Shale oil horizontal well volume fracturing segmented productivity prediction method

Abstract

The invention relates to a shale oil horizontal well volume fracturing staged productivity prediction method. The method comprises the following steps: obtaining average fluid production profile dynamic distribution of fracturing fluid flow-back periods and oil-seeing stable production periods of all fracturing sections of shale oil horizontal wells of the same block and the same development layer series on site; establishing a multi-level evaluation system of the shale oil horizontal well volume fracturing productivity influence factors by using an analytic hierarchy process; a productivity influence factor evaluation model is established, and weight factors of different influence factors are calculated and sorted; coupling productivity influence factors, establishing a productivity prediction model, calculating similar factors of a test section and a prediction section of the known fluid production profile of the horizontal well, and dynamically predicting the productivity of the prediction section according to a dynamic test result of the known fluid production profile. According to the productivity prediction method, the reservoir capacity, the fracture network complexity and the volume fracturing transformation strength factors are coupled, the productivity prediction model is established, rapid quantitative prediction is conducted on the liquid production profile, accuracy and reliability are achieved, and the productivity prediction method is also suitable for prediction of the volume fracturing liquid production profile of the oil reservoir horizontal well of the same kind.

Description

Technical Field

[0001] The invention relates to the technical field of oil and natural gas development, and specifically designs a method for predicting the segmented capacity of shale oil horizontal well volume fracturing. Background Art

[0002] The geological characteristics of basin shale oil are quite different from those of North America. It is difficult to achieve scale-effective development by directly copying the North American fracturing model. Some horizontal wells still face problems such as rapid production decline and short stable production period after volume fracturing. The volume fracturing technology of horizontal wells that is more compatible with the reservoir still needs to be continuously researched. Among them, the production capacity contribution of each fracturing section of the horizontal well is an important means to directly evaluate the effect of volume fracturing and the applicability of the process. However, there are many factors affecting the production capacity of each fracturing section and the relationship is complex, which affects the production capacity contribution to varying degrees. At the same time, the long test cycle and high economic cost make the prediction of production capacity contribution more difficult. Therefore, there is an urgent need for a faster and more economical production capacity contribution prediction method to provide strong support for the optimization of shale oil horizontal well volume fracturing parameters and promote the efficient development of shale oil.

[0003] At present, there are mainly the following methods for testing and predicting the productivity contribution of each fracturing stage in horizontal wells:

[0004] Patent 1 "A Liquid Production Profile Test String for Multi-stage Fracturing Horizontal Wells" (Publication No.: CN111663929B, Publication Date: 2023-04-07) tests the oil and water in different perforation layers by designing a liquid production profile test string combined with a tracer. However, since multi-stage fracturing horizontal wells usually have more perforation clusters, a large number of liquid production profile test strings need to be designed, which increases the economic cost. At the same time, the test string needs to be re-inserted under high temperature and high pressure conditions in the wellbore after hydraulic fracturing, which has a greater engineering risk. From an economic and safety perspective, its practicality is subject to certain limitations.

[0005] Patent 2 "A tubing string, system and method for testing the production profile of a horizontal well in an oil field" (publication number: CN111441763A, publication date: 2020-07-24) also tests the production profile by designing a tubing string, but this method can only indirectly and qualitatively determine the location of high water content in the oil well by testing the wellbore temperature.

[0006] Patent three "A method for testing the production profile of horizontal wells using quantum dot tracers" (publication number: CN110805432A, publication date: 2020-02-18) uses different quantum dot tracers to be injected following the fracturing construction process to quantitatively test the oil, gas and water production profiles at different times after hydraulic fracturing. This method has good engineering feasibility and strong operability in field applications. It realizes quantitative characterization of oil, gas and water and is a testing method worthy of promotion, but its disadvantage is that the testing cycle is long. Summary of the invention

[0007] The purpose of the present invention is to provide a method for predicting the production capacity of shale oil horizontal well volume fracturing segments, which solves the problems of high cost and only indirect qualitative judgment of the high water content position of the oil well in the prior art.

