A shale gas reservoir porosity calculation method based on elements and nuclear magnetic logging

Abstract

The application discloses a shale gas reservoir porosity calculation method based on elements and nuclear magnetic logging, and belongs to the technical field of shale gas reservoir evaluation. The shale gas reservoir porosity calculation method based on elements and nuclear magnetic logging disclosed by the application improves the problem that the variable matrix density logging is relatively single, the density logging value is small due to the gas excavation effect, the fluid density value is large, and the variable matrix density porosity calculation is large; and the problem that the nuclear magnetic logging porosity of a gas layer is small due to the fact that the hydrogen index of natural gas is smaller than that of oil and water, and the relatively long longitudinal relaxation time of the natural gas is not fully polarized. The shale gas reservoir evaluation and reserve calculation have obvious practical application effects.

Description

Technical Field

[0001] This invention belongs to the field of shale gas reservoir evaluation technology, specifically relating to a method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging. Background Technology

[0002] Shale porosity is a key parameter for measuring the physical properties, gas content, and reserves of shale reservoirs, and is of great significance for resource prediction and the selection of favorable exploration and development areas.

[0003] Shale reservoirs have finer pores and more complex structures than conventional reservoirs. Experimental measurements of porosity are often insufficient to meet the effective lower limit of conventional reservoirs. Furthermore, shale reservoirs exhibit both inorganic and organic pore types, well-developed microfractures, and diverse fluid occurrence states. Conventional logging systems have limited signal response to pores, leading to difficulties in calculating porosity with insufficient accuracy.

[0004] Currently, shale gas reservoir porosity calculations mainly employ conventional curve multivariate fitting, variable framework density, and nuclear magnetic resonance (NMR) logging methods. The conventional curve multivariate fitting method establishes a regression relationship between the logging sensitivity curve and core analysis porosity using mathematical statistics. However, this method does not consider the impact of changes in mineral content along the reservoir's horizontal and vertical axes on the conventional curve, resulting in low calculation accuracy and poor regional adaptability; it is only applicable under conditions of stable lithology in both the horizontal and vertical axes. While the variable framework density method considers the impact of mineral content changes on the logging curve, it does not account for the presence of a large amount of free gas in the shale gas reservoir. This leads to a significant "gas-digging effect" on the logging curve, where, in formations containing natural gas, the water in the pores is replaced by gas compared to formations saturated with fresh water. This results in lower gas density and hydrogen index, causing underestimation of the measured density and inaccurate fluid density values, ultimately leading to an overestimation of the porosity calculated using the variable framework density method. Nuclear magnetic resonance (NMR) logging can directly obtain porosity in shale reservoirs that is independent of lithology. However, this method is also affected by the presence of a large amount of free gas in shale gas reservoirs. The hydrogen index of natural gas is lower than that of oil and water, and its relatively long vertical relaxation time usually means that it is not fully polarized. Therefore, when using NMR logging data, the NMR porosity obtained is too small. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method for calculating the porosity of shale gas reservoirs based on elemental and nuclear magnetic resonance logging, so as to solve the technical problem of porosity calculation error in existing shale gas reservoir porosity logging calculation methods.

[0006] To achieve the above objectives, the present invention employs the following technical solution: This invention discloses a method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging, comprising the following steps: S1: Obtain variable skeleton density porosity curves, nuclear magnetic resonance porosity curves, and the influence intensity curve of the mining effect based on the logging interpretation data of shale gas wells; S2: Based on the variable skeleton density porosity curve, NMR porosity curve and measured porosity, establish a graph showing the relationship between the influence intensity of the excavation effect and the porosity correction coefficient; S3: Calculate the porosity of shale gas reservoirs based on the relationship between the influence intensity curve of the mining effect and the porosity correction coefficient.

[0007] Furthermore, the logging data includes elemental logging interpretation data, nuclear magnetic resonance logging interpretation data, and conventional logging interpretation data.

[0008] Furthermore, the conventional logging interpretation data includes neutron curves and density curves of shale gas wells; the elemental logging interpretation data includes mass fraction curves of mineral components.

[0009] Furthermore, the mineral components include quartz, feldspar, carbonate rock minerals, clay minerals, pyrite, and kerogen.

[0010] Furthermore, the variable skeleton density porosity curve is obtained from elemental logging interpretation data; the nuclear magnetic resonance porosity curve is obtained from nuclear magnetic resonance logging interpretation data; the nuclear magnetic resonance porosity curve is expressed as a percentage; and the influence intensity curve of the excavation effect is obtained from conventional logging interpretation data.

