CN108009705A - A kind of shale reservoir compressibility evaluation method based on support vector machines technology - Google Patents
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Abstract
The present invention relates to a kind of shale reservoir compressibility evaluation method based on support vector machines technology.Comprise the following steps：（1）Mineral constituent quantitative analysis and uniaxial compressive experiment are carried out first；（2）Extraction is used for the logging characteristic parameters for identifying crack；（3）Maximum, the minimum horizontal principal stress of different layers position are calculated by cluster spring model, then calculates stress sensitive degree；（4）Establish compressibility evaluation model；（5）Discrete layer position stratum compressibility evaluation sample space is resettled, while correlation analysis is carried out to the relation between compressibility evaluation result and different response logs.Beneficial effect is：The present invention considers compressibility influence factor more perfectly, shale gas reservoir compressibility model is established in terms of brittleness index, intrinsic fracture development degree and stress sensitivity three, the deficiency that existing evaluation method considers this factor is compensate for, preferably embodies and distinguishes the integration capability that shale forms complex fracture network in volume fracturing.
Description
Technical Field
The invention relates to the field of development of unconventional oil and gas fields, in particular to a shale reservoir compressibility evaluation method based on a support vector machine technology.
Background
With the rapid development and progress of society, it has become urgent to find resources other than conventional coal, oil and natural gas resources. Shale gas has the characteristics of abundant reserves, wide distribution, long production period and the like, and the great success of the commercial exploitation of the shale gas in the United states in the last decade has attracted the extensive attention of other countries in the world. According to estimation, the shale gas resources in China are widely distributed in basins such as Sichuan, Erdos, Tarim, Querconaire, Songliao and the like, and the estimated mining storage amount is up to 36 billion cubic meters. Therefore, the efficient and scientific exploration and development of shale gas resources become national strategies for guaranteeing the energy safety of China and realizing the economic stability and rapid development.
The efficient exploitation of shale gas mainly benefits from the progress of the large-scale horizontal well volume fracturing modification technology, and the progress of the novel fracturing technology with infinite layers enables the industrial airflow yield of shale gas to exponentially rise, so that the development momentum is strong. Because of the unique reservoir formation occurrence of shale gas and the mechanical characteristics of a reservoir, the development degree of a fracture network in the volume fracturing process is controlled by various factors, such as the ground stress state, the rock mechanical properties, the development degree of natural fractures of the reservoir, the formation permeability and porosity, the diagenesis and other reservoir properties and geomechanical factors, and also comprises the fracturing construction discharge capacity, the pumping mode, the fracturing process, the types of fracturing fluid and propping agent, the well completion mode, the flowback mode, the production system and other engineering factors, so that the volume fracturing efficiency is effectively improved, the volume fracturing blindness is reduced under the condition of a certain engineering scale, and the research hotspot of the shale gas volume fracturing matched with the reservoir is formed all the time.
The reservoir compressibility only reflects physical and geomechanical characteristics of stratum oil reservoirs, and the comprehensive influence of pumping high-pressure fracturing fluid on mechanical behaviors such as fracture initiation and expansion of rock body cracks in the volume fracturing process is irrelevant to volume fracturing construction. The current compressibility evaluation methods mainly include qualitative and quantitative evaluation methods. The qualitative method mainly adopts a mode of directly comparing research block data with shale gas high-yield dominant layer positions, well positions and block data, and selects the well positions and the layer positions of shale gas fracturing through geology, rock mechanics, well logging, geophysical, trial production and other data, but the method needs a large amount of early-stage historical data, is suitable for mature shale gas development blocks, and has larger errors sometimes. The quantitative method is mainly based on a brittleness index or a compressibility index, the brittleness index is used as a core index for selecting a favorable stratum position for reservoir fracturing, and the method is the most widely applied reservoir compressibility evaluation method, but the conventional brittleness index evaluation method only can reflect the permanent destructive capacity of rock after inelastic strain under a simple mechanical environment, and cannot consider the influence of an external environment (such as ground stress and the like) on the rock destruction. Meanwhile, the brittleness index needs to be indirectly obtained by other parameters, and cannot be directly tested. The compressibility evaluation method may generally take into account factors other than the brittleness index factor, such as the difference in ground stress, the degree of natural fracture development, and the like. However, the brittleness index and compressibility index methods generally need sufficient indoor experimental data, so that the method has the defects of high test cost, long test period and the like, and at the same time, the core is sometimes insufficient on site, so that only discrete compressibility evaluation data of different layers can be obtained, and the fracturing design work of the whole well section is difficult to support.
