Shale brittleness mineral evaluation method, data processing terminal and readable storage medium

Abstract

The application discloses a shale brittle mineral evaluation method, a data processing terminal and a readable storage medium, can more accurately determine the shale mineral content, realize the quantitative evaluation of shale brittle mineral. Including collecting and sorting the basic data of shale layer system to be evaluated well, determining the calibration well which is relatively consistent with the layer position and lithology of the well to be evaluated, complete and representative, and collecting all data thereof, using the calibration well data, respectively establishing shale natural gamma logging, resistivity logging, total organic carbon single factor felsic rock content and clay content calculation model. Through multiple linear regression, the final shale mineral felsic rock and clay content calculation formula is further obtained, and the overall significance fitting goodness test is carried out by using the whole rock analysis data and the calculation result. The final shale mineral felsic rock and clay content calculation formula is used to calculate the felsic rock and clay content results of the well to be evaluated, and the quantitative evaluation of the shale brittle mineral of the well to be evaluated is completed.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir exploration and development technology, and in particular to a method for evaluating brittle minerals in shale based on multiple regression, a data processing terminal, and a readable storage medium. Background Technology

[0002] Shale oil and shale gas refer to the oil and natural gas resources contained in shale formations, which are mainly composed of shale. Globally, shale oil and shale gas resources are abundant, with technically recoverable reserves of 469 × 10⁻⁶. 8 t, of which China is 44.8 × 10 8 Due to its extremely low permeability and lack of natural production capacity, shale oil and gas are difficult to develop and utilize under conventional technical conditions. In recent years, with the widespread application of horizontal well + multi-stage fracturing technology both domestically and internationally, the efficient utilization of shale oil and gas has been achieved, resulting in a significant increase in shale oil and gas production.

[0003] Shale brittleness is a crucial indicator for evaluating the compressibility of shale oil and gas reservoirs. For conventional reservoirs such as sandstone and carbonate rocks, core analysis and well logging calculations are relatively mature methods for determining rock brittleness. Currently, most shale brittleness assessments utilize elastic parameter methods, but these methods are influenced by numerous factors and have poor universality. Studies have found that minerals with higher brittleness can form large-scale fracture networks after effective fracturing, improving oil and gas flow channels and thus increasing shale oil and gas production. However, shale mineral types are complex and vary rapidly vertically, and there is currently no method to quantitatively determine the mineral content of shale, which is a major challenge in shale brittleness assessment. The quantitative evaluation of brittle minerals is indispensable for engineering evaluation of shale oil and gas, sweet spot selection, horizontal well fracturing assessment, and well testing design.

[0004] Shale minerals are complex, but mainly composed of quartz, feldspar, calcite, dolomite, and clay minerals. It also contains small amounts of siderite, pyrite, and other minerals formed through biological processes or chemical deposition in oxygen-deficient environments. Based on rock properties, physical characteristics, and oil-bearing capacity, shale can generally be classified into three major mineral assemblages: felsite (including quartz and feldspar), carbonate rocks (including calcite, dolomite, siderite, pyrite, etc.), and clay. Whole-rock analysis uses XRD technology for quantitative analysis of rock minerals. It is unaffected by physical properties and oil-bearing capacity, and can accurately determine the mineral content of shale. However, core sampling and analysis are relatively expensive, especially in horizontal wells with long sections, making it not universally applicable. Natural gamma ray and resistivity logging are affected by lithology and oil-generating capacity, and cannot accurately calculate the mineral content of shale, but they are closely related to the content of felsite and clay. Studies have shown that total organic carbon (TOC) is also closely related to felsite and clay. Summary of the Invention

[0005] The technical problem to be solved by this invention is to propose a method for evaluating brittle minerals in shale, a data processing terminal, and a readable storage medium based on multiple linear regression by utilizing natural gamma, resistivity logging, and pyrolysis analysis data.