[0011] Furthermore, the variable skeleton density porosity curve is obtained by the following formula: POR rg =(ρ ma -DEN) / (ρ) ma -ρ f ) × 100%; Among them, POR rg Variable skeleton density porosity, in %; ρ ma ρ is the density of the rock skeleton. f For pore fluids, DEN is the measured density value, in g / cm³. 3 ; Where, ρ ma It is equal to the reciprocal of the sum of the ratios of the mass fractions of each mineral component to the skeletal density value of that mineral component.

[0012] Furthermore, the influence intensity curve of the excavation effect is obtained by the following formula: S=(CNL L -CNL) / (CNL L –CNL r )-(DEN-DEN L ) / (DEN r –DEN L); Where S represents the intensity of the excavation effect, dimensionless; CNL is the neutron measurement; DEN is the density measurement; CNL L The left-hand value of the neutron curve scale; CNL r DEN is the right-hand value of the neutron curve scale. L This represents the left-hand value of the density curve scale; DEN r This represents the right-hand value of the density curve scale.

[0013] Further, the steps for establishing a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient are as follows: Based on well logging interpretation data, obtain the measured porosity of core samples from at least five shale gas wells at different depths, introduce the porosity correction coefficient w, and combine the variable skeleton density porosity curve, NMR porosity curve, and porosity correction coefficient. Set the calculated result equal to the measured porosity. After formula refinement, obtain the calculation formula for the porosity correction coefficient at different depths and obtain the porosity correction coefficient curve. Then, following the method for obtaining the intensity curve of the mining effect, extract the numerical values ​​of the intensity of the mining effect at several depths corresponding to the porosity correction coefficient, and then establish a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient.

[0014] Furthermore, the porosity correction coefficient curves corresponding to different depths of the shale gas well are obtained by the following formula: w=(1-POR1) / (POR cmr -POR rg ) Where w is the porosity correction coefficient; POR1 is the measured porosity; The relationship between the intensity of the excavation effect and the porosity correction coefficient is established by the following formula: w = S × a + b; Where a is the slope and b is a constant.

[0015] Furthermore, the porosity of shale gas reservoirs is calculated using the following formula: POR = (1-w) × POR cmr +w×POR rg ; POR stands for shale gas reservoir porosity.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance (NMR) logging. By introducing a porosity correction coefficient to correct and compensate for variable framework density porosity and NMR porosity, it combines the advantages of both elemental and NMR logging. Compared to relying solely on elemental logging data to obtain variable framework density porosity, this method improves upon the problem of overestimating variable framework density porosity due to underestimating density logging values ​​and overestimating fluid density values ​​caused by the gas tapping effect. Compared to relying solely on NMR logging data, it improves upon the problem of underestimating gas reservoir porosity in NMR logging due to the lower hydrogen index of natural gas compared to oil and water, and its relatively long vertical relaxation time, which often results in insufficient polarization. This method has good regional adaptability, is unaffected by changes in framework mineral lithology, and avoids the influence of the natural gas tapping effect on porosity calculation. This technical solution avoids the poor universality of the empirical formula method based on multiple regression in mathematical statistics for calculating porosity. It also solves the problems of overestimating porosity using the variable framework density method and underestimating porosity using nuclear magnetic resonance (NMR) logging due to the presence of large amounts of free gas. Furthermore, elemental and NMR logging data are more accurate than conventional logging data, resulting in more reliable and precise porosity calculations. According to relevant results, when using this invention to calculate shale gas reservoir porosity, the relative error can be reduced to approximately 10% compared to other methods, significantly improving calculation accuracy. Therefore, the shale gas reservoir porosity calculation method based on elemental and NMR logging provided in this application can accurately determine shale gas reservoir porosity and has significant practical application effects in shale gas reservoir evaluation and reserve calculation. Attached Figure Description

[0017] Figure 1 This is a flowchart of the shale gas reservoir porosity calculation method based on elemental and nuclear magnetic resonance logging disclosed in this invention; Figure 2 This is a regression analysis chart showing the influence intensity S of the excavation effect and the porosity correction coefficient w in this invention. Figure 3 This is a comparison chart of the calculation results in Example 1 with the calculation results of variable skeleton density porosity and NMR porosity alone. Detailed Implementation

[0018] To enable those skilled in the art to understand the features and effects of the present invention, the terms and expressions used in the specification and claims are explained and defined in general below. Unless otherwise specified, all technical and scientific terms used herein have the ordinary meaning understood by those skilled in the art regarding the present invention, and in case of conflict, the definitions in this specification shall prevail.

