Shale reservoir water saturation detection method, device, equipment and medium

Abstract

The invention discloses a shale reservoir water saturation detection method, device and equipment and a medium. The method comprises the following steps: determining target interpretation result information corresponding to target organic matter-rich shale; determining a target detection model correspondingly adopted by the target organic matter-rich shale, wherein the target detection model can be used for describing the porosity, the volume fractions of all the conductive substances, the equivalent resistivity of all the conductive substances, and the relationship between the shale resistivity of the target organic matter-rich shale and the water saturation; and based on the target interpretation result information, determining the water saturation of the target organic matter-rich shale through the target detection model. According to the technical scheme, the shale water saturation is determined by establishing the equivalent resistivity water saturation prediction model of all deep conductive substances, the shale water saturation prediction accuracy is improved, and a foundation is laid for accurately predicting the shale gas saturation and the gas content.

Description

Technical Field

[0001] The present invention relates to the technical field of shale gas development, and in particular to a method, device, equipment and medium for detecting water saturation of a shale reservoir. Background Art

[0002] Shale gas has been widely used as a clean energy source. In order to ensure the geological reserves of clean energy, shale gas exploration and development work has begun to gradually move deeper. Compared with medium and shallow shale (currently buried at a depth of less than 3500m), deep shale (currently buried at a depth of more than 3500m) has the characteristics of high thermal maturity of organic matter and high pyrite content. Because highly mature organic matter and high pyrite content have good electrical conductivity, the water saturation calculated using the commonly used resistivity mathematical model is significantly higher than the water saturation measured experimentally, resulting in lower predicted shale gas saturation and gas content, which restricts the evaluation and optimization of sweet spots for deep shale gas. Summary of the Invention

[0003] The present invention provides a shale reservoir water saturation detection method, device, equipment and medium. By establishing an equivalent resistivity water saturation prediction model for all deep conductive materials, the shale water saturation is determined, thereby improving the accuracy of shale water saturation prediction and laying the foundation for accurately predicting shale gas saturation and gas content.

[0004] According to one aspect of the present invention, a method for detecting water saturation in a shale reservoir is provided, which is applied to a shale reservoir water saturation detection device, wherein the shale reservoir water saturation detection device is disposed at a well site. The method comprises:

[0005] Determining target interpretation result information corresponding to a target organic-rich shale, the target interpretation result information including a pyrite volume fraction, a clay mineral volume fraction, an organic matter volume fraction, and porosity corresponding to the target organic-rich shale, wherein the target organic-rich shale is a high-overmature organic-rich shale;

[0006] Determining a target detection model for the target organic-rich shale, wherein the target detection model can be used to describe the porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, and the relationship between the shale resistivity and water saturation of the target organic-rich shale;

[0007] Based on the target interpretation result information, the water saturation of the target organic-rich shale is determined by the target detection model.

[0008] According to another aspect of the present invention, a shale reservoir water saturation detection device is provided, comprising:

[0009] a target result information determination module, configured to determine target interpretation result information corresponding to a target organic-rich shale, wherein the target interpretation result information includes pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, and porosity corresponding to the target organic-rich shale, and the target organic-rich shale is a high-overmature organic-rich shale;

[0010] A target detection model determination module is used to determine the target detection model corresponding to the target organic-rich shale. The target detection model can be used to describe the porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, and the relationship between the shale resistivity and water saturation corresponding to the target organic-rich shale.

[0011] The water saturation determination module is configured to determine the water saturation of the target organic-rich shale through the target detection model based on the target interpretation result information.

[0012] According to another aspect of the present invention, an electronic device is provided, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the shale reservoir water saturation detection method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the shale reservoir water saturation detection method according to any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention determines the shale water saturation by establishing a deep shale resistivity water saturation prediction model, thereby improving the accuracy of shale water saturation prediction.

[0018] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a method for detecting water saturation in a shale reservoir provided by an embodiment of the present invention;

[0021] Figure 2 This is a technical roadmap of a method for detecting water saturation in a shale reservoir provided according to an embodiment of the present invention;

[0022] Figure 3 The formation factor F and φ+Vol provided in the embodiment of the present invention are py +Vol sh S w +Vol om The relationship curve diagram;

[0023] Figure 4 The resistivity increase coefficient I provided by the embodiment of the present invention is The relationship curve diagram;

[0024] Figure 5 This is a diagram showing the prediction results of shale water saturation in a single well in the study area provided by an embodiment of the present invention;

[0025] Figure 6 2. It is a structural schematic diagram of a shale reservoir water saturation detection device provided by an embodiment of the present invention;

[0026] Figure 7 It is a structural schematic diagram of an electronic device for implementing the shale reservoir water saturation detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Figure 1 The present invention provides a flow chart of a method for detecting water saturation in a shale reservoir. This embodiment is applicable to detecting water saturation in a shale reservoir. The method can be performed by a shale reservoir water saturation detection device. The shale reservoir water saturation detection device can be implemented in the form of hardware and / or software. The shale reservoir water saturation detection device can be configured in any electronic device with network communication function. Figure 1 As shown, the method includes:

[0030] S110. Determine target interpretation result information corresponding to the target organic-rich shale, wherein the target interpretation result information includes a pyrite volume fraction, a clay mineral volume fraction, an organic matter volume fraction, and a porosity corresponding to the target organic-rich shale, and the target organic-rich shale is a high-to-overmature organic-rich shale.