[0006] To address the aforementioned technical problems, the present invention employs the following technical solution: a method for evaluating brittle minerals in shale, comprising: collecting data from wells to be evaluated; determining calibration wells and collecting data; using the calibration well data, establishing calculation models for natural gamma ray, resistivity, organic carbon felsite content, and clay content in shale through multiple linear regression; combining the goodness-of-fit test of XRD whole-rock analysis data with the calculation results to obtain the final calculation formulas for felsite and clay content in shale; and applying these formulas to calculate the felsite and clay content in the wells to be evaluated, thus completing the quantitative evaluation of brittle minerals in the shale of the wells to be evaluated.

[0007] Specifically, the following steps are included:

[0008] Step 1: Collect and organize data on shale oil wells to be evaluated. The collected data includes: depth, stratigraphy, lithology, geochemical TOC analysis data, as well as natural gamma and resistivity logging data.

[0009] Step 2: Select a calibration well G1 within the well area to be evaluated or in an area with similar geological conditions, and collect data from calibration well G1. The data from well G1 includes depth, stratigraphy, lithology, geochemical TOC analysis data, as well as natural gamma and resistivity logging data.

[0010] Step 3: Establish a single-factor mineral calculation model

[0011] (1) Using data from calibration well G1, establish a CYY logging system for shale natural gamma ray wells in felsite. G And clay NT G Computational model: Linear regression was performed between the CCY content of felsite and the NT content of clay obtained from the whole-rock XRD analysis of calibration well G1 and the natural gamma ray spectroscopy (GR) to obtain the CYY content of the shale natural gamma felsite. G And clay NT G Calculation formula:

[0012] CYY G = a × GR + b, where a and b are undetermined coefficients.

[0013] NT G = c × GR + d, where c and d are undetermined coefficients.

[0014] a, b, c, and d were obtained through regression analysis using data from calibrated well G1, and T-tests and F-tests were performed.

[0015] (2) Using data from calibration well G1, establish the CYY model for shale resistivity felsite. R And clay NT R Calculation model: The CCY and NT contents of felsite obtained from whole-rock XRD analysis of calibration well G1 are compared with the formation resistivity LgRt on a logarithmic scale, and linear regression is performed to obtain the shale resistivity felsite CYY. R And clay NT R Calculation formula:

[0016] CYY R =e×lgRt+f, where e and f are undetermined coefficients.

[0017] NT R = g × lgRt + h, where g and h are undetermined coefficients.

[0018] e, f, g, h were obtained from regression analysis using data from calibrated well G1, and T-tests and F-tests were performed.

[0019] (3) Using data from calibration well G1, establish the CYY of total organic carbon felsite in shale. T And clay NT T Computational model;

[0020] Linear regression analysis was performed on the CCY and NT contents of felsite and total organic carbon (TOC) obtained from whole-rock XRD analysis of well G1 to obtain the organic carbon (CYY) content of shale felsite. T And clay NT T Calculation formula:

[0021] CYY T = i × TOC + j, where i and j are undetermined coefficients.

[0022] NT T = k × TOC + l, where k and l are undetermined coefficients.

[0023] i, j, k, l were obtained from regression analysis using data from calibrated well G1, and T-test and F-test were performed.

[0024] Step 4: Multiple linear regression to establish the final mineral calculation model and complete the goodness-of-fit test;

[0025] (1) Using gamma, resistivity logging, and organic carbon data, multiple linear regression was performed to obtain the calculation formulas for CYY of shale mineral felsite and NT of clay:

[0026] CYY = m × CYY G +n×CYY R +o×CYY T +p, where m, n, o, and p are undetermined coefficients NT = q × NT G+r×NT R +s×NT T +t, where q, r, s, t are undetermined coefficients m, n, o, p, q, r, s, t are obtained using multiple linear regression, and the independent variable is CYY. G CYY R CYY T For the dependent variable CYY and the independent variable NT G NT R NT T All variables are linearly significant with respect to the dependent variable NT, and the independent variables are linearly significant with respect to the dependent variable as a whole.

