Develop a method to evaluate the degree of endpoint control in shale reservoir sweet spots

Abstract

The present invention discloses a method for evaluating the sweet spot body of a shale reservoir based on the degree of control of the development endpoint, the method comprising: 1. Standardizing and preprocessing the acquired evaluation parameter data; 2. Threshold control of the standardized data; 3. Based on the evaluation parameters of step 1 and the parameters obtained by threshold control in step 2, respectively establishing a reserve abundance model and a plane control model, and calculating the oil abundance and the plane control degree; 4. Based on the parameters obtained by threshold control, calculating the vertical penetration degree; 5. Based on the parameters obtained by threshold control and the acquired basic physical property parameters of the reservoir such as oil abundance, plane control degree, and vertical penetration degree, the sweet spot body index model is obtained by fusing. The method is mainly based on the degree of control of the development endpoint, and the geological model is characterized from three aspects: reserve abundance characteristics, plane control area, and vertical penetration degree, to establish a shale reservoir sweet spot body evaluation model, select the target layer, and provide strong support for shale oil development.

Description

Technical field:

[0001] The invention relates to a method for evaluating a shale reservoir sweet spot body for the degree of development endpoint control, which is applicable to continental shale oil reservoirs in large freshwater lake basins and belongs to the field of unconventional oil and gas exploration and development. Background technology:

[0002] China's continental source rock formations are rich in oil resources, which is an important area for vigorously enhancing domestic oil exploration and development efforts. At present, the evaluation of shale oil sweet spots is mainly based on the seven-property relationship based on electrical properties, lithology, physical properties, oil content, source rock characteristics, brittleness, and geostress anisotropy. It can be divided into main factor evaluation method, multi-parameter comparative evaluation method, comprehensive information superposition method, comprehensive evaluation index method, weight factor method and other modes. The existing methods provide many parameters that can be used for shale oil sweet spot evaluation, and also provide a detailed introduction to the acquisition methods of some of the parameters. On the whole, from the two perspectives of geology and engineering, the reservoir sweet spot evaluation model has been deeply explored, and many models have been established to guide development and production and achieve certain results. However, the above methods still have shortcomings, mainly in the following two aspects.

[0003] (1) There are defects in the evaluation model structure.

[0004] Single parameter main control evaluation method: reservoir evaluation is only carried out from a certain angle, which is not comprehensive and has great limitations; at the same time, the evaluation standard or classification limit value given is regional geologically specific and difficult to be directly used by other oil fields.

[0005] Multi-parameter parallel evaluation method: (multi-parameter comparative evaluation method, comprehensive information superposition method) The determination of a certain standard requires the simultaneous satisfaction of multiple conditions, the data analysis workload is large, the process is cumbersome and the applicability is weak; the final detailed grading standards are often based on a single parameter, which leads to the final evaluation standards being more subject to geographical limitations and relatively poor generalizability.

[0006] Multi-parameter normalization evaluation method: (comprehensive evaluation index method, weight factor method) Each evaluation parameter is often calculated at the same level, resulting in unclear logical relationship between different parameters; the final evaluation model is mostly obtained through numerical analysis, so the weight coefficient corresponding to each parameter and the operation rules between different parameters do not have clear geological meaning.

[0007] (2) There are defects in the evaluation principle and mechanism.

[0008] Since the existing shale oil reservoir sweet spot evaluation model only evaluates an isolated point inside the reservoir, the evaluation results can only reflect the geological characteristics of this point itself in terms of development. The spatial isolation of the evaluation mechanism breaks the connectivity of the reservoir space and the continuity of the formation, and cannot reflect the actual control degree of the reservoir and the overall utilization space during the development process.

[0009] The above methods have optimized many shale oil reservoir evaluation parameters and established calculation methods for multiple sweet spot evaluations. Many research results have been achieved in single parameter master control evaluation, multi-parameter parallel evaluation and multi-parameter normalization evaluation. However, no evaluation model that can correspond to the geological meaning has been given, making it difficult to solve other shale reservoir evaluations such as the Gulong Shale in the Songliao Basin and effectively guide exploration and development related issues. Summary of the invention:

[0010] The present invention aims to overcome the problems existing in the background technology and provide a method for evaluating the sweet spot body of a shale reservoir with a development endpoint control degree. The method for evaluating the sweet spot body of a shale reservoir with a development endpoint control degree involves three parts: development endpoint reservoir abundance, plane control area, and vertical crossing range. Through point-plane-body three-level calculation, a shale reservoir sweet spot body is established to evaluate the shale reservoir, thereby optimizing the horizontal well target layer and the perforation depth.

[0011] The present invention solves the problem through the following technical solution: a method for evaluating a shale reservoir sweet spot body by developing an endpoint control degree, comprising the following steps:

[0012] Step 1: obtaining evaluation parameters obtained from indoor tests or well logging, and performing standardization (pre-)processing on the obtained evaluation parameter data;

[0013] Step 2, performing threshold control on the evaluation parameters after standardization (pre)processing in step 1;

[0014] Step 3: Based on the evaluation parameters in step 1 and the parameters obtained by threshold control in step 2, a reserve abundance model is established to calculate the oil abundance;

[0015] Step 4: Based on the evaluation parameters in step 1 and the parameters obtained by threshold control in step 2, a plane control model is established to calculate the plane control degree;

[0016] Step 5: Based on the parameters obtained by threshold control in step 2, a vertical crossing model is established to calculate the vertical crossing degree;

[0017] Step 6: Based on the parameters obtained by threshold control in step 2 and the basic reservoir physical parameters such as oil abundance, plane control degree, vertical penetration degree obtained in the above steps, a sweet spot volume index model is obtained to calculate the sweet spot volume index.

[0018] Preferably, the evaluation parameter obtained by indoor testing or logging in step 1 is the free hydrocarbon content S 1 , total organic carbon content TOC, organic matter maturity Ro, porosity Brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h , lamella density ρ, permeability μ; the evaluation parameters for threshold control in step 2 are free hydrocarbon content S 1 , total organic carbon content TOC, porosity Brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h , permeability μ; the evaluation parameters for calculating oil abundance in step 3 are free hydrocarbon content S 1 , total organic carbon content TOC, organic matter maturity Ro; the evaluation parameters for calculating the plane control degree in step 4 are free hydrocarbon content S 1 , horizontal principal stress difference σ H -σ h , lamella density ρ; the evaluation parameters for calculating the vertical penetration degree in step 5 are σ H +σ; the evaluation parameters for calculating the sweet spot body index in step 6 are porosity Permeability μ and oil abundance O, plane control parameter S, depth of bottom of longitudinal sweep section H 1 , the top depth of the longitudinal wave segment H 2 Six parameters.

