Methods, apparatus, and electronic and storage media for determining the mineral composition of shale reservoirs

Abstract

This application provides a method, apparatus, electronic equipment, and storage medium for determining the mineral composition of shale reservoirs. By pre-classifying lithology, it calculates mineral components such as total organic carbon content and clay content as quantitative constraints on the response equation. Furthermore, based on the cross-plot of mineral components from core analysis, inequality constraints can be established. The calculated parameters and inequality constraints can be used as constraints on the mineral-sensitive logging response equation, resulting in higher accuracy and better alignment with actual conditions when using optimization methods.

Description

Technical Field

[0001] This application relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, electronic equipment and storage medium for determining the mineral composition of shale reservoirs. Background Technology

[0002] Determining mineral composition is crucial for reservoir sedimentary environment analysis, reservoir parameter calculation, rock mechanics parameter prediction, and successful well completion and fracturing. Fully exploiting the mineral content information of shale reservoirs contained in conventional logging data is not only fundamental to solving the aforementioned geological and engineering problems, but also significantly reduces the cost of engineering construction and evaluation, providing a scientific basis for the economic development of unconventional reservoirs. Lithology scanning logging and elemental capture energy dispersive spectroscopy (EDS) logging are effective logging methods for evaluating the mineral composition of complex reservoirs, and both domestically and internationally, these logging data are being used to evaluate the complex mineral composition and content of unconventional reservoirs. However, due to high costs, most appraisal and development wells lack access to these new logging data in addition to conventional logging data. Mineral composition calculations using conventional logging methods typically rely on core experimental data to establish univariate or multivariate linear regression models, but the accuracy of these models often does not meet production requirements. As early as the 1980s, optimization methods were applied to quantitatively evaluate the mineral composition of complex lithologies such as volcanic rocks, carbonate rocks, and complex clastic rocks. These methods fully utilize various well logging response information, establishing response equations based on well logging response values ​​of different mineral frameworks, and solving these equations using optimization methods yields the formation's mineral composition. However, for shale reservoirs, the mineral composition calculated directly using optimization methods often has low accuracy, particularly in total organic carbon content, carbonate mineral content, and iron mineral content, which differ significantly from core analysis. This is because shale reservoirs, especially terrestrial and marine-continental transitional facies reservoirs, exhibit diverse sedimentary environments and lithologies, resulting in significant differences in well logging responses compared to conventional sandstone and mudstone reservoirs. Summary of the Invention

[0003] To address the aforementioned problems, this application provides a method, apparatus, electronic device, and storage medium for determining the mineral composition of shale reservoirs, which can improve the calculation accuracy of mineral composition in shale reservoirs.

[0004] This application provides a method for determining the mineral composition of shale reservoirs, including:

[0005] Obtain characteristic information of mineral components of shale reservoir, well logging curves of shale reservoir, relationship between clay content and water saturation of shale reservoir, and cross-plot of mineral components of shale reservoir core;

[0006] The mineral category is determined based on the aforementioned feature information, and the mineral-sensitive logging response equation is determined based on the different mineral components and the logging curves.

[0007] The lithology of the shale reservoir is determined using the baseline overlap method based on the well logging curves.

[0008] Based on the lithological constraints corresponding to the lithology category, the clay content is calculated based on the well logging curves.

[0009] Based on the lithological constraints corresponding to the lithological category, the organic carbon content is calculated based on the well logging curve and the clay content, and the iron content is calculated using a multiple regression method based on the well logging curve.

[0010] The inequality relationships between mineral components are determined based on the cross-plot of the mineral components in the core.

[0011] The air saturation is determined based on the relationship between the clay content and the water saturation.

[0012] Based on the clay content, organic carbon content, and iron content as quantitative constraints, the inequality relationships between mineral components as inequality constraints, and the range of mineral components as boundary constraints, under these constraints, the well logging curves are corrected for the excavation effect according to the gas saturation, water saturation, and porosity. The mineral composition of the shale reservoir is obtained by iteratively solving the mineral-sensitive well logging response equation using a constrained optimization method.

[0013] In some embodiments, the logging curves include: gamma curves, resistivity curves, neutron curves, and density curves. The determination of the lithology of the shale reservoir using the baseline overlap method based on the logging curves includes:

[0014] The first characteristic line is obtained by overlaying the gamma curve and the resistivity curve at each sampling point;

[0015] The second characteristic line is obtained by overlaying the neutron curve and the density curve at each sampling point;

[0016] The lithology of the shale reservoir is classified based on the curve overlap baseline, lithology threshold, first characteristic line, and second characteristic line.

[0017] In some embodiments, the first feature line is represented as:

[0018]

[0019] Where: RD is the resistivity curve, RD s RD is the baseline for limestone resistivity. c Baseline for mudstone resistivity; GR c The baseline for mudstone GR, GR s Baseline for limestone GR;

[0020] The second characteristic line is represented as:

[0021] Among them: DEN s DEN is the density baseline for limestone. c Baseline for mudstone density; CNL c For mudstone neutron baseline, CNL s The baseline for limestone neutrons.