[0031] In the examples of this application, it should be noted that the present invention treats multiple conductive substances (highly mature organic matter, pyrite, clay minerals, formation water) in organic-rich shale as equivalent to a single conductive substance, thereby establishing a corresponding resistivity model. This model predicts the water saturation of deep shale. The target interpretation result information refers to the information parameters involved in determining the water saturation of organic shale, including the volume fractions of multiple conductive substances and shale porosity data. See Figure 2 The technical roadmap of the shale reservoir water saturation detection method of this scheme is shown.

[0032] S120. Determine a target detection model corresponding to the target organic-rich shale, where the target detection model can be used to describe the relationship between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of the target organic-rich shale, and water saturation, and can also describe the correlation between the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, and shale resistivity of the organic-rich shale.

[0033] In the examples of this application, it should be noted that the present invention establishes a water saturation model to predict the water saturation of deep shale. Commonly used shale resistivity calculation models include the Archie model, the Simandoux model, the improved Simandoux model, the Total Shale model, the Indonesian model, the improved Indonesian model, the Dual-Water model, and the Waxman-Smits model. The water saturation model of this solution first determines the resistivity calculation model to be used, and then improves on the existing resistivity model.

[0034] As an optional but non-limiting implementation, the target detection model includes a water saturation calculation function and an equivalent resistivity calculation function for all conductive materials. The water saturation calculation function is a calculation function constructed based on equivalent conductive materials and conductivity theory to describe the correlation between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of organic-rich shale, and water saturation. The equivalent resistivity calculation function for all conductive materials includes a first calculation function and a second calculation function. The first calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, volume fraction of organic matter, and equivalent resistivity of all conductive materials when the organic matter resistivity is less than or equal to the logging resistivity; the second calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, and equivalent resistivity of all conductive materials when the organic matter resistivity is greater than the logging resistivity.

[0035] In this embodiment, the target detection model refers to the resistivity calculation model of this solution. The water saturation calculation function is determined by saturating a dry shale sample with pressurized distilled water. This calculation function uses the equivalent water-saturated shale as a conductive material and conductivity theory to determine the porosity, resistivity, and water saturation of the shale sample. Resistivity is a physical quantity used to represent the electrical resistance of various materials, reflecting the material's resistance to electric current.

[0036] The calculation formula of equivalent resistivity and water saturation of all conductive materials compares the organic resistivity and logging resistivity at the same depth. Logging resistivity refers to the method of measuring the resistivity of rock (including the fluid therein) by using power supply electrodes and measuring electrodes arranged at different positions in the borehole. Logging resistivity is recorded by direct data of the resistivity of the samples in the area measured experimentally. When the organic resistivity R om Less than the logging resistivity RT, the resistivity of the equivalent conductive material inside the shale is obtained by the first calculation function. Organic resistivity R omIf the resistivity is greater than the logging resistivity RT, the organic matter is not considered conductive. The resistivity of the equivalent conductive material within the shale is calculated using the second calculation function. It should be noted that the difference between the two calculation functions lies in whether the organic matter volume fraction and organic matter resistivity are considered.

[0037] As an optional but non-limiting implementation, the target detection model is constructed in the following steps A1-A5:

[0038] Step A1: Determine a plurality of candidate organic-rich shale samples from candidate organic-rich shales in the same study area and having the same reservoir conditions as the target organic-rich shale.

[0039] In the embodiment of the present application, the shale area with a depth of more than 3500m is taken as the research area. The specific operation is to select 9 deep fresh shale core samples in the same structural unit and obtain the logging resistivity value RT of the corresponding depth. The same sample is prepared into a cylindrical sample with a length of 5 cm and a width of 2.5 cm and two powder samples of 10g each.

[0040] Step A2: Determine the porosity and water saturation corresponding to the multiple candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the multiple candidate organic-rich shale samples.

[0041] In the embodiment of the present application, the porosity and water saturation corresponding to the multiple shale samples are determined. First, for the above-mentioned fresh cylindrical sample, the mass m under the original conditions of the formation is first weighed. 原始 , and then bake it in a baking oven at 60℃ for 48 hours, and measure the dry sample mass m 干样 , determine the volume V of water in the original sample by calculating the mass difference of the sample before and after drying 原始 , according to the formula After confirmation, the dried cylindrical sample was vacuumed and pressurized with saturated distilled water for 72 hours, and the mass m after saturation was measured. 饱和 The volume V of water saturated in the sample is determined by calculating the mass difference of the dry sample before and after saturation. 饱和水 , according to the formula The quotient of the volume of saturated water and the volume of the sample is the porosity Φ of the sample, which is determined by the formula Determine the initial water saturation S of the fresh cylindrical sample w Available through and The water saturation of shale under original formation conditions is obtained by The calculation formula is determined. Among them, m 原始 is the mass of the fresh cylinder, g; m 干样 is the mass of the cylindrical dry sample, g; m 饱和水is the mass of the cylinder after saturation with water, g; ρ 水 is the density of distilled water, which is 1.0 g / cm 3 ; V 原始 is the volume of water in the fresh cylindrical sample, cm 3 ; V 饱和水 is the maximum volume of distilled water that can fill the cylinder, cm 3 ; V 圆柱 is the apparent volume of the cylinder, which is 24.53 cm 3 ; φ is the porosity of the sample, dimensionless; S w is the water saturation of shale under formation conditions, dimensionless.

