Brittleness index logging calculation method and system based on shale macrostructure classification

Abstract

The application discloses a brittle index logging calculation method and system based on shale macrostructure classification, and the method comprises the following steps: performing triaxial compression experiments on rock samples of a target layer section in a formation, and constructing a brittle index calculation model of the macrostructure of the rock samples; and combining electrical imaging logging data and element logging data, and calculating and obtaining the brittle index of the formation by using the brittle index calculation model. The application can solve the problems that the classical mineral method has low brittle index precision, the elastic parameter method has unsensitive brittle index change, and the velocity radial profile method cannot be quantitatively compared.

Description

Technical Field

[0001] This invention relates to the field of engineering technology for geological exploration and development of petroleum and natural gas, and in particular to a method and system for calculating the brittleness index based on the macroscopic structure classification of shale. Background Technology

[0002] Brittleness is a crucial aspect of evaluating the seven properties of shale oil, and the accuracy of brittleness assessment directly impacts the precise location of sweet spots in shale oil. Currently, there are three main categories of methods for evaluating brittleness both domestically and internationally: ① Stress-strain method: This method extracts sensitive parameters from the stress-strain relationship in multiaxial compressive strength tests of rocks. These parameters (such as static Young's modulus, static Poisson's ratio, and static bulk modulus) are combined to form a static brittleness expression, which then quantitatively evaluates the brittleness of the core. While this method yields relatively intuitive and accurate results, core sampling is expensive, its application is limited, and it cannot continuously evaluate formation conditions at depths. ② Dynamic elastic parameter method: This method uses P-wave and S-wave transit time logging data to continuously calculate dynamic elastic parameters (such as dynamic Young's modulus and dynamic Poisson's ratio), and combines these parameters for brittleness evaluation. Although this method can continuously evaluate formations, when formation compaction is strong, the sonic transit time often changes little, making the brittleness variation in the elastic parameter method insensitive. ③ Mineral method: This method uses the ratio of brittle mineral content to total mineral content to evaluate the strength of brittleness. This type of method ignores the fact that even with similar lithology, different stress conditions can lead to differences in brittleness. Furthermore, it does not consider the influence of plastic minerals on the brittleness index. ④ Radial velocity profile: A radial variation profile of P-wave or S-wave velocity is obtained from array sonic logging data. Differences in the radial velocity variation characteristics are used to qualitatively determine the brittleness of adjacent reservoirs. The limitation of this method is that it is only limited to qualitative judgment of the brittleness of adjacent reservoirs within the same well; it cannot be used for quantitative comparison and evaluation over a large area at continuous depths, nor can it be used for quantitative comparison and evaluation of the brittleness strength of the same formation in multiple wells. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for calculating the brittleness index based on the macroscopic structure classification of shale, which can solve the problems of low accuracy of the brittleness index in the classical mineral method, insensitivity to changes in the brittleness index in the elastic parameter method, and inability to make quantitative comparisons in the velocity radial profile method.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A well logging method for calculating the brittleness index based on the classification of shale macrostructure, the method comprising: conducting triaxial compressive strength tests on rock samples from the target stratum in the formation, and constructing a brittleness index calculation model for the macrostructure of the rock samples;

[0006] The brittleness index calculation model combines electrical imaging logging data and elemental logging data to calculate the brittleness index of the formation.

[0007] Specifically, the brittleness index calculation model for constructing the macroscopic structure of the rock sample includes,

[0008] Obtain rock samples from the target stratum;

[0009] Triaxial compressive strength tests were conducted on rock samples from the target stratum to obtain the static elastic parameters of the core, the fracture morphology of the rock samples, and the macroscopic structure of the rock samples.

[0010] Based on the static elastic parameters of the core, a normalized static brittleness index is constructed.

[0011] Whole-rock mineral analysis was performed on the rock samples after triaxial compressive strength test to obtain the mineral types and contents of the rock samples;

[0012] By combining the correlation analysis of mineral type and content of rock samples with the static brittleness index, a calculation model for the brittleness index of macroscopic structures is constructed.

[0013] Specifically, the static elastic parameters include one or more of the static Young's modulus and the static Poisson's ratio.