[0022] In some embodiments, the well logging curve includes a gamma curve and a neutron curve, and the calculation of clay content based on the well logging curve includes:

[0023] Calculate the first clay content based on the gamma curve;

[0024] The content of the second clay was calculated based on the neutron curve.

[0025] The clay content was calculated using a weighted average algorithm based on the first clay content and the second clay content.

[0026] In some embodiments, the calculation of iron content based on well logging curves using a multiple regression method includes:

[0027] Based on the response relationship between iron content and logging curves, sensitive logging curves are selected from the logging curves.

[0028] A multiple regression prediction model for iron content was established based on the aforementioned sensitive logging curves;

[0029] The iron content is calculated based on the aforementioned multiple regression prediction model.

[0030] In some embodiments, the cross-plot between mineral components includes: a cross-plot of silica and clay content, and a cross-plot of calcareous and clay content; determining the inequality relationships between mineral components based on the cross-plot of mineral components in the core includes: determining the inequality relationships between mineral components and the upper and lower boundaries of each mineral component based on the cross-plot of silica and clay content, and the cross-plot of calcareous and clay content.

[0031] In some embodiments, the step of iteratively solving the mineral composition of the shale reservoir using an optimization method based on the mineral-sensitive logging response equation includes:

[0032] The mineral-sensitive logging response equation is converted into matrix form to obtain a matrix function;

[0033] The objective function is determined based on the matrix function, and the objective function is: Where U is the weight of the logging curves involved in the calculation, B is each logging curve, and A is the logging response value corresponding to each mineral component;

[0034] The mineral composition of the shale reservoir is obtained by iteratively solving the objective function using a constrained optimization method.

[0035] In some embodiments, the gas saturation is hydrocarbon saturation, and the logging curve includes a neutron curve. The step of correcting the logging curve for excavation effects based on gas saturation, water saturation, and porosity includes:

[0036] The hydrogen content index in the pores is determined based on hydrocarbon saturation and water saturation.

[0037] The correction amount for the influence of oil and gas on the neutron curve is determined based on the hydrogen content index.

[0038] Determine the correction amount for the mining effect based on the influence correction amount;

[0039] The neutron curve is corrected based on the mining effect correction.

[0040] This application provides a device for determining the mineral composition of a shale reservoir, comprising:

[0041] The acquisition module is used to acquire characteristic information of mineral components of shale reservoirs, well logging curves of shale reservoirs, the relationship between clay content and water saturation of shale reservoirs, and cross-plots of mineral components in shale reservoir cores.

[0042] The first determining module is used to determine the mineral category based on the feature information, and to determine the mineral-sensitive logging response equation based on different mineral components and the logging curve;

[0043] The second determination module is used to determine the lithology of the shale reservoir based on the well logging curves using the baseline overlap method.

[0044] The third determining module is used to calculate the clay content based on the well logging curve, based on the lithological constraints corresponding to the lithology category.

[0045] The fourth determination module is used to calculate the organic carbon content based on the well logging curve and the clay content, and to calculate the iron content based on the well logging curve using a multiple regression method, based on the lithological constraints corresponding to the lithological category.

[0046] The fifth determining module is used to determine the inequality relationships between mineral components based on the cross-plot of the mineral components in the core.

[0047] The sixth determining module is used to determine the air saturation based on the relationship between the clay content and the water saturation.

[0048] The calculation module is used to perform mining effect correction on the logging curve based on the clay content, organic carbon content, and iron content as quantitative constraints, the inequality relationship between mineral components as inequality constraints, and the range of mineral components as boundary constraints. Under the constraints, it performs mining effect correction on the logging curve according to the gas saturation, water saturation, and porosity, and iteratively solves the problem using a constrained optimization method based on the mineral-sensitive logging response equation to obtain the mineral composition of the shale reservoir.

[0049] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs any of the above-described methods for determining the mineral composition of shale reservoirs.

[0050] This application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement the method for determining the mineral composition of shale reservoirs described in any of the above claims.

[0051] This application provides a method, apparatus, electronic equipment, and storage medium for determining the mineral composition of shale reservoirs. By pre-classifying lithology, it calculates mineral components such as total organic carbon content and clay content as quantitative constraints on the response equation. Furthermore, based on the cross-plot of mineral components from core analysis, inequality constraints can be established. The calculated parameters and inequality constraints can be used as constraints on the mineral-sensitive logging response equation, resulting in higher accuracy and better alignment with actual conditions when using optimization methods. Attached Figure Description

[0052] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0053] Figure 1 A schematic diagram illustrating the implementation process of a method for determining the mineral composition of a shale reservoir, provided in an embodiment of this application;

[0054] Figure 2 A schematic diagram illustrating the implementation flow of another method for determining the mineral composition of shale reservoirs provided in this application embodiment;

[0055] Figure 3 A schematic diagram of mineral composition and mineral categories provided for embodiments of this application;

[0056] Figure 4 A schematic diagram of lithology identification established using the baseline overlap method of well logging curves is provided as an embodiment of this application;

[0057] Figure 5 This is a schematic diagram of clay content calculation results provided in an embodiment of this application;

[0058] Figure 6 A schematic diagram illustrating the calculation of total organic carbon content using conventional well logging curves and clay content, provided in this application embodiment;

[0059] Figure 7 A schematic diagram illustrating the relationship between clay content and water saturation is provided for an embodiment of this application.

