoir to be measured. The inversion data volume includes the P-wave velocity volume, the S-wave velocity volume, and the density volume. The rock reservoir to be measured and the target rock reservoir belong to the same study area.

[0296] The calculation module is also used to perform calculations based on the inversion data volume and the brittleness indicator factor model to obtain the brittleness indicator factor of the rock reservoir to be tested.

[0297] The third construction module 300 is also used to convert the brittleness indicator factor of the rock reservoir to be tested based on the mapping relationship, so as to obtain the actual brittleness index of the rock reservoir to be tested.

[0298] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0299] Example 5:

[0300] Another embodiment of this application relates to an electronic device, such as... Figure 16 As shown, it includes: at least one processor 901; and a memory 902 communicatively connected to at least one processor 901; wherein the memory 902 stores instructions executable by at least one processor 901, the instructions being executed by at least one processor 901 to enable at least one processor 901 to perform the brittleness prediction method for rock reservoirs in the above embodiments.

[0301] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0302] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0303] Example 6:

[0304] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements an embodiment of the above-described method for predicting the brittleness of rock reservoirs.

[0305] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0306] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for predicting the brittleness of rock reservoirs, characterized in that, include: Based on well logging data of the target rock reservoir, a rock physical model is constructed for the target section of the target rock reservoir according to rock composition and rock physical characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least major brittle minerals. The rock physical model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the major brittle minerals. A model of brittleness indicator factors is constructed based on the rock physics model and the changes in the content of the main brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass. A mapping relationship between the brittleness indicator factor and the actual brittleness index is constructed to convert the brittleness indicator factor into the actual brittleness index, which is used to characterize the actual brittleness degree of the rock mass.

2. The method for predicting the brittleness of rock reservoirs according to claim 1, characterized in that, Based on well logging data of the target rock reservoir, a rock physical model is constructed for the target interval of the target rock reservoir according to rock composition and rock physical characteristic parameters, including: The logging data of the target rock reservoir is divided into upper target layer data and lower target layer data; Based on the rock composition and rock physical characteristics, the rock physical model is constructed for the upper segment of the target layer and the lower segment of the target layer, respectively. The target layer includes the upper segment and the lower segment of the target layer.

3. The method for predicting the brittleness of rock reservoirs according to claim 1, characterized in that, Based on rock composition and rock physical characteristics, a rock physical model is constructed for the target interval of the target rock reservoir, including: The rock physical characteristic parameters are calculated based on the rock composition of the target layer, wherein the rock composition also includes porosity and saturation; the rock physical characteristic parameters include at least: the equivalent elastic modulus of the mixed minerals, the equivalent elastic modulus of the fluid portion in the rock pores, and the elastic modulus of the dry rock skeleton. The bulk modulus and the shear modulus are calculated based on the rock physical characteristic parameters. The rock density was calculated based on multiple minerals in the target stratigraphic section.

4. The method for predicting the brittleness of rock reservoirs according to claim 3, characterized in that, A model for brittleness indicator factors is constructed based on the aforementioned rock physics model and the changes in the content of the major brittle minerals, including: By changing the content of the main brittle minerals in the rock physical model, elastic parameters corresponding to different contents of the main brittle minerals are obtained; the elastic parameters are determined based on the bulk modulus, shear modulus and density of the rock. The brittleness characterization parameters are determined based on the changes in elastic parameters corresponding to different contents of the main brittle minerals. The brittleness characterization parameters are elastic parameters that are sensitive to changes in the content of the main brittle minerals. A model of the brittleness indicator factor is constructed based on the brittleness characterization parameters.

5. The method for predicting the brittleness of rock reservoirs according to claim 4, characterized in that, By changing the content of the main brittle minerals in the rock physics model, elastic parameters corresponding to different contents of the main brittle minerals are obtained; including: By substituting the different contents of the main brittle minerals into the rock physics model, the corresponding bulk modulus, shear modulus and density are obtained; Based on the bulk modulus, the shear modulus, and the density, the elastic parameters corresponding to different contents of the main brittle minerals are calculated.