[0014] Specifically, constructing the normalized static brittleness index based on the static elastic parameters of the rock core includes:

[0015] Based on the static elastic parameters of the core, a static brittleness index is constructed;

[0016] The static brittleness index is normalized to obtain a normalized static brittleness index.

[0017] Specifically, the normalized calculation of the static fragility index is expressed as follows:

[0018]

[0019] Among them, BI N BI is the normalized static fragility index. min BI represents the minimum static brittleness index obtained experimentally. max This represents the maximum static brittleness index obtained from the experiment.

[0020] Specifically, the fracture morphology of the rock sample includes one or more of the following: fracture along bedding planes and random fracture morphology.

[0021] The macroscopic structure of the rock sample includes one or more of the following: lamellar, thin-layered, and homogeneous.

[0022] Specifically, the correlation analysis between the mineral type and content of the rock sample and the static brittleness index is used to construct a macroscopic structural brittleness index calculation model, including:

[0023] Mineral-sensitive parameters are constructed based on the content of brittle and ductile minerals;

[0024] The mineral sensitivity parameters were fitted and analyzed with the static brittleness index to establish a brittleness index calculation model for macrostructure.

[0025] Specifically, the fragility index calculation model for macroscopic structures includes one or more fragility index calculation models for lamellar, thin-layer, and homogeneous structures.

[0026] Specifically, the mineral-sensitive parameters are constructed based on the content of brittle and ductile minerals, and the formula is expressed as follows:

[0027]

[0028] Where m1, m2, ..., m n Indicates the content of different brittle minerals; p1, p2, ..., p q Indicates the content of different plastic minerals; a1, a2, ... a n Weighting coefficients representing different brittle minerals; b1, b2, ..., b q The weighting coefficients represent the different plastic minerals, where,

[0029] The weighting coefficient of any brittle mineral satisfies the following relationship:

[0030]

[0031] in, The correlation coefficient between brittle minerals and the static brittleness index; the weighting coefficient of any ductile mineral satisfies the following relationship:

[0032]

[0033] in, This represents the correlation coefficient between ductile minerals and the static brittleness index.

[0034] Specifically, the brittleness index calculation model, combined with electrical imaging logging data, calculates the brittleness index of the formation, including:

[0035] Based on electrical imaging logging data, calculate the thickness H of any bedding in the formation. j ;

[0036] Based on the thickness H of any bedding in the strata j To determine the type of macroscopic structure of the strata;

[0037] Based on the macroscopic structural type of the strata, determine the corresponding brittleness index calculation model; based on elemental logging data, obtain the content of different types of minerals in the target layer;

[0038] The brittleness index of the stratum is obtained by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

[0039] Specifically, the calculated layer thickness H j Its expression is:

[0040] H j =h i+1 -h i

[0041] Among them, h i The depth of the i-th bedding plane or lithological abrupt change surface identified from electrical imaging data; h i+1 The depth of the (i+1)th bedding plane or lithological abrupt change surface identified from electrical imaging data; H j It refers to the thickness of a certain layer.

[0042] Specifically, the classification of the macroscopic structure of strata based on bedding thickness includes:

[0043] When 0 <H j When ≤x, H j The strata in question are lamellar.

[0044] When x <H j When ≤y, H j The strata in question are thin-layered.

[0045] When y <H j At that time, H j The strata in which it is located are homogeneous, among which,

[0046] x is a constant representing the maximum thickness of lamellar bedding in a certain stratum; y is a constant representing the maximum thickness of thin-layer bedding in a certain stratum.

[0047] Specifically, based on the results of the classification of the macroscopic structure of the strata, the content of different types of minerals in the target layer is obtained, including but not limited to quartz, potassium feldspar, sodium feldspar, pyrite, calcite, dolomite, and clay minerals.

[0048] A brittleness index logging calculation system based on shale macrostructure classification, the system comprising,

[0049] The module is used to conduct triaxial compressive strength tests on rock samples from the target layer and to construct a brittleness index calculation model for different macroscopic structures of the rock samples.

[0050] The calculation module is used to calculate the brittleness index of the formation by combining the brittleness index calculation model with electrical imaging logging data.