[0019] Preferably, the step 1 performs standardization (pre-)processing on the acquired evaluation parameter data, limiting them to the same or similar order of magnitude, so as to reduce the error caused by the difference in the dimension of the data;

[0020] The data standardization process can be performed in the following two ways:

[0021]

[0022]

[0023] Where:

[0024] a I : The standardized value of the i-th 0-n in the data column a (dimensionless);

[0025] a i : The i-th original value in data column a (dimensioned);

[0026] n: magnitude parameter;

[0027] in:

[0028] Formula 1-1 regulates the same data distribution boundaries and the same data fluctuations;

[0029] Formula 1-2 regulates the same data distribution boundaries and retains the original data fluctuations.

[0030] and / or,

[0031] Methods for selecting the level of standardization include:

[0032] The standardization level of evaluation parameter data selection: the data is standardized by using 0-10 standardization, 0-2 standardization and 0-1 standardization, which correspond to strong impact parameters, medium impact parameters and weak impact parameters respectively;

[0033] The organic matter maturity Ro, porosity is a weakly influential parameter, standardized by 0-1; the lamella density ρ is a medium-influence parameter, standardized by 0-2; the free hydrocarbon content S 1 , total organic carbon content TOC, brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h , permeability μ are classified as strongly influencing parameters and are standardized using a range of 0-10.

[0034] Preferably, the method for threshold control in step 2 includes:

[0035] According to the distribution of the standardized values ​​of different evaluation parameters, based on the geological meanings corresponding to different evaluation parameters and combined with the regional reservoir development characteristics, the available threshold is selected;

[0036] The data outside the threshold of each parameter is weakened or eliminated before participating in the next step of calculation.

[0037] Preferably, the threshold control is divided into two types: left-type threshold control and right-type threshold control. The specific control methods of the left-type threshold and the right-type threshold include:

[0038] The left-type threshold is to select the minimum threshold, retaining all data greater than the threshold to participate in the next step of calculation, corresponding to those parameters whose larger values ​​contribute more to the evaluation of the sweet spot;

[0039] The right-type threshold is to select the maximum value threshold, retain all data less than the threshold to participate in the next step of calculation, corresponding to those parameters whose smaller values ​​contribute more to the evaluation of the sweet spot.

[0040] Preferably, corresponding to the parameters whose larger values ​​contribute more to the evaluation of the dessert body, there are S 1 、TOC、BI、σ H +σ h , μ; corresponding to those parameters whose smaller values ​​contribute more to the evaluation of the sweet spot, there are σ H -σ h .

[0041] Preferably, the method of establishing a reserve abundance model in step 3 comprises:

[0042] Based on S obtained in step 2 1 , TOC and Ro obtained in step 1 to establish a reserve abundance model;

[0043] Calculate oil abundance based on the established reserve abundance model;

[0044] and / or,

[0045] The reserve abundance model is:

[0046]

[0047] Where:

[0048] O: oil abundance parameter (dimensionless);

[0049] S 1 : free hydrocarbon content (0-10 standardized value, dimensionless);

[0050] TOC: total organic carbon content (0-10 normalized value, dimensionless);

[0051] Ro: organic matter maturity (0-1 standardized value, dimensionless).

[0052] Preferably, the plane control model establishment method in step 4 comprises:

[0053] The size of the plane control area is described by obtaining two parameters, namely, the brittleness index and the plane principal stress difference. The plane control area is generally controlled by the length and width of the fracture. Since the fracturing effect is affected by the brittleness and the anisotropy of the ground stress, the brittleness index and the plane principal stress difference are used to describe the size of the plane control area. Here, the stress difference must be obtained first, and then the result is standardized by 0-10.

[0054] Obtaining the density of lamellae fractures is used to describe the size of the plane control area. When the injection volume of fracturing fluid is sufficient, the lamellae fractures will promote and continue the extension of the fractures on the plane, thereby playing a role in expanding the plane control area.

[0055] and / or,

[0056] The plane control model is:

[0057]

[0058] Where:

[0059] σ H : Maximum principal stress (MPa);

[0060] σ h : Minimum principal stress (MPa);

[0061] σ H -σ h : stress difference (0-10 standardized value, dimensionless);

[0062] ρ: lamella density (0-2 normalized value, dimensionless);

[0063] BI: brittleness index (0-10 normalized value, dimensionless);

[0064] S: plane control parameter (dimensionless);

[0065] Preferably, the method for establishing the vertical crossing model in step 5 includes:

[0066] Get σ H +σ h Parameters: During fracturing, the fracture height will cause penetration in the vertical direction; the fracture height is affected by the maximum principal stress, and the high stress area is easy to penetrate into the low stress area. Therefore, by comparing the longitudinal stress evolution characteristics, a method to describe the fracture height is established;

[0067] Based on the calculated fracture height, obtain the bottom depth H of the longitudinal swept section 1 and the top depth of the longitudinal swept segment H 2 ;

[0068] and / or,

[0069] The relationship describing the seam height is:

[0070] k·f(σ H +σ h )=H 1-5

[0071]

[0072]

[0073] Where:

[0074] k: monitoring seam height conversion coefficient;

[0075] f(x): vertical sweep function;

[0076] H: seam height (m);

[0077] H 1 : depth of the bottom of the longitudinal swept section (m);

[0078] H 2 : Depth of the top of the longitudinal affected section (m).

[0079] Preferably, the method for establishing the sweet body index model in step 6 comprises:

[0080] Get the value obtained in step 2 μ, the porosity and permeability reflect the reservoir storage capacity and seepage capacity respectively. Since the main development target of shale oil is the space inside the fracture, and the space outside the fracture is equivalent to a secondary development target and plays a smaller role, when these two parameters are standardized, it can be considered to reduce one order of magnitude to participate in the calculation of the final sweet spot body evaluation;

[0081] Based on the results obtained in step 2 μ and O, S, H obtained in steps 3 to 5 1 , H 2 Six parameters, using the sweet spot body evaluation formula, calculate the sweet spot body index;

[0082] and / or,

[0083] The calculation formula for the dessert evaluation is:

[0084]

[0085] Where:

[0086] Porosity (0-1 normalized value, dimensionless);

[0087] μ: permeability (0-1 normalized value, dimensionless);

[0088] O: oil abundance parameter (dimensionless);

[0089] SE: Dessert body evaluation index (dimensionless).

[0090] After the calculation of the SE sweet spot body evaluation index is completed, according to the distribution range and change of the SE index at different vertical depths of the whole well, the favorable development section and the advantageous development end point depth domain corresponding to the highest value are selected to determine the target layer of the horizontal well.

[0091] Compared with the above background technology, the present invention has the following beneficial effects:

[0092] (1) Model establishment: The present invention adopts the idea of ​​establishing mathematical operation methods based on geological meanings. According to the structural characteristics of the geological model in the shale reservoir development process, the operation structure is established and the point-surface-volume three-level operation is performed. This fills the gap in the existing reservoir evaluation algorithm when connecting the geological model and the development process, and realizes the unification of the geological model and the mathematical model in the sweet spot volume evaluation process.