[0060] Figure 8 This is a schematic diagram illustrating the inequality relationship between calcium content and clay content provided in an embodiment of this application.

[0061] Figure 9 A schematic diagram illustrating the inequality relationship between clay content and silica content provided in an embodiment of this application;

[0062] Figure 10 A schematic diagram of a calculation result provided for an embodiment of this application;

[0063] Figure 11 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application.

[0064] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0066] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0067] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0069] To address the problems existing in related technologies, this application provides a method for determining the mineral composition of shale reservoirs. This method is applied to electronic devices, such as computers and mobile terminals. The functions implemented by the method for determining the mineral composition of shale reservoirs provided in this application can be achieved by the processor of the electronic device calling program code, which can be stored in a computer storage medium.

[0070] Example 1

[0071] This application provides a method for determining the mineral composition of shale reservoirs. Figure 1 This is a schematic diagram illustrating the implementation process of a method for determining the mineral composition of a shale reservoir, as provided in an embodiment of this application. Figure 1 As shown, it includes:

[0072] Step S101: Obtain the characteristic information of mineral components of the shale reservoir, the well logging curve of the shale reservoir, the relationship between clay content and water saturation of the shale reservoir, and the cross-plot of mineral components in the core of the shale reservoir.

[0073] In this embodiment, the characteristic information of mineral components can be obtained through XRD analysis of the shale reservoir. The logging curves may include gamma curves, resistivity curves, neutron curves, density curves, etc. Experiments are conducted on the shale reservoir to establish the relationship between clay content and water saturation. Core samples from the shale reservoir can be analyzed to determine cross-plots of mineral components.

[0074] In the embodiments of this application, the mineral components may include: calcite, dolomite, quartz, potassium feldspar, plagioclase, siderite, pyrite, illite, kaolinite, water, gas, kerogen, etc.

[0075] Step S102: Determine the mineral category based on the feature information, and determine the mineral-sensitive logging response equation based on different mineral components and the logging curve.

[0076] In this embodiment of the application, the mineral category can be determined based on feature information, such as carbonate minerals, silica minerals, iron minerals, clay minerals, water, gas, kerogen, etc.

[0077] In this embodiment of the application, a mineral-sensitive logging response equation can be determined based on different mineral compositions and the logging curves. For example, the mineral-sensitive logging response equation is:

[0078]

[0079] Where: i = 1, 2, ..., m represents the various minerals selected; GR i TH i CNL i DEN i AC i These represent the GR, TH, CNL, DEN, and AC response values ​​for various logging curves corresponding to different minerals; V i This refers to the volume content of various minerals; Porosity; S w Water saturation; S h This represents the hydrocarbon saturation level.

[0080] Step S103: Based on the well logging curves, the baseline overlap method is used to determine the lithology of the shale reservoir.

[0081] In this embodiment, a first characteristic line is obtained by overlaying the gamma curve and resistivity curve based on each sampling point; a second characteristic line is obtained by overlaying the neutron curve and density curve based on each sampling point; and the lithology of the shale reservoir is classified based on the curve overlap baseline, lithology threshold, the first characteristic line and the second characteristic line.

[0082] The first feature line is represented as:

[0083] Where: RD is the resistivity curve, RD s RD is the baseline for limestone resistivity. c Baseline for mudstone resistivity; GR c The baseline for mudstone GR, GR s Baseline for limestone GR;

[0084] The second characteristic line is represented as:

[0085] Among them: DEN s DEN is the density baseline for limestone. c Baseline for mudstone density; CNL c For mudstone neutron baseline, CNL s The baseline for limestone neutrons.

[0086] In this embodiment of the application, the threshold values ​​are exemplarily defined as α1, α2, δ1, and δ2. Table 1 is a schematic table of lithological classification provided in this embodiment of the application. As shown in Table 1,

[0087] Table 1 is a schematic table of lithological classification provided in the embodiments of this application.

[0088]

[0089] The lithological category of the shale reservoir is determined by the curve overlap baseline, lithological threshold, first characteristic line and second characteristic line. For example, it can be argillaceous limestone, siltstone, coal, mudstone shale, bauxite, etc.

[0090] Step S104: Based on the lithological constraints corresponding to the lithological category, calculate the clay content based on the well logging curve.

[0091] In the embodiments of this application, different lithologies have different lithological constraints, which can be pre-set. For example, the lithological constraints corresponding to argillaceous limestone and siltstone are different.

