A method for predicting the reserves controlled by fracture network in volume fracturing of horizontal wells in tight oil reservoirs

Abstract

The application provides a method for predicting fracture network controlled reserves of compact oil reservoir horizontal well volume fracturing, which comprises the following steps: firstly, a fracture network controlled volume prediction model coupled with different influencing factors is established according to the predicted horizontal well basic big data, and the fracture network controlled volume of the whole well section microseismic monitoring is predicted; secondly, a geological model is established according to the reservoir physical property parameters of the horizontal well, and the fracture network controlled volume of the whole well section microseismic monitoring is introduced into the geological model to obtain a productivity prediction model; finally, the effective fracture network controlled volume is calculated by using the productivity prediction model according to the horizontal well production dynamic parameters, the fracture network controlled reserves are further quantitatively calculated according to the reservoir physical property parameters, the fracture network controlled reserves index of the horizontal well is defined, and the development effect of the horizontal well volume fracturing is quantitatively evaluated, so that the problem of low prediction precision of reservoir numerical simulation is solved, the method has the advantages of simple calculation and strong operability, and has a wide application prospect and an important guiding role for fracturing optimization design.

Description

Technical Field

[0001] This invention relates to the field of oil and gas development, and particularly to a method for predicting the volumetric fracturing network-controlled reserves in horizontal wells of tight oil reservoirs within the field of hydraulic fracturing. Background Technology

[0002] With the continuous development of tight oil reservoirs in the basin, the matching of volumetric fracturing technology with reservoirs faces many challenges. For example, big data from production profile tests of each fracturing section in horizontal wells in the field show significant differences in the production capacity contribution of each fracturing section. Whether the volumetric fracturing parameters of horizontal wells are optimally matched with the reservoir's geomechanical parameters is currently difficult to evaluate. Among them, the fracture-controlled reserves after horizontal well fracturing (i.e., fracture-controlled reserves) is a direct and important indicator for evaluating whether the volumetric fracturing parameters are matched with the reservoir. The larger the value, the better the fracturing effect. Therefore, fracture-controlled reserves are crucial for improving the volumetric fracturing technology of horizontal wells. However, due to the strong heterogeneity of reservoir properties, the large differences in fracturing construction parameters, and the different effects of each parameter on the fracturing effect at different levels, determining the fracture-controlled reserves is very difficult.

[0003] Currently, microseismic monitoring technology and reservoir numerical simulation methods are mainly used to determine the fracture network control volume. Microseismic monitoring technology can directly obtain the morphology of hydraulic fractures and the fracture network control volume. However, many production-related numerical simulations show that the effective fracture network control volume is much smaller than the microseismic monitoring volume, making it impossible to obtain the true hydraulic fracture network control volume and thus inaccurately calculate fracture network-controlled reserves. Furthermore, unconventional reservoirs typically employ multi-stage, multi-cluster fracturing, making it impractical to utilize microseismic monitoring technology for every stage due to its long testing cycle and high cost, hindering its application across the entire well section in oilfields. Reservoir numerical simulation methods can quickly obtain the fracture network control volume of the entire horizontal well section and then calculate the fracture network-controlled volume reserves. However, these models rely on numerous laboratory experiments to obtain physical property parameters and make many assumptions, leading to significant discrepancies between simulation results and actual conditions, making it difficult to evaluate the effectiveness of hydraulic fracturing. Therefore, a reliable and economical method for predicting the hydraulic fracture network control volume reserves in horizontal wells is urgently needed to further promote the efficient development of unconventional oil and gas. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, the present invention aims to provide a method for determining the volumetric fracture network-controlled reserves in horizontal wells of tight oil reservoirs. This method first establishes a fracture network-controlled volume prediction model coupled with different influencing factors based on large datasets of geomechanical and volumetric fracturing parameters for each fractured section of the horizontal well. It then quantitatively calculates the similarity coefficient between the predicted fractured section and the measured fractured section, predicting the microseismic monitoring fracture network-controlled volume for the entire horizontal well. Secondly, a heterogeneous geological model is established based on the reservoir properties of the horizontal well, and the fracture network-controlled volume for the entire well is incorporated into this geological model to obtain a production capacity prediction model. Finally, based on the horizontal well's production dynamic parameters, the effective fracture network-controlled volume is calculated using the production capacity prediction model, and further, based on the reservoir properties, the fracture network-controlled reserves are quantitatively calculated, thus achieving the desired determination.

[0005] To achieve the above technical objectives, the present invention provides the following technical solutions:

[0006] A method for predicting reserves controlled by volumetric pressure fracture networks in horizontal wells of tight oil reservoirs includes the following steps:

[0007] S1. Based on the predicted big data of the horizontal well foundation, establish a fracture network control volume prediction model coupled with different influencing factors, and further predict the microseismic monitoring fracture network control volume of the entire horizontal well section.

[0008] S2. Based on the reservoir properties of the horizontal well, a heterogeneous geological model is established, and the control volume of the microseismic monitoring fracture network of the entire horizontal well section is imported into the geological model to obtain a production capacity prediction model.