[0051] Specifically, the building modules include:

[0052] The first acquisition unit is used to conduct triaxial compressive strength tests on rock samples from the target layer to obtain the static elastic parameters, fracture morphology, and macroscopic structure of the rock samples.

[0053] The first building unit is used to build a normalized static brittleness index based on the static elastic parameters of the core.

[0054] The second acquisition unit is used to perform whole-rock mineral analysis on the rock sample after the triaxial compressive test to obtain the mineral type and content of the rock sample;

[0055] The second building unit is used to construct a macroscopic structural brittleness index calculation model by combining the correlation analysis of the mineral type and content of the rock sample with the static brittleness index.

[0056] Specifically, the computing module includes,

[0057] The first calculation unit is used to calculate the thickness H of any bedding in the formation based on electrical imaging logging data. j ;

[0058] Type determination unit, used to determine the thickness H of any bedding in the strata. j To determine the type of macroscopic structure of the strata;

[0059] The model determination unit is used to determine the corresponding brittleness index calculation model based on the macroscopic structure type of the stratum.

[0060] The acquisition unit obtains the content of different types of minerals in the target layer based on elemental logging data;

[0061] The second calculation unit is used to calculate the brittleness index of the stratum by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

[0062] The technical effects and advantages of this invention are as follows:

[0063] 1. Compared with the stress-strain method: While core fragmentation experiments can determine static elastic parameters, obtaining cores is costly and it is impossible to obtain a considerable number of cores from every well. This innovative method, after being calibrated with cores in a specific exploration area, can be widely applied within that area without the need for further substantial expenditures on core testing and measurement.

[0064] 2. Comparison with the dynamic elastic parameter method: The dynamic elastic parameter method not only needs to consider the conversion relationship with static elastic parameters, but also the anomalies in dynamic elastic parameters caused by changes in underground stress. In contrast, this innovative method is based on macroscopic structural classification and improves the mineral method for calculating the brittleness index, eliminating the need to consider stress changes and the conversion between dynamic and static parameters.

[0065] 3. Compared with the mineral method: Previous mineral methods have been effective in strata with a single type of brittle mineral. However, for mixed sedimentary strata, there are multiple types of brittle minerals, and the lithological indicator method does not consider the control of plastic minerals on the brittleness index. This innovative approach assigns weight coefficients to different brittle and plastic minerals according to the correlation between individual minerals and the static brittleness index, and establishes a conversion relationship between the ratio of the brittle mineral index to the plastic mineral index and the static brittleness index.

[0066] 4. Compared with the shear wave radial profiling method: The shortcoming of this method is that it is limited to qualitative judgment of the brittleness of adjacent reservoirs in the same well, and cannot be used for quantitative comparison and evaluation over a large area at continuous depths, nor can it be used for quantitative comparison and evaluation of the brittleness of the same layer in multiple wells. This innovative method does not use shear wave data, therefore it does not have the above problems.

[0067] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0068] Figure 1a These are CT scan images of layered rock samples after triaxial compressive strength tests according to embodiments of the present invention;

[0069] Figure 1b These are core photographs of layered rock samples from embodiments of the present invention after triaxial compressive strength tests;

[0070] Figure 2a These are CT scan images of thin-layered rock samples after triaxial compressive strength tests according to embodiments of the present invention;

[0071] Figure 2b These are core photographs of thin-layer rock samples after triaxial compressive strength tests, according to embodiments of the present invention.

[0072] Figure 3a These are CT scan images and core photographs of homogeneous rock samples after triaxial compressive strength tests, according to embodiments of the present invention.

[0073] Figure 3b These are core photographs of homogeneous rock samples after triaxial compressive strength tests, according to embodiments of the present invention.

[0074] Figure 4a This is a single correlation analysis diagram between quartz content and static brittleness index in an embodiment of the present invention;

[0075] Figure 4b This is a single correlation analysis diagram between potassium feldspar and the static brittleness index in an embodiment of the present invention;

[0076] Figure 4c This is a single correlation analysis diagram between albite and the static brittleness index in an embodiment of the present invention;

[0077] Figure 4d This is a single correlation analysis diagram between calcite and the static brittleness index in an embodiment of the present invention;

[0078] Figure 4e This is a single correlation analysis diagram between dolomite and the static brittleness index in an embodiment of the present invention;

[0079] Figure 4f This is a single correlation analysis diagram between pyrite and the static brittleness index in an embodiment of the present invention;

[0080] Figure 4g This is a single correlation analysis diagram between clay minerals and the static brittleness index in an embodiment of the present invention;

[0081] Figure 5 The fitting relationship between mineral sensitive parameters and static brittleness index in the embodiments of the present invention is divided into three types: lamellar type, thin-layer type, and homogeneous type.