[0092] In this embodiment of the application, the content of the first clay is calculated based on the gamma curve;

[0093] The content of the second clay was calculated based on the neutron curve.

[0094] The clay content was calculated using a weighted average algorithm based on the first clay content and the second clay content.

[0095] For example, the first clay content Vsh_kth is calculated based on the gamma curve;

[0096] The second clay content Vsh_cnl was calculated based on the neutron curve.

[0097] The clay content Vsh is calculated by weighted averaging Vsh_kth and Vsh_cnl, i.e.:

[0098] V clay =a×V sh_cnl +b×V sh_kth .

[0099] Where a and b are weights.

[0100] Step S105: Based on the lithological constraints corresponding to the lithological category, calculate the organic carbon content based on the well logging curve and the clay content, and calculate the iron content based on the well logging curve using a multiple regression method.

[0101] In this embodiment, different calculation methods are used for different strata. In the coal-bearing strata, the bulk density model is used to calculate the total organic carbon content; in the bauxite strata, the clay content is used to calculate the total organic carbon content; and in other lithological strata, a multiple regression prediction model for total organic carbon is established using clay content and conventional well logging curves. In this embodiment, the bulk density model can be obtained based on well logging curves and clay content, or it can be used to calculate the organic carbon content based on the clay content within the clay content.

[0102] In this embodiment, a sensitive logging curve is selected from the logging curves based on the response relationship between iron content and logging curves; a multiple regression prediction model for iron content is established based on the sensitive logging curve; and the iron content is calculated based on the multiple regression prediction model. In this embodiment, the larger the response relationship, the greater the sensitivity; therefore, the logging curve with the largest response relationship can be selected as the sensitive logging curve. The sensitive logging curve can be any of the following: gamma curve, resistivity curve, neutron curve, density curve, etc.

[0103] Step S106: Determine the inequality relationships between mineral components based on the cross-plot of the mineral components in the core.

[0104] In this embodiment of the application, the cross-plot between the mineral components includes: a cross-plot of silica and clay content, and a cross-plot of calcareous and clay content. Determining the inequality relationship between the mineral components based on the cross-plot of the mineral components in the core includes: determining the inequality relationship between the mineral components and the upper and lower boundaries of each mineral component based on the cross-plot of silica and clay content and the cross-plot of calcareous and clay content.

[0105] Step S107: Determine the air saturation based on the relationship between the clay content and the water saturation.

[0106] In this embodiment of the application, a water saturation calculation model can be established based on lithological analysis of mineral components and rock saturation, wherein the water saturation calculation model is S w =c×V clay +d. After calculating the water saturation, the gas saturation can be determined.

[0107] Step S108: Based on the clay content, organic carbon content, and iron content as quantitative constraints, the inequality relationship between mineral components as inequality constraints, and the range of mineral components as boundary constraints, under the constraints, the well logging curve is corrected for the excavation effect according to the gas saturation, water saturation, and porosity, and the mineral composition of the shale reservoir is obtained by iteratively solving the mineral-sensitive well logging response equation using an optimization method.

[0108] In this embodiment of the application, the constraints may include:

[0109]

[0110] Among them, V clay Vtoc represents clay content, Vtoc represents organic carbon content, and V<sub fe This represents the iron content. e, k, m, and n are coefficients, and g and r are boundary conditions.

[0111] In this embodiment of the application, after the constraints are determined, the well logging curve can be corrected for the excavation effect based on the gas saturation, water saturation and porosity.

[0112] In this embodiment, the logging curve can be a neutron curve. The hydrogen content index in the pores can be determined based on hydrocarbon saturation and water saturation. A correction amount for the influence of oil and gas on the neutron curve is determined based on the hydrogen content index. A mining effect correction amount is determined based on the influence correction amount. The neutron curve is then corrected based on the mining effect correction amount. In this embodiment, in addition to fluid correction, mining effect correction is also required for the neutron logging curve. When residual oil and gas are present in the pores, the hydrogen content index H... f for:

[0113] H f =S w H w +S h H h ;

[0114] In the formula: H w The hydrogen content index of water, H h This represents the hydrogen content index of oil and gas. For hydrocarbons, each carbon atom corresponds to x hydrogen atoms. The hydrogen content index is:

[0115]

[0116] Where ρh is the density of the oil / gas. For methane, x = 4, Hh = 2.25ρh.

[0117] The correction factor (CNHc) for the effect of oil and gas on neutrons is:

[0118] CNH c =φ(1-H f );

[0119] Mining effect correction factor (CNEx):

[0120] CNE x =(ADG 2 / 7.02)(2φH f +0.4)CNH c ;

[0121] In the formula: ADG is the particle density.

[0122] The neutron logging values ​​after excavation effect correction are:

[0123] CNL*=CNL+CNE x ;

[0124] In the formula: CNL is the actual measured value.

[0125] While correcting for the mining effect, the objective equation is also solved.