[0009] S3. Based on the production dynamic parameters of the horizontal well, the effective fracture network control volume is calculated using the production capacity prediction model. Furthermore, the fracture network control reserves are quantitatively calculated based on the reservoir physical property parameters, and the horizontal well fracture network control reserve index is defined to quantitatively evaluate the horizontal well volumetric fracturing development effect.

[0010] Further, step S1, acquiring and establishing a fracture network control volume prediction model coupled with different influencing factors based on the predicted horizontal well basic big data, and further predicting the microseismic monitoring fracture network control volume of the entire horizontal well section, specifically includes:

[0011] Step S101, acquire basic big data: including collecting geomechanical parameters and volumetric fracturing parameters of each fracturing section of the predicted horizontal well, as well as the fracture control volume of the measured section of the horizontal well on the same platform;

[0012] Step S102: Based on the basic big data obtained in step S101, analyze the influencing factors of the volumetric fracture network controlling the reserves of horizontal wells in tight oil reservoirs, and further establish a fracture network control volume prediction matrix M and a prediction reference column M0, wherein the elements of the prediction matrix are the geomechanical parameters and volumetric fracturing parameters of the measured section of the horizontal well; the elements of the prediction reference column are the geomechanical parameters and volumetric fracturing parameters of the unmeasured section of the horizontal well.

[0013] Step S103: Couple the prediction matrix M and the prediction reference series M0 from step S102 to establish the volumetric pressure fracture network control volume prediction model A for horizontal wells.

[0014] Step S104: Standardize the elements of the fracture network control volume prediction matrix, the prediction reference series elements, and the elements of the horizontal well volume prediction model established in step S102 and step S103, respectively.

[0015] Step S105: Based on the standardized elements of the fracture control volume prediction matrix obtained in step S104, quantitatively calculate the similarity coefficient between the predicted fractured section and the measured fractured section of the horizontal well.

[0016] Step S106: Sort the similarity coefficients between the predicted fracturing section and the measured fracturing section of the horizontal well calculated in step S105, and assign the fracture control volume of the measured fracturing section corresponding to the largest similarity coefficient to the predicted fracturing section.

[0017] Step S107: Based on the single-segment microseismic monitoring fracture control volume predicted in step S106, calculate the microseismic monitoring fracture control volume of the entire horizontal well section.

[0018] Furthermore, in step S101, the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure; the volumetric fracturing modification parameters include fracture density, fracturing flow rate, fracturing fluid volume, and proppant dosage.

[0019] Furthermore, in step S102, the seam control volume prediction matrix M and the prediction reference column M0 are calculated using formula (I) and formula (II), respectively:

[0020]

[0021] M0=[M0(1), M0(2),…,M0(n)] (Ⅱ)

[0022] In the formula: M is the prediction matrix for the control volume of the seam mesh;

[0023] M i (j) represents the elements of the prediction matrix;

[0024] m represents the number of measured sections in the horizontal well volumetric fracturing;

[0025] n represents the number of factors affecting the volumetric control of the fracture network in the measured section of the horizontal well.

[0026] M0 is the prediction reference column, and the elements of the prediction reference column are the geomechanical parameters of the untested section of the horizontal well and the volumetric fracturing construction parameters.

[0027] Furthermore, in step S103, the volumetric fracture network control volume prediction model A for the horizontal well is calculated using formula (Ⅲ):

[0028]

[0029] In the formula: A is the volumetric pressure fracture network control volume prediction model for horizontal wells.

[0030] In step S104, the standardized elements of the seam control volume prediction matrix are standardized using formula (IV), and the standardized elements of the seam control volume prediction model are standardized using formula (V).

[0031]

[0032]

[0033] In the formula: Standardize the elements of the control volume prediction matrix for the seam mesh.

[0034] A * The normalized matrix is ​​used for the prediction matrix of the control volume of the seam mesh.

[0035] Furthermore, in step S105, the similarity coefficient between the predicted fracturing section and the measured fracturing section of the horizontal well is calculated using formula (VI):

[0036]

[0037] In the formula: SI i is the similarity coefficient between the predicted fractured section and the measured fractured section in a horizontal well, and is dimensionless;

[0038] Standardize the elements of the seam control volume prediction matrix;

[0039] Standardize the elements of the reference column for prediction.

[0040] Furthermore, in step S107, the control volume of the microseismic monitoring fracture network for the entire horizontal well section is calculated using formula (Ⅶ):

[0041]

[0042] In the formula: SRV is the control volume of the microseismic monitoring fracture network for the entire horizontal well section, 10 4 m 3 ;

[0043] SRV i For the control volume of the microseismic monitoring fracture network in a single section of a horizontal well, 10 4 m 3 ;

[0044] k represents the number of fracturing stages in a horizontal well.