The earth logging information contains rich and perfect stratum information and is the comprehensive embodiment of the lithology and physical property of the stratum. The logging data can be used for evaluating geological characteristics and a pore structure to obtain a reservoir quadrisexual relation, and simultaneously can be used for explaining oil deposit and geomechanical data such as shale content, porosity, permeability, saturation, pore pressure, rock mechanical parameters, ground stress and the like. However, when conventional well logging data are used for explaining elastography parameters, the defects of large calculation error and the like caused by the lack of shear wave data often exist. Moreover, the relation between the mechanical parameters of the stratum rock and each logging data is difficult to identify by a traditional linear and nonlinear model, and even random and fuzzy complex relation is generated between the logging data and the mechanical parameters due to the influence of high pressure and high temperature. The support vector machine is a small sample machine learning method based on a statistical learning theory, and is widely applied to problems of pattern recognition, regression analysis, function fitting and the like. The method has outstanding results in the field of petroleum engineering, such as reservoir identification, lithology judgment, pore permeability prediction and the like, and a set of solid theoretical foundation is established. However, no scholars apply the method to evaluation and calculation of reservoir compressibility, and the relationship between logging data and indoor rock physical experiment data is deeply excavated by utilizing the extremely strong nonlinear learning capacity of a support vector machine, so that a new method is established and a new way is opened up for optimization of reservoir oil and gas production improvement by carrying out whole-well-section compressibility continuous section interpretation.
In summary, there are two problems that need to be solved by shale gas: (1) combining block characteristics, and considering a compressibility evaluation model of comprehensive factors; (2) the formed compressibility evaluation result can not only guide the compressibility evaluation result of the key layer, but also form a continuous section of the whole well section, and is beneficial to field popularization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the shale reservoir compressibility evaluation method based on the support vector machine technology, overcomes the defects in the prior art, has higher operability and accuracy, and provides a new decision method for shale gas reservoir fracturing stratum selection optimization.
The invention provides a shale reservoir compressibility evaluation method based on a support vector machine technology, which comprises the following steps:
(1) firstly, carrying out quantitative analysis of mineral components and uniaxial compression test on mineral content and cores taken from different layers through QEMSCAN technology and rock mechanics test, calculating brittleness index based on mineral components and brittleness index based on rock mechanics parameters, and if no core exists, calculating brittleness index based on elastic parameters through logging data;
(2) then, extracting logging characteristic parameters for identifying the fractures, such as one or more of logging curve change rate, rock pore structure index, stratum factor ratio, saturation ratio, three-porosity ratio, skeleton index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, cracking coefficient, deep and shallow resistivity ratio, relative borehole diameter abnormality, natural potential abnormality and sound wave time difference ratio, respectively obtaining natural fracture development degrees of different scales of the whole well section by using a fractal technology and a comprehensive probability scale method, and if imaging logging or rock core fracture statistical data exist, restricting the natural fracture development degrees by using the logging characteristic parameters;
(3) secondly, calculating the maximum and minimum horizontal principal stresses of different layers through a combined spring model, and then calculating the stress sensitivity, wherein if borehole wall caving data and small fracturing data exist, the ground stress data can be restrained;
(4) finally, establishing a compressibility evaluation model, wherein a related weight coefficient can be obtained by inverting the actual compressibility evaluation result of an adjacent well, and if the brittleness index and the natural fracture development degree are positive indexes and the stress difference coefficient is a negative index, the positive index is larger, namely the index value is better, and the reverse index is smaller, namely the index value is better, and a range transformation standardization method is adopted to normalize the quantified parameters to obtain the following four indexes with the range between 0 and 1;
(5) then, a discrete horizon stratum compressibility evaluation sample space is established, correlation analysis is conducted on the relation between the compressibility evaluation result and different response logging curves, support vector machine model input parameters are determined, whole-well compressibility prediction is conducted through a support vector machine method, standard deviation and absolute average error are used as standards, weight is adjusted, and the weight and the input parameters are optimized.