[0082] Figure 6 This is a comparison chart of the calculation results of different brittleness index calculation methods for a single well. Detailed Implementation

[0083] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0084] To address the shortcomings of existing technologies, this invention discloses a brittleness index logging calculation method based on shale macrostructure classification. The method includes conducting triaxial compressive strength tests on rock samples from the target formation and constructing a brittleness index calculation model for the macrostructure of the rock samples. The brittleness index calculation model, combined with electrical imaging logging data and elemental logging data, calculates the brittleness index of the formation.

[0085] Furthermore, the calculation model for the brittleness index of the macroscopic structure of the rock sample includes obtaining the core of the target layer, conducting triaxial compressive strength tests, obtaining the static elastic parameters of the core (such as static Young's modulus, static Poisson's ratio, and static bulk modulus), and then constructing a normalized static brittleness index based on the static elastic parameters of the core.

[0086] Obtain CT scan images of rock samples after triaxial compressive testing to determine the fracture morphology of the rock samples (fracture along bedding planes, random fracture, etc.); obtain photographs of plunger rock samples after triaxial compressive testing to determine the macroscopic structure of the rock samples (laminated, thin-layered, homogeneous, etc.).

[0087] Whole-rock mineral analysis was performed on rock samples after triaxial compressive strength testing to obtain the mineral types and contents. Generally, a correlation analysis was conducted between the mineral types of the formation and the obtained static brittleness index; a positive correlation indicated brittle minerals, and a negative correlation indicated ductile minerals. Mineral sensitivity parameters were constructed using the contents of brittle and ductile minerals.

[0088]

[0089] Where m1, m2, ..., m n It represents the content of brittle minerals, in percent; p1, p2, ..., p q This refers to the content of plastic minerals, in percent. a1, a2, ... a n These are the weighting coefficients for brittle minerals; b1, b2, ..., b q It is the weighting coefficient for plastic minerals.

[0090] The weighting coefficient of any brittle mineral satisfies the following relationship:

[0091]

[0092] in, is the correlation coefficient between brittle minerals and the static brittleness index, dimensionless.

[0093] The weighting coefficient of any plastic mineral satisfies the following relationship:

[0094]

[0095] in, is the correlation coefficient between ductile minerals and the static brittleness index, dimensionless.

[0096] By fitting the mineral-sensitive parameter ms with the static brittleness index, a brittleness index calculation model BI = f(ms) for different macrostructures is established (the fitting relationship can be linear, exponential, or logarithmic). The brittleness index calculation model for the macrostructure includes one or more brittleness index calculation models for lamellar, thin-layer, and homogeneous types.

[0097] Furthermore, the brittleness index calculation model, combined with electrical imaging logging data, calculates the brittleness index of the formation, including the following steps: calculating the thickness H of any bedding in the formation based on the electrical imaging logging data. j ;

[0098] Based on the thickness H of any bedding in the strata j To determine the type of macroscopic structure of the strata;

[0099] Based on the macroscopic structural type of the strata, determine the corresponding brittleness index calculation model; based on elemental logging data, obtain the content of different types of minerals in the target layer;

[0100] The brittleness index of the stratum is obtained by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

[0101] Furthermore, the calculated layer thickness H j Its expression is:

[0102] H j =h i+1 -h i

[0103] Among them, h i The depth of the i-th bedding plane or lithological abrupt change surface identified from electrical imaging data; h i+1 The depth of the (i+1)th bedding plane or lithological abrupt change surface identified from electrical imaging data; H j It refers to the thickness of a certain layer.

[0104] Furthermore, the classification of the macroscopic structure of strata based on bedding thickness includes:

[0105] When 0 <H j When ≤x, H j The strata in question are lamellar.