[0093] (2) Raw data processing: First, the present invention establishes a complete data standardization method, breaking through the data barriers formed by dimensional differences between different parameters; second, the present invention adopts a "proportional evaluation" model, which minimizes the algorithm necessity, regional limitations, assignment subjectivity and result randomness of the "weight coefficient" in the addition operation, thereby enhancing the fidelity of the original basic experimental data.

[0094] (3) Operation structure: Each geological element corresponding to the evaluation model established by the present invention uses a unique evaluation index to participate in the construction of the final model. This method avoids logical errors in geological meaning caused by repeated calculations of certain parameters; at the same time, each geological model included in the evaluation model adopts independent modular calculations, which provides a convenient channel for the later upgrade and expansion of the algorithm.

[0095] Based on experiments and logging data, the present invention has established a new reservoir evaluation model, achieved rapid comparative evaluation of sweet spots, and convenient optimization and selection of target layers, providing strong support for shale exploration and development deployment. Description of the drawings:

[0096] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to explain the technical solutions of the present invention.

[0097] Figure 1 It is a technical roadmap of the SE sweet spot evaluation method for shale reservoirs of the present invention;

[0098] Figure 2 Schematic diagram of SRV control area according to an embodiment of the present invention;

[0099] Figure 3 Scatter diagram of the relationship between microseismic monitoring and tracer monitoring of fracture height in an embodiment of the present invention;

[0100] Figure 4 Scatter plot of the relationship between the plane control parameters and the microseismic monitoring fracturing plane area in the embodiment of the present invention;

[0101] Figure 5 A scatter plot of the relationship between vertical sweep parameters and tracer monitoring fracture height in an embodiment of the present invention;

[0102] Figure 6This is a JHA well logging comprehensive curve diagram of an embodiment of the present invention;

[0103] Figure 7 This is a standardized comprehensive curve diagram of JHA logging parameters according to an embodiment of the present invention;

[0104] Figure 8 A frequency distribution histogram of the threshold control operation according to an embodiment of the present invention;

[0105] Fig. 9 This is a comprehensive curve diagram of JHA logging parameter threshold control according to an embodiment of the present invention;

[0106] Fig.10 It is a scatter plot of abundance calculation results (O) of an embodiment of the present invention;

[0107] Fig.11 It is a scatter plot of the simulation results of the fracturing plane area (S) of the embodiment of the present invention;

[0108] Fig.12 It is a scatter diagram of the simulation results of the vertical penetration thickness (H) of the hydraulic fracturing according to the embodiment of the present invention;

[0109] Fig.13 This is a dessert comprehensive evaluation (SE) result curve of an embodiment of the present invention. Specific implementation method:

[0110] In order to have a clearer understanding of the technical features, purposes and beneficial effects of the present invention, the technical solution of the present invention is now described in detail below, but it should not be construed as limiting the applicable scope of the present invention.

[0111] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0112] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the obtained embodiment is only an embodiment of a part of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present invention.

[0113] The present invention mainly utilizes shale oil exploration drilling coring, well logging, and supporting geological experimental analysis methods, and adopts S 1 、TOC、Ro、BI、σ H , σ h ,ρ, In actual work, the present invention plays a guiding role in the optimization of the main development layer section and the target layer of the horizontal well, and can meet the needs of shale oil exploration and development deployment.

[0114] In order to incorporate the comprehensive development capability of the reservoir into the final sweet spot body evaluation algorithm, the following three parts were taken into consideration in the process of establishing the evaluation model: first, the geological characteristics of the development endpoint itself (the development endpoint is the starting point of fracturing) should be considered, and the threshold should be optimized according to different parameter characteristics; second, the plane control range of the point during the development process, as well as the oil-bearing characteristics and reserve abundance in the plane should be explored; finally, the vertical penetration capability in the process of reservoir transformation should be explored, and the movable bodies in three-dimensional space (such as Figure 1 shown).

[0115] The present invention relates to developing endpoint reservoir abundance, plane control area, and vertical crossing range. Through point-surface-body three-level calculation, a shale reservoir sweet spot body is established to carry out reservoir evaluation, which includes the following steps:

[0116] Step (1): Data preprocessing

[0117] The S obtained by indoor tests or logging 1 、TOC、Ro、BI、σ H +σ h , σ H -σ h ,ρ, The evaluation parameters such as μ are standardized and limited to the same or similar order of magnitude to reduce the error caused by the difference in data dimensions. The data standardization method can be adopted in the following two ways (Formula 1-1~2):

[0118]

[0119]

[0120] Where:

[0121] a I : The standardized value of the i-th 0-n in the data column a (dimensionless)

[0122] a i : The i-th original value in data column a (dimensioned)

[0123] n: order of magnitude parameter

[0124] Remark:

[0125] Formula 1-1 regulates the same data distribution boundaries and the same data fluctuations;

[0126] Formula 1-2 regulates the same data distribution boundaries and retains the original data fluctuations.

[0127] ① Selecting the standardization level: In the present invention, data standardization is performed using three methods: 0-10 standardization, 0-2 standardization, and 0-1 standardization, which correspond to strong influence parameters, medium influence parameters, and weak influence parameters, respectively.

[0128] Organic matter maturity Ro is standardized by 0-1. Ro is a parameter that changes continuously with depth. Although it reflects the maturity of the formation, the data itself has an obvious linear law at the same depth. Therefore, it is classified as a weakly influential parameter in the present invention to reduce the influence of depth on the final evaluation result.

[0129] Porosity The porosity and permeability μ are standardized by 0-1. Although porosity and permeability reflect the reservoir storage capacity and seepage capacity respectively, these two parameters mainly correspond to the original reservoir space outside the fracture. Since the main development target of shale oil is the space inside the fracture, and the space outside the fracture is equivalent to a secondary development target and plays a smaller role, these two parameters are classified as weakly influential parameters in the present invention.

[0130] The density of laminae ρ is standardized by 0-2. Laminae in shale provide the main storage space for oil and gas, but not all laminae are filled with oil and gas, and have a medium relationship with the evaluation of sweet spot, and are classified as medium-influence parameters in the present invention.

[0131] The remaining parameters are closely related to the evaluation of the dessert body and are classified as strongly influential parameters in the present invention, and are all standardized using a scale of 0-10.

[0132] ②Select the standardization formula:

[0133] In the selection of the standardization method, since the lamella density ρ itself spans multiple orders of magnitude, while other parameters themselves only span two orders of magnitude, in order to reduce the impact of parameter ρ on the final sweet spot body evaluation results, the parameter ρ is standardized using formula 1-1 to reduce the fluctuation of the data; and the remaining parameters are standardized using formula 1-2 to retain the original fluctuation of the data itself.

[0134] Step (2), performing threshold control on the data standardized in step (1).

[0135] According to the distribution of the values ​​after standardization of different parameters, based on the geological meanings corresponding to different evaluation parameters and combined with the regional reservoir development characteristics, the available threshold is selected, and the data outside the threshold of each parameter is weakened or eliminated before participating in the next step of calculation. Here, the maturity Ro has a monotonically increasing gradual change with the deepening of depth, and the lamella density ρ is not used as the main determining variable in the evaluation process, so there is no need to perform threshold control calculations for both.