The shale reservoir compressibility evaluation method based on the support vector machine technology specifically comprises the following steps:
(1) calculating the brittleness index；
(2) Calculating natural crack development degree index by using comprehensive probability method and fractal technology；
(3) Calculating stress sensitivity index of rock；
(4) Establishing a shale gas reservoir compressibility evaluation model by using a combined weight method, and calculating a compressibility evaluation index；
(5) Determining sensitive logging input data of a selected target block influencing the compressibility evaluation result based on the correlation analysis;
(6) and carrying out deep learning by adopting a support vector machine technology to realize regression analysis and establishing a continuous compressibility index profile of the shale gas reservoir target layer well section.
Preferably, the above-mentioned brittleness index is calculated, and two methods can be mainly adopted for the brittleness index evaluation method: 1. mineralogical analysis; 2. the rock mechanical parameter interpretation method is characterized in that the mineral component analysis method mainly analyzes rock mineral components through QEMSCAN technology and X-ray diffraction technology, and determines the mineral brittleness index based on the mineralogy according to the brittle mineral mass fractionThe calculation formula is shown as follows;
（1）
wherein,Ｂ t brittleness index calculated for the mineral composition method;W qua the percentage content of the quartz feldspar is;W calyis the percentage content of calcite;W dolomiteis the percentage content of dolomite;W calythe percentage content of the clay is shown as the percentage content,，，，，correlation coefficients obtained by regression of different shale gas reservoirs;
determining brittleness index based on rock mechanical characteristics according to elastic mechanical parameters
（2）
Is the elastic modulus of the shale formation GPa;is the poisson's ratio of the shale formation; subscript、The minimum and maximum values of the parameter are indicated, respectively. Under the condition of complete longitudinal and transverse wave logging data, the formula (2) can obtain a continuous brittleness index explanation profile, and if the profile has a standard core, the Young modulus can be respectively calculated through rock mechanics experimentsAnd poisson's ratio；
The comprehensive brittleness index of the rock based on mineral composition and elastic mechanical parameters is shown as the following formula:
（3）。
preferably, the comprehensive probability method is to compare each characteristic parameter curve with the core fracture description data of the core well and analyze each characteristic parameter reflection fractureThe seam capacity, the weight coefficient is determined, all characteristic parameters are comprehensively calculated, and finally, the comprehensive index value is obtained through calculationAnd is andthe larger the value, the more developed the crack,
（4）
in the formula,is the percentage of the various characteristic parameters that reflect the thickness of the fracture,is the weight coefficient of various characteristic parameters reflecting the crack,is the firstCharacteristic parameter values reflecting cracks are planted;
the fractal technology refers to that research objects with self-similarity in form, function and information are called fractal, natural fractures developing in a reservoir are caused by rock fracture, the fracture process of the rock and the geometric form after fracture have self-similar fractal structures, a plane where a curve of a target interval is analyzed is successively added with grids, and the number of the grids sequentially passed by the curve is respectively as follows:
、、、……、。
the fractal dimension of the logging curve is used for calculating the formula as follows:
（5）
in the formula,is the firstThe number of the grids after the sub-grid encryption,；is a log value;
and considering the logging data to conform to a fractal relation, and defining a fractal set as follows:
（6）
wherein,the number of objects with fractal characteristics is counted;is a fractal dimension;a scale of characteristic objects; after taking logarithm on both sides, can obtainAndthe fractal dimension is the slope;
fractal dimension of reservoir fracturesThe value reflects the development degree of reservoir cracks, and the fractal dimension valueHigher indicates more developed fractures;
after the comprehensive probability and fractal dimension of the natural fracture are obtained, the natural fracture development index is calculated through the following formula：
（7）。
Preferably, a single-point compressibility evaluation model is established by using a combined weight method:
（11）
in the formula,the weight coefficients of various characteristic parameters reflecting different compressibility parameters can pass through the existing fracturing wellPerforming regression on the microseism and volume fracturing data;
and dividing the fracturing grades of the reservoir by utilizing the comprehensive fracturing coefficient, wherein the comprehensive fracturing coefficient is considered to be a low-grade fracturing grade below 0.33, the stratum belongs to a medium-grade fracturing grade if the fracturing index is between 0.33 and 0.66, and the stratum belongs to a high-grade fracturing grade if the fracturing index is between 0.66 and 1.