[0027] (2) Based on the calculated felsite CYY and clay NT, the formula for calculating shale mineral carbonate rock TSY is further obtained:

[0028] TSY = 100-CYY-NT

[0029] Complete the quantitative evaluation of brittle minerals in the shale of the well to be evaluated.

[0030] The calibration well G1 and the evaluation well Gx have similar stratigraphic positions and lithologies, and the data are complete and representative.

[0031] A data processing terminal for implementing the above-mentioned method for evaluating brittle minerals in shale.

[0032] A computer-readable storage medium includes instructions that, when executed on a computer, cause the computer to perform the aforementioned method for evaluating brittle minerals in shale.

[0033] The beneficial effects of this invention are: it can more accurately evaluate the brittle mineral content of shale, thereby providing favorable support for the engineering evaluation of shale oil and gas, the selection of sweet spots, the evaluation of the fracturing capability of horizontal wells, and the design of oil testing schemes. Attached Figure Description

[0034] Figure 1 This is a flowchart of the shale brittle mineral evaluation method of the present invention;

[0035] Figure 2A This is a graph showing the relationship between felsite content and GR in an embodiment of the present invention;

[0036] Figure 2B This is a graph showing the relationship between clay content and GR in an embodiment of the present invention;

[0037] Figure 3A This is a graph showing the relationship between felsite content and RT in an embodiment of the present invention;

[0038] Figure 3B This is a graph showing the relationship between clay content and RT in an embodiment of the present invention;

[0039] Figure 4A This is a graph showing the relationship between felsite content and TOC in an embodiment of the present invention;

[0040] Figure 4B This is a graph showing the relationship between felsite content and TOC in an embodiment of the present invention;

[0041] Figure 5 This is an evaluation chart of the brittle mineral content of the well to be evaluated in an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] This invention presents a method for evaluating brittle minerals in shale based on multiple regression, targeting shale oil and gas reservoirs. First, data from the wells to be evaluated are collected. Then, calibration wells are identified and their data are collected. Using the calibration well data, multiple linear regression is used to establish calculation models for shale natural gamma ray, resistivity, organic carbon felsite content, and clay content. Combining the goodness-of-fit test between XRD whole-rock analysis data and the calculation results, the final calculation formulas for shale felsite and clay content are obtained. Applying these formulas, the felsite and clay contents of the wells to be evaluated are calculated, ultimately completing the quantitative evaluation of brittle minerals in the shale.

[0044] Specifically, it includes:

[0045] Step 1: Data collection of shale oil wells to be evaluated: Collect and organize the data of the shale oil wells to be evaluated;

[0046] The collected data includes: depth, stratigraphy, lithology, geochemical TOC analysis data, as well as natural gamma ray and resistivity logging data;

[0047] Step 2: Determining the calibration well and collecting data

[0048] Within the well area to be evaluated or in an area with similar geological conditions, select one calibration well G1;

[0049] The calibration well G1 should be comparable to the well Gx to be evaluated in terms of stratigraphy, lithology, data, and representativeness.

[0050] The data from the G1 calibration well includes depth, stratigraphy, lithology (including felsite, carbonate rocks, and clay minerals), geochemical TOC analysis data, and natural gamma and resistivity logging data. The more abundant and well-matched the collected data, the higher the reliability of the evaluation results.

[0051] Step 3: Establish a single-factor mineral calculation model

[0052] Using data from calibration well G1, a CYY logging model for shale natural gamma ray wells was established for felsite. G And clay NT G Computational model;

[0053] The distribution of naturally occurring radionuclides in strata follows certain patterns, primarily related to the lithology and mineral composition of the rocks. Finer lithology, higher clay content, and larger rock particle surface area result in a stronger ability to adsorb radioactive minerals, manifested as a high natural gamma ray. Conversely, coarser lithology and lower clay content lead to a weaker ability to adsorb radioactive minerals, resulting in a low natural gamma ray. For sandstone and carbonate reservoirs, quantitative calculation of sandstone and carbonate rock content using natural gamma logging is a mature method. Shale is affected by factors such as bedding and oil-generating capacity, making precise lithology calculation difficult; however, higher felsic content and lower clay content result in a weaker ability to adsorb radioactive minerals, manifested as a lower amplitude on the natural gamma ray curve.