[0042] Step A3: Using a rock resistivity meter, measure the equivalent resistivity of all conductive materials at different water saturations of multiple candidate organic-rich shale samples.

[0043] In the embodiment of the present application, the rock resistivity meter can measure the resistivity values ​​at different water saturations. The resistivity values ​​of 9 selected deep fresh shale core samples at different water saturations were measured using the rock resistivity meter.

[0044] Step A4: Substitute the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, equivalent resistivity of all conductive materials at different water saturations, and corresponding water saturations corresponding to the multiple candidate organic-rich shale samples into the formation factor function F and the resistivity increase coefficient function I determined according to the oil and gas reservoir conductivity theory to obtain the lithology coefficient a and cementation index of the formation factor function F and the constant coefficient b and saturation index n in the resistivity increase coefficient function I.

[0045] In the embodiment of this application, the experimental data of 9 core samples are shown in Table 1, including the sampling depth of the sample core, the logging resistivity RT, the volume fraction of clay minerals Vol sh , whole rock mineral composition and organic matter mass fraction TOC, organic matter volume fraction Vol om 、Volume fraction of pyrite Vol py , the resistivity R of organic matter om , the porosity φ of the sample, and the water saturation S of the shale under formation conditions w Among them, the volume fraction of organic matter is given by the formula Get, DEN is the formation density, unit is g / cm 3 , the formation density DEN can be obtained from the density curve of well logging, ρ om is the density of organic matter, usually 1.4 g / cm 3 . R om It can be obtained through the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer.

[0046] Table 1. Basic experimental data of wells in the study area

[0047]

[0048] The experimental data in the table above were used according to the oil and gas reservoir conductivity theory, and the resistivity of multiple cylindrical samples under different water saturation conditions was measured using a rock resistivity meter. The results were obtained by F-(φ+Vol py +Vol sh S w +Vol om ) or F—(φ+Vol py +Vol sh S w ) to determine the lithology coefficient a and cementation index m; using or The relationship between the determination coefficient b and the saturation index n. Figure 3 Shows the relationship between F and φ+Vol py +Vol sh S w +Vol om The relationship curve of the lithology coefficient a and the cementation index m were finally determined to be 3×10 -7 , 14.3; Figure 4 Shows I and The relationship curve diagram shows that the coefficient b and the saturation index n are finally determined to be 1.11 and 41.23 respectively.

[0049] Step A5: derive and construct a target detection model using the solved formation factor function F, resistivity increase coefficient function I, and equivalent resistivity calculation function of all conductive materials.

[0050] In the embodiment of the present application, a target detection model is constructed to predict the water saturation of shale. First, the equivalent resistivity of all conductive materials is predicted by establishing a resistivity model. The resistivity model consists of a formation factor function F, a resistivity increase coefficient function I, and a resistivity calculation function of the equivalent conductive material in the shale. Jointly derive the calculation.

[0051] As an optional but non-limiting implementation, the formation factor function F is:

[0052] or

[0053] Where F represents the formation factor, dimensionless; R0 represents the saturated resistivity of the cylindrical sample, Ω·m; a represents the lithology coefficient, dimensionless; and m represents the cementation index, dimensionless.

[0054] In the present embodiment, it can be understood that the organic resistivity R om Less than the logging resistivity RT, the formation factor function F is composed of the left function Calculate the organic resistivity R om Greater than the logging resistivity RT, organic matter is not considered as a conductive material, the resistivity of the equivalent conductive material inside the shale, the formation factor function F is given by the function on the right To obtain.

[0055] As an optional but non-limiting implementation, the resistivity increase coefficient function I is:

[0056] or

[0057] Where I represents the resistivity increase coefficient, dimensionless; R t represents the resistivity of a cylindrical sample under a certain water saturation condition, Ω·m; b represents the constant coefficient, dimensionless; n represents the saturation index, dimensionless; S w Represents the water saturation of the cylindrical sample, dimensionless.

[0058] In the present embodiment, it can be understood that the organic resistivity R om Less than the logging resistivity RT, the resistivity increase coefficient function I is the function on the left Calculate the organic resistivity R om If the resistivity is greater than the logging resistivity RT, the organic matter is not considered as a conductive material. The resistivity of the equivalent conductive material inside the shale and the resistivity increase coefficient function I are given by the function on the right. To obtain.

[0059] As an optional but non-limiting implementation, the target detection model is:

[0060]

[0061] Among them, S w Indicates the water saturation of shale, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; Rom The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρ om Indicates the density of organic matter, usually 1.4g / cm 3 .