[0019] The theories or mechanisms described and disclosed herein, whether right or wrong, should not in any way limit the scope of the invention, that is, the contents of the invention can be implemented without being limited by any particular theory or mechanism.

[0020] This invention discloses a method for calculating the porosity of shale gas reservoirs based on elemental and nuclear magnetic resonance logging, characterized by the following steps: S1: Obtain variable skeleton density porosity curves, nuclear magnetic resonance porosity curves, and the influence intensity curve of the mining effect based on the logging interpretation data of shale gas wells; S2: The steps for establishing a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient are as follows: Based on well logging interpretation data, obtain the measured porosity at different depths from no fewer than 5 shale gas wells, introduce the porosity correction coefficient curve w, and combine the variable skeleton density porosity curve, NMR porosity curve, and porosity correction coefficient. Set the calculated result equal to the measured core porosity. After formula refinement, obtain the calculation formula for the porosity correction coefficient at different depths and obtain the porosity correction coefficient curve. Then, following the method for obtaining the intensity curve of the mining effect, extract the numerical values ​​of the intensity of the mining effect at several depths corresponding to the porosity correction coefficient, and then establish a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient. S3: Calculate the porosity of shale gas reservoirs based on the relationship between the influence intensity curve of the mining effect and the porosity correction coefficient.

[0021] The conventional well logging interpretation data includes neutron curves and density curves of shale gas wells; the elemental well logging interpretation data includes mass fraction curves of mineral components, and the elemental well logging interpretation results must include mass fraction curves of each mineral component; the nuclear magnetic resonance (NMR) well logging interpretation results must include NMR porosity curves and must be converted to percentage form; the variable framework density porosity curve is obtained from the elemental well logging interpretation data; the NMR porosity curve is obtained from the NMR well logging interpretation data; the influence intensity curve of the mining effect is obtained from the conventional well logging interpretation data; the mineral components include quartz, feldspar, carbonate minerals, clay minerals, pyrite, and kerogen, etc., and may be more or less than these minerals.

[0022] Furthermore, the variable skeleton density porosity curve is obtained by the following formula: POR rg =(ρ ma -DEN) / (ρ) ma -ρ f ) × 100%; Among them, POR rg Variable skeleton density porosity, in %; ρ ma ρ is the density of the rock skeleton. fFor pore fluids, DEN is the measured density value, in g / cm³. 3 ; Where, ρ ma It is equal to the reciprocal of the sum of the ratios of the mass fractions of each mineral component to the skeletal density value of that mineral component.

[0023] Furthermore, the influence intensity curve of the excavation effect is obtained by the following formula: S=(CNL L -CNL) / (CNL L –CNL r )-(DEN-DEN L ) / (DEN r –DEN L ); Where S represents the intensity of the excavation effect, dimensionless; CNL is the neutron measurement; DEN is the density measurement; CNL L The left-hand value of the neutron curve scale; CNL r DEN is the right-hand value of the neutron curve scale. L This represents the left-hand value of the density curve scale; DEN r This represents the right-hand value of the density curve scale.

[0024] Furthermore, the porosity correction coefficient curves corresponding to different depths of the shale gas well are obtained by the following formula: w=(1-POR1) / (POR cmr -POR rg ) Where w is the porosity correction coefficient; POR1 is the measured porosity of the core. The relationship between the intensity of the excavation effect and the porosity correction coefficient is established by the following formula: w = S × a + b; Where a is the slope and b is a constant.

[0025] Furthermore, the porosity of shale gas reservoirs is calculated using the following formula: POR = (1-w) × POR cmr +w×PORrg; POR stands for shale gas reservoir porosity.

[0026] In this article, unless otherwise specified, “contains,” “includes,” “containing,” “has,” or similar terms cover the meanings of “composed of” and “mainly composed of,” for example, “A contains a” covers the meanings of “A contains a and others” and “A contains only a.”

[0027] For the sake of brevity, not all possible combinations of the technical features in each implementation scheme or embodiment are described herein. Therefore, as long as there is no contradiction in the combination of these technical features, the technical features in each implementation scheme or embodiment can be combined arbitrarily, and all possible combinations should be considered within the scope of this specification.