[0045] Further, step S2 establishes a heterogeneous geological model based on the reservoir properties of the horizontal well, and imports the control volume of the microseismic monitoring fracture network throughout the horizontal well into the geological model to obtain a production capacity prediction model, specifically including:

[0046] Step S201, Establishment of basic parameter database: This includes obtaining basic parameters of horizontal wells and basic parameters of the tight oil reservoir in which the horizontal wells are located. The basic parameters of horizontal wells include the length of the horizontal section, the number of fractured sections, and the cumulative oil production in the first year. The basic parameters of the tight oil reservoir in which the horizontal wells are located include the reservoir depth, the depth of the completed well, the distance between adjacent wells, the reservoir pressure, the reservoir temperature, the reservoir porosity, permeability, oil saturation, reservoir thickness, and reservoir fluid parameters of each fractured section of the horizontal well.

[0047] Step S202: Based on the basic parameter database established in step S201, a horizontal well geological model is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field.

[0048] Step S203: The microseismic monitoring fracture network control volume of the entire horizontal well section obtained in step S1 is implanted into the heterogeneous geological model to obtain the production capacity prediction model.

[0049] Further, step S3 calculates the effective fracture network control volume based on the horizontal well production dynamic parameters and using a production capacity prediction model. It then quantitatively calculates the fracture network control reserves based on reservoir physical property parameters and defines a horizontal well fracture network control reserve index to quantitatively evaluate the horizontal well volumetric fracturing development effect. Specifically, this includes:

[0050] Step S301: Use the production capacity prediction model established in step S2 to predict the cumulative oil production of the horizontal well in the first year, and combine the historical cumulative oil production of the horizontal well in the first year with the microseismic monitoring fracture control volume of the entire well section predicted in step S1 to quantitatively calculate the effective fracture control volume of the horizontal well.

[0051] Step S302: Based on the porosity and oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located, and the effective fracture control volume of the horizontal well calculated in step S301, the fracture control reserves are quantitatively calculated; and a horizontal well fracture control reserve index is defined to quantitatively evaluate the volumetric fracturing development effect of the horizontal well. The larger the value, the better the development effect.

[0052] Furthermore, in step S301, the effective fracture network control volume of the horizontal well is calculated using formula (VIII):

[0053]

[0054] In the formula: ESRV is the effective fracture network control volume of the horizontal well, 10 4 m 3 ;

[0055] Q H The cumulative oil production in the first year of the horizontal well's historical production, in tons;

[0056] Q P The cumulative oil production for the first year of a horizontal well is predicted in tons.

[0057] Furthermore, in step S302, the fractured network controlled reserves are calculated using formula (IX), and the horizontal well fractured network controlled reserves index is calculated using formula (X):

[0058]

[0059]

[0060] In the formula: ESRV is the effective fracture network control volume of the horizontal well, 10 4 m 3 ;

[0061] φ i Porosity of the i-th fracturing stage in a horizontal well, %;

[0062] S oi The oil saturation of the i-th fracturing stage in a horizontal well is expressed as %.

[0063] k represents the number of fracturing stages in a horizontal well;

[0064] Q R To control reserves using a fractured mesh in horizontal wells, t;

[0065] EQ is the horizontal well fracture network control reserve index, t;

[0066] L is the length of the horizontal segment, in meters;

[0067] d is the distance between adjacent wells, in meters;

[0068] h is the reservoir thickness, in meters (m).

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. This invention can comprehensively consider reservoir physical property parameters and volumetric fracturing stimulation parameters that affect the volumetric fracturing network controlled reserves in horizontal wells, and establish a multi-level evaluation system, which improves the accuracy of evaluation and avoids the unscientific nature of making decisions based on a single evaluation index. At the same time, this method is also applicable to the evaluation and prediction of factors affecting the fracture-controlled reserves in vertical wells.

[0071] 2. Compared with previous methods, this prediction method calculates the fracture network control reserve index by using big data from actual tests in horizontal well fields. This allows for a quantitative evaluation of the volumetric fracturing effect of horizontal wells, solving the problem of low accuracy in reservoir numerical simulation prediction. Furthermore, this method has the advantages of simple calculation and strong operability. It is also applicable to the prediction of fracture network control reserves in other unconventional tight oil reservoirs, and has broad application prospects. It also plays an important guiding role in fracturing optimization design.

[0072] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other design solutions and drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a diagram illustrating the multi-level evaluation system of factors influencing the controlled reserves of horizontal well volumetric pressure fracture network according to the present invention.

[0075] Figure 2 This is a diagram showing the calculation results and ranking of the similarity coefficient of the predicted segment Fra20 in this invention;

[0076] Figure 3 This is a diagram showing the calculation results and ranking of the similarity coefficient of the predicted segment Fra21 in this invention.

[0077] To more clearly illustrate the present invention, the following description, in conjunction with preferred embodiments, further clarifies the invention. Those skilled in the art should understand that the specific descriptions below are illustrative rather than restrictive, and should not be construed as limiting the scope of protection of the present invention. Detailed Implementation

[0078] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0080] A method for predicting reserves controlled by volumetric pressure fracture networks in horizontal wells of tight oil reservoirs includes the following steps:

[0081] S1. Based on the predicted big data of the horizontal well foundation, establish a fracture network control volume prediction model coupled with different influencing factors, and further predict the microseismic monitoring fracture network control volume of the entire horizontal well section.