Preferably, in the invention, a data sample based on a single-point compressibility evaluation model is established, firstly, correlation analysis is carried out on the relationship between the single-point compressibility index and the logging data related to the corresponding point, a sensitive logging curve influencing the compressibility index is determined, and the number of input variables of the support vector machine is determined.
The invention has the beneficial effects that: the method has the advantages that compressibility influence factors are considered more completely, a shale gas reservoir compressibility model is established from three aspects of brittleness index, natural fracture development degree and stress sensitivity, the defect of the existing evaluation method in consideration of the factors is made up, and the comprehensive capability of distinguishing complex fracture networks formed by shale in volume fracturing is better reflected; secondly, a support vector machine method is introduced, the logging data and the result of a rock physical experiment are effectively combined, the application of the logging data in evaluating the formation compressibility is deeply excavated, and the dependence on the experimental data and the transverse wave logging data is reduced; the formed support vector machine stratum compressibility prediction model has high generalization capability and robustness, can be used for quickly evaluating the shale reservoir quality of different layers of a fracturing well, and has important engineering practical value;
the pressability evaluation technology of the support vector machine also comprises the processing of the input variables of the support vector machine by the dimensionality reduction methods such as principal component analysis and the like, and the variation and derivative models of different types of support vector machine technologies.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of the results of a specific compressibility analysis in the compressibility evaluation of a shale gas block of the Quchuanbasin Longmaxi model according to the present invention;
in the upper diagram: the well contains raw log data as follows: gamma, neutron porosity, density, deep lateral resistivity and acoustic wave time difference, wherein a continuous curve is a compressibility index obtained by inversion, and a red dot represents a brittleness index obtained by calculation of a brittle mineral.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to the attached figure 1, the shale reservoir compressibility evaluation method based on the support vector machine technology, provided by the invention, comprises the following steps:
(1) firstly, carrying out quantitative analysis of mineral components and uniaxial compression test on mineral content and cores taken from different layers through QEMSCAN technology and rock mechanics test, calculating brittleness index based on mineral components and brittleness index based on rock mechanics parameters, and if no core exists, calculating brittleness index based on elastic parameters through logging data;
(2) then, extracting logging characteristic parameters for identifying the fractures, such as one or more of logging curve change rate, rock pore structure index, stratum factor ratio, saturation ratio, three-porosity ratio, skeleton index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, cracking coefficient, deep and shallow resistivity ratio, relative borehole diameter abnormality, natural potential abnormality and sound wave time difference ratio, respectively obtaining natural fracture development degrees of different scales of the whole well section by using a fractal technology and a comprehensive probability scale method, and if imaging logging or rock core fracture statistical data exist, restricting the natural fracture development degrees by using the logging characteristic parameters;
(3) secondly, calculating the maximum and minimum horizontal principal stresses of different layers through a combined spring model, and then calculating the stress sensitivity, wherein if borehole wall caving data and small fracturing data exist, the ground stress data can be restrained;
(4) finally, establishing a compressibility evaluation model, wherein a related weight coefficient can be obtained by inverting the actual compressibility evaluation result of an adjacent well, and if the brittleness index and the natural fracture development degree are positive indexes and the stress difference coefficient is a negative index, the positive index is larger, namely the index value is better, and the reverse index is smaller, namely the index value is better, and a range transformation standardization method is adopted to normalize the quantified parameters to obtain the following four indexes with the range between 0 and 1;
(5) then, a discrete horizon stratum compressibility evaluation sample space is established, correlation analysis is conducted on the relation between the compressibility evaluation result and different response logging curves, support vector machine model input parameters are determined, whole-well compressibility prediction is conducted through a support vector machine method, standard deviation and absolute average error are used as standards, weight is adjusted, and the weight and the input parameters are optimized.