[0136] Threshold control is divided into two types: left-type threshold and right-type threshold. The left-type threshold selects the minimum threshold and retains all data greater than the threshold to participate in the next step of calculation. For parameters with larger values, the greater contribution to the evaluation of the sweet spot is S. 1 、TOC、BI、σ H +σ h , μ; the right-type threshold is to select the maximum value threshold, retain all data less than the threshold to participate in the next step of calculation, corresponding to those parameters whose smaller values ​​contribute more to the evaluation of the sweet spot, there is σ H -σ h .

[0137] In the application process of the present invention, the threshold control operation is performed through the frequency distribution histogram. According to the left type and the right type, the boundary value of the maximum frequency near the left or right interval is selected as the control threshold, and all data points on the left or right side of the threshold are discarded by assigning a value of 0.

[0138] Step (3): Establishing a reserve abundance model

[0139] Based on S obtained in step (2) 1 The oil abundance is calculated based on the three parameters of TOC and Ro obtained in step (1).

[0140] When evaluating the reserve abundance of shale reservoirs, two parameters are mainly considered: free hydrocarbon content and adsorbed hydrocarbon content. 1 Characterization, the latter can be expressed by converting TOC and Ro (Formula 1-3):

[0141]

[0142] Where:

[0143] O: Oil abundance parameter (dimensionless)

[0144] S 1 : Free hydrocarbon content (0-10 standardized value, dimensionless)

[0145] TOC: Total organic carbon content (0-10 normalized value, dimensionless)

[0146] Ro: organic matter maturity (0-1 standardized value, dimensionless)

[0147] There are two ways to select values ​​in the process of abundance evaluation calculation. The first is to assume that the oil abundance is homogeneous on the plane, and the reserve abundance parameter O calculated by the development endpoint represents the regional oil abundance; the second is to take into account the heterogeneity of the reservoir on the plane, and take the average value of the abundance parameters of different detection points before participating in the next step of calculation.

[0148] Step (4): Establishing a plane control model

[0149] Based on BI and σ obtained in step (2) H -σ h The plane control degree is calculated with the three parameters ρ obtained in step (1).

[0150] The plane control area is generally controlled by the length and width of the fracture. Since the fracturing effect is affected by brittleness and anisotropy of ground stress, the brittleness index and plane principal stress difference are used to describe the size of the plane control area. Here, the stress difference must be obtained first, and then the result is standardized by 0-10. If the two are standardized separately and then subtracted, the mathematical logic between the two principal stresses of the plane is broken and there is no practical significance. At the same time, considering that when the fracturing fluid injection volume is sufficient, the lamellae fractures on the plane will promote and continue the extension of the fractures, thereby playing a role in expanding the plane control area, and the lamellae fracture density is introduced into the formula to participate in the calculation (1-4).

[0151]

[0152] Where:

[0153] σ H : Maximum principal stress (MPa)

[0154] σ h :Minimum principal stress (MPa)

[0155] σ H -σ h : Stress difference (0-10 standardized value, dimensionless)

[0156] ρ: lamella density (0-2 standardized value, dimensionless)

[0157] BI: Brittleness index (0-10 standardized value, dimensionless)

[0158] S: plane control parameter (dimensionless)

[0159] Step (5): Establish vertical crossing model

[0160] Based on σ obtained in step (2) H +σ h Calculate the vertical penetration extent.

[0161] During fracturing, the fracture height will cause penetration in the vertical direction. The fracture height is affected by the maximum principal stress, and the high stress area is easy to penetrate into the low stress area. Therefore, by comparing the longitudinal stress evolution characteristics, a method to describe the fracture height is established (Formulas 1-5 to 7):

[0162] k·f(σ H +σ h )=H 1-5

[0163]

[0164]

[0165] Where:

[0166] k: Monitoring seam height conversion coefficient

[0167] f(x): vertical sweep function

[0168] H: Seam height (m)

[0169] H 1 : Depth of the bottom of the longitudinal sweep section (m)

[0170] H 2 : Depth of the top of the longitudinal swept section (m)

[0171] Formula 1-5 is specifically expanded into Formula 2-1, which is described in detail in the third part of the specific implementation method, SRV simulation of fracturing.

[0172] Since fracture heights vary greatly under different geological conditions, the description of fracture height in the present invention needs to be closely combined with the results of oilfield fracturing monitoring. The present invention provides a method for calculating fracture height, and the fracture height parameter H value in the corresponding model evaluation process can also be directly assigned after being obtained through other methods such as numerical simulation and fracturing simulation. The built-in function f(x) in formula 1-5 is a vertical sweep function, and its specific features will be introduced in the specific implementation method.

[0173] Step (6): Fusion of basic reservoir physical parameters to obtain a sweet spot index model

[0174] Based on the result obtained in step (2) μ and O, S, H obtained in steps (3) to (5) 1 , H 2 Six parameters are used to calculate the sweet spot index.

[0175] Although porosity and permeability reflect reservoir storage capacity and seepage capacity respectively, these two parameters mainly correspond to the original reservoir space outside the fracture. Since the main development target of shale oil is the space inside the fracture, and the space outside the fracture is equivalent to a secondary development target and plays a smaller role, when these two parameters are standardized, it can be considered to reduce one order of magnitude to participate in the calculation of the final sweet spot body evaluation (Formula 1-8).

[0176]

[0177] Where:

[0178] Porosity (0-1 normalized value, dimensionless)

[0179] μ: Permeability (0-1 normalized value, dimensionless)

[0180] SE: Dessert Body Evaluation Index (dimensionless)

[0181] After completing the calculation of the SE sweet spot evaluation index, according to the distribution range and change of the SE index at different vertical depths of the whole well, the favorable development section and the advantageous development endpoint depth domain corresponding to the highest value are selected to determine the target layer of the horizontal well.

[0182] The following describes in detail the evaluation method for shale reservoir sweet spot body of the development endpoint control degree in the development process of the present invention and the specific application examples from the aspects of the evaluation model structure and the evaluation principle mechanism.

[0183] 1. Basic geological evaluation model

[0184] During the development of shale reservoirs, the reserves and recovery rate within the control range of the development endpoint determine the production capacity of the point. Therefore, the sweet spot evaluation is the optimal choice of the development endpoint based on the contribution to production capacity. When the reservoir is subjected to large-scale hydraulic fracturing, the fracture network will not only expand in a planar manner in a symmetrical direction on the plane, but will also cross different foliation planes, lithological interfaces or layers in the vertical direction. Since the reconstruction space of the fracture network basically limits the available space of the reservoir controlled by the development endpoint, the development endpoint control area can be directly described by the three parameters of fracture length, fracture width and fracture height after fracturing.