[0082] S2. Based on the reservoir properties of the horizontal well, a heterogeneous geological model is established, and the control volume of the microseismic monitoring fracture network of the entire horizontal well section is imported into the geological model to obtain a production capacity prediction model.

[0083] S3. Based on the production dynamic parameters of the horizontal well, the effective fracture network control volume is calculated using the production capacity prediction model. Furthermore, the fracture network control reserves are quantitatively calculated based on the reservoir physical property parameters. The horizontal well fracture network control reserve index is defined to quantitatively evaluate the development effect of horizontal well volume fracturing. The larger the value, the better the development effect.

[0084] Further, step S1, acquiring and establishing a fracture network control volume prediction model coupled with different influencing factors based on the predicted horizontal well basic big data, and further predicting the microseismic monitoring fracture network control volume of the entire horizontal well section, specifically includes:

[0085] Step S101, Acquire basic big data: This includes collecting the geomechanical parameters and volumetric fracturing parameters of each fracturing segment of the predicted horizontal well, as well as the fracture network control volume of the measured segments of the horizontal well on the same platform; wherein, the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure; the volumetric fracturing parameters include fracture density, fracturing displacement, fracturing fluid volume, and proppant dosage;

[0086] Step S102: Based on the basic big data obtained in step S101, analyze the influencing factors of the volumetric fracturing network controlling the reserves of horizontal wells in tight oil reservoirs, and further establish the volumetric fracturing network control prediction matrix M and the prediction reference column M0, as shown in formulas (I) and (II). The elements of the prediction matrix are the geomechanical parameters and volumetric fracturing stimulation parameters of the measured section of the horizontal well; the elements of the prediction reference column are the geomechanical parameters and volumetric fracturing stimulation parameters of the unmeasured section of the horizontal well.

[0087]

[0088] M0=[M0(1), M0(2),…,M0(n)] (Ⅱ)

[0089] In the formula: M is the prediction matrix for the control volume of the seam mesh;

[0090] M i (j) represents the elements of the prediction matrix;

[0091] m represents the number of measured sections in the horizontal well volumetric fracturing;

[0092] n represents the number of factors affecting the volumetric control of the fracture network in the measured section of the horizontal well.

[0093] M0 is the prediction reference column, and the elements of the prediction reference column are the geomechanical parameters of the unmeasured section of the horizontal well and the volumetric fracturing construction parameters.

[0094] Step S103: Couple the prediction matrix M and prediction reference series M0 from step S102 to establish the volumetric pressure fracture network control volume prediction model A for horizontal wells, as shown in equation (Ⅲ):

[0095]

[0096] In the formula: A is the volumetric pressure fracture network control volume prediction model for horizontal wells;

[0097] Step S104: The elements of the fracture network control volume prediction matrix and the prediction reference series elements established in step S102 are processed as follows (IV); the elements of the horizontal well volume prediction model established in step S103 are standardized as follows (V):

[0098]

[0099]

[0100] In the formula: Standardize the elements of the control volume prediction matrix for the seam mesh.

[0101] A * The normalized matrix is ​​the prediction matrix for the control volume of the mesh.

[0102] Step S105: Based on the standardized elements of the fracture control volume prediction matrix obtained in step S104, the similarity coefficient between the predicted fractured section and the measured fractured section of the horizontal well is quantitatively calculated using formula (VI).

[0103]

[0104] In the formula: SI i is the similarity coefficient between the predicted fractured section and the measured fractured section in a horizontal well, and is dimensionless;

[0105] Standardize the elements of the seam control volume prediction matrix;

[0106] Standardize elements for the reference column for prediction;

[0107] Step S106: Sort the similarity coefficients between the predicted fracturing section and the measured fracturing section of the horizontal well calculated in step S105, and assign the fracture control volume of the measured fracturing section corresponding to the largest similarity coefficient to the predicted fracturing section.

[0108] Step S107: Based on the single-segment microseismic monitoring fracture network control volume predicted in step S106, calculate the microseismic monitoring fracture network control volume of the entire horizontal well section using formula (Ⅶ):

[0109]

[0110] In the formula: SRV is the control volume of the microseismic monitoring fracture network for the entire horizontal well section, 10 4 m 3 ;

[0111] SRV i For the control volume of the microseismic monitoring fracture network in a single section of a horizontal well, 10 4 m 3 ;

[0112] k represents the number of fracturing stages in a horizontal well.

[0113] Further, step S2 establishes a heterogeneous geological model based on the reservoir properties of the horizontal well, and imports the control volume of the microseismic monitoring fracture network throughout the horizontal well into the geological model to obtain a production capacity prediction model, specifically including:

[0114] Step S201, Establishment of basic parameter database: This includes obtaining basic parameters of horizontal wells and basic parameters of the tight oil reservoir in which the horizontal wells are located. The basic parameters of horizontal wells include the length of the horizontal section, the number of fractured sections, and the cumulative oil production in the first year. The basic parameters of the tight oil reservoir in which the horizontal wells are located include the reservoir depth, the depth of the completed well, the distance between adjacent wells, the reservoir pressure, the reservoir temperature, the reservoir porosity, permeability, oil saturation, reservoir thickness, and reservoir fluid parameters of each fractured section of the horizontal well.