[0054] As mentioned above, CYY G There is a high linear positive correlation between NT and GR. G There is a high linear negative correlation between them and GR;

[0055] Linear regression analysis was performed on the CCY content of felsite and the NT content of clay obtained from whole-rock XRD analysis of well G1, along with natural gamma ray (GR), to obtain the CYY content of the shale natural gamma felsite. G And clay NT G Calculation formula, i.e.

[0056] CYY G = a × GR + b, where a and b are undetermined coefficients.

[0057] NT G = c × GR + d, where c and d are undetermined coefficients.

[0058] a, b, c, and d were obtained through regression analysis using data from well G1, and T-tests and F-tests were performed.

[0059] Using data from calibration well G1, a CYY model of shale resistivity felsite was established. R And clay NT R Computational model;

[0060] The conductivity of a reservoir is generally determined by the conductivity of solid rock particles and pore fluids. Shale lithology is mainly felsite, carbonate rock and clay. Therefore, the higher the felsite content, the higher the resistivity. Shale oil and shale gas, which are integrated source and reservoir, do not have mobile water. The higher the clay content, the higher the clay-bound water content, and the stronger the rock conductivity, which is reflected as a low value on the resistivity curve.

[0061] The aforementioned shale felsite content CYY R It shows a close linear positive correlation with the formation resistivity value (LgRt) on the logarithmic scale, and the clay content NT R It shows a strong linear negative correlation with the formation resistivity value (LgRt) on the logarithmic scale;

[0062] Linear regression was performed on the felsite CCY and clay NT contents obtained from whole-rock XRD analysis of well G1 and the formation resistivity (LgRt) on a logarithmic scale to obtain the shale resistivity felsite CYY. R And clay NT R Calculation formula:

[0063] CYY R =e×lgRt+f, where e and f are undetermined coefficients.

[0064] NT R = g × lgRt + h, where g and h are undetermined coefficients.

[0065] e, f, g, and h were obtained using regression analysis of data from the calibration well G1, and T-tests and F-tests were performed.

[0066] Using data from calibration well G1, a CYY model for total organic carbon felsite in shale was established. T And clay NT T Computational model;

[0067] The lithology of lacustrine shale is mainly influenced by the supply of clastic sediments, which determines the content of felsite, carbonate rocks, and clay in the study area. Externally introduced organic matter is an important source of kerogen; higher organic matter content correlates with stronger oil-generating capacity. Also influenced by sediment supply, shale lithology is closely related to total organic carbon (TOC) content. Analysis of the organic matter abundance of shale minerals revealed that high felsite content corresponds to high TOC values, while high low-resistivity clay content results in low TOC values.

[0068] The aforementioned TOC and CYY T There is a good linear positive correlation with clay NT. T There is a good linear negative correlation;

[0069] Linear regression analysis was performed on the CCY and NT contents of felsite and total organic carbon (TOC) obtained from whole-rock XRD analysis of well G1 to obtain the organic carbon (CYY) content of shale felsite. T And clay NT T Calculation formula:

[0070] CYY T = i × TOC + j, where i and j are undetermined coefficients.

[0071] NT T = k × TOC + l, where k and l are undetermined coefficients.

[0072] i, j, k, l were obtained from regression analysis using data from well G1, and T-tests and F-tests were performed.