[0126] In this embodiment of the application, the mineral-sensitive logging response equation can be converted into matrix form to obtain a matrix function; based on the matrix function, an objective function is determined, and the objective function is: Wherein, U is the weight of the logging curves involved in the calculation, B is each logging curve, and A is the response value corresponding to each mineral component; the mineral components of the shale reservoir are obtained by iteratively solving the optimization method based on the objective function.

[0127] In this embodiment of the application, the mineral-sensitive logging response equation can be simplified to matrix form:

[0128] Ax = B;

[0129] in:

[0130]

[0131]

[0132] Here, x is the mineral component that needs to be solved.

[0133] In this embodiment of the application, the above-mentioned linear least squares problem can be transformed into a quadratic programming problem, and a suitable optimization algorithm can be used to solve it to obtain the mineral composition of the reservoir rock.

[0134] This application provides a method, apparatus, electronic equipment, and storage medium for determining the mineral composition of shale reservoirs. By pre-classifying lithology, it calculates mineral components such as total organic carbon content and clay content as quantitative constraints on the response equation. Furthermore, based on the cross-plot of mineral components from core analysis, inequality constraints can be established. The calculated parameters and inequality constraints can be used as constraints on the mineral-sensitive logging response equation, resulting in higher accuracy and better alignment with actual conditions when using optimization methods.

[0135] Example 2

[0136] Based on the foregoing embodiments, this application further provides a method for determining the mineral composition of shale reservoirs. Figure 2 Another method for determining the mineral composition of shale reservoirs provided in this application embodiment, the method comprising:

[0137] Step S1: Based on the mineral composition characteristics of the target strata analyzed by XRD, determine the main mineral categories, analyze the relationship between different mineral components and logging curves, and determine the mineral-sensitive logging response.

[0138] Figure 3A schematic diagram of mineral composition and mineral categories provided for embodiments of this application, such as... Figure 3 As shown, the mineral components may include: calcite, dolomite, quartz, potassium feldspar, plagioclase, siderite, pyrite, illite, kaolinite, water, air, kerogen, etc. Mineral categories: carbonate minerals, siliceous minerals, ferruginous minerals, clay minerals, water, air, kerogen, etc.

[0139] Step S2: The baseline overlap method of conventional logging curves is used to classify the main lithology categories.

[0140] Figure 4 A schematic diagram of lithology identification established using the well logging curve baseline overlap method is provided as an embodiment of this application, such as... Figure 4 As shown, lithology can include: coal, shale, siltstone, argillaceous limestone, bauxite, etc.

[0141] Step S3: Based on lithological constraints, calculate the clay content using neutron and uranium-depleted gamma curves.

[0142] Figure 5 This is a schematic diagram of clay content calculation results provided in an embodiment of this application, as shown below. Figure 5 As shown.

[0143] Step S4: Calculate the total organic carbon content using conventional well logging curves and clay content based on lithological constraints.

[0144] Figure 6 A schematic diagram illustrating the calculation of total organic carbon content using conventional well logging curves and clay content is provided as an embodiment of this application. Figure 6 As shown.

[0145] Step S5: Calculate the iron content using a multiple regression method;

[0146] Step S6: Establish the relationship between reservoir clay content and water saturation based on experimental data.

[0147] Figure 7 A schematic diagram illustrating the relationship between clay content and water saturation is provided for an embodiment of this application, as shown below. Figure 7 As shown.

[0148] Step S7: Based on the cross-plot of mineral components from the core analysis, establish the inequality relationship of mineral content.

[0149] In this embodiment, the inequalities include: the inequality between calcium content and clay content, and the inequality between clay content and silica content. Figure 8 This application provides a schematic diagram illustrating the inequality relationship between calcium content and clay content in an embodiment of the present application. Figure 8 As shown. Figure 9A schematic diagram illustrating the inequality relationship between clay content and silica content provided in this application embodiment is shown below. Figure 9 As shown.

[0150] Step S8: Establish a multi-mineral logging response equation system, using clay content, organic carbon content, and pyrite content as quantitative constraints, the relationships between mineral components as inequality constraints, and the range of mineral components as boundary constraints. Correct for neutron logging curve excavation effects based on gas saturation and porosity. Iterate the equation system using an optimization method until convergence or a given number of iterations is reached. The solution to the equations at this point represents the mineral composition of the desired formation. The calculation results are as follows: Figure 10 As shown.

[0151] This application provides a geologically constrained method for optimizing and inverting the mineral composition of shale reservoirs. The method includes: classifying major lithological categories using the baseline overlap method; calculating clay content using neutron and uranium-depleted gamma curves; calculating total organic carbon content using conventional logging curves and clay content; establishing mineral content inequalities based on core analysis results; establishing a multi-mineral logging response equation set, using clay content, organic carbon content, and pyrite content as quantitative constraints, and the relationships between mineral components as inequality constraints; iteratively solving the equation set using an optimization method until convergence or a given number of iterations is reached. The solution to the equations is then the desired mineral composition of the formation.