[0115] Step S202: Based on the basic parameter database established in step S201, a horizontal well geological model is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field.

[0116] Step S203: The microseismic monitoring fracture network control volume of the entire horizontal well section obtained in step S1 is implanted into the heterogeneous geological model to obtain the production capacity prediction model.

[0117] Further, step S3 calculates the effective fracture network control volume based on the horizontal well production dynamic parameters and using a production capacity prediction model. It then quantitatively calculates the fracture network control reserves based on reservoir physical property parameters and defines a horizontal well fracture network control reserve index to quantitatively evaluate the horizontal well volumetric fracturing development effect. Specifically, this includes:

[0118] Step S301: Using the production capacity prediction model established in step S2, predict the cumulative oil production of the horizontal well in the first year. Combined with the historical cumulative oil production of the horizontal well in the first year and the microseismic monitoring fracture network control volume of the entire well section predicted in step S1, calculate the effective fracture network control volume of the horizontal well quantitatively using formula (VIII):

[0119]

[0120] In the formula: ESRV is the effective fracture network control volume of the horizontal well, 10 4 m 3 ;

[0121] Q H The cumulative oil production in the first year of the horizontal well's historical production, in tons;

[0122] Q P For predicting the cumulative oil production in the first year for horizontal wells, t

[0123] Step S302: Based on the porosity and oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located, and the effective fractured network control volume of the horizontal well calculated in step S301, the fractured network control reserves are quantitatively calculated using formula (IX); the fractured network control reserve index of the horizontal well is calculated using formula (X) to quantitatively evaluate the volumetric fracturing development effect of the horizontal well. Specifically, formulas (IX) and (X) are as follows:

[0124]

[0125]

[0126] In the formula: ESRV is the effective fracture network control volume of the horizontal well, 10 4 m 3 ;

[0127] φ i Porosity of the i-th fracturing stage in a horizontal well, %;

[0128] S oi The oil saturation of the i-th fracturing stage in a horizontal well is expressed as %.

[0129] k represents the number of fracturing stages in a horizontal well;

[0130] Q R To control reserves using a fractured mesh in horizontal wells, t;

[0131] EQ is the horizontal well fracture network control reserve index, t;

[0132] L is the length of the horizontal segment, in meters;

[0133] d is the distance between adjacent wells, in meters;

[0134] h is the reservoir thickness, in meters (m).

[0135] Example 1

[0136] The following describes in detail the specific embodiments of the present invention with reference to the accompanying drawings and the tight oil reservoirs in the Ordos Basin as examples, illustrating the practicality of the method.

[0137] The Ordos Basin boasts abundant tight oil reservoir resources, primarily distributed within the Yanchang Formation, with immense development potential. Years of practice have led to the development of a technical model for tight oil reservoirs characterized by "large well clusters, long horizontal wells, fine-grained fracturing, soluble ball seats, and factory-style production." Downhole microseismic monitoring shows a significant increase in fracture network coverage, achieving volumetric fracturing and a substantial increase in production. However, the basin's shale oil exhibits characteristics such as low pressure coefficients, low brittleness indexes, and multiple vertical interlayers, differing significantly from North American shale oil. With the continued development of shale oil, the compatibility of volumetric fracturing technology with the reservoir faces numerous challenges. For example, evaluating the effectiveness of volumetric fracturing and determining whether existing process parameters are optimally matched with the reservoir remains difficult to answer. Among these challenges, the reserve control capacity of a single well with fracture network coverage is the most important indicator for evaluating the adaptability of volumetric fracturing technology. The example predicted horizontal well WY1 is located in the main development test area of ​​the tight oil reservoir in the basin. The reservoir is buried at a depth of 2135m, with a horizontal section length of 1980m and a reservoir thickness of 14.2m. The well spacing between adjacent wells is 500m. It has strong heterogeneity, which makes the prediction of the fracture network control reserves after volumetric fracturing of the horizontal well very challenging.

[0138] This example provides a complete method for predicting reserves controlled by volumetric pressure fracture networks in horizontal wells of tight oil reservoirs, as detailed below:

[0139] 1. Based on big data of geological and volumetric fracturing parameters of each fracturing section in a horizontal well, an evaluation matrix of fracture network control volume coupled with different influencing factors is established. The calculated similarity coefficient between the predicted fracturing section and the measured fracturing section is quantitatively calculated, and the microseismic monitoring fracture network control volume of the entire horizontal well is predicted. The specific content is as follows:

[0140] (1) Collect the control volume of the microseismic monitoring fracture network of the predicted horizontal well or the same fracturing process used in the same layer of the same platform. As shown in Table 2.