[0073] Step 4: Multiple linear regression to establish the final mineral calculation model and complete the goodness-of-fit test;

[0074] Studies have shown that the contents of shale minerals felsite and clay, in addition to exhibiting good correlations with natural gamma logging (GR) and formation resistivity (LgRt) on a logarithmic scale, are also closely related to total organic carbon (TOC) content. Using gamma, resistivity, and organic carbon data, multiple linear regression can be performed to derive calculation formulas for felsite CYY and clay NT, i.e.

[0075] CYY = m × CYY G +n×CYY R +o×CYY T +p, where m, n, o, and p are undetermined coefficients.

[0076] NT = q × NT G +r×NT R +s×NT T +t, where q, r, s, t are undetermined coefficients m, n, o, p, q, r, s, t are obtained using multiple linear regression, and the independent variable is CYY. G CYY R CYY T For the dependent variable CYY and the independent variable NT G NT R NT T All variables are linearly significant with respect to the dependent variable NT, and the independent variables are linearly significant with respect to the dependent variable as a whole.

[0077] The goodness-of-fit test, T-test, and F-test show that the model is generally significant.

[0078] Shale lithology can generally be divided into three major mineral categories: felsite, carbonate rocks, and clay. Using the above formula to calculate the CYY of felsite and the NT of clay, we can further derive the formula for calculating the TSY of shale minerals and carbonate rocks:

[0079] TSY = 100-CYY-NT

[0080] In the formula above:

[0081] CYY, the final felsite content calculated by multiple regression.

[0082] CYY G Natural gamma ray GR linear regression calculation of felsite content

[0083] CYY R Linear regression calculation of formation resistivity (LgRt) on a logarithmic scale to determine felsite content

[0084] CYY T The total organic carbon (TOC) content of felsite was calculated by linear regression.

[0085] NT, the clay content calculated by the final multiple regression.

[0086] NT G Natural gamma GR linear regression calculation of clay content

[0087] NT R Clay content calculated by linear regression of formation resistivity (LgRt) on a logarithmic scale.

[0088] NT T Total organic carbon (TOC) was calculated using linear regression to determine clay content.

[0089] TSY, carbonate rock content

[0090] The following description, in conjunction with embodiments of the present invention, illustrates a method for evaluating brittle minerals in shale based on multiple regression, including quantitative evaluation of brittle minerals in shale oil and gas reservoirs. Specifically, this involves two aspects: data collection from the wells to be evaluated and determination of calibration wells. Figure 1 Steps S101 and S102 are shown below:

[0091] Step S101: Data collection of wells to be evaluated.

[0092] In this embodiment, the collected Gx data of the well to be evaluated specifically includes: depth, stratification, lithology, TOC, natural gamma ray, resistivity logging data, etc. The collected data are shown in Table 1.

[0093] Table 1. Data on the shale section of well Gx to be evaluated.

[0094]

[0095]

[0096] Step S102: Determine the scale well and collect data.

[0097] In this embodiment, the calibration well G1 is selected. It should be comparable to the Gx stratigraphic position, have the same lithology, have relatively complete data, and be representative. The collected data is shown in Table 2.

[0098] Table 2 Data for Shale Section of Well G1 (Graded Well)

[0099]

[0100] In this embodiment, the quantitative evaluation of brittle minerals in the shale of the well to be evaluated includes three aspects: establishing a single-factor mineral calculation model, obtaining the final calculation formula for the content of felsite and clay in the shale minerals through multiple linear regression, model fit test, and quantitative evaluation of the brittle minerals in the shale of the well to be evaluated. Figure 1 Steps S103 to S105 shown below:

[0101] Step S103: Using data from calibration well G1, obtain the shale gamma ray, resistivity, and organic carbon felsite content (CYY). G CYY R CYY T and clay content NT G NT R NT T Calculation formula.

[0102] The shale mineral composition obtained from whole-rock XRD analysis can be considered as the actual content of felsite, carbonate rock, and clay.

[0103] CYYG=-1.2239*GR+162.46 (Formula 1)

[0104] Among them, R 2 =0.5243, the relationship between GR and CYYG is relatively significant.