[0028] like Figure 1 As shown, this invention provides a method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging, comprising the following steps: S1: Obtain conventional, elemental, and nuclear magnetic resonance logging data of a shale gas appraisal well reservoir in an oilfield. The logging data must include neutron and density curves. The elemental interpretation data must include result data, i.e., mass fraction curves of each mineral component. The nuclear magnetic resonance logging interpretation data must include result data, i.e., nuclear magnetic porosity curves (in percentage form). Subsequently, the variable skeleton density porosity curve was calculated based on the well logging data. The calculation model is as follows: First, calculate the skeleton density curve: ρ ma =1 / (walb / ρalb+wchl / ρchl+wclc / ρclc+wdol / ρdol+will / ρill+ wort / ρort+wpyr / ρpyr +wqua / ρqua+wkero / ρkero+……); In the formula, ρ ma This represents the density of the rock skeleton, in g / cm³. 3 ;walb, wchl, wclc, wdol, will, wort, wpyr, wqua, and wkero represent the molecular weight fractions (w / w) of albite, chlorite, calcite, dolomite, illite, orthoclase, pyrite, quartz, and kerogen, respectively; ρ alb ρ chl ρ clc ρ dol ρ ill ρ ort ρ pyr ρ qua ρ kero The skeletal density values ​​(g / cm³) of the mineral components albite, chlorite, calcite, dolomite, illite, orthoclase, pyrite, quartz, and kerogen are respectively. 3 ; Then, the variable skeleton density porosity curve is calculated, obtained from the following formula: POR rg =(ρ ma -DEN) / (ρ) ma -ρ f ) × 100%; Among them, POR rg For variable skeleton density porosity, ρ ma ρ is the skeleton density. f For pore fluid, DEN is the measured density value; The nuclear magnetic resonance porosity curve POR cmr It can be directly obtained from the nuclear magnetic resonance logging interpretation curve, and the unit is a percentage. The intensity curve of the excavation effect is obtained by the following steps: Using conventional well logging data, the density and neutron curves are superimposed to ensure overlap in non-source rock sections. However, a certain amplitude difference will appear in the main shale gas producing sections. This amplitude difference is defined as the influence intensity S of the mining effect. The influence intensity S curve of the mining effect is obtained using the following formula: S=(CNL L -CNL) / (CNL L –CNL r )-(DEN-DEN L ) / (DEN r –DEN L ); Where S is the intensity of the excavation effect, dimensionless; CNL is the neutron measurement value, in v / v; and DEN is the density measurement value, in g / cm³. 3 CNL L The left-hand value of the neutron curve scale; CNL r DEN is the right-hand value of the neutron curve scale. L This represents the left-hand value of the density curve scale; DEN r This represents the right-hand value of the density curve scale.

[0029] S2: Establish a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient. Take porosity experimental analysis data from several uniformly distributed shale gas reservoir sections within the study area, introduce the porosity correction coefficient w, and combine it with the NMR porosity curve and the variable skeleton density porosity curve. Make the calculation result equal to the porosity data obtained from core experiments at the corresponding depth, thereby obtaining the value of the porosity correction coefficient W at different depths. Use the following formula to obtain the porosity correction coefficient W curve: w=(1-POR1) / (POR cmr -POR rg ) Where w is the porosity correction coefficient; POR1 is the porosity measured in the core experiment, in percentage (%). Secondly, for the conventional curves of these shale gas wells, the numerical values ​​of the mining effect intensity S at the corresponding depth points of the porosity correction coefficient w were extracted according to the method for obtaining the mining effect intensity S. Using cross-plots, the correspondence between the porosity correction coefficient w and the mining effect intensity S was established: w = S × a + b; Where a is the slope and b is a constant; S3: The porosity of shale gas reservoirs is calculated using the following formula: POR = (1-w) × POR cmr +W×POR rg ; POR stands for shale gas reservoir porosity.

[0030] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0031] The following examples use instruments and equipment conventional in the art. Experimental methods in the following examples, unless otherwise specified, are generally performed under conventional conditions or as recommended by the manufacturer. All raw materials used in the following examples are conventional commercially available products with specifications conventional in the art. In this specification and the following examples, unless otherwise specified, "%" refers to weight percentage, "parts" refers to parts by weight, and "ratio" refers to weight proportion.