[0152] Example 3

[0153] Based on the foregoing embodiments, this application provides a device for determining the mineral composition of shale reservoirs. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0154] This application provides a device for determining the mineral composition of a shale reservoir, the device comprising:

[0155] The acquisition module is used to acquire characteristic information of mineral components of shale reservoirs, well logging curves of shale reservoirs, the relationship between clay content and water saturation of shale reservoirs, and cross-plots of mineral components in shale reservoir cores.

[0156] The first determining module is used to determine the mineral category based on the feature information, and to determine the mineral-sensitive logging response equation based on different mineral components and the logging curve;

[0157] The second determination module is used to determine the lithology of the shale reservoir based on the well logging curves using the baseline overlap method.

[0158] The third determining module is used to calculate the clay content based on the well logging curve, based on the lithological constraints corresponding to the lithology category.

[0159] The fourth determination module is used to calculate the organic carbon content based on the well logging curve and the clay content, and to calculate the iron content based on the well logging curve using a multiple regression method, based on the lithological constraints corresponding to the lithological category.

[0160] The fifth determining module is used to determine the inequality relationships between mineral components based on the cross-plot of the mineral components in the core.

[0161] The sixth determining module is used to determine the air saturation based on the relationship between the clay content and the water saturation.

[0162] The calculation module is used to perform mining effect correction on the logging curve based on the clay content, organic carbon content, and iron content as quantitative constraints, the inequality relationship between mineral components as inequality constraints, and the range of mineral components as boundary constraints. Under the constraints, it performs mining effect correction on the logging curve according to the gas saturation, water saturation, and porosity, and iteratively solves the problem using a constrained optimization method based on the mineral-sensitive logging response equation to obtain the mineral composition of the shale reservoir.

[0163] In some embodiments, the logging curves include: gamma curves, resistivity curves, neutron curves, and density curves; the first determining module includes:

[0164] The first determining unit is used to overlay the gamma curve and the resistivity curve based on each sampling point to obtain the first characteristic line;

[0165] The second determining unit is used to overlap the neutron curve and the density curve based on each sampling point to obtain the second characteristic line;

[0166] The third determining unit is used to classify the lithology of the shale reservoir based on the curve overlap baseline, lithology threshold, the first characteristic line and the second characteristic line.

[0167] In some embodiments, the first feature line is represented as:

[0168]

[0169] Where: RD is the resistivity curve, RD s RD is the baseline for limestone resistivity. c Baseline for mudstone resistivity; GR c The baseline for mudstone GR, GR s Baseline for limestone GR;

[0170] The second characteristic line is represented as:

[0171] Among them: DEN s DEN is the density baseline for limestone. c Baseline for mudstone density; CNL c For mudstone neutron baseline, CNL s The baseline for limestone neutrons.

[0172] In some embodiments, the logging curves include: gamma curves and neutron curves, and the third determining module includes:

[0173] The first calculation unit is used to calculate the first clay content based on the gamma curve;

[0174] The second calculation unit is used to calculate the second clay content based on the neutron curve;

[0175] The third calculation unit is used to calculate the clay content based on the first clay content and the second clay content using a weighted average algorithm.

[0176] In some embodiments, the fourth determining module includes:

[0177] The selection unit is used to select sensitive logging curves from the logging curves based on the response relationship between iron content and logging curves.

[0178] A unit is established to build a multiple regression prediction model for iron content based on the sensitive logging curves.

[0179] The fourth calculation unit is used to calculate the iron content based on the multivariate regression prediction model.

[0180] In some embodiments, the cross-plot between the mineral components includes: a cross-plot of silica and clay content, a cross-plot of calcareous and clay content, and a fifth determining module, including:

[0181] The fourth determining unit is used to determine the upper and lower boundaries of each mineral component based on the cross-plot of the silica and clay mineral content and the cross-plot of the calcareous and clay mineral content.

[0182] The fifth determining unit is used to determine the inequality relationship between mineral components based on the upper and lower boundaries.

[0183] In some embodiments, the computing module includes:

[0184] The conversion unit is used to convert the mineral-sensitive logging response equation into matrix form to obtain a matrix function.

[0185] The sixth determining unit is used to determine the objective function based on the matrix function, wherein the objective function is: Where U is the weight of the logging curves involved in the calculation, B is each logging curve, and A is the logging response value corresponding to each mineral component;

[0186] The fifth calculation unit is used to perform iterative optimization based on the objective function to obtain the mineral composition of the shale reservoir.

[0187] In some embodiments, the gas saturation is hydrocarbon saturation, the logging curve includes a neutron curve, and the calculation module includes:

[0188] The seventh determining unit is used to determine the hydrogen content index in the pores based on the hydrocarbon saturation and water saturation.

[0189] The eighth determining unit is used to determine the correction amount for the influence of oil and gas on the neutron curve based on the hydrogen content index;

[0190] The ninth determining unit is used to determine the mining effect correction amount based on the influence correction amount;

[0191] The correction unit is used to correct the neutron curve based on the mining effect correction amount.