6. The method for predicting the brittleness of rock reservoirs according to claim 5, characterized in that, The brittleness characterization parameters are determined based on the changes in elastic parameters corresponding to different contents of the main brittle minerals, including: The brittleness sensitivity of each elastic parameter is obtained by analyzing the variation law of each elastic parameter; the brittleness sensitivity is the amount of change of the elastic parameter obtained by the change of the content of the main brittle mineral; The brittle sensitivity of each elastic parameter is arranged in descending order, and the first elastic parameter and the second elastic parameter corresponding to the two brittle sensitivities at the top of the list are determined as brittle characterization parameters.

7. The method for predicting the brittleness of rock reservoirs according to claim 6, characterized in that, Constructing a model for the brittleness indicator factor based on the brittleness characterization parameters includes: The main brittle minerals corresponding to the brittleness characterization parameters are classified according to their different contents; Linear fitting is performed on the data points of the brittleness characterization parameters corresponding to different contents of the main brittle minerals to obtain coordinate rotation parameters, which include coordinate rotation axes and rotation parameters; The model of the brittleness indicator factor is obtained based on the coordinate rotation parameters.

8. The method for predicting the brittleness of rock reservoirs according to claim 7, characterized in that, The rotation parameters include rotation angle and translation parameters, and the coordinate rotation axis is calculated using the following formula: Y = tan(θ_BI)*X + b_BI); In the formula, θ_BI is the rotation angle, b_BI is the translation parameter, and X is the X-axis; The model for obtaining the brittleness indicator factor based on the coordinate rotation parameters includes: The brittleness indicator factor is constructed using the following formula: Rotate_X_BI=E*cos(θ_BI)+(Poisson-b_BI)*sin(θ_BI); Rotate_Y_BI=(Poisson-b_BI)*cos(θ_BI)-E*sin(θ_BI)+b_BI; In the formula, Rotate_X_BI is the X-coordinate parameter after coordinate rotation, Rotate_Y_BI is the Y-coordinate parameter after coordinate rotation, and represents the brittleness indicator factor BI_RPM; E and Poisson are both the brittleness characterization parameters.

9. The method for predicting the brittleness of rock reservoirs according to claim 8, characterized in that, Constructing the mapping relationship between the brittleness indicator factor and the actual brittleness index includes: The mapping relationship between the brittleness indicator factor and the actual brittleness index is established using the following formula: BI_map = k * BI_RPM + b; In the formula, BI_map is the actual fragility index after mapping, BI_RPM is the fragility indicator factor, and k and b are mapping parameters.

10. The method for predicting the brittleness of rock reservoirs according to claim 9, characterized in that, Also includes: The original seismic data of the rock reservoir to be tested are acquired and seismic inversion is performed to obtain the inversion data volume of the rock reservoir to be tested. The inversion data volume includes the P-wave velocity volume, the S-wave velocity volume and the density volume. The rock reservoir to be tested and the target rock reservoir belong to the same study area. The brittleness indicator factor of the rock reservoir to be tested is obtained by calculating based on the model of the inversion data volume and the brittleness indicator factor. Based on the mapping relationship, the brittleness indicator factor of the rock reservoir to be tested is transformed to obtain the actual brittleness index of the rock reservoir to be tested.

11. A device for predicting the brittleness of rock reservoirs, characterized in that, include: The module is used to construct a rock physics model based on well logging data of the target rock reservoir, according to rock composition and rock physics characteristic parameters. The rock composition includes at least a variety of minerals, and the minerals include at least a major brittle mineral. The rock physics model is used to characterize the relationship between the bulk modulus, shear modulus, rock density and the content of the major brittle mineral. The determination module is used to construct a model of brittleness indicator factors based on the rock physics model and the changes in the content of the main brittle minerals. The brittleness indicator factors are used to characterize the relative brittleness of the rock mass. The conversion module is used to construct a mapping relationship between the brittleness indicator factor and the actual brittleness index, so as to convert the brittleness indicator factor into the actual brittleness index, which is used to characterize the actual brittleness degree of the rock mass.

12. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the brittleness prediction method for rock reservoirs as described in any one of claims 1 to 10.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the brittleness prediction method for rock reservoirs as described in any one of claims 1 to 10.

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