[0032] Example 1 Taking well XX in a certain oilfield as an example, a method for calculating the porosity of shale gas reservoirs based on elemental and nuclear magnetic resonance logging includes the following steps: S1: Obtain conventional, elemental, and nuclear magnetic resonance logging data of the shale gas appraisal well reservoir in well XX of a certain oilfield; calculate the variable skeleton density porosity curve (POR) based on the logging data. rg : First, the skeleton density curve is obtained based on elemental logging data, and the calculation model is as follows: ρ ma =1 / (walb / 2.61+wchl / 3.01+wclc / 2.71+wdol / 2.87+will / 2.79+ wort / 2.57+wpyr / 5+wqua / 2.65+wkero / 1.4) (Formula 1); In the formula, ρ ma This is the density value of the rock skeleton, in g / cm³. 3; walb, wchl, wclc, wdol, will, wort, wpyr, wqua, and wkero represent the mass fractions of albite, chlorite, calcite, dolomite, illite, orthoclase, pyrite, quartz, and kerogen, respectively, in w / w.

[0033] Then, the variable skeleton density porosity is calculated using the following model: POR rg =(ρ ma -DEN) / (ρ) ma -ρ f ) × 100% (Equation 2); In the formula, ρ ma ρ f DEN is divided into rock skeleton, pore fluid, and density measurement value, with the unit being g / cm³. 3 ;POR rg Variable skeleton density porosity, in %; ρ f The value is 1.0 g / cm³. 3 .

[0034] Porosity curves can be directly obtained from nuclear magnetic resonance (NMR) logging data. Converting the units to % yields the NMR porosity curve (POR). cmr ; Then, using conventional logging data, the density and neutron curves are superimposed, ensuring overlap between them in non-source rock sections. In the main shale gas producing sections, a certain amplitude difference will appear, defined as the intensity S of the exploitation effect. The density curve's scale range from left to right is 2.2 g / cm³. 3 ~2.9g / cm 3 The neutron curve ranges from 0.45 V / V to 0 V / V from left to right. The amplitude difference between the two curves, i.e., the envelope area S, is calculated to obtain the intensity of the excavation effect. The calculation formula is: S = (0.45 - CNL) / (0.45 - 0) - (DEN - 2.2) / (2.9 - 2.2) (Equation 3); In the formula, S represents the intensity of the excavation effect, which is dimensionless; CNL and DEN are neutron and density measurements, respectively, with units of v / v and g / cm³. 3 .

[0035] S2: Five shale gas wells evenly distributed within the oilfield study area are selected. A porosity correction coefficient w is introduced, and the nuclear magnetic resonance porosity, variable skeleton density porosity, and porosity correction coefficient w are combined to make the calculation result equal to the porosity data obtained from core porosity experiments. This allows the acquisition of the porosity correction coefficient w values ​​at different depths. After refinement, the calculation formula is as follows: w=(1-POR1) / (PORcmr -POR rg (Equation 4); In the formula, POR1 and POR cmr POR rg These represent core-measured porosity, NMR porosity, and variable framework density porosity, all in percentage terms. Using the conventional curves from these five shale gas wells, the influence intensity S of the mining effect at the corresponding depth point was extracted according to the method for determining the influence intensity curve of the mining effect. A cross-plot was then used to establish a graph depicting the relationship between the porosity correction coefficient w and the mining effect intensity S, as shown below. Figure 2 Japanese Style 5: w = S × 2.0326 + 0.0268 (Equation 5); Correlation coefficient R 2 =0.8566; S3: The porosity calculation of the XX well reservoir section of shale gas in the study area can be obtained from the influence intensity S of the mining effect in Equation 3, and then the porosity correction coefficient w can be calculated according to Equation 5. The shale porosity POR calculation model based on elemental and nuclear magnetic resonance logging is as follows: POR = (1-w) × POR cmr +w×POR rg (Equation 6) In the formula, POR and POR cmr POR rg These represent shale reservoir porosity, nuclear magnetic resonance porosity, and variable framework density porosity, respectively, all in units of %; w is the porosity correction coefficient, dimensionless.

[0036] Figure 3 This is a diagram showing the porosity calculation results for well XX. Specifically, conventional, elemental, and nuclear magnetic resonance logging data of well XX in the study area, for which shale porosity needs to be calculated, were collected. The variable skeleton density porosity (POR) of this well was calculated using formulas 1 and 2. rg Based on nuclear magnetic resonance logging data, the nuclear magnetic porosity (POR) was directly obtained. cmr Secondly, according to Formula 4, the mining effect intensity S of Well XX is calculated. Then, according to Formula 5, the porosity correction coefficient w curve of Well XX is calculated. Finally, according to Formula 6, the porosity POR of the shale reservoir of Well XX can be obtained.