The shale reservoir compressibility evaluation method based on the support vector machine technology specifically comprises the following steps:
(1) calculating the brittleness index；
(2) Calculating natural crack development degree index by using comprehensive probability method and fractal technology；
(3) Calculating stress sensitivity index of rock；
(4) Establishing a shale gas reservoir compressibility evaluation model by using a combined weight method, and calculating a compressibility evaluation index；
(5) Determining sensitive logging input data of a selected target block influencing the compressibility evaluation result based on the correlation analysis;
(6) and carrying out deep learning by adopting a support vector machine technology to realize regression analysis and establishing a continuous compressibility index profile of the shale gas reservoir target layer well section.
Preferably, the above-mentioned brittleness index is calculated, and two methods can be mainly adopted for the brittleness index evaluation method: 1. mineralogical analysis; 2. the rock mechanical parameter interpretation method is characterized in that the mineral component analysis method mainly analyzes rock mineral components through QEMSCAN technology and X-ray diffraction technology, and determines the mineral brittleness index based on the mineralogy according to the brittle mineral mass fractionThe calculation formula is shown as follows;
（1）
wherein,Ｂ t brittleness index calculated for the mineral composition method;W qua the percentage content of the quartz feldspar is;W calyis the percentage content of calcite;W dolomiteis the percentage content of dolomite;W calythe percentage content of the clay is shown as the percentage content,，，，，correlation coefficients obtained by regression of different shale gas reservoirs;
determining brittleness index based on rock mechanical characteristics according to elastic mechanical parameters
（2）
Is the elastic modulus of the shale formation GPa;is the poisson's ratio of the shale formation; subscript、The minimum and maximum values of the parameter are indicated, respectively. Under the condition of complete longitudinal and transverse wave logging data, the formula (2) can obtain a continuous brittleness index explanation profile, and if the profile has a standard core, the Young modulus can be respectively calculated through rock mechanics experimentsAnd poisson's ratio；
The comprehensive brittleness index of the rock based on mineral composition and elastic mechanical parameters is shown as the following formula:
（3）。
preferably, the comprehensive probability method includes comparing each characteristic parameter curve with core fracture description data of the core well, analyzing the fracture reflecting capacity of each characteristic parameter, determining a weight coefficient, performing comprehensive calculation on all characteristic parameters, and finally calculating to obtain a comprehensive index numerical valueAnd is andthe larger the value, the more developed the crack,
（4）
in the formula,is the percentage of the various characteristic parameters that reflect the thickness of the fracture,is the weight coefficient of various characteristic parameters reflecting the crack,is the firstCharacteristic parameter values reflecting cracks are planted;
the fractal technology refers to that research objects with self-similarity in form, function and information are called fractal, natural fractures developing in a reservoir are caused by rock fracture, the fracture process of the rock and the geometric form after fracture have self-similar fractal structures, a plane where a curve of a target interval is analyzed is successively added with grids, and the number of the grids sequentially passed by the curve is respectively as follows:
、、、……、。
the fractal dimension of the logging curve is used for calculating the formula as follows:
（5）
in the formula,is the firstThe number of the grids after the sub-grid encryption,；is a log value;
and considering the logging data to conform to a fractal relation, and defining a fractal set as follows:
（6）
wherein,the number of objects with fractal characteristics is counted;is a fractal dimension;a scale of characteristic objects; after taking logarithm on both sides, can obtainAndthe fractal dimension is the slope;
fractal dimension of reservoir fracturesThe value reflects the development degree of reservoir cracks, and the fractal dimension valueHigher indicates more developed fractures;
after the comprehensive probability and fractal dimension of the natural fracture are obtained, the natural fracture development index is calculated through the following formula：
（7）。
Preferably, a single-point compressibility evaluation model is established by using a combined weight method:
（11）
in the formula,the weight coefficients of various characteristic parameters reflecting different compressibility parameters can be obtained by microseism of the existing fracturing well and regression of volume fracturing data;
and dividing the fracturing grades of the reservoir by utilizing the comprehensive fracturing coefficient, wherein the comprehensive fracturing coefficient is considered to be a low-grade fracturing grade below 0.33, the stratum belongs to a medium-grade fracturing grade if the fracturing index is between 0.33 and 0.66, and the stratum belongs to a high-grade fracturing grade if the fracturing index is between 0.66 and 1.