[0008] The technical solution adopted by the present invention is a method for predicting the production capacity of shale oil horizontal well volume fracturing stages, which is specifically implemented according to the following steps:

[0009] Step 1: obtaining the dynamic distribution of the average production profile of the fracturing fluid flowback period and the oil-discovering stable production period of each fracturing stage of the shale oil horizontal well in the same block and the same development layer on site;

[0010] Step 2: Establish a multi-level evaluation system for factors affecting shale oil horizontal well volume fracturing productivity using the analytic hierarchy process;

[0011] Step 3: According to the multi-level evaluation system of capacity influencing factors, establish a capacity influencing factor evaluation model, calculate the weight factors of different influencing factors and rank them;

[0012] Step 4: Sort the coupled production capacity influencing factors according to the weight factors, establish a production capacity prediction model, calculate the similarity factor between the test section and the prediction section of the known production profile of the horizontal well, and dynamically predict the production capacity of the prediction section based on the dynamic test results of the known production profile.

[0013] The present invention is also characterized in that:

[0014] Step 1 is specifically as follows: using quantum dot tracers to test the fluid production profile of each fracturing section of shale oil horizontal wells in the same block and development formation, and obtaining the average fluid production profile dynamic distribution during the fracturing fluid flowback period and the oil-seeking stable production period after volume fracturing.

[0015] In step 2, factors affecting productivity include reservoir capacity, fracture network complexity, and volume fracturing intensity.

[0016] Reservoir capacity factors include: transformation length of horizontal oil layer, porosity, permeability, oil saturation, and mud content; fracture network complexity factors include: brittleness index, horizontal stress difference, and fracture pressure; volume fracturing transformation strength factors include: fracture density, liquid injection strength, sand addition strength, and construction displacement.

[0017] Step 3 is implemented as follows:

[0018] Step 3.1, parameter set acquisition: According to the logging interpretation results, the parameter set of the factors affecting the productivity of each fracturing stage of the shale oil horizontal well is collected, and the method of obtaining is the average method;

[0019] Step 3.2, establish an evaluation matrix of production capacity influencing factors: according to the parameter set of production capacity influencing factors and the measured fluid production profile of the fracturing section of the shale oil horizontal well, establish an evaluation matrix X and an evaluation reference column X0 respectively, wherein the evaluation matrix elements are the production capacity influencing factors of the measured fracturing section, and the evaluation standard column is the measured fluid production profile of the fracturing section;

[0020]

[0021] X0=(X1(0),…,X i (0),…,X m (0)) T i=1,2,…,m (2)

[0022] Where: X is the evaluation matrix; X i (j) is the evaluation matrix element; m is the number of horizontal well fracturing stages; n is the number of productivity influencing factors; X0 is the evaluation standard column;

[0023] Step 3.3, standardization of evaluation matrix data: Use the maximum value method to standardize the evaluation matrix and evaluation criteria column. The calculation formula is as follows:

[0024]

[0025] Where: is the standardized element of the evaluation matrix; (X i (j)) max is the maximum value of the jth influencing factor parameter set;

[0026] Step 3.4, gray correlation calculation: Calculate the gray correlation between different influencing factors and the evaluation criteria column according to the standardized evaluation matrix and the evaluation criteria column. The calculation expression is as follows:

[0027]

[0028] Where: r j is the grey correlation degree; in The data were normalized for the evaluation reference column; is the data after the evaluation matrix elements are standardized; ρ is the resolution coefficient;

[0029] Step 3.5, weight factor calculation: According to the gray correlation between each capacity influencing factor and the evaluation standard column, calculate the weight factors of different capacity influencing factors to quantitatively characterize the degree of impact on capacity, and rank the weight factors of each capacity influencing factor. The larger the value, the greater the impact on capacity.

[0030] The calculation formula of the weight factor of factors affecting production capacity is as follows:

[0031]

[0032] Where: c j is the weight factor of the factors affecting production capacity; r j is the grey correlation degree.