[0185] Shale has a layered characteristic and can be divided into different "layers" vertically by different lithological interfaces or lamellae. When only a certain depth point is considered, the movable area of ​​the reservoir in the layer at that point can be evaluated by the length and width of the fracture; the comprehensive development capacity of the "layer" at that point can be expressed by the reservoir characteristics at that point. However, when the vertical penetration effect caused by fracturing is taken into account, the vertical affected area must be included in the evaluation model.

[0186] The shale reservoir has a rapid vertical phase change and strong interlayer heterogeneity. The lithology reserves, fracture length and fracture width at different depths are quite different, and even present a mutation state without clear rules. Therefore, it is necessary to calculate the vertical sweep degree of each development endpoint cumulatively to obtain the sweet spot evaluation index corresponding to the development endpoint.

[0187] 2. Methods for constructing mathematical evaluation models

[0188] (1) Evaluation of oil abundance

[0189] In the present invention, the evaluation of oil abundance in shale reservoirs mainly uses S 1 , TOC, and Ro. These three parameters can be quickly obtained through post-processing by logging methods, and there is a monitoring data point every 0.125 meters. Sufficient data volume, mature calculation methods and convenient data acquisition methods provide a solid foundation for the applicability of the oil abundance evaluation module in the present invention.

[0190] The occurrence of crude oil in shale reservoirs is mainly divided into two states: free hydrocarbons and adsorbed hydrocarbons. With the increase of maturity, organic matter gradually begins to generate hydrocarbons. Among the generated oil and gas, the part that is separated from the original organic matter is free hydrocarbons, and the remaining part that is not discharged from the original organic matter is adsorbed hydrocarbons in an adsorbed state. Regardless of the state of hydrocarbons, they will contribute to production capacity during the development of shale reservoirs, but there will be differences in the utilization stage and development rules. Therefore, in the present invention, the oil abundance is characterized by the average value of the two.

[0191] In practical applications, the free hydrocarbon content is directly calculated by logging S 1 Indicates that S must be 1 The 0-10 standardization process is performed. The content of hydrocarbons in the adsorbed state in the shale reservoir is controlled by the organic matter content and is also affected by maturity. The organic matter provides the basis for hydrocarbon generation. The greater the organic matter content, the greater the hydrocarbon generation potential. Maturity characterizes the hydrocarbon generation stage. The higher the maturity, the more hydrocarbons have been formed. In this regard, in the present invention, TOC and R O Because the relationship between hydrocarbon generation and maturity is not clear, TOC is standardized to 0-10 before calculation, and Ro is standardized to 0-1 to reduce the influence of Ro on the final result during multiplication.

[0192] (2) Fracturing SRV evaluation

[0193] The fracturing SRV evaluation method provided in the present invention is based on the ideal fracturing engineering conditions of sufficient injection pressure and unlimited sand supply, and then establishes an ideal geological environment where the fracturing effect is only affected by reservoir factors. At the same time, it reduces the proportion of engineering accidental factors in the evaluation model and is more suitable for the needs of sweet spot evaluation.

[0194] ① Plane parallel seam

[0195] For the fracture network formed on the bedding plane after fracturing, its fracture length and width are mainly controlled by the reservoir brittleness index and stress difference. Since the influencing factors of the two parameters of fracture length and fracture width overlap, if the dual-parameter evaluation method is adopted, not only is it difficult to establish the model and the law is not easy to grasp, but it is also likely to cause repeated calculations of the evaluation parameters in the model, resulting in confusion in the operation logic. In order to improve the accuracy, the dual parameters are converted into a single parameter, and the plane fracture network area (fracture length × fracture width) is selected, and a proportional comparison is performed through qualitative calculation.

[0196] ②Vertical interlayer seam

[0197] The evaluation model needs to accumulate the evaluation parameters obtained from the previous calculation of the corresponding depth segment according to the fracture height data, so a fracture height quantitative simulation method must be established based on the fracture height data monitored on site. Since the height of the hydraulic fracture is subject to regional restrictions, its main control parameters and control laws may be different. Therefore, a vertical sweep function f(x) based on ground stress is given in the model, and the functional relationship is defined according to the monitored fracture height. The fracture height provides a limited area for the cumulative calculation, so even if the stress parameters have been used in other modules, the evaluation model still does not have repeated calculations (such as Figure 2 shown).

[0198] (3) Initial reservoir property evaluation

[0199] Reservoir physical parameters can first reflect the reservoir storage space, and then indirectly reflect the oil content. The oil abundance has been evaluated in other modules and does not need to be considered again here. Secondly, it can reflect the original seepage characteristics of the reservoir, but compared with the seepage channels constructed by the medium-pressure fracture network during development, the contribution is relatively weak.

[0200] Considering the role of physical property parameters in the initial characteristics of reservoirs and the evaluation of productivity contribution, the physical property parameters are used as positive coefficients and multiplied with other plane evaluation parameters before the fracture height accumulation operation. During the operation, in order to reduce the influence of physical property parameters and ensure the stability of the order of magnitude distribution of other parameter calculation results, the physical property parameters are standardized by 0-1.

[0201] 3. Fracturing SRV simulation method

[0202] The establishment process of the fracturing SRV simulation method provided by the present invention is as follows:

[0203] (1) Optimization of monitoring parameters

[0204] In the process of monitoring the fracturing of shale oil wells in the Songliao Basin, there are two methods: microseismic monitoring and tracer monitoring. However, there is a big gap between the final monitoring values ​​of the two. According to the fracture height data of the JHA well fracturing monitoring: the microseismic monitoring single-segment fracture height (vibration peak height) is distributed between 27-35m, while the tracer monitoring single cluster fracture height (radial fracture height) is between 7.5-10.9m. According to the scatter plot comparison analysis of the maximum monitored fracture heights of the two in the same fracturing section, the two monitored fracture heights are positively correlated (such as Figure 3 shown).

[0205] Since microseismic monitoring detects the location where vibration is transmitted rather than the location where the rock cracks, the vibration fracture height may exceed the actual fracture height; for the tracer monitoring method, it cannot be guaranteed that all fractures are filled with radioactive tracers in engineering, and radioactivity may be missing at the end of some fracture networks, so the radioactive fracture height may be lower than the actual fracture height. Considering that the vibration fracture height is greater than the actual fracture height and greater than the radioactive fracture height, and there is a proportional relationship between them. Therefore, the values ​​obtained by different monitoring methods are not contradictory. It is recommended to take the minimum parameter as the limit for quantitative calculation.

[0206] (2) Plane simulation

[0207] First, since the greater the brittleness, the easier it is for the fracture network to extend, and the smaller the stress difference, the weaker the interference and restriction on the fracture network, the plane control parameter (S) in the evaluation model is positively correlated with the brittleness index (BI) and the maximum and minimum principal stress difference (σ H -σ h ), the brittleness index of the above three parameters is standardized by 0-10 during the calculation process, and the stress difference result is standardized by 0-1. Secondly, under ideal fracturing conditions, the denser the lamellae fractures in the reservoir, the easier it is for the fractures to open and extend to the far end. Therefore, the density of lamellae fractures (ρ) is used as a positive relationship coefficient in the calculation. The distribution range of (ρ) during the calculation is 0-2000 / m, and 0-2 is used in the standardization process. Here, the original 0 value of the non-laminated area should be converted to 1 before participating in the standardization, because when the lamellae fractures are not developed, it does not mean that fracturing is impossible. The error caused by the value of 0 should be prevented here.