[0106] When x <H j When ≤y, H j The strata in question are thin-layered.

[0107] When y <H j At that time, H jThe strata in question are homogeneous, where x is a constant representing the maximum thickness of lamellar bedding in a given stratum, and y is a constant representing the maximum thickness of thin-layer bedding in a given stratum.

[0108] Elemental logging data of the target layer was obtained, and the content of different mineral types in the formation was calculated based on the elemental logging data of the target layer. The mineral types and contents of the target layer are consistent with the mineral types and contents of the rock sample.

[0109] Furthermore, based on the identification of macroscopic structural classification using electrical imaging logging data, the mineral type and content of the target layer are incorporated into the brittleness index model to continuously calculate the brittleness index of the formation.

[0110] This invention also discloses a brittleness index logging calculation system based on shale macrostructure classification. The system includes a construction module for performing triaxial compressive strength tests on rock samples from the target formation to construct a brittleness index calculation model for the macrostructure of the rock samples; and a calculation module for calculating the brittleness index of the formation by combining the brittleness index calculation model with electrical imaging logging data.

[0111] The construction module includes: a first acquisition unit, used to conduct triaxial compressive strength tests on rock samples from the target layer to obtain the static elastic parameters of the core, the fracture morphology of the rock sample, and the macroscopic structure of the rock sample; a first construction unit, used to construct a normalized static brittleness index based on the static elastic parameters of the rock core; a second acquisition unit, used to perform whole-rock mineral analysis on the rock sample after the triaxial compressive strength test to obtain the mineral types and contents of the rock sample; and a second construction unit, used to combine the correlation analysis of the mineral types and contents of the rock sample with the static brittleness index to construct a brittleness index calculation model for the macroscopic structure.

[0112] The calculation module includes a first calculation unit, used to calculate the thickness H of any bedding in the formation based on electrical imaging logging data. j ; Type determination unit, used to determine the thickness H of any bedding in the strata. j The system comprises: a first unit for determining the type of macroscopic structure of the formation; a second unit for determining the model for calculating the brittleness index based on the type of macroscopic structure of the formation; an acquisition unit for acquiring the content of different types of minerals in the target layer based on the elemental logging data of the target layer; and a third unit for calculating the brittleness index of the formation by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

[0113] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0114] The technical solution of the present invention will be further described below with reference to specific embodiments.

[0115] In one embodiment of the present invention, triaxial compressive strength tests are first conducted on rock samples from the target layer in the formation to obtain the static elastic parameters (static Young's modulus, static Poisson's ratio) of the rock core, and a static brittleness index is constructed:

[0116]

[0117] Where BI is the static brittleness index, MPa; E s The static Young's modulus is expressed in MPa; u s It is Poisson's ratio, dimensionless.

[0118] For ease of comparison, the brittleness index is usually calculated in a normalized form, and its expression is:

[0119]

[0120] Among them, BI N BI is the normalized static fragility index, dimensionless. min BI represents the minimum static brittleness index obtained experimentally, in MPa. max The maximum static brittleness index obtained from the experiment is expressed in MPa.

[0121] Rocks will fracture and develop cracks after undergoing triaxial compressive strength testing. The morphology of the cracks will vary depending on the bedding development of the shale. In this embodiment, for example... Figures 1a-3b CT scans and core images show the fracture morphology of three types of shale: Figure 1a and 1b The fracture morphology of a typical lamellar rock sample is shown: such rocks have many thin bedding planes, and the fractures extend along the bedding planes or lithological abrupt changes. In this example, the thickness of a single lamellar ranges from 0.001m to 0.05m. Figure 2a and 2b The fracture morphology of a typical thin-layered rock sample is shown: the bedding thickness of this type of rock is significantly greater than that of laminated rocks, and at lithological abrupt changes, fractures tend to extend along these changes. However, within homogeneous bedding, fracture extension exhibits a random pattern. In this example, the thickness of a single thin layer in the sample ranges from 0.05 m to 0.15 m. Figure 3a and 3b The fracture morphology of a typical homogeneous rock sample is shown: this type of rock has the thickest bedding, and generally no bedding is developed within the visual range of the rock sample. In this example, the thickness of a single homogeneous layer is generally greater than 0.15m. The lithology within the bedding is homogeneous, and the fracture extension is random.