[0033] Step 4 is implemented as follows:

[0034] Step 4.1, coupling the factors affecting capacity, establish the capacity prediction matrix B,

[0035]

[0036] Where: B capacity forecast matrix; B i (j) is the element of the production capacity prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k; h is the number of measured fracturing stages; k is the number of parameters affecting the production capacity of the measured fracturing stages;

[0037] The elements of the capacity prediction matrix are the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set corresponding to the measured fracturing section, and the prediction reference series is the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set of the predicted section;

[0038] Step 4.2: Based on the established capacity forecast matrix, standardize the elements of the capacity forecast matrix to obtain a standardized capacity forecast matrix;

[0039] Step 4.3, according to the standardized matrix of production capacity prediction, calculate the similarity factor between the test section and the predicted section of the known production profile of the horizontal well;

[0040] Step 4.4, sort the similarity factors between the known fracturing sections and the predicted fracturing sections. The larger the value, the greater the similarity. The fluid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the predicted section. The dynamic change results of the fluid production profile of the predicted section at different stages are obtained based on the dynamic test results of the fluid production profile of the measured fracturing section.

[0041] Step 4.2 is as follows:

[0042] According to the distribution range of the parameter values ​​of the capacity influencing factors, the elements of the capacity forecast matrix are standardized, and the calculation formula is as follows:

[0043]

[0044]

[0045] Where: is the mean of each parameter set of the capacity prediction matrix column vector; Standardize elements for the capacity forecast matrix;

[0046] The elements of the capacity forecast matrix are standardized using formulas (7) and (8):

[0047]

[0048] Where: B * Normalize the matrix for capacity forecasting.

[0049] Step 4.3 is as follows: first, the maximum distance between each parameter set of the known fracturing section and each parameter set of the prediction section in the prediction matrix is ​​calculated, and then the similarity factor is calculated by combining the standardized matrix of capacity prediction. The calculation formula is as follows:

[0050]

[0051]

[0052] Where: SI i is the similarity factor; D is the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section.

[0053] The beneficial effects of the present invention are:

[0054] The method for predicting the production capacity of shale oil horizontal wells by volume fracturing segmentation of the present invention can simultaneously couple the factors affecting the production capacity, the complexity of the fracture network and the intensity of the volume fracturing transformation, and clarify the main controlling factors. At the same time, a production capacity prediction model is established to quickly quantitatively predict the production profile. The calculation method is accurate and reliable, which solves the problems of high risk, long testing cycle and high cost in mine construction, and greatly saves time and economic costs. It is also applicable to the prediction of the production profile of volume fracturing of horizontal wells in similar oil reservoirs, has good application prospects, and can provide strong support for the evaluation of the post-fracture effect of shale oil horizontal wells and the optimization of fracturing parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a cross-sectional diagram of oil and water production in the measured fracturing section of the horizontal well H1-1 during the flowback period of Example 1 of the present invention;

[0056] Figure 2 This is a cross-sectional diagram of oil and water production in the measured fracturing section of the horizontal well H1-1 during the production period of Example 1 of the present invention;

[0057] Figure 3 This is a profile of the measured fluid production of the fracturing section of the horizontal well H1-1 during the flowback period of Example 1 of the present invention;

[0058] Figure 4 This is a profile diagram of the measured fluid production of the fracturing section of the horizontal well H1-1 in the production period of Example 1 of the present invention;

[0059] Figure 5A multi-level evaluation system diagram of productivity influencing factors in the shale oil horizontal well volume fracturing staged productivity prediction method of the present invention;

[0060] Figure 6 It is a ranking diagram of productivity influencing factors in the shale oil horizontal well volume fracturing staged productivity prediction method of the present invention;

[0061] Figure 7 This is a similarity factor ranking diagram between the measured fracturing section and the predicted section Fr14 of the horizontal well H1-1 in Example 1 of the present invention;

[0062] Figure 8 This is a similarity factor ranking diagram between the measured fracturing section and the predicted section Fr15 of the horizontal well H1-1 in Example 1 of the present invention;