[0208] According to formula 1-4, the plane control parameter S of the five fracturing stages of the JHA well was obtained, and the fracturing plane control area was calculated according to the monitoring results. Since the proportional comparison analysis was carried out here, the fracturing plane control area was directly obtained by multiplying the microseismic monitoring fracture length and fracture width. The scatter plot comparison analysis shows that the plane control parameter is positively correlated with the fracturing plane control area, and the linear fitting formula fitting accuracy R 2is 0.79, which fully proves the accuracy of the theoretical basis of plane control parameters and the applicability of formulas 1-4 (such as Figure 4 shown).

[0209] (3) Vertical simulation

[0210] The vertical fracture height simulation of fracturing is based on the ground stress characteristics of the development endpoint, exploring the functional relationship between stress and tracer monitoring fracture height, and establishing a vertical sweep function. There is a more obvious functional relationship between the sum of the maximum and minimum principal stresses on the plane at the development endpoint and the fracture height (e.g. Figure 5 The stress and distribution of JHA well are between 95-120MPa. They are first normalized to 0-10 and then shifted to the left by 2 units of the horizontal axis to obtain the vertical sweep parameters. The vertical sweep parameters are logarithmically related to the fracture height, and the fitting accuracy is R 2 When it exceeds 0.86, the vertical sweep function established by comparison has strong applicability (Formula 2-1).

[0211]

[0212] Since the distribution range of the monitored fracture height is 0-10, which is the same as the stress and standardization range, the fracture height conversion coefficient k corresponding to formula 1-5 is 1. In the process of establishing the vertical sweep function fitting, in order to better explore the fracture height change law, the monitoring data was optimized and selected based on geological theory. A total of 12 data points were obtained from the fracture height monitoring. First, the minimum stress and the same extreme point corresponding to the maximum fracture height were discarded in the trend analysis process; secondly, two points far from the trend line were discarded in the formula fitting process. These two data points were symmetrically distributed on both sides of the trend line. Finally, 9 data points were used for formula fitting, and the data utilization rate was 75%.

[0213] 4. Embodiment

[0214] The implementation process of the method of the present invention is described below by taking the continental shale reservoir sweet spot evaluation method in the JHA well test area in the northern large freshwater lake basin of the Songliao Basin in the Daqing exploration area as an example.

[0215] 1. Research background

[0216] The JHA block in the study area is located in the central depression area in the northern part of the Songliao Basin. The internal structure of the JHA block is relatively flat, and the faults are mainly developed on the east and west sides, thus forming two fault zones with a horst in the middle. The central horst structure is U-shaped, with the middle being the low part of the structure and the north and south being the high parts of the structure. The exploration target layer of the JHA block is the Q 1 ~Q 9The deepest top surface elevation of each oil layer is located in the northeastern fault zone of the JHA well, and the shallowest top surface elevation is located beside the fault in the southeastern part of the study area, with an average structural height difference of 133m. In order to realize the three-dimensional development and utilization of the strata, it is necessary to select the best development layer section, so it is of great significance to establish a convenient and complete sweet spot evaluation method.

[0217] 2. Standardization of geological parameters

[0218] In the process of applying the present invention to the sweet spot evaluation of the JHA well, the free hydrocarbon content S 1 (0-20%), total organic carbon content TOC (0-4%), organic matter maturity Ro (1-2%), porosity Brittleness index BI (20-60 dimensionless), horizontal principal stress difference σ H -σ h (0-6MPa), horizontal principal stress and σ H +σ h (90-120MPa), lamella density ρ (0-2000 / m), a total of 8 parameters are involved in the calculation (see Figure 6 JHA logging comprehensive curve diagram).

[0219] The values ​​of different parameters range from 0.01 to 12,000,000, spanning 10 orders of magnitude, and need to be standardized. The 0-1 standardization is adopted, the ρ standardization is selected as 0-2, and the other five parameters are standardized by 0-10. In terms of the standardization method, the parameter ρ is standardized by formula 1-1, and the other parameters are standardized by formula 1-2. After standardization, the values ​​of all parameters are distributed between 0.5 and 10, spanning only three orders of magnitude (see Figure 7 Standardized comprehensive curve of JHA logging parameters).

[0220] 3. Threshold control operation

[0221] In the application process of the present invention, the threshold control operation can be performed through the frequency distribution histogram. This time, the boundary value of the interval adjacent to the maximum frequency is selected as the control threshold, and all data points in a certain direction of the threshold are discarded by assigning a value of 0. The threshold control operation is performed on the basis of the standardized data, and can be divided into two types according to the direction of the discarded area: the left type threshold that retains the high value area and the right type threshold that retains the low value area. Among them, S 1 The selected threshold is 4.3, the selected threshold for TOC is 4.04, The selected threshold is 0.62, the selected threshold for BI is 6.64, the stress and σ H +σ h The selected threshold is 2.51. The above five parameters are all left-type thresholds; stress difference σH -σ h The selected threshold is 4.67, which is a right-shaped threshold (such as Figure 8 After preliminary data processing, the logging curve after threshold control is finally obtained (as shown in Fig. 9 shown).

[0222] 4. Reserve abundance model calculation

[0223] Based on the S obtained after the threshold control operation 1 The three parameters Ro obtained after standardization of TOC and geological parameters are calculated by formula 1-3. The O values ​​at different depths in the target layer of JHA well are used to select seven favorable sweet spots. The first section is 2463.625-2464.75m, with a thickness of 1.125m and an extreme value of 7.7; the second section is 2469.125-2471.75m, with a thickness of 2.625m and an extreme value of 8.6; the third section is 2549.25-2550.5m, with a thickness of 1.25m and an extreme value of 7.7; the fourth section is 2561.125-2563.875m, with a thickness of 2.75m and an extreme value of 9.9; the fifth section is 2572.625-2575m, with a thickness of 2.375m and an extreme value of 9.5; the sixth section is 2575.375-2576.375m, with a thickness of 1m and an extreme value of 8.3; the seventh section is 2577.125-2577.75m, with a thickness of 0.625m and an extreme value of 7.2( Fig.10 ). The reserve abundance parameter O increases with depth and has a gradual change characteristic.