[0062] In the embodiment of the present application, the target detection model can measure the water saturation of shale under different formation conditions. It is composed of the above five formulas. By comparing the organic resistivity and logging resistivity at the same depth, the corresponding formula is selected for calculation to finally obtain the water saturation of shale. This technical solution extracts the logging interpretation results of pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction and porosity of deep shale sub-sections with a depth greater than 3500 meters, and uses the target detection model to predict the water saturation of shale in this sub-section of the area. See Figure 5 The results of water saturation prediction of shale subsections in the study area are shown in Figure 2. Figure 5 It shows that the water saturation prediction results of this method are in good agreement with the measured results, laying the foundation for the accurate prediction of shale gas saturation and gas content.

[0063] As an optional but non-limiting implementation, the first calculation function is:

[0064]

[0065] The second calculation function is:

[0066]

[0067] Among them, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; R om The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρom Indicates the density of organic matter, usually 1.4g / cm 3 .

[0068] In the embodiment of the present application, it can be understood that by comparing the organic resistivity and the logging resistivity at the same depth, the organic resistivity R om Less than the logging resistivity RT, the resistivity of the equivalent conductive material inside the shale By the formula Calculate the organic resistivity R om Greater than the logging resistivity RT, organic matter is not considered as a conductive material, and the resistivity of the equivalent conductive material inside the shale is By the formula To obtain.

[0069] As an optional but non-limiting implementation, determining the porosity and water saturation corresponding to the multiple candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the multiple candidate organic-rich shale samples, includes the following steps B1-B5:

[0070] Step B1: For each candidate organic-rich shale sample, prepare the same candidate organic-rich shale sample into one candidate organic-rich shale cylindrical sample and two candidate organic-rich shale powder samples.

[0071] In the embodiment of the present application, 9 deep fresh shale core samples were selected in the same structural unit and the logging resistivity value RT of the corresponding depth was obtained. The same sample was prepared into a cylindrical sample with a length of 5 cm and a width of 2.5 cm and two powder samples of 10 g each.

[0072] Step B2: Dry the first candidate organic-rich shale powder sample by baking it in a baking oven at 60° C. for 48 hours, and test the first candidate organic-rich shale powder sample using an X-ray diffractometer and a carbon-sulfur analyzer to obtain the whole-rock mineral composition and organic matter mass fraction of the first candidate organic-rich shale powder sample; and extract organic matter from the second candidate organic-rich shale powder sample and dry it, and measure the resistivity of the organic matter using a powder resistivity tester.

[0073] In the embodiment of the present application, the whole-rock mineral composition and organic mass fraction after drying are obtained from the first powdered shale sample, and the organic resistivity is obtained from the second powdered shale sample.

[0074] Step B3: Weigh the mass of the candidate organic-rich shale cylindrical sample under original formation conditions and determine it as the first mass. Then, bake the candidate organic-rich shale cylindrical sample in a baking oven at 60° C. for 48 hours. Measure the mass of the baked and dried candidate organic-rich shale cylindrical sample and determine it as the second mass.

[0075] In the embodiment of the present application, the first mass is the mass m of the simulated cylindrical shale sample under the original conditions of the formation. 原始 The second mass is the mass of the cylindrical shale sample after drying and dehydration m 干样 The volume V of water in the cylindrical shale sample under the original formation conditions can be calculated by the difference between these two masses. 原始 .

[0076] Step B4: Determine the volume of water in the candidate organic-rich shale cylindrical sample by calculating the mass difference between the first mass and the second mass of the sample before and after drying, and determine it as the first volume. Then, evacuate the dried cylindrical sample and pressurize it with saturated distilled water for 72 hours. Measure the mass after saturation and determine it as the third mass. Determine the volume of water in the candidate organic-rich shale cylindrical sample after saturation by calculating the mass difference between the third mass and the second mass of the dried sample before and after saturation, and determine it as the second volume.

[0077] In the embodiment of the present application, the dried cylindrical sample is pressurized and saturated with distilled water to calculate the mass m of the cylindrical shale sample when it is saturated with water. 饱和 , the volume of water in the saturated sample V is determined by calculating the mass difference of the dry sample before and after saturation 饱和水 The first volume is the volume V of water in the cylindrical shale sample under the original conditions of the formation. 原始 , according to the formula The second volume is the volume V of the dry cylindrical shale sample before and after saturation. 饱和水 , according to the formula Calculate, where ρ 水 is the density of distilled water, which is 1.0 g / cm 3 .

[0078] Step B5: Determine the ratio of the first volume to the cylindrical apparent volume of the candidate organic-rich shale cylindrical sample as the pore size of the candidate organic-rich shale cylindrical sample, and determine the ratio of the first volume to the second volume as the original water saturation of the candidate organic-rich shale cylindrical sample.