[0063] Fig. 9 This is a profile diagram of the fluid production of Fr14 and Fr15 in the predicted sections of the horizontal well H1-1 during the flowback period of Example 1 of the present invention;

[0064] Fig.10 This is a fluid production profile diagram of predicted sections Fr14 and Fr15 of horizontal well H1-1 during the production period of Example 1 of the present invention. DETAILED DESCRIPTION

[0065] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] The method for predicting the production capacity of shale oil horizontal well volume fracturing segmentation of the present invention is specifically implemented according to the following steps:

[0067] Step 1: Obtain the dynamic distribution of the average fluid production profile of each fracturing stage during the fracturing fluid flowback period and the oil-seeking stable production period of the shale oil horizontal wells in the same block and the same development formation on site.

[0068] Quantum dot tracers were used to test the fluid production profile of each fracturing section of shale oil horizontal wells in the same block and development formation, and the dynamic distribution of the average fluid production profile during the fracturing fluid flowback period and the oil-seeking stable production period after volume fracturing was obtained.

[0069] Step 2: Use the analytic hierarchy process to establish a multi-level evaluation system for factors affecting the productivity of shale oil horizontal well volume fracturing, including factors such as storage capacity, fracture network complexity, and volume fracturing intensity;

[0070] The storage capacity factors include: the transformation length of the horizontal oil layer, porosity, permeability, oil saturation, and mud content; the fracture network complexity factors include: brittleness index, horizontal stress difference, and fracture pressure; the volume fracturing transformation strength factors include: fracture density, liquid injection strength, sand addition strength, and construction displacement.

[0071] Step 3: Based on the multi-level evaluation system of factors affecting production capacity, establish a production capacity influencing factor evaluation model, calculate the weight factors of different influencing factors, and rank them. The larger the value, the greater the contribution to production capacity.

[0072] In step 3, according to the multi-level evaluation system of production capacity factors, a production capacity factor evaluation model is established, the weight factors of different influencing factors are calculated, and they are ranked to clarify the main control factors affecting production capacity. Specifically, it includes the following contents:

[0073] Step 3.1, parameter set acquisition: According to the logging interpretation results, the parameter set of factors affecting the productivity of each fracturing stage of shale oil horizontal wells is collected, and the method of obtaining is the average value method.

[0074] Step 3.2, establish an evaluation matrix of influencing factors: According to the parameter set of production capacity influencing factors and the measured fluid production profile of the fracturing section of the shale oil horizontal well, establish an evaluation matrix X and an evaluation reference column X0, respectively, where the evaluation matrix elements are the production capacity influencing factors of the measured fracturing section, and the evaluation standard column is the measured fluid production profile of the fracturing section.

[0075]

[0076] X0=(X1(0),…,X i (0),…,X m (0)) T i=1,2,…,m (2)

[0077] Where: X is the evaluation matrix; X i (j) is the evaluation matrix element; m is the number of horizontal well fracturing stages; n is the number of productivity influencing factors; X0 is the evaluation standard column.

[0078] Step 3.3, standardization of evaluation matrix data: Since the dimensions and physical meanings of different influencing factors vary greatly, it is necessary to standardize the evaluation matrix and evaluation criteria columns. The maximum value method is used, and the calculation formula is as follows:

[0079]

[0080] Where: is the standardized element of the evaluation matrix; (X i (j)) max is the maximum value in the jth influencing factor parameter set.

[0081] Step 3.4, gray correlation calculation: Calculate the gray correlation between different influencing factors and the evaluation criteria column according to the standardized evaluation matrix and the evaluation criteria column. The calculation expression is as follows:

[0082]

[0083] Where: rj is the grey correlation degree; in The data were normalized for the evaluation reference column; is the data after the evaluation matrix elements are standardized; ρ is the resolution coefficient, which is usually taken as 0.5.

[0084] Step 3.5, weight factor calculation: According to the gray correlation between each capacity influencing factor and the evaluation standard column, further calculate the weight factors of different influencing factors to quantitatively characterize the degree of impact on capacity. The calculation formula is shown in (5). And sort the weight factors of each capacity influencing factor. The larger the value, the greater the impact on capacity.