Preferably, in the invention, a data sample based on a single-point compressibility evaluation model is established, firstly, correlation analysis is carried out on the relationship between the single-point compressibility index and the logging data related to the corresponding point, a sensitive logging curve influencing the compressibility index is determined, and the number of input variables of the support vector machine is determined.
The invention fully considers three main control factors influencing the formation of the complex seam network of the shale gas reservoir: the method has the advantages that the brittleness index, the natural fracture development degree and the stress sensitivity are effectively integrated, the relation between indoor test and acoustic logging response is effectively integrated, the dependence on indoor test data and transverse wave logging data is reduced, particularly, a continuous section of the compressibility index of the whole well section is established by adopting a support vector machine technology, the operability is realized, and the method has practical significance for guiding the hydraulic fracturing design, well selection and other work of shale gas.
The above description is only a few of the preferred embodiments of the present invention, and any person skilled in the art may modify the above-described embodiments or modify them into equivalent ones. Therefore, any simple modifications or equivalent substitutions made in accordance with the technical solution of the present invention are within the scope of the claims of the present invention.
Claims (6)
1. A shale reservoir compressibility evaluation method based on a support vector machine technology is characterized by comprising the following steps:
(1) firstly, carrying out quantitative analysis of mineral components and uniaxial compression test on mineral content and cores taken from different layers through QEMSCAN technology and rock mechanics test, calculating brittleness index based on mineral components and brittleness index based on rock mechanics parameters, and if no core exists, calculating brittleness index based on elastic parameters through logging data;
(2) then, extracting logging characteristic parameters for identifying the fractures, such as one or more of logging curve change rate, rock pore structure index, stratum factor ratio, saturation ratio, three-porosity ratio, skeleton index, equivalent elastic modulus difference ratio, resistivity invasion correction difference ratio, cracking coefficient, deep and shallow resistivity ratio, relative borehole diameter abnormality, natural potential abnormality and sound wave time difference ratio, respectively obtaining natural fracture development degrees of different scales of the whole well section by using a fractal technology and a comprehensive probability scale method, and if imaging logging or rock core fracture statistical data exist, restricting the natural fracture development degrees by using the logging characteristic parameters;
(3) secondly, calculating the maximum and minimum horizontal principal stresses of different layers through a combined spring model, and then calculating the stress sensitivity, wherein if borehole wall caving data and small fracturing data exist, the ground stress data can be restrained;
(4) finally, establishing a compressibility evaluation model, wherein a related weight coefficient can be obtained by inverting the actual compressibility evaluation result of an adjacent well, and if the brittleness index and the natural fracture development degree are positive indexes and the stress difference coefficient is a negative index, the positive index is larger, namely the index value is better, and the reverse index is smaller, namely the index value is better, and a range transformation standardization method is adopted to normalize the quantified parameters to obtain the following four indexes with the range between 0 and 1;
(5) then, a discrete horizon stratum compressibility evaluation sample space is established, correlation analysis is conducted on the relation between the compressibility evaluation result and different response logging curves, support vector machine model input parameters are determined, whole-well compressibility prediction is conducted through a support vector machine method, standard deviation and absolute average error are used as standards, weight is adjusted, and the weight and the input parameters are optimized.
2. The shale reservoir compressibility evaluation method based on the support vector machine technology as claimed in claim 1, wherein the specific implementation method comprises the following steps:
(1) calculating the brittleness index；
(2) Calculating natural crack development degree index by using comprehensive probability method and fractal technology；
(3) Calculating stress sensitivity index of rock；
(4) Establishing a shale gas reservoir compressibility evaluation model by using a combined weight method, and calculating a compressibility evaluation index；
(5) Determining sensitive logging input data of a selected target block influencing the compressibility evaluation result based on the correlation analysis;
(6) and carrying out deep learning by adopting a support vector machine technology to realize regression analysis and establishing a continuous compressibility index profile of the shale gas reservoir target layer well section.