[0079] In the embodiment of the present application, it can be understood that the pores in the shale sample under the original formation conditions contain water. After the water under the original formation conditions is removed, the pores are approximately filled with air. Therefore, the original formation water volume V 原始 The porosity Φ of cylindrical shale samples is determined by the formula Calculate, where V圆柱 is the apparent volume of the cylinder, which is 24.53 cm 3 The water saturation of the original shale formation is given by the formula Calculation, S w is the water saturation of shale under formation conditions, dimensionless.

[0080] S130. Based on the target interpretation result information, determine the water saturation of the target organic-rich shale through the target detection model.

[0081] In this embodiment, the target interpretation result information is the aforementioned parameter information, including the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, and resistivity results for the target layer of a single well. The target detection water saturation model is used to predict the water saturation of the shale in the target layer based on experiments.

[0082] As an optional but non-limiting implementation, based on the target interpretation result information, the water saturation of the target organic-rich shale is determined by the target detection model, including the following steps C1-C5:

[0083] Step C1: inputting the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction and porosity corresponding to the organic-rich shale in the target interpretation result information into the target detection model.

[0084] In the embodiments of the present application, it should be noted that the target detection water saturation model requires the volume fractions of minerals and organic matter in the shale and the shale porosity data.

[0085] Step C2: In the target detection model, the organic resistivity of the target organic-rich shale at the same depth is compared with the well logging resistivity at the corresponding depth.

[0086] In this embodiment, the equivalent resistivity calculation function for all conductive materials with different organic resistivities is compared with the logging resistivity at corresponding depths. When the organic resistivity is less than the logging resistivity, the shale is considered to be an equivalent conductive material. If the organic resistivity is greater than the logging resistivity, the organic matter is not considered a conductive material.

[0087] Step C3: If the organic resistivity is less than or equal to the logging resistivity, the equivalent resistivity of all conductive materials in the target organic-rich shale is determined by a first calculation function, which is an equivalent resistivity calculation function of all conductive materials, based on the porosity, volume fraction of pyrite, volume fraction of clay minerals, and volume fraction of organic matter of the target organic-rich shale.

[0088] In an embodiment of the present application, when the organic resistivity is less than the logging resistivity, the equivalent conductive material inside the shale is determined by the first calculation function of the equivalent resistivity of all conductive materials in the target. The first calculation function is related to the porosity of the organic shale, the volume fraction of pyrite, the volume fraction of clay minerals, and the volume fraction of organic matter.

[0089] Step C4: If the organic resistivity is greater than the logging resistivity, the equivalent resistivity of all conductive materials in the target organic-rich shale is determined using a second calculation function, which is an equivalent resistivity calculation function for all conductive materials, based on the porosity, volume fraction of pyrite, and volume fraction of clay minerals corresponding to the target organic-rich shale.

[0090] In an embodiment of the present application, when the organic matter resistivity is greater than the logging resistivity, the organic matter is not considered as a conductive material, and the equivalent resistivity of all conductive materials of the target is determined by the second calculation function of the equivalent resistivity of all conductive materials. The second calculation function is related to the porosity of the organic shale, the volume fraction of pyrite, and the volume fraction of clay minerals.

[0091] Step C5: Based on the porosity corresponding to the target organic-rich shale, the volume fraction of all conductive materials, the equivalent resistivity of all conductive materials, and the shale resistivity corresponding to the target organic-rich shale, the water saturation of the shale corresponding to the target organic-rich shale is determined using a water saturation calculation function.

[0092] In the embodiment of the present application, the water saturation of the shale corresponding to the target layer is obtained through a water saturation calculation function based on the porosity and equivalent resistivity values ​​of all conductive materials calculated under different conditions.

[0093] The present invention discloses a method for detecting water saturation in shale reservoirs. The method comprises: determining target interpretation result information corresponding to a target organic-rich shale, the target interpretation result information including the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, and porosity corresponding to the target organic-rich shale, wherein the target organic-rich shale is a high-to-overmature organic-rich shale; determining a target detection model corresponding to the target organic-rich shale, the target detection model being capable of describing the relationship between porosity, the volume fraction of all conductive materials, the equivalent resistivity of all conductive materials, and the shale resistivity of the target organic-rich shale and water saturation; and determining the water saturation of the target organic-rich shale using the target detection model based on the target interpretation result information. The technical solution of the present invention determines shale water saturation by establishing a deep-layer all-conductive-material equivalent resistivity water saturation prediction model, thereby improving the accuracy of shale water saturation prediction and laying the foundation for accurately predicting shale gas saturation and gas content.

[0094] Figure 6The schematic diagram of the structure of a shale reservoir water saturation detection device provided by an embodiment of the present invention. Figure 6 As shown, the device includes:

[0095] a target result information determination module 610 for determining target interpretation result information corresponding to a target organic-rich shale, wherein the target interpretation result information includes the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, and porosity corresponding to the target organic-rich shale, wherein the target organic-rich shale is a high-to-overmature organic-rich shale;

[0096] a target detection model determination module 620 for determining a target detection model corresponding to the target organic-rich shale. The target detection model can be used to describe the relationship between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of the target organic-rich shale, and water saturation, and can also describe the relationship between the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, and shale resistivity of the organic-rich shale;

[0097] The water saturation determination module 630 is configured to determine the water saturation of the target organic-rich shale using the target detection model based on the target interpretation result information.