[0085]

[0086] Where: c j is the weight factor of the factors affecting production capacity; r j is the grey correlation degree.

[0087] Step 4: Sort the coupled production capacity influencing factors according to the weight factors, establish a production capacity prediction model, and calculate the similarity factor between the test section and the predicted section of the horizontal well with known production profiles. The larger the value, the higher the similarity, and the production profiles are equivalent. Further, the production capacity of the predicted section is dynamically predicted based on the dynamic test results of the known production profile.

[0088] Specifically, the factors influencing the coupled production capacity are sorted according to the weight factors, a production capacity prediction model is established, and the similarity factor between the test section and the predicted section of the horizontal well with known production profile is calculated. The larger the value, the higher the similarity, and the production profile is equivalent. The production profile of the predicted section is further dynamically predicted through the dynamic test results of the known production profile. Specifically, it includes the following contents:

[0089] Step 4.1, coupling the factors affecting capacity, establishing the capacity prediction matrix B, where the prediction matrix elements are the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set corresponding to the measured fracturing section, and the prediction reference series is the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set of the predicted section, as shown in expression (6).

[0090]

[0091] Where: B capacity forecast matrix; B i (j) is the element of the production capacity prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k; h is the number of measured fracturing stages; k is the number of parameters of the factors affecting the production capacity of the measured fracturing stages.

[0092] Since there are many factors that affect production capacity, the dimensions of different factors vary greatly, and it is difficult to compare absolute values. Therefore, on the basis of the establishment of the production capacity forecast matrix, the matrix elements are further standardized. The specific contents are as follows:

[0093] Step 4.2: Standardize the capacity forecast matrix elements according to the distribution range of the capacity influencing factor parameters. The calculation formula is as follows:

[0094]

[0095]

[0096] Where: is the mean of each parameter set of the capacity prediction matrix column vector; Standardize elements for the capacity forecast matrix.

[0097] The elements of the capacity forecast matrix are standardized using formula (7) and formula (8), as follows:

[0098]

[0099] Where: B * Normalize the matrix for capacity forecasting.

[0100] Step 4.3: Calculate the similarity factor between the test section and the predicted section of the known production profile of the horizontal well according to the standardized matrix of capacity prediction. First, calculate the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section in the prediction matrix. The calculation formula is:

[0101]

[0102] The similarity factor is further calculated by combining the standardized matrix of capacity forecasting. The calculation formula is as follows:

[0103]

[0104] Where: SI i is the similarity factor; D is the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section.

[0105] Step 4.4, sort the similarity factors between the known fracturing sections and the predicted fracturing sections. The larger the value, the greater the similarity. The fluid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the predicted section. Then, according to the dynamic test results of the fluid production profile of the measured fracturing section, the dynamic change results of the fluid production profile at different stages of the predicted section are obtained.

[0106] The method for predicting the production capacity of shale oil horizontal well volume fracturing segmentation of the present invention first obtains the dynamic distribution of the production profile of each fracturing segment of the shale oil horizontal well at different production times; secondly, a multi-level evaluation system of factors affecting the production capacity of shale oil horizontal wells is established by using the hierarchical analysis method, including storage capacity, fracture network complexity and volume fracturing transformation intensity factors. Then, a production capacity influencing factor evaluation model is established, and the weight factors of different influencing factors are calculated and sorted. The larger the value, the greater the contribution to the production capacity. Finally, the coupled production capacity influencing factors are sorted according to the weight factor, and a production capacity prediction model is established. The similarity factor of the test segment and the prediction segment of the known production profile of the horizontal well is calculated. The larger the value, the higher the similarity, and the equivalent production capacity contribution, thereby achieving the purpose of dynamically predicting the production capacity of the shale oil horizontal well volume fracturing segmentation.

[0107] Example 1

[0108] The production capacity prediction is carried out by taking the prediction of the production profile of a shale oil horizontal well in the Ordos Basin as an example.