[0105] CYYR=31.845*LgRt+9.8317 (Formula 2)

[0106] Among them, R 2 =0.7480, the relationship between LgRt and CYYR is significant.

[0107] CYYT=11.3*TOC+20.604 (Equation 3)

[0108] Among them, R 2 =0.7617, TOC and CYYT are significantly related.

[0109] NTG = 0.199 * GR - 8.9459 (Equation 4)

[0110] Among them, R 2 =0.5356, the relationship between GR and NTG is relatively significant.

[0111] NTR=-4.8339*LgRt+15.41 (Formula 5)

[0112] Among them, R 2 =0.6656, the relationship between LgRt and NTR is significant.

[0113] NTT = -1.7025 * TOC + 13.738 (Equation 6)

[0114] Among them, R 2 =0.6677, TOC and NTT are significantly related.

[0115] Step S104: Using data from calibration well G1, multiple regression analysis is performed to establish calculation formulas for the contents of shale felsite (CYY), clay (NT), and carbonate rock (TSY), and the goodness-of-fit test of the model is completed.

[0116] CYY = -0.4777 * CYY G +18.8537*CYY R +2.8920*CYY T +62.0265 (Equation 7)

[0117] Among them, regression coordination R 2 =0.8569, indicating that the model has a high goodness of fit; the T-test shows that each independent variable CYY G CYY R CYY T The independent variables CYY are linearly significant; the F-test Prob(F-statistic) = 7.17E-177, indicating that the independent variables are linearly significant with respect to the dependent variable as a whole. The goodness-of-fit test, T-test and F-test show that the model is significant overall.

[0118] NT = 0.1051 * NT G -2.7398*NT R -0.3665*NT T +4.2172 (Equation 8)

[0119] Among them, regression coordination R 2 =0.8047, indicating that the model has a high goodness of fit; the T-test shows that for each independent variable NT G NT R NT TThe independent variables are linearly significant for the dependent variable NT; the F-test Prob(F-statistic) = 1.618E-148, indicating that the independent variables are linearly significant for the dependent variable as a whole. The goodness-of-fit test, T-test and F-test show that the model is significant overall.

[0120] Shale lithology can generally be divided into three major mineral categories: felsite, carbonate rocks, and clay. The tests conducted using formulas 7 and 8 show that the above model is generally significant. By calculating the CYY content of felsite and the NT content of clay, the formula for calculating the TSY content of shale carbonate rocks can be further derived.

[0121] TSY = 100 - CYY - NT (Equation 9)

[0122] Step S105: Using the established calculation formulas (Equations 7, 8, and 9) for felsite, carbonate rock, and clay minerals, the brittleness of the shale section in the well Gx to be evaluated is assessed. If CYY < 37% and NT > 13%, the felsite content is low, the clay content is high, and the brittle mineral content is low, classifying it as a Class III shale sweet spot based on engineering compressibility. If 37% ≤ CYY < 60% and 13% ≥ NT > 10%, the felsite content is relatively high, the clay content is relatively low, and it belongs to the medium brittle mineral category, classifying it as a Class II shale engineering sweet spot. If CYY ≥ 60% and NT ≤ 10%, the felsite content is high, the clay content is low, and the brittle mineral content is high, classifying it as a Class I shale engineering sweet spot.

[0123] Table 3. Brittleness Evaluation Table of Shale Section in Well Gx (to be evaluated)

[0124]

[0125] It should be noted that although the above practice was implemented for the Kong-2 section shale oil reservoir in the Cangdong Depression, the technical solution provided by this invention is also applicable to the quantitative evaluation of brittle minerals in shale oil reservoirs in other regions, and has great reference and promotion significance in this industry and field.

[0126] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0127] In summary, the content of this invention is not limited to the above-described embodiments. Those skilled in the art can easily propose other embodiments within the technical guiding principles of this invention, but such embodiments are all included within the scope of this invention.