[0141] (2) A multi-level evaluation system for controlling volumetric factors influencing the volumetric fracture network of horizontal wells in tight oil reservoirs was established using the analytic hierarchy process (AHP). (See Appendix) Figure 1 This includes geomechanical parameters and volumetric fracturing parameters. The geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure of each fractured section of the horizontal well. The volumetric fracturing parameters include fracture density, fracturing flow rate, fracturing fluid volume, and proppant dosage, as shown in Tables 1 and 2.

[0142] Table 1 Geomechanical parameters of each fractured section in horizontal well WY1

[0143]

[0144] Table 2. Volumetric fracturing parameters and microseismic monitoring fracture network control volume for each fracturing section of horizontal well WY1.

[0145]

[0146] (3) Based on the multi-level evaluation system factor data in steps (1) and (2), establish the fracture network control volume prediction matrix M and the prediction reference column M0. The expression of the prediction matrix M is shown in (XI). The elements of the prediction reference column M0 are the unmeasured section geomechanical parameters and volume fracturing transformation parameters in Tables 1 and 2. The expression of the prediction reference column M0 is shown in (XII).

[0147]

[0148]

[0149] (4) Couple the prediction matrix M and the prediction reference series M0 to establish the volumetric pressure fracture network control volume prediction model A for horizontal wells. The prediction reference series is taken as Fra20 and calculated as shown in expression (XIII).

[0150]

[0151] (5) The elements of the control volume prediction model of the horizontal well volumetric pressure fracture network established in step (4) are standardized using formula (Ⅳ).

[0152] (6) Based on the standardized elements of the fracture control volume prediction matrix, the similarity coefficient between the predicted fractured section Fra20 and the measured fractured sections Fra1 to Fra19 in the horizontal well is quantitatively calculated using formula (VI). The calculation results are shown in Table 3.

[0153] Table 3. Ranking of similarity coefficients between predicted segments Fra20~Fra21 and measured segments Fra1~Fra19

[0154]

[0155]

[0156] (7) Sort the similarity coefficients between the predicted fracturing section and the measured fracturing section of the horizontal well calculated in step (6), see Appendix Figure 2 and attached Figure 3 Among them, the predicted fracturing segment Fra20 has the highest similarity coefficient with the measured fracturing segment Fra11. Therefore, the fracture control volume of the predicted segment Fra20 is the same as that of Fra11, which is 253 × 10⁻⁶. 4 m 3 Repeating steps (4) to (6) using the same method yields the fracturing section Fra21, which has the highest similarity coefficient with the measured fracturing section Fra14. Therefore, the fracture control volume of the predicted section Fra21 is the same as that of Fra14, which is 240 × 10⁻⁶. 4 m 3 .

[0157] (8) Based on the control volume of the single-segment microseismic monitoring fracture network predicted in step (7), the control volume of the microseismic monitoring fracture network for the entire horizontal well section is calculated to be 4537 × 10⁻⁶. 4 m 3 .

[0158] 2. Based on the reservoir physical properties of the horizontal well, a heterogeneous geological model was established using the reservoir numerical simulation software Eclipse. The microseismic monitoring fracture network control volume of the entire horizontal well section was then imported into the geological model to obtain a production capacity prediction model. The specific details are as follows:

[0159] (1) Establishment of basic parameter database: basic parameters of horizontal wells and basic parameters of tight oil reservoirs where horizontal wells are located, including the basic horizontal section length, number of fracturing sections, and cumulative oil production in the first year of horizontal wells. The basic parameters of tight oil reservoirs where horizontal wells are located include reservoir burial depth, completed well depth, distance between adjacent wells, reservoir thickness, formation pressure, formation temperature, average reservoir porosity, average permeability, average oil saturation, reservoir fluids, etc., as shown in Table 4.

[0160] Table 4. Basic geological parameters of the reservoir where horizontal well WY1 is predicted.

[0161] parameter numerical values parameter numerical values Reservoir depth (m) 2135 Oil saturation (%) 55.2 Complete drilling depth (m) 4034 Porosity (%) 12.1 Distance between adjacent wells (m) 500 Formation crude oil volume factor ( / ) 1.30 Reservoir thickness (m) 14.2 Crude oil viscosity (mPa.s) 1.50 Formation pressure (MPa) 19.7 Horizontal segment length (m) 1980 Formation temperature (°C) 68.2 Number of fracturing stages (stages) 21 Permeability (mD) 0.13 Cumulative oil production in Year 1 (t) 3560

[0162] (2) Based on the basic parameter database, a heterogeneous geological model of a horizontal well is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field.

[0163] (3) The microseismic monitoring fracture network control volume of the entire horizontal well section obtained in step 2) is implanted into the geological model to obtain the production capacity prediction model.

[0164] 3. Based on the dynamic production parameters of horizontal wells, the effective fracture network control volume is calculated using a production capacity prediction model. Furthermore, the fracture network control reserves are quantitatively calculated based on reservoir physical properties. Details are as follows:

[0165] (1) Using the production capacity prediction model established in step 2, the cumulative oil production of the horizontal well in the first year is predicted to be 13692t. Combined with the historical cumulative oil production of the horizontal well in the first year (Table 4) and the microseismic monitoring fracture network control volume of the entire well section predicted in step 1, the effective fracture network control volume of the horizontal well is quantitatively calculated using formula (VIII) to be 1179.6×10 4 m 3 .