[0192] It should be noted that, in the embodiments of this application, if the above-described method for determining the mineral composition of shale reservoirs is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0193] Accordingly, this application provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the method for determining the mineral composition of shale reservoirs provided in the above embodiments.

[0194] Example 4

[0195] This application provides an electronic device; Figure 11 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 11 As shown, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, and a memory 705. The communication bus 702 is configured to enable communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include standard wired and wireless interfaces. The processor 701 is configured to execute a program stored in the memory for determining the mineral composition of shale reservoirs, to implement the steps in the method for determining the mineral composition of shale reservoirs provided in the above embodiments.

[0196] The descriptions of the display device and storage medium embodiments above are similar to those of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0197] It should be noted that the descriptions of the storage medium and device embodiments above are similar to the descriptions of the method embodiments above, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0198] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0199] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0200] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0201] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0202] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0203] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0204] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part 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 mobile storage devices, ROMs, magnetic disks, or optical disks.

[0205] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for determining the mineral composition of a shale reservoir, characterized in that, include: Obtain characteristic information of mineral components of shale reservoir, well logging curves of shale reservoir, relationship between clay content and water saturation of shale reservoir, and cross-plot of mineral components of shale reservoir core; The mineral category is determined based on the aforementioned feature information, and the mineral-sensitive logging response equation is determined based on the different mineral components and the logging curves. The mineral-sensitive logging response equation is: ； Where: i = 1, 2, … , m represent the various minerals selected; GR i TH i CNL i DEN i AC i These represent the logging response values ​​for natural gamma, thorium, neutrons, density, and sonic transit time for various minerals; V i φ represents the volume content of various minerals; φ represents porosity; S represents the volume content of various minerals. w Water saturation; S h , where represents hydrocarbon saturation; GR, TH, CNL, DEN, and AC are the logging response values ​​of natural gamma, thorium, neutron, density, and sonic transit time corresponding to the shale reservoir, respectively. The lithology of the shale reservoir is determined using the baseline overlap method based on the well logging curves. Based on the lithological constraints corresponding to the lithology category, the clay content is calculated based on the well logging curves. Based on the lithological constraints corresponding to the lithological category, the organic carbon content is calculated based on the well logging curve and the clay content, and the iron content is calculated using a multiple regression method based on the well logging curve. The inequality relationships between mineral components are determined based on the cross-plot of the mineral components in the core. The air saturation is determined based on the relationship between the clay content and the water saturation. Based on the clay content, organic carbon content, and iron content as quantitative constraints, the inequality relationship between mineral components as inequality constraints, and the range of mineral components as boundary constraints, under the constraints, the well logging curve is corrected for the excavation effect according to the gas saturation, water saturation, and porosity, and the mineral composition of the shale reservoir is obtained by iteratively solving the constraint optimization method based on the mineral-sensitive well logging response equation. The constraints include: ； Among them, V clay V represents clay content, KTH represents uranium-reduced gamma ray logging value, LITH represents lithology, F(CNL, KTH, LITH) represents the functional relationship with respect to neutrons, uranium-reduced gamma ray, and lithology, and V represents clay content. toc M represents the organic carbon content. toc DEN represents the mass percentage of total organic carbon. toc V represents the density of organic matter. fe Let G(logs) represent the iron content, where G(logs) is the multiple regression model used to calculate the iron content from the well logging curve, e, k, m, and n are coefficients, g and r are boundary conditions, and V is the value of V. i V j V ii V jj This indicates the volume content of different minerals.

2. The method according to claim 1, characterized in that, The logging curves include: gamma curves, resistivity curves, neutron curves, and density curves. The determination of the lithology of the shale reservoir using the baseline overlap method based on the logging curves includes: The first characteristic line is obtained by overlaying the gamma curve and the resistivity curve based on each sampling point; The second characteristic line is obtained by overlaying the neutron curve and the density curve at each sampling point; The lithology of the shale reservoir is classified based on the curve overlap baseline, lithology threshold, first characteristic line, and second characteristic line.

3. The method according to claim 2, characterized in that, The first feature line is represented as: ； Where: RD is the resistivity curve, RD s As the baseline for limestone resistivity, RD c Baseline for mudstone resistivity; GR c The baseline for mudstone GR, GR s Baseline for limestone GR; The second characteristic line is represented as: ; Among them: DEN s DEN serves as the baseline for limestone density. c Baseline for mudstone density; CNL c For mudstone neutron baseline, CNL s The neutron baseline for limestone; GR represents natural gamma, DEN represents density, and CNL represents neutron porosity; logRD2GR represents the difference between the normalized value of the resistivity curve after logarithmic scaling and the normalized value of the gamma curve; DEN2CNL represents the difference between the normalized value of the density curve and the normalized value of the neutron curve.