[0122] When calculating the brittleness index using mineral content, it is necessary to clarify the influence of different types of minerals on brittleness. Correlation analysis between individual minerals and the static brittleness index is performed; a positive correlation indicates brittle minerals, and a negative correlation indicates ductile minerals. Figures 4a-4g In this embodiment, the positively correlated minerals are quartz and dolomite; the negatively correlated minerals are clay minerals, calcite, potassium feldspar, sodium feldspar, and pyrite. It is also important to pay attention to the correlation coefficient in the correlation analysis; the higher the correlation coefficient for a particular mineral, the greater its influence on brittleness, and vice versa. In this embodiment, the correlation coefficients of the brittle minerals, from highest to lowest, are: R... 石英 =0.5, R 白云石 =0.29; The correlation coefficients of plastic minerals, from largest to smallest, are: R 方解石 =0.53, R 钠长石 =0.38, R 黄铁矿 =0.31, R 钾长石 =0.3, R 粘土矿物 =0.27.

[0123] The greater the brittleness of a rock, the higher its content of brittle minerals and the lower its content of ductile minerals. Simultaneously, the total mineral composition of the strata should be 100%, with brittle and ductile minerals exhibiting an inverse relationship. Therefore, mineral-sensitive parameters can be constructed:

[0124]

[0125] Among them, Dw 石英 Quartz content, %; Dw 白云石 Dolomite content, %; Dw 方解石 Calcite, %; Dw 钾长石 Potassium feldspar content, %; Dw 钠长石 The content of albite is %, Dw 黄铁矿 Pyrite content, %; Dw 粘土矿物 a1 is the weighting coefficient of quartz, dimensionless; a2 is the weighting coefficient of dolomite, dimensionless; b1 is the weighting coefficient of calcite, dimensionless; b2 is the weighting coefficient of potassium feldspar, dimensionless; b3 is the weighting coefficient of albite, dimensionless; b4 is the weighting coefficient of pyrite, dimensionless; b5 is the weighting coefficient of clay minerals, dimensionless.

[0126] In this embodiment, the weighting coefficient for quartz is:

[0127]

[0128] The weighting coefficient for dolomite is:

[0129]

[0130] The weighting coefficient for calcite is:

[0131]

[0132] The weighting factor for potassium feldspar is:

[0133]

[0134] The weighting factor for albite is:

[0135]

[0136] The weighting factor for pyrite is:

[0137]

[0138] The weighting coefficient for clay minerals is:

[0139]

[0140] Substituting the aforementioned weighting coefficients into equation (3) yields the mineral sensitivity parameter ms. Regression is then performed using the mineral sensitivity parameter ms and the normalized static brittleness index obtained from equation (2) to obtain brittleness index calculation models for different macroscopic structural types. This embodiment employs a linear regression model ( Figure 5 The X-axis represents the mineral sensitivity parameter, and the y-axis represents the normalized static brittleness index. Based on the linear formulas for lamellar, thin-layer, and homogeneous brittleness index calculation models, the following can be derived:

[0141] The linear formula for the lamellar type is: y = 3.4874x + 24.529

[0142] The linear formula for the thin-layer type is: y = 4.7068x + 5.3134

[0143] The linear formula for homogeneous data is: y = 1.7364x + 17.441

[0144] Based on the above linear formula, the calculation models for the brittleness index of lamellar, thin-layer, and homogeneous types can be derived as follows:

[0145] BI 纹层 =3.4874*ms+24.529 (4)

[0146] BI 薄层 =4.7068*ms+5.3134 (5)

[0147] BI 均质 =1.7364*ms+17.441 (6)

[0148] Among them, BI 纹层BI is the normalized brittleness index for lamellar types, dimensionless; 薄层 BI is the normalized brittleness index for lamellar types, dimensionless; 薄层 It is a homogeneous, normalized fragility index, dimensionless.