[0166] (2) Based on the reservoir properties (porosity and oil saturation) of each fractured section of the horizontal well (see Table 1) and the effective fractured network control volume of the horizontal well, the fractured network control reserves were quantitatively calculated using formula (IX) to be 6.2328 × 10⁻⁶. 5 m 3 Meanwhile, the fracture network control reserve index for horizontal wells was calculated using formula (X) to be 0.891. The fracture network control reserve index can be used to quantitatively evaluate the effectiveness of volumetric fracturing development in horizontal wells; the higher the value, the better the development effect.

[0167] The fracture network control reserve prediction method provided by this invention calculates the fracture network control reserve index using big data from actual tests in horizontal well fields. This method can quantitatively evaluate the volumetric fracturing effect of horizontal wells, solving the problem of low accuracy in reservoir numerical simulation prediction. Furthermore, this method has the advantages of simple calculation and strong operability. It is also applicable to the prediction of fracture network control reserves in other similar unconventional tight oil reservoirs, and has broad application prospects. It also plays an important guiding role in fracturing optimization design.

[0168] The present invention has been specifically described above through embodiments. It should be noted that these embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way, nor are they limited to the forms disclosed herein, and should not be construed as excluding other embodiments. Modifications and simple variations made by those skilled in the art that do not depart from the technical concept and scope of the present invention are all within the protection scope of the present invention.

Claims

1. A method for predicting reserves controlled by volumetric pressure fracture networks in horizontal wells of tight oil reservoirs, characterized in that, Includes the following steps: S1. Based on the predicted big data of the horizontal well foundation, establish a fracture network control volume prediction model coupled with different influencing factors, and further predict the microseismic monitoring fracture network control volume of the entire horizontal well section. In step S1, the analytic hierarchy process (AHP) is used to establish a multi-level evaluation system for the volumetric pressure fracture network control factors of horizontal wells in tight oil reservoirs, and the coupled influencing factors are screened out based on this system. S2. Based on the reservoir properties of the horizontal well, a heterogeneous geological model is established, and the control volume of the microseismic monitoring fracture network of the entire horizontal well section is imported into the geological model to obtain a production capacity prediction model. In step S2, the control volume of the microseismic monitoring fracture network of the entire horizontal well section is imported into the heterogeneous geological model to obtain the production capacity prediction model. Specifically, it includes: Step S201, Establishment of basic parameter database: This includes obtaining basic parameters of horizontal wells and basic parameters of the tight oil reservoir in which the horizontal wells are located. The basic parameters of horizontal wells include the length of the horizontal section, the number of fractured sections, and the cumulative oil production in the first year. The basic parameters of the tight oil reservoir in which the horizontal wells are located include the reservoir depth, the depth of the completed well, the distance between adjacent wells, the reservoir pressure, the reservoir temperature, the reservoir porosity, permeability, oil saturation, reservoir thickness, and reservoir fluid parameters of each fractured section of the horizontal well. Step S202: Based on the basic parameter database established in step S201, a horizontal well geological model is established using the reservoir numerical simulation software Eclipse, including the reservoir porosity distribution field, permeability distribution field, oil saturation distribution field, and formation pressure distribution field. Step S203: Import the microseismic monitoring fracture network control volume of the entire horizontal well section obtained in step S1 into the heterogeneous geological model to obtain the production capacity prediction model. S3. Based on the production dynamic parameters of the horizontal well, the effective fracture control volume is calculated using the production capacity prediction model. Furthermore, the fracture control reserves are quantitatively calculated based on the reservoir physical property parameters, and the horizontal well fracture control reserve index is defined to quantitatively evaluate the horizontal well volumetric fracturing development effect. Among them, the horizontal well fracture network control reserve index defined in step S3 is used to quantitatively evaluate the development effect of horizontal well volumetric fracturing. The larger the value, the better the development effect. Specifically, it includes: Step S301: Use the production capacity prediction model established in step S2 to predict the cumulative oil production of the horizontal well in the first year, and combine the historical cumulative oil production of the horizontal well in the first year with the microseismic monitoring fracture control volume of the entire well section predicted in step S1 to quantitatively calculate the effective fracture control volume of the horizontal well. Step S302: Based on the porosity and oil saturation in the basic parameters of the tight oil reservoir where the horizontal well is located, and the effective fracture control volume of the horizontal well calculated in step S301, the fracture control reserves are quantitatively calculated; and the horizontal well fracture control reserve index is defined to quantitatively evaluate the horizontal well volumetric fracturing development effect.

2. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 1, characterized in that, Step S1 involves acquiring and establishing a fracture network control volume prediction model coupled with different influencing factors based on the predicted basic big data of the horizontal well, and further predicting the microseismic monitoring fracture network control volume of the entire horizontal well section. Specifically, this includes: Step S101, acquire basic big data: including collecting geomechanical parameters and volumetric fracturing parameters of each fracturing section of the predicted horizontal well, as well as the fracture control volume of the measured section of the horizontal well on the same platform; Step S102: Based on the basic big data obtained in step S101, analyze the influencing factors of the volumetric fracture network controlling the reserves of horizontal wells in tight oil reservoirs, and further establish a fracture network control volume prediction matrix M and a prediction reference column M0, wherein the elements of the prediction matrix are the geomechanical parameters and volumetric fracturing parameters of the measured section of the horizontal well; the elements of the prediction reference column are the geomechanical parameters and volumetric fracturing parameters of the unmeasured section of the horizontal well. Step S103: Couple the prediction matrix M and the prediction reference series M0 from step S102 to establish the volumetric pressure fracture network control volume prediction model A for horizontal wells. Step S104: Standardize the elements of the fracture network control volume prediction matrix, the prediction reference series elements, and the elements of the horizontal well volume prediction model established in step S102 and step S103, respectively. Step S105: Based on the standardized elements of the fracture control volume prediction matrix obtained in step S104, quantitatively calculate the similarity coefficient between the predicted fractured section and the measured fractured section of the horizontal well. Step S106: Sort the similarity coefficients between the predicted fracturing section and the measured fracturing section of the horizontal well calculated in step S105, and assign the fracture control volume of the measured fracturing section corresponding to the largest similarity coefficient to the predicted fracturing section. Step S107: Based on the single-segment microseismic monitoring fracture control volume predicted in step S106, calculate the microseismic monitoring fracture control volume of the entire horizontal well section.

3. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 2, characterized in that, In step S101, the geomechanical parameters include porosity, permeability, oil saturation, clay content, brittleness index, horizontal stress difference, and fracturing pressure; the volumetric fracturing modification parameters include fracture density, construction displacement, fracturing fluid volume, and proppant dosage.

4. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 2, characterized in that, In step S102, the seam control volume prediction matrix M and the prediction reference column M0 are calculated using formula (I) and formula (II), respectively: （Ⅰ） （Ⅱ） In the formula: M is the prediction matrix for the control volume of the seam mesh; M i (j) represents the elements of the prediction matrix; m represents the number of measured sections in the horizontal well volumetric fracturing; n represents the number of factors affecting the volumetric control of the fracture network in the measured section of the horizontal well. M0 is the prediction reference column, and the elements of the prediction reference column are the geomechanical parameters of the untested section of the horizontal well and the volumetric fracturing construction parameters.

5. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 2, characterized in that, In step S103, the volumetric pressure fracture network control volume prediction model A for the horizontal well is calculated using formula (Ⅲ). In step S104, the standardized elements of the fracture network control volume prediction matrix are standardized using formula (Ⅳ), and the elements of the fracture network control volume prediction model are standardized using formula (Ⅴ). （Ⅲ） （Ⅳ） （Ⅴ） In the formula: A is the volumetric pressure fracture network control volume prediction model for horizontal wells; Standardize the elements of the control volume prediction matrix for the seam mesh. The normalized matrix is ​​used for the prediction matrix of the control volume of the seam mesh.

6. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 2, characterized in that, In step S105, the similarity coefficient between the predicted fracturing section and the measured fracturing section of the horizontal well is calculated using formula (VI): （Ⅵ） In the formula: is the similarity coefficient between the predicted fractured section and the measured fractured section in a horizontal well, and is dimensionless; Standardize the elements of the seam control volume prediction matrix; Standardize the elements of the reference column for prediction.

7. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 2, characterized in that, In step S107, the control volume of the microseismic monitoring fracture network for the entire horizontal well section is calculated using formula (Ⅶ): （Ⅶ） In the formula: SRV is the control volume of the microseismic monitoring fracture network for the entire horizontal well section, 10 4 m 3 ; SRV i For the control volume of the microseismic monitoring fracture network in a single section of a horizontal well, 10 4 m 3 ; k represents the number of fracturing stages in a horizontal well.

8. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 1, characterized in that, In step S301, the effective fracture network control volume of the horizontal well is calculated using formula (VIII): （Ⅷ） In the formula: To effectively control the volume of the fracture network in horizontal wells, 10 4 m 3 ; The cumulative oil production in the first year of the horizontal well's historical production, in tons; The cumulative oil production for the first year of a horizontal well is predicted in tons.

9. The method for predicting reserves controlled by volumetric pressure fracture network in horizontal wells of tight oil reservoirs as described in claim 1, characterized in that, In step S302, the fractured mesh controlled reserves are calculated using formula (IX), and the horizontal well fractured mesh controlled reserves index is calculated using formula (X): （Ⅸ） （Ⅹ） In the formula: To effectively control the volume of the fracture network in horizontal wells, 10 4 m 3 ; φ i For horizontal wells i Porosity of the fracturing section, % S oi For horizontal wells i Oil saturation in the fracturing section, % k The number of fracturing stages in a horizontal well; Q R To control reserves using a fractured mesh in horizontal wells, t; EQ For horizontal well fracture network control of reserve index, t; L The length of the horizontal segment is in meters (m). d The distance between adjacent wells, in meters; h Let be the reservoir thickness, in meters (m).

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