[0224] 5. Fracturing plane scale simulation

[0225] Based on the BI,σ obtained after the threshold control operation H -σ hThe three parameters ρ obtained after standardization with geological parameters are used to calculate the S values ​​at different depths in the target layer of the JHA well according to formulas 1-4, and ten favorable sweet spots are selected. The first section is 2452.125-2452.375m, with a thickness of 0.25m and an extreme value of 10.1; the second section is 2475.625-2475.875m, with a thickness of 0.25m and an extreme value of 12.6; the third section is 2510.25-2510.5m, with a thickness of 0.25m and an extreme value of 10.6; the fourth section is 2514.875-2515.625m, with a thickness of 0.75m and an extreme value of 11.1; the fifth section is 2532.5-2533.125m, with a thickness of 0.625m and an extreme value of 11.9; the sixth section 2536.875-2537m, thickness 0.125m, extreme value 10.1; the seventh section 2550.625-2550.875m, thickness 0.25m, extreme value 14.5; the eighth section 2568.625-2569m, thickness 0.375m, extreme value 14.6; the ninth section 2569.375-2570.125m, thickness 0.75m, extreme value 14.3; the tenth section 2583.75-2584 thickness 0.25m, extreme value 10.3 ( Fig.11 ). The plane control parameter S increases with depth and has a mutation characteristic.

[0226] 6. Simulation of vertical penetration of fracturing thickness

[0227] Based on the threshold control operation, the obtained σ H +σ h According to formula 2-1, the H values ​​at different depths in the target layer of the JHA well were calculated, and nine favorable sweet spots were selected. The first section is 2465.125-2465.375m, with a thickness of 0.25m and an extreme value of 10.1; the second section is 2488-2488.25m, with a thickness of 0.25m and an extreme value of 10.2; the third section is 2522.875-2523m, with a thickness of 0.125m and an extreme value of 10; the fourth section is 2528.5-2529m, with a thickness of 0.5m and an extreme value of 10.1; the fifth section is 2529.375-2530.75m, with a thickness of 0.25m and an extreme value of 10.2; The sixth section is 2532.75-2533.625m thick, 0.875m thick, and the extreme value is 10.7; the seventh section is 2537.125-2537.5m thick, 0.375m thick, and the extreme value is 10.2; the eighth section is 2544.125-2544.5m thick, 0.375m thick, and the extreme value is 10.3; the ninth section is 2583.5-2584m thick, 0.5m thick, and the extreme value is 10.3 ( Fig.12 ). The vertical penetration thickness H increases with depth and has the characteristics of periodic fluctuation.

[0228] 7. Final dessert evaluation

[0229] Based on the threshold control operation μ and O obtained from the reserve abundance model, S obtained from the plane control model, and H obtained from the vertical crossing model 1 , H 2 The six parameters were used to calculate the SE values ​​at different depths in the target layer of the JHA well according to formula 1-8, and seven favorable development sections were finally divided. The development endpoints were optimized according to the SE index variation characteristics within each development section ( Fig.13 ).

[0230] In the first development section of 2450-2457m, four advantageous development endpoint depth domains (SE>420) were selected: (1) 2450.625-2450.75m, with an average SE index of 423; (2) 2452.125-2453.25m, with an average SE index of 440; (3) 2454.125-2454.75m, with an average SE index of 440; (4) 2455.375-2456.25m, with an average SE index of 486.

[0231] The second development section is 2500-2511m, and two advantageous development endpoint depth domains (SE>400) are selected: (1) 2507.25-2508.75m, with an average SE index of 478; (2) 2509.125-2509.875m, with an average SE index of 411.

[0232] In the third development section 2512-2526m, four advantageous development endpoint depth domains (SE>450) were selected: (1) 2515.25-2515.75m, average SE index 460; (2) 2518.125-2518.375m, average SE index 451; (3) 2520.125-2520.5m, average SE index 455; (4) 2522.625-2523m, average SE index 458.

[0233] In the fourth development section 2536-2542m, two advantageous development endpoint depth domains (SE>410) were selected: (1) 2537.5-2537.75m, with an average SE index of 412; (2) 2538.5-2539.25m, with an average SE index of 422.

[0234] In the fifth development section 2546-2556m, three advantageous development endpoint depth domains (SE>865) were selected: (1) 2548.375-2549.125m, with an average SE index of 868; (2) 2550.25-2551m, with an average SE index of 909; (3) 2552.5-2553.25m, with an average SE index of 878.

[0235] In the sixth development section, 2557-2568 m, two advantageous development endpoint depth domains (SE>1620) were selected: (1) 2562.375-2563 m, with an average SE index of 1638; (2) 2563.375-2564.5 m, with an average SE index of 1678;

[0236] In the seventh development section 2569-2581m, five advantageous development endpoint depth domains (SE>1260) were selected: (1) 2572.75-2573m, average SE index 1263; (2) 2573.875-2575.375m, average SE index 1495; (3) 2576.125-2576.5m, average SE index 1418; (4) 2577.25-2577.75m, average SE index 1392; (5) 2578.75-2578.875m, average SE index 1263.

[0237] The present invention takes the JHA well test area of ​​the continental shale reservoir in the large freshwater lake basin of the Songliao Basin as an example to carry out the sweet spot body evaluation research and verify the invented method. Firstly, in terms of data preprocessing, in order to reduce the errors caused by the dimensional differences of different parameters, two data standardization methods are used in a targeted manner. In order to reflect the control degree of independent parameters in the sweet spot evaluation process, the model accommodates the threshold operation steps; secondly, in terms of fracturing simulation, according to the results of microseismic and tracer field monitoring and combined with the characteristics of ground stress, a fracturing SRV simulation method is established, and the simulation formulas of plane control parameters and vertical sweep parameters are provided; finally, in terms of model construction, the mode of "plane independent operation, vertical superposition and accumulation" is adopted. Through the three-level operation of point-surface-body, the spatial isolation of the existing sweet spot evaluation mechanism is broken, and the description of the actual control degree of the reservoir and the overall utilization space during the development process is realized. Based on the JHA well, seven favorable development sections and 22 advantageous development endpoint depth domains were finally selected, which effectively guided the exploration, development and production of shale oil and enriched the connotation of unconventional oil and gas geology and the basic theory of shale oil exploration and development.

[0238] The above is only a partial embodiment of the present invention, but the present invention should not be limited to the contents disclosed in the embodiment and the drawings. Therefore, any equivalent or modification completed without departing from the technical solution disclosed in the present invention shall fall within the scope of protection of the present invention.