3. The shale reservoir compressibility evaluation method based on support vector machine technology as claimed in claim 2, wherein: the brittleness index is calculated, and the brittleness index evaluation method mainly adopts two methods: 1. mineralogical analysis; 2. the rock mechanical parameter interpretation method is characterized in that the mineral component analysis method mainly analyzes rock mineral components through QEMSCAN technology and X-ray diffraction technology, and determines the mineral brittleness index based on the mineralogy according to the brittle mineral mass fractionThe calculation formula is shown as follows;
（1）
wherein,Ｂ t brittleness index calculated for the mineral composition method;W qua the percentage content of the quartz feldspar is;W calyis the percentage content of calcite;W dolomiteis the percentage content of dolomite;W calythe percentage content of the clay is shown as the percentage content,，，，，correlation coefficients obtained by regression of different shale gas reservoirs;
determining brittleness index based on rock mechanical characteristics according to elastic mechanical parameters
（2）
Is the elastic modulus of the shale formation GPa;is the poisson's ratio of the shale formation; subscript、Respectively representing the minimum value and the maximum value of the parameter;
under the condition of complete longitudinal and transverse wave logging data, the formula (2) can obtain a continuous brittleness index explanation profile, and if the profile has a standard core, the Young modulus can be respectively calculated through rock mechanics experimentsAnd poisson's ratio；
The comprehensive brittleness index of the rock based on mineral composition and elastic mechanical parameters is shown as the following formula:
（3）。
4. the shale reservoir compressibility evaluation method based on support vector machine technology as claimed in claim 2, wherein: the comprehensive probability method is characterized in that each characteristic parameter curve is compared with rock core fracture description data of a core well, the capability of each characteristic parameter in reflecting fractures is analyzed, weight coefficients are determined, all characteristic parameters are comprehensively calculated, and finally, comprehensive index numerical values are obtained through calculationAnd is andthe larger the value, the more developed the crack,
（4）
in the formula,is the percentage of the various characteristic parameters that reflect the thickness of the fracture,is the weight coefficient of various characteristic parameters reflecting the crack,is the firstCharacteristic parameter values reflecting cracks are planted;
the fractal technology refers to that research objects with self-similarity in form, function and information are called fractal, natural fractures developing in a reservoir are caused by rock fracture, the fracture process of the rock and the geometric form after fracture have self-similar fractal structures, a plane where a curve of a target interval is analyzed is successively added with grids, and the number of the grids sequentially passed by the curve is respectively as follows:
、、、……、；
the fractal dimension of the logging curve is used for calculating the formula as follows:
（5）
in the formula,is the firstThe number of the grids after the sub-grid encryption,；is a log value;
and considering the logging data to conform to a fractal relation, and defining a fractal set as follows:
（6）
wherein,the number of objects with fractal characteristics is counted;is a fractal dimension;a scale of characteristic objects; after taking logarithm on both sides, can obtainAndthe fractal dimension is the slope;
fractal dimension of reservoir fracturesThe value reflects the development degree of reservoir cracks, and the fractal dimension valueHigher indicates more developed fractures;
after the comprehensive probability and fractal dimension of the natural fracture are obtained, the natural fracture development index is calculated through the following formula：
（7）。
5. The shale reservoir compressibility evaluation method based on support vector machine technology as claimed in claim 2, wherein: establishing a single-point compressibility evaluation model by using a combined weight method:
（11）
in the formula,the weight coefficients of various characteristic parameters reflecting different compressibility parameters can be obtained by microseism of the existing fracturing well and regression of volume fracturing data;
and dividing the fracturing grades of the reservoir by utilizing the comprehensive fracturing coefficient, wherein the comprehensive fracturing coefficient is considered to be a low-grade fracturing grade below 0.33, the stratum belongs to a medium-grade fracturing grade if the fracturing index is between 0.33 and 0.66, and the stratum belongs to a high-grade fracturing grade if the fracturing index is between 0.66 and 1.
6. The shale reservoir compressibility evaluation method based on support vector machine technology of claim 5, characterized by: establishing a data sample based on a single-point compressibility evaluation model, firstly, carrying out correlation analysis on the relation between the single-point compressibility index and the corresponding point related logging information, determining a sensitive logging curve influencing the compressibility index, and determining the number of input variables of the support vector machine.
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