[0149] To perform well logging calculations for different macrostructural brittleness indices, it is necessary to first calculate the thickness of arbitrary bedding in the formation using electrical imaging logging data:

[0150] H j =h i+1 -h i

[0151] Among them, h i The depth, in meters (m), of the i-th bedding plane or lithological abrupt change surface identified from electrical imaging data; h i+1 The depth, in meters (m), of the (i+1)th bedding plane or lithological abrupt change surface identified from electrical imaging data; H j It is the thickness of a certain layer, in meters (m).

[0152] Then, the macroscopic structure type of the strata is determined based on the thickness of the bedding layers:

[0153] 0 <H 纹层 ≤x<H 薄层 ≤y <H 均质

[0154] Among them, H 纹层 H represents the thickness of the lamellar layer, in meters (m). 薄层 For thin-layered bedding, m; H 均质 denoted as homogeneous bedding thickness (m); x is the maximum thickness of lamellar bedding in a certain stratum (m), which is 0.05m in this embodiment; y is the maximum thickness of thin-layer bedding in a certain stratum (m), which is 0.15m in this embodiment.

[0155] Based on the continuous identification of the macrostructure of the formation, the mineral content calculated by elemental logging is obtained and substituted into equation (3) to calculate the mineral sensitivity parameters. Then, the brittleness index of different macrostructures is calculated using equations (4), (5) and (6).

[0156] To verify the application effect of this invention, this embodiment selects actual well logging data that was not used in modeling for processing. For example... Figure 6As shown, the first column represents the resistivity channel, the second the depth channel, the third a comparison channel between the brittleness index calculated by the elastic parameter method and the core brittleness index, the fourth a comparison channel between the brittleness index calculated by the classical mineral method and the core brittleness index, the fifth a comparison channel between the brittleness index calculated by the new method and the core brittleness index, the sixth a mineral content channel, and the seventh a macrostructure channel. Macrostructure classification: short lines indicate homogeneous type, medium-length lines indicate thin-layered type, and long lines indicate laminated type. The brittleness index calculated by the elastic parameter method has no correlation with the core brittleness index, while the brittleness index calculated by the classical mineral method has some correlation but poor accuracy (relative error of 40%). The brittleness index calculated in this application has high accuracy (relative error of 8.2%), meeting the needs of well logging reservoir evaluation.

[0157] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A well logging calculation method for brittleness index based on shale macrostructure classification, characterized in that, The method includes, Triaxial compressive strength tests were conducted on rock samples from the target stratum to construct a brittleness index calculation model for the macroscopic structure of the rock samples, including: Triaxial compressive strength tests were conducted on rock samples from the target stratum to obtain the static elastic parameters of the core, the fracture morphology of the rock samples, and the macroscopic structure of the rock samples. Based on the static elastic parameters of the core, a normalized static brittleness index is constructed. Whole-rock mineral analysis was performed on the rock samples after triaxial compressive strength test to obtain the mineral types and contents of the rock samples; Based on the correlation analysis of the mineral type and content of the rock sample with the static brittleness index, a macroscopic structural brittleness index calculation model is constructed, including the construction of mineral sensitive parameters based on the content of brittle minerals and ductile minerals. The mineral sensitivity parameters were fitted and analyzed with the static brittleness index to establish a brittleness index calculation model for macrostructures. The brittleness index calculation model, combining electrical imaging logging data and elemental logging data, calculates the brittleness index of the formation, including: Based on electrical imaging logging data, calculate the thickness H of any bedding in the formation. j ; Based on the thickness H of any bedding in the strata j To determine the type of macroscopic structure of the strata; Based on the macroscopic structural type of the strata, determine the corresponding brittleness index calculation model; based on elemental logging data, obtain the content of different types of minerals in the target layer; The brittleness index of the stratum is obtained by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

2. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, The static elastic parameters include one or more of the static Young's modulus and the static Poisson's ratio.

3. A well logging calculation method for brittleness index based on shale macrostructure classification according to claim 1 or 2, characterized in that, The process of constructing a normalized static brittleness index based on the static elastic parameters of the rock core includes: Based on the static elastic parameters of the core, a static brittleness index is constructed; The static brittleness index is normalized to obtain a normalized static brittleness index.