Claims

1. A method for evaluating the sweet spot of a shale reservoir by developing the degree of endpoint control, characterized by: The following steps are involved: Step 1: obtaining evaluation parameters obtained from indoor tests or well logging, and performing standardized preprocessing on the obtained evaluation parameter data; Step 2, performing threshold control on the evaluation parameters after the standardized preprocessing in step 1; Step 3: Based on the evaluation parameters in step 1 and the parameters obtained by threshold control in step 2, a reserve abundance model is established to calculate the oil abundance; Step 4: Based on the evaluation parameters in step 1 and the parameters obtained by threshold control in step 2, a plane control model is established to calculate the plane control degree; Step 5: Based on the parameters obtained by threshold control in step 2, a vertical crossing model is established to calculate the vertical crossing degree; Step 6: Based on the parameters obtained by threshold control in step 2 and the basic physical parameters of the reservoir such as oil abundance, plane control degree, vertical penetration degree obtained in the above steps, a sweet spot volume index model is obtained, and the sweet spot volume index is calculated; The evaluation parameters obtained by indoor test or logging in step 1 are free hydrocarbon content S1, total organic carbon content TOC, organic matter maturity Ro, porosity Brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h , lamella density ρ, permeability μ; the evaluation parameters for threshold control in step 2 are free hydrocarbon content S1, total organic carbon content TOC, porosity Brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h , permeability μ; the evaluation parameters for calculating the degree of plane control in step 4 are brittleness index BI, horizontal principal stress difference σ H -σ h , lamella density ρ; the evaluation parameters for calculating the vertical penetration degree in step 5 are σ H +σ h The evaluation parameters of the dessert body index calculated in step 6 are porosity Six parameters: permeability μ and oil abundance O, plane control parameter S, depth of bottom of longitudinal sweep section H1, depth of top of longitudinal sweep section H2; The method for performing threshold control in step 2 includes: According to the distribution of the standardized values ​​of different evaluation parameters, based on the geological meanings corresponding to different evaluation parameters and combined with the regional reservoir development characteristics, the available threshold is selected; The data outside the threshold of each parameter is weakened or eliminated before participating in the next step of calculation; The plane control model establishment method in step 4 includes: Obtain two parameters, brittleness index and plane principal stress difference, to describe the size of the plane control area; here, the stress difference must be obtained first, and then the result is standardized to 0-10; Obtaining the density of lamellae fractures is used to describe the size of the plane control area. When the injection volume of fracturing fluid is sufficient, the lamellae fractures will promote and continue the extension of the fractures on the plane, thereby playing a role in expanding the plane control area. The plane control model is: σ H : Maximum principal stress, MPa; σ h : minimum principal stress, MPa; σ H -σ h : stress difference, 0-10 standardized value, dimensionless; ρ: lamella density, 0-2 normalized value, dimensionless; BI: brittleness index, 0-10 normalized value, dimensionless; S: plane control parameter, dimensionless; The method for establishing the vertical crossing model in step 5 includes: Get σ H +σ h Parameters: During fracturing, the fracture height will cause penetration in the vertical direction; the fracture height is affected by the maximum principal stress, and the high stress area is easy to penetrate into the low stress area. By comparing the longitudinal stress evolution characteristics, a method to describe the fracture height is established; Based on the calculated fracture height, the bottom depth H1 of the longitudinal wave section and the top depth H1 of the longitudinal wave section are obtained. 2； The relationship describing the seam height is: k·f(σ H +s h )=H 1-5 In the formula: k: monitoring seam height conversion coefficient; f(x): vertical sweep function; H: seam height, m; H1: depth of the bottom of the longitudinal swept section, m; H2: Depth of the top of the longitudinal swept segment, m.

2. A method for evaluating the sweet spot of a shale reservoir according to claim 1, characterized in that: The evaluation parameters for calculating the oil abundance in step 3 include free hydrocarbon content S1, total organic carbon content TOC, and organic matter maturity Ro.

3. A method for evaluating a shale reservoir sweet spot according to claim 1 or 2, characterized in that: Step 1: standardize and preprocess the acquired evaluation parameter data to limit them to the same or similar order of magnitude; The data standardization process adopts the following two methods: Where: a I : The standardized value of the i-th 0-n in the data column a, dimensionless; a i : The i-th original value in data column a, dimensioned; n: magnitude parameter; in: Formula 1-1 regulates the same data distribution boundaries and the same data fluctuations; Formula 1-2 regulates the same data distribution boundaries and retains the original data fluctuations; and / or, Methods for selecting the level of standardization include: Standardization level of evaluation parameter data selection: data standardization adopts 0-10 standardization, 0-2 standardization and 0-1 standardization, which correspond to strong impact parameters, medium impact parameters and weak impact parameters respectively; The organic matter maturity Ro, porosity Permeability μ is a weakly influential parameter and is standardized using 0-1; lamella density ρ is a moderately influential parameter and is standardized using 0-2; free hydrocarbon content S1, total organic carbon content TOC, brittleness index BI, horizontal principal stress difference σ H -σ h , horizontal principal stress and σ H +σ h Parameters classified as strongly affected were standardized using a value between 0 and 10.

4. A method for evaluating the sweet spot of a shale reservoir according to claim 2, characterized in that: The threshold control is divided into two categories: left-type threshold control and right-type threshold control. The specific control methods of the left-type threshold and the right-type threshold include: The left-type threshold is to select the minimum threshold, retaining all data greater than the threshold to participate in the next step of calculation, corresponding to those parameters whose larger values ​​contribute more to the evaluation of the sweet spot; The right-type threshold is to select the maximum value threshold, retain all data less than the threshold to participate in the next step of calculation, corresponding to those parameters whose smaller values ​​contribute more to the evaluation of the sweet spot; and / or, The parameters whose larger values ​​contribute more to the evaluation of the sweet spot are S1, TOC, BI, σ H +σ h , μ; corresponding to those parameters whose smaller values ​​contribute more to the evaluation of the sweet spot, there are σ H -σ h .

5. A method for evaluating the sweet spot of a shale reservoir according to claim 2, characterized in that: The method of establishing a reserve abundance model in step 3 comprises: The reserve abundance model is established based on the three parameters S1, TOC obtained in step 2 and Ro obtained in step 1. Calculate oil abundance based on the established reserve abundance model; and / or, The reserve abundance model is: Where: O: oil abundance parameter, dimensionless; S1: free hydrocarbon content, 0-10 standardized value, dimensionless; TOC: total organic carbon content, 0-10 normalized value, dimensionless; Ro: organic matter maturity, 0-1 standardized value, dimensionless.

6. A method for evaluating a shale reservoir sweet spot according to claim 1, characterized in that: The method for establishing the sweet body index model in step 6 includes: Get the value obtained in step 2 μ, the porosity and permeability reflect the reservoir storage capacity and seepage capacity respectively. The main development target of shale oil is the space inside the fracture, while the space outside the fracture is equivalent to a secondary development target and plays a smaller role. When these two parameters are standardized, they are reduced by one order of magnitude to participate in the calculation of the final sweet spot body evaluation; Based on the results obtained in step 2 The sweet spot body index is calculated using the sweet spot body evaluation formula using μ and the six parameters O, S, H1, and H2 obtained in steps 3 to 5; and / or, The calculation formula for the dessert body evaluation is: Porosity, 0-1 normalized value, dimensionless; μ: permeability, 0-1 normalized value, dimensionless; O: oil abundance parameter, dimensionless; SE: Dessert body evaluation index, dimensionless.

7. A method for evaluating the sweet spot of a shale reservoir according to claim 6, characterized in that: After the calculation of the SE sweet spot body evaluation index is completed, according to the distribution range and change of the SE index at different vertical depths of the whole well, the favorable development section and the advantageous development end point depth domain corresponding to the highest value are selected to determine the target layer of the horizontal well.

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