[0109] The shale oil in the basin has the characteristics of low rock brittleness index, low oil layer pressure coefficient, and low single well production. The long horizontal well advance energy storage subdivision cutting volume fracturing technology is a key technology for the efficient development of shale oil. The production profile test is currently one of the important means to evaluate the effect of horizontal well fracturing. The horizontal well H1 in the main development test area of ​​shale oil in the basin was selected, and the long horizontal well body subdivision cutting volume fracturing transformation was adopted. A total of 15 sections were fractured, numbered Fr1~Fr15. The quantum dot tracer method was used to obtain the average production profile distribution of 13 sections (F1-F13) after volume fracturing in the backflow period and oil appearance period. The shale oil horizontal well volume fracturing segmented capacity prediction method of the present invention was used to predict the production profile of the fracturing section Fr14~Fr15, as follows:

[0110] Step 1: Obtain the dynamic distribution of the fluid production profile at different production times of each fracturing stage after volume fracturing of the example well H1-1, including the fracturing fluid return period and the oil-seeking stable production period. The water production and oil production profiles are as follows: Figure 1 and Figure 2 As shown in Figure 2, the liquid production profile is as follows: Figure 3 and Figure 4 shown.

[0111] Step 2: Use the analytic hierarchy process to establish a multi-level evaluation system for factors affecting the productivity of shale oil horizontal well volume fracturing, including reservoir capacity, fracture network complexity, and volume fracturing transformation strength factors. The reservoir capacity factors include: the transformation length of the horizontal oil layer, porosity, permeability, oil saturation, and mud content; the fracture network complexity factors include: brittleness index, horizontal stress difference, and fracture pressure; the volume fracturing transformation strength factors include: seam density, liquid injection strength, sand addition strength, and construction displacement. Figure 5 shown.

[0112] Step 3: Based on the multi-level evaluation system of production capacity factors, establish a production capacity factor evaluation model, calculate the weight factors of different influencing factors, rank them, and clarify the main control factors affecting production capacity. Specifically include the following:

[0113] 1) According to the logging interpretation results, the parameter set of factors affecting the productivity of each fracturing stage of shale oil horizontal wells is collected, and the method of obtaining is the average value method, as shown in Tables 1 and 2.

[0114] Table 1 Geomechanical parameters of each fracturing stage of horizontal well H1-1

[0115]

[0116]

[0117] Table 2 Parameters of fracturing transformation and fluid production profile of each fracturing stage of horizontal well H1-1

[0118]

[0119] 2) According to the parameter set of productivity influencing factors and the measured fluid production profile of the fracturing section of the shale oil horizontal well, an evaluation matrix and an evaluation standard column are established, as shown in Expressions (12) and (13).

[0120]

[0121] X0=(4, 6, 5, 8, 3, 2, 3, 3, 13, 24, 19, 6, 4) T (13)

[0122] 3) Formula (3) is used to standardize the evaluation matrix and evaluation standard column data, and formulas (4) and (5) are used to calculate the gray correlation degree and weight factor between different influencing factors of capacity and the evaluation standard column, as shown in Table 3.

[0123] Table 3 Calculation table of weight factors affecting production capacity

[0124]

[0125]

[0126] 4) According to the weight factors, the contribution of different influencing factors to production capacity is ranked from large to small as follows: transformation length, sand addition intensity, seam density, permeability, liquid inflow intensity, construction displacement, porosity, horizontal stress difference, brittleness index, fracture pressure, oil saturation, and mud content. Figure 6 shown.

[0127] Step 4: Sort the coupled production capacity influencing factors according to the weight factors, establish a production capacity prediction model, and calculate the similarity factor between the test section and the predicted section of the horizontal well with known production profiles. The larger the value, the higher the similarity and the more similar the production profiles are. Further, the production profile of the predicted section is dynamically predicted based on the dynamic test results of the known production profile.