4. The brittleness index logging calculation method based on shale macrostructure classification according to claim 3, characterized in that, The normalized calculation of the static brittleness index is expressed as follows: Among them, BI N BI is the normalized static fragility index. min BI represents the minimum static brittleness index obtained experimentally. max This represents the maximum static brittleness index obtained from the experiment.

5. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, The fracture morphology of the rock sample includes one or more of the following: fracture along bedding planes and random fracture morphology. The macroscopic structure of the rock sample includes one or more of the following: lamellar, thin-layered, and homogeneous.

6. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, The fragility index calculation model for macroscopic structures includes one or more fragility index calculation models for lamellar, thin-layer, and homogeneous structures.

7. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, The mineral-sensitive parameters are constructed based on the content of brittle and ductile minerals, and the formula is expressed as follows: Where m1, m2, ..., m n Indicates the content of different brittle minerals; p1, p2, ..., p q Indicates the content of different plastic minerals; a1, a2, … a n These represent the weighting coefficients for different brittle minerals; b1, b2, ..., b q The weighting coefficients represent the different plastic minerals, where, The weighting coefficient of any brittle mineral satisfies the following relationship: in, The correlation coefficient between brittle minerals and the static brittleness index; The weighting coefficient of any plastic mineral satisfies the following relationship: in, This represents the correlation coefficient between ductile minerals and the static brittleness index.

8. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, The thickness H of any bedding in the calculated strata. j Its expression is: Among them, h i The depth of the i-th bedding plane or lithological abrupt change surface identified from electrical imaging data; h i+1 The depth of the (i+1)th bedding plane or lithological abrupt change surface identified from electrical imaging data; H j It refers to the thickness of a certain layer.

9. The brittleness index calculation method based on shale macrostructure classification according to claim 1, characterized in that, The thickness H of any bedding in the strata j To determine the type of macroscopic structure of the strata, including: When 0 <H j When ≤x, H j The strata in question are lamellar. When x <H j When ≤y, H j The strata in question are thin-layered. When y <H j At that time, H j The strata in which it is located are homogeneous, among which, x is a constant representing the maximum thickness of lamellar bedding in a certain stratum; y is a constant representing the maximum thickness of thin-layer bedding in a certain stratum.

10. The brittleness index logging calculation method based on shale macrostructure classification according to claim 1, characterized in that, Based on the results of the classification of the macroscopic structure of the strata, the content of different types of minerals in the target layer is obtained, including but not limited to quartz, potassium feldspar, sodium feldspar, pyrite, calcite, dolomite, and clay minerals.

11. A brittleness index logging calculation system based on shale macrostructure classification, characterized in that, The system employs the brittleness index logging calculation method based on shale macrostructure classification as described in any one of claims 1-10, comprising: The module is used to conduct triaxial compressive strength tests on rock samples from the target layer and to construct a brittleness index calculation model for different macroscopic structures of the rock samples. The calculation module is used to calculate the brittleness index of the formation by combining the brittleness index calculation model with electrical imaging logging data.

12. The brittleness index logging calculation system based on shale macrostructure classification according to claim 11, characterized in that, The building blocks include, The first acquisition unit is used to conduct triaxial compressive strength tests on rock samples from the target layer to obtain the static elastic parameters, fracture morphology, and macroscopic structure of the rock samples. The first building unit is used to build a normalized static brittleness index based on the static elastic parameters of the core. The second acquisition unit is used to perform whole-rock mineral analysis on the rock sample after the triaxial compressive test to obtain the mineral type and content of the rock sample; The second building unit is used to construct a macroscopic structural brittleness index calculation model by combining the correlation analysis of the mineral type and content of the rock sample with the static brittleness index.

13. A brittleness index logging calculation system based on shale macrostructure classification according to claim 11, characterized in that, The computing module includes, The first calculation unit is used to calculate the thickness H of any bedding in the formation based on electrical imaging logging data. j ; Type determination unit, used to determine the thickness H of any bedding in the strata. j To determine the type of macroscopic structure of the strata; The model determination unit is used to determine the corresponding brittleness index calculation model based on the macroscopic structure type of the stratum. The acquisition unit obtains the content of different types of minerals in the target layer based on elemental logging data; The second calculation unit is used to calculate the brittleness index of the stratum by combining the content of different types of minerals in the target layer with the brittleness index calculation model.

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