[0037] Taking well XX in the case study area as an example, such as Figure 3 It is known that the relative error of nuclear magnetic resonance porosity is 39.12%, and the relative error of variable density porosity is 49.12%. The relative error calculated by the patented method is 9.91%, showing a significant improvement in calculation accuracy. Therefore, the shale gas reservoir porosity calculation method based on elemental and nuclear magnetic resonance logging provided in this application can accurately determine the porosity of shale gas reservoirs, and has significant practical application effects in shale gas reservoir evaluation and reserve calculation.

[0038] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for calculating the porosity of shale gas reservoirs based on elemental analysis and nuclear magnetic resonance logging, characterized in that, Includes the following steps: S1: Obtain variable skeleton density porosity curves, nuclear magnetic resonance porosity curves, and the influence intensity curve of the mining effect based on the logging interpretation data of shale gas wells; S2: Determine the porosity correction coefficient based on the variable skeleton density porosity curve, nuclear magnetic resonance porosity curve and measured porosity, and establish a graph showing the relationship between the influence intensity of the excavation effect and the porosity correction coefficient; S3: Calculate the porosity of shale gas reservoirs based on the relationship between the intensity curve of the mining effect and the porosity correction coefficient. The intensity curve of the excavation effect is obtained by the following formula: S=(CNL L -CNL) / (CNL L –CNL r )-(THE-THE L ) / (THE r –THE L ); Where S represents the intensity of the excavation effect, dimensionless; CNL is the neutron measurement; DEN is the density measurement; CNL L The left-hand value of the neutron curve scale; CNL r DEN is the right-hand value of the neutron curve scale. L This represents the left-hand value of the density curve scale; DEN r This represents the right-hand value of the density curve scale; The steps for establishing a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient are as follows: Based on well logging interpretation data, obtain the measured porosity of core samples from at least five shale gas wells at different depths. Introduce the porosity correction coefficient w, and combine the variable skeleton density porosity curve, the nuclear magnetic resonance porosity curve, and the porosity correction coefficient w. Set the calculated result equal to the measured porosity. After formula refinement, obtain the calculation formula for the porosity correction coefficient at different depths and obtain the porosity correction coefficient curve. Then, following the method for obtaining the intensity curve of the mining effect, extract the numerical values ​​of the intensity of the mining effect at several depths corresponding to the porosity correction coefficients. Finally, establish a graph showing the relationship between the intensity of the mining effect and the porosity correction coefficient. The porosity correction coefficient curves for different depths of the shale gas well are obtained by the following formula: w=(1-POR1) / (POR cmr -POR rg ) Where w is the porosity correction coefficient; POR1 is the measured porosity from the core experiment; POR cmr The NMR porosity curve; POR rg For variable skeleton density porosity; The established graph showing the relationship between the intensity of the excavation effect and the porosity correction coefficient is obtained from the following formula: w = S × a + b; Where a is the slope and b is a constant; The porosity of shale gas reservoirs is calculated using the following formula: POR=(1-in)×POR cmr +w×POR rg ; POR stands for shale gas reservoir porosity.

2. The method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging according to claim 1, characterized in that, The well logging interpretation data includes elemental well logging interpretation data, nuclear magnetic resonance well logging interpretation data, and conventional well logging interpretation data.

3. The method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging according to claim 2, characterized in that, The conventional logging interpretation data includes neutron curves and density curves of shale gas wells; the elemental logging interpretation data includes mass fraction curves of mineral components.

4. The method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging according to claim 3, characterized in that, The mineral composition includes quartz, feldspar, carbonate rock minerals, clay minerals, pyrite, and kerogen.

5. The method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging according to claim 3, characterized in that, The variable skeleton density porosity curve was obtained from elemental logging interpretation data; the nuclear magnetic resonance porosity curve was obtained from nuclear magnetic resonance logging interpretation data; the nuclear magnetic resonance porosity curve was expressed as a percentage; and the influence intensity curve of the excavation effect was obtained from conventional logging interpretation data.

6. The method for calculating shale gas reservoir porosity based on elemental and nuclear magnetic resonance logging according to claim 5, characterized in that, The variable skeleton density porosity curve is obtained by the following formula: POR rg =(ρ ma -DEN) / (p ma -r f )×100%; Among them, POR rg Variable skeleton density porosity, in %; ρ ma ρ is the density of the rock skeleton. f For pore fluids, DEN is the measured density value, in g / cm³. 3 ; Where, ρ ma It is equal to the reciprocal of the sum of the ratios of the mass fractions of each mineral component to the skeletal density value of that mineral component.

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