Claims

1. A method for evaluating the brittle minerals in shale, characterized in that, Includes the following steps: Step 1: Collect and organize data on shale oil wells to be evaluated. The collected data includes: depth, stratigraphy, lithology, geochemical TOC analysis data, as well as natural gamma and resistivity logging data. Step 2: Select a calibration well G1 within the well area to be evaluated or in an area with similar geological conditions, and collect data from calibration well G1. The data from well G1 includes depth, stratigraphy, lithology, geochemical TOC analysis data, as well as natural gamma and resistivity logging data. Step 3: Establish a single-factor mineral calculation model (1) Using data from calibration well G1, establish the CYY model for natural gamma-ray logging of shale felsite. G And clay NT G Computational model: Linear regression was performed between the CCY content of felsite and the NT content of clay obtained from the whole-rock XRD analysis of calibration well G1 and the natural gamma ray spectroscopy (GR) to obtain the CYY content of the shale natural gamma felsite. G And clay NT G Calculation formula: CYY G =a×GR +b, where a and b are undetermined coefficients; NT G =c×GR +d, where c and d are undetermined coefficients; a, b, c, and d were obtained through regression analysis using data from calibrated well G1, and T-tests and F-tests were performed. (2) Using data from calibration well G1, establish the CYY model for shale resistivity felsite. R And clay NT R Calculation model: The CCY and NT contents of felsite obtained from whole-rock XRD analysis of calibration well G1 are compared with the formation resistivity lgRt on a logarithmic scale, and linear regression is performed to obtain the shale resistivity felsite CYY. R And clay NT R Calculation formula: CYY R =e×lgRt+f, where e and f are undetermined coefficients; NT R =g×lgRt+h, where g and h are undetermined coefficients; e, f, g, h were obtained from regression analysis using data from calibrated well G1, and T-tests and F-tests were performed. (3) Using data from calibration well G1, establish the CYY of total organic carbon felsite in shale. T And clay NT T Computational model; Linear regression analysis was performed on the CCY and NT contents of felsite and total organic carbon (TOC) obtained from whole-rock XRD analysis of well G1 to obtain the organic carbon (CYY) content of shale felsite. T And clay NT T Calculation formula: CYY T =i×TOC+j, where i and j are undetermined coefficients; NT T =k×TOC+l, where k and l are undetermined coefficients; i, j, k, l were obtained from regression analysis using data from calibrated well G1, and T-test and F-test were performed. Step 4: Multiple linear regression to establish the final mineral calculation model and complete the goodness-of-fit test; (1) Using gamma, resistivity logging, and organic carbon data, multiple linear regression was performed to obtain the calculation formulas for CYY of shale mineral felsite and NT of clay: CYY=m×CYY G +n×CYY R +o×CYY T +p, where m, n, o, and p are undetermined coefficients; NT = q×NT G +r×NT R +s×NT T +t, where q, r, s, and t are undetermined coefficients; m, n, o, p, q, r, s, t were obtained using multiple linear regression, with the independent variable CYY. G CYY R CYY T For the dependent variable CYY and the independent variable NT G NT R NT T All variables are linearly significant with respect to the dependent variable NT, and the independent variables are linearly significant with respect to the dependent variable as a whole. (2) Based on the calculated CYY of shale mineral felsite and NT of clay, the calculation formula for TSY of shale mineral carbonate rock is further obtained: TSY=100-CYY-NT Complete the quantitative evaluation of brittle minerals in the shale of the well to be evaluated.

2. The method for evaluating brittle minerals in shale according to claim 1, characterized in that, The calibration well G1 and the evaluation well Gx have similar stratigraphic positions and lithologies, and the data are complete and representative.

3. A data processing terminal for implementing the shale brittle mineral evaluation method according to claim 1 or 2.

4. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the shale brittle mineral evaluation method as described in claim 1 or 2.

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