4. The method according to claim 1, characterized in that, The well logging curves include gamma curves and neutron curves. The calculation of clay content based on the well logging curves includes: Calculate the first clay content based on the gamma curve; The content of the second clay was calculated based on the neutron curve. The clay content was calculated using a weighted average algorithm based on the first clay content and the second clay content.

5. The method according to claim 1, characterized in that, The calculation of iron content based on well logging curves using a multiple regression method includes: Based on the response relationship between iron content and logging curves, sensitive logging curves are selected from the logging curves. A multiple regression prediction model for iron content was established based on the aforementioned sensitive logging curves; The iron content is calculated based on the aforementioned multiple regression prediction model.

6. The method according to claim 1, characterized in that, The cross-plots between the mineral components include: cross-plots of silica and clay content, and cross-plots of calcareous and clay content; the determination of inequality relationships between mineral components based on the cross-plots of the mineral components in the core includes: determining the inequality relationships between mineral components and the upper and lower boundaries of each mineral component based on the cross-plots of silica and clay content, and the cross-plots of calcareous and clay content.

7. The method according to claim 1, characterized in that, The mineral composition of the shale reservoir is obtained by iteratively solving the mineral-sensitive logging response equation using an optimization method, including: The mineral-sensitive logging response equation is converted into matrix form to obtain a matrix function; The objective function is determined based on the matrix function, and the objective function is: Where U is the weight of the logging curves involved in the calculation, B is each logging curve, and A is the logging response value corresponding to each mineral component; The mineral composition of the shale reservoir is obtained by iteratively solving the objective function using a constrained optimization method.

8. The method according to claim 1, characterized in that, The gas saturation is hydrocarbon saturation, and the logging curve includes a neutron curve. The step of correcting the logging curve for excavation effects based on gas saturation, water saturation, and porosity includes: The hydrogen content index in the pores is determined based on hydrocarbon saturation and water saturation. The correction amount for the influence of oil and gas on the neutron curve is determined based on the hydrogen content index. Determine the correction amount for the mining effect based on the influence correction amount; The neutron curve is corrected based on the mining effect correction.

9. A device for determining the mineral composition of a shale reservoir, characterized in that, include: The acquisition module is used to acquire characteristic information of mineral components of shale reservoirs, well logging curves of shale reservoirs, the relationship between clay content and water saturation of shale reservoirs, and cross-plots of mineral components in shale reservoir cores. The first determining module is used to determine the mineral category based on the feature information, and to determine the mineral-sensitive logging response equation based on different mineral components and the logging curve; The mineral-sensitive logging response equation is: ； Where: i = 1, 2, … , m represent the various minerals selected; GR i TH i CNL i DEN i AC i These represent the logging response values ​​for natural gamma, thorium, neutrons, density, and sonic transit time for various minerals; V i φ represents the volume content of various minerals; φ represents porosity; S represents the volume content of various minerals. w Water saturation; S h , where represents hydrocarbon saturation; GR, TH, CNL, DEN, and AC are the logging response values ​​of natural gamma, thorium, neutron, density, and sonic transit time corresponding to the shale reservoir, respectively. The second determination module is used to determine the lithology of the shale reservoir based on the well logging curves using the baseline overlap method. The third determining module is used to calculate the clay content based on the well logging curve, based on the lithological constraints corresponding to the lithology category. The fourth determination module is used to calculate the organic carbon content based on the well logging curve and the clay content, and to calculate the iron content based on the well logging curve using a multiple regression method, based on the lithological constraints corresponding to the lithological category. The fifth determining module is used to determine the inequality relationships between mineral components based on the cross-plot of the mineral components in the core. The sixth determining module is used to determine the air saturation based on the relationship between the clay content and the water saturation. The calculation module is used to perform mining effect correction on the logging curve based on the clay content, organic carbon content and iron content as quantitative constraints, the inequality relationship between mineral components as inequality constraints, and the range of mineral components as boundary constraints. Under the constraints, it performs mining effect correction on the logging curve based on the gas saturation, water saturation and porosity, and iteratively solves the mineral composition of the shale reservoir using the constrained optimization method based on the mineral sensitive logging response equation. The constraints include: ； Among them, V clay V represents clay content, KTH represents uranium-reduced gamma ray logging value, LITH represents lithology, F(CNL, KTH, LITH) represents the functional relationship with respect to neutrons, uranium-reduced gamma ray, and lithology, and V represents clay content. toc M represents the organic carbon content. toc DEN represents the mass percentage of total organic carbon. toc V represents the density of organic matter. fe Let G(logs) represent the iron content, where G(logs) is the multiple regression model used to calculate the iron content from the well logging curve, e, k, m, and n are coefficients, g and r are boundary conditions, and V is the value of V. i V j V ii V jj , indicates the volume content of different minerals.

10. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the method for determining the mineral composition of a shale reservoir as described in any one of claims 1 to 8.

11. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for determining the mineral composition of shale reservoirs as described in any one of claims 1 to 8.

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