[0098] Optionally, determining the target detection model module 620 includes:

[0099] The target detection model includes a water saturation calculation function and an equivalent resistivity calculation function for all conductive materials. The water saturation calculation function is a calculation function constructed based on equivalent conductive materials and conductivity theory, and is used to describe the correlation between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of organic-rich shale, and water saturation. The equivalent resistivity calculation function for all conductive materials includes a first calculation function and a second calculation function. The first calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, volume fraction of organic matter, and equivalent resistivity of all conductive materials when the organic matter resistivity is less than or equal to the logging resistivity; the second calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, and equivalent resistivity of all conductive materials when the organic matter resistivity is greater than the logging resistivity.

[0100] Optionally, the target detection model is constructed by the following steps A1-A5:

[0101] Step A1: Determine a plurality of candidate organic-rich shale samples from candidate organic-rich shales in the same study area and having the same reservoir conditions as the target organic-rich shale.

[0102] Step A2: Determine the porosity and water saturation corresponding to the multiple candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the multiple candidate organic-rich shale samples.

[0103] Step A3: Using a rock resistivity meter, measure the equivalent resistivity of all conductive materials at different water saturations of multiple candidate organic-rich shale samples.

[0104] Step A4: Substitute the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, equivalent resistivity of all conductive materials at different water saturations, and corresponding water saturations corresponding to the multiple candidate organic-rich shale samples into the formation factor function F and the resistivity increase coefficient function I determined according to the oil and gas reservoir conductivity theory to obtain the lithology coefficient a and cementation index of the formation factor function F and the constant coefficient b and saturation index n in the resistivity increase coefficient function I.

[0105] Step A5: derive and construct a target detection model using the obtained formation factor function F, resistivity increase coefficient function I, and equivalent resistivity calculation functions of all conductive materials.

[0106] Optionally, the formation factor function F is:

[0107] or

[0108] Where, F represents the formation factor, dimensionless; R0 represents the saturated resistivity of the cylindrical sample, Ω·m; a represents the lithology coefficient, dimensionless; m represents the cementation index, dimensionless;

[0109] The resistivity increase coefficient function I is:

[0110] or

[0111] Where I represents the resistivity increase coefficient, dimensionless; R t represents the resistivity of a cylindrical sample under a certain water saturation condition, Ω·m; b represents the constant coefficient, dimensionless; n represents the saturation index, dimensionless; S w It represents the water saturation of the cylindrical sample, dimensionless;

[0112] The target detection model is:

[0113]

[0114] Among them, S w Indicates the water saturation of shale, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; R om The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρ om Indicates the density of organic matter, usually 1.4g / cm 3 .

[0115] Optionally, the first calculation function is:

[0116]

[0117] The second calculation function is:

[0118]

[0119] Among them, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; R om The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρ om Indicates the density of organic matter, usually 1.4g / cm 3 .

[0120] Optionally, determining the porosity and water saturation corresponding to the plurality of candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the plurality of candidate organic-rich shale samples, comprises the following steps B1-B5:

[0121] Step B1: For each candidate organic-rich shale sample, prepare the same candidate organic-rich shale sample into one candidate organic-rich shale cylindrical sample and two candidate organic-rich shale powder samples.

[0122] Step B2: Dry the first candidate organic-rich shale powder sample by baking it in a baking oven at 60° C. for 48 hours, and test the first candidate organic-rich shale powder sample using an X-ray diffractometer and a carbon-sulfur analyzer to obtain the whole-rock mineral composition and organic matter mass fraction of the first candidate organic-rich shale powder sample; and extract organic matter from the second candidate organic-rich shale powder sample and dry it, and measure the resistivity of the organic matter using a powder resistivity tester.

[0123] Step B3: Weigh the mass of the candidate organic-rich shale cylindrical sample under original formation conditions and determine it as the first mass. Then, bake the candidate organic-rich shale cylindrical sample in a baking oven at 60° C. for 48 hours. Measure the mass of the baked and dried candidate organic-rich shale cylindrical sample and determine it as the second mass.

[0124] Step B4: Determine the volume of water in the candidate organic-rich shale cylindrical sample by calculating the mass difference between the first mass and the second mass of the sample before and after drying, and determine it as the first volume. Then, evacuate the dried cylindrical sample and pressurize it with saturated distilled water for 72 hours. Measure the mass after saturation and determine it as the third mass. Determine the volume of water in the candidate organic-rich shale cylindrical sample after saturation by calculating the mass difference between the third mass and the second mass of the dried sample before and after saturation, and determine it as the second volume.

[0125] Step B5: Determine the ratio of the first volume to the cylindrical apparent volume of the candidate organic-rich shale cylindrical sample as the pore size of the candidate organic-rich shale cylindrical sample, and determine the ratio of the first volume to the second volume as the original water saturation of the candidate organic-rich shale cylindrical sample.

[0126] Optionally, module 630 includes the following steps C1-C5:

[0127] Step C1: inputting the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction and porosity corresponding to the organic-rich shale in the target interpretation result information into the target detection model.

[0128] Step C2: In the target detection model, the organic resistivity of the target organic-rich shale at the same depth is compared with the well logging resistivity at the corresponding depth.

[0129] Step C3: If the organic resistivity is less than or equal to the logging resistivity, then based on the porosity, volume fraction of pyrite, volume fraction of clay minerals, and volume fraction of organic matter of the target organic-rich shale, the equivalent resistivity of all conductive materials in the target organic-rich shale is determined by a first calculation function.

[0130] Step C4: If the organic resistivity is greater than the logging resistivity, the equivalent resistivity of all conductive materials in the target organic-rich shale is determined by a second calculation function based on the porosity, volume fraction of pyrite, and volume fraction of clay minerals corresponding to the target organic-rich shale.

[0131] Step C5: Based on the porosity corresponding to the target organic-rich shale, the volume fraction of all conductive materials, the equivalent resistivity of all conductive materials, and the shale resistivity corresponding to the target organic-rich shale, the water saturation of the shale corresponding to the target organic-rich shale is determined using a water saturation calculation function.

[0132] The shale reservoir water saturation detection device provided in the embodiment of the present invention can execute the shale reservoir water saturation detection method provided in any embodiment of the present invention mentioned above, and has the corresponding functions and beneficial effects of executing the shale reservoir water saturation detection method. For detailed process, please refer to the relevant operations of the shale reservoir water saturation detection method in the above embodiment.

[0133] Figure 7 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0134] like Figure 7As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0135] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0136] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for detecting water saturation in a shale reservoir.

[0137] In some embodiments, the shale reservoir water saturation detection method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the shale reservoir water saturation detection method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the shale reservoir water saturation detection method in any other suitable manner (e.g., via firmware).

[0138] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0139] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0140] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0141] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0142] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0143] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0144] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0145] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for detecting water saturation of a shale reservoir, characterized in that: The method comprises: Determining target interpretation result information corresponding to a target organic-rich shale, the target interpretation result information including a pyrite volume fraction, a clay mineral volume fraction, an organic matter volume fraction, and porosity corresponding to the target organic-rich shale, wherein the target organic-rich shale is a high-overmature organic-rich shale; Determining a target detection model corresponding to the target organic-rich shale, wherein the target detection model can be used to describe the relationship between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of the target organic-rich shale, and water saturation, and can also describe the correlation between the volume fraction of pyrite, volume fraction of clay minerals, volume fraction of organic matter, porosity, and shale resistivity of the organic-rich shale; Based on the target interpretation result information, the water saturation of the target organic-rich shale is determined by the target detection model.

2. The method according to claim 1, characterized in that The target detection model includes a water saturation calculation function and an equivalent resistivity calculation function for all conductive materials. The water saturation calculation function is a calculation function constructed based on equivalent conductive materials and conductivity theory, and is used to describe the correlation between porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, shale resistivity of target organic-rich shale, and water saturation. The equivalent resistivity calculation function for all conductive materials includes a first calculation function and a second calculation function. The first calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, volume fraction of organic matter, and equivalent resistivity of all conductive materials when the organic matter resistivity is less than or equal to the logging resistivity; the second calculation function is a calculation function used to describe the correlation between porosity, volume fraction of pyrite, volume fraction of clay minerals, and equivalent resistivity of all conductive materials when the organic matter resistivity is greater than the logging resistivity.

3. The method according to claim 2, characterized in that Based on the target interpretation result information, determining the water saturation of the target organic-rich shale through the target detection model includes: Inputting the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction and porosity corresponding to the organic-rich shale in the target interpretation result information into the target detection model; In the target detection model, the organic resistivity of the target organic-rich shale at the same depth is compared with the well logging resistivity at the corresponding depth; If the organic matter resistivity is less than or equal to the well logging resistivity, determining the equivalent resistivity of all conductive materials in the target organic-rich shale using a first calculation function based on the porosity, volume fraction of pyrite, volume fraction of clay minerals, and volume fraction of organic matter of the target organic-rich shale; If the organic matter resistivity is greater than the logging resistivity, then the equivalent resistivity of all conductive materials in the target organic-rich shale is determined by a second calculation function based on the porosity, volume fraction of pyrite, and volume fraction of clay minerals corresponding to the target organic-rich shale; Based on the porosity corresponding to the target organic-rich shale, the volume fraction of all conductive materials, the equivalent resistivity of all conductive materials, and the shale resistivity corresponding to the target organic-rich shale, the water saturation of the shale corresponding to the target organic-rich shale is determined by a water saturation calculation function.

4. The method according to claim 2, characterized in that The construction process of the target detection model is as follows: identifying a plurality of candidate organic-rich shale samples from candidate organic-rich shales in the same study area and having the same reservoir conditions as the target organic-rich shale; Determining the porosity and water saturation corresponding to the plurality of candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the plurality of candidate organic-rich shale samples; The equivalent resistivity of all conductive materials in multiple candidate organic-rich shale samples at different water saturations was measured using a rock resistivity meter. Substituting the pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, porosity, equivalent resistivity of all conductive materials at different water saturations, and corresponding water saturations corresponding to the multiple candidate organic-rich shale samples into the formation factor function F and the resistivity increase coefficient function I determined according to the oil and gas reservoir conductivity theory to obtain the lithology coefficient a and cementation index of the formation factor function F and the constant coefficient b and saturation index n in the resistivity increase coefficient function I; The target detection model is constructed by deducing the formation factor function F, the resistivity increase coefficient function I, and the equivalent resistivity calculation function of all conductive materials.

5. The method according to claim 4, characterized in that The formation factor function F is: or Where, F represents the formation factor, dimensionless; R0 represents the saturated resistivity of the cylindrical sample, Ω·m; a represents the lithology coefficient, dimensionless; m represents the cementation index, dimensionless; The resistivity increase coefficient function I is: or Where I represents the resistivity increase coefficient, dimensionless; R t represents the resistivity of a cylindrical sample under a certain water saturation condition, Ω·m; b represents the constant coefficient, dimensionless; n represents the saturation index, dimensionless; S w It represents the water saturation of the cylindrical sample, dimensionless; The target detection model is: Among them, S w Indicates the water saturation of shale, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; R om The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρ om Indicates the density of organic matter, usually 1.4g / cm 3 .

6. The method according to claim 2, characterized in that The first calculation function is: The second calculation function is: Among them, dimensionless Indicates the equivalent resistivity of various conductive materials, Ω·m; R w Indicates formation water resistivity, Ω·m; Vol py represents the volume fraction of pyrite, dimensionless; R py Represents the resistivity of pyrite, which can be obtained through research and is usually taken as 0.1Ω·m; Vol sh represents the volume fraction of clay minerals, dimensionless; R sh Represents the resistivity of clay minerals, which can be obtained by the scatter plot of logging resistivity and clay mineral volume fraction in the organic-poor layer, Ω·m; Vol om represents the volume fraction of organic matter, dimensionless; R om The resistivity of organic matter can be obtained by plotting the resistivity of organic matter-poor intervals against the volume fraction of clay minerals in the well logging plot (Ω·m); DEN represents the formation density, which can be obtained from the density curve of the well logging plot (g / cm 3 ρ om Indicates the density of organic matter, usually 1.4g / cm 3 .

7. The method according to claim 4, characterized in that Determining the porosity and water saturation corresponding to the plurality of candidate organic-rich shale samples, as well as the whole-rock mineral composition, organic matter mass fraction, and organic matter resistivity corresponding to the plurality of candidate organic-rich shale samples, includes: For each candidate organic-rich shale sample, the same candidate organic-rich shale sample is prepared into one candidate organic-rich shale cylinder sample and two candidate organic-rich shale powder samples; The first candidate organic-rich shale powder sample was dried by baking at 60°C for 48 hours in a baking oven, and the first candidate organic-rich shale powder sample was tested by an X-ray diffractometer and a carbon-sulfur analyzer to obtain the whole-rock mineral composition and organic matter mass fraction of the first candidate organic-rich shale powder sample; and the second candidate organic-rich shale powder sample was subjected to organic matter extraction and drying, and the resistivity of the organic matter was measured using a powder resistivity tester; The mass of the candidate organic-rich shale cylinder sample under original formation conditions is weighed and determined as the first mass. The sample is then baked in a baking oven at 60°C for 48 hours. The mass of the baked and dried candidate organic-rich shale cylinder sample is measured and determined as the second mass. The volume of water in the candidate organic-rich shale cylindrical sample is determined as the first volume by calculating the mass difference between the first mass and the second mass of the sample before and after drying, and then the dried cylindrical sample is vacuumed and pressurized to saturate with distilled water for 72 hours, and the mass after saturation is measured and determined as the third mass. The volume of water in the candidate organic-rich shale cylindrical sample after saturation is determined as the second volume by calculating the mass difference between the third mass and the second mass of the dried sample before and after saturation. The ratio of the first volume to the cylindrical apparent volume of the candidate organic-rich shale cylindrical sample is determined as the pores of the candidate organic-rich shale cylindrical sample, and the ratio of the first volume to the second volume is determined as the original water saturation of the candidate organic-rich shale cylindrical sample.

8. A shale reservoir water saturation detection device, characterized in that: include: a target result information determination module, configured to determine target interpretation result information corresponding to a target organic-rich shale, wherein the target interpretation result information includes pyrite volume fraction, clay mineral volume fraction, organic matter volume fraction, and porosity corresponding to the target organic-rich shale, and the target organic-rich shale is a high-overmature organic-rich shale; A target detection model determination module is used to determine a target detection model corresponding to a target organic-rich shale. The target detection model can be used to describe the porosity, volume fraction of all conductive materials, equivalent resistivity of all conductive materials, and the relationship between the shale resistivity and water saturation corresponding to the target organic-rich shale. The water saturation determination module is configured to determine the water saturation of the target organic-rich shale through the target detection model based on the target interpretation result information.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the shale reservoir water saturation detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the shale reservoir water saturation detection method according to any one of claims 1 to 7 when executed.

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