[0128] 1) Sort the factors affecting coupled capacity according to the weight factors, and take the forecast segment F14 as an example to establish a capacity forecast model as follows:

[0129]

[0130] 2) The elements of the capacity prediction model are standardized using formulas (7) and (8), and the similarity factors between the measured fracturing sections and the predicted fracturing sections are calculated using formulas (9) and (10), as shown in Table 4.

[0131] Table 4 Calculation table of similarity factors of Fr14 and Fr15 between the measured fracturing section and the predicted fracturing section

[0132]

[0133]

[0134] 3) Sort the similarity factors between the known fracturing sections and the predicted fracturing sections Fr14, such as Figure 7 and Figure 8 As shown, the larger the value, the greater the similarity. The maximum similarity factor between the fracturing section Fr8 and the predicted section Fr14 is 0.9999. Therefore, the change law of the liquid production profile after the two sections of volume fracturing is consistent. According to the test results of the liquid production profile of the fracturing section Fr8 in step 1, the average liquid production profiles of the fracturing fluid backflow period and the stable production period of oil in the predicted section Fr14 are 2.1 and 3%, respectively. Repeating 1) to 4) in step 4 using the same method can obtain that the maximum similarity factor between the fracturing section Fr9 and the predicted section Fr15 is 0.9649. Therefore, the change law of the liquid production profile after the two sections of volume fracturing is consistent. The average liquid production profiles of the fracturing fluid backflow period and the stable production period of oil in the predicted section Fr15 are 0.8 and 13, respectively. Fig. 9 and Fig.10 shown.

[0135] Example 2

[0136] The method for predicting the production capacity of shale oil horizontal well volume fracturing stage is implemented in the following steps:

[0137] Step 1: obtaining the dynamic distribution of the average production profile of the fracturing fluid flowback period and the oil-discovering stable production period of each fracturing stage of the shale oil horizontal well in the same block and the same development layer on site;

[0138] Step 2: Establish a multi-level evaluation system for factors affecting shale oil horizontal well volume fracturing productivity using the analytic hierarchy process;

[0139] Step 3: According to the multi-level evaluation system of capacity influencing factors, establish a capacity influencing factor evaluation model, calculate the weight factors of different influencing factors and rank them;

[0140] Step 4: Sort the coupled production capacity influencing factors according to the weight factors, establish a production capacity prediction model, calculate the similarity factor between the test section and the prediction section of the known production profile of the horizontal well, and dynamically predict the production capacity of the prediction section based on the dynamic test results of the known production profile.

[0141] Example 3

[0142] Based on Example 2, step 1 is specifically as follows: using quantum dot tracers to test the fluid production profile of each fracturing section of shale oil horizontal wells in the same block and development formation, and obtaining the dynamic distribution of the average fluid production profile during the fracturing fluid backflow period and the stable oil production period after volume fracturing.

[0143] In step 2, the factors affecting productivity include reservoir capacity, fracture network complexity and volume fracturing intensity. The reservoir capacity factors include: the length of the horizontal oil layer, porosity, permeability, oil saturation and mud content. The fracture network complexity factors include: brittleness index, horizontal stress difference and fracture pressure. The volume fracturing intensity factors include: fracture density, liquid injection intensity, sand addition intensity and construction displacement.

[0144] Example 4

[0145] Based on Example 3, step 3 is specifically implemented according to the following steps:

[0146] Step 3.1, parameter set acquisition: According to the logging interpretation results, the parameter set of the factors affecting the productivity of each fracturing stage of the shale oil horizontal well is collected, and the method of obtaining is the average method;

[0147] Step 3.2, establish an evaluation matrix of production capacity influencing factors: according to the parameter set of production capacity influencing factors and the measured fluid production profile of the fracturing section of the shale oil horizontal well, establish an evaluation matrix X and an evaluation reference column X0 respectively, wherein the evaluation matrix elements are the production capacity influencing factors of the measured fracturing section, and the evaluation standard column is the measured fluid production profile of the fracturing section;

[0148]

[0149] X0=(X1(0),…,X i (0),…,X m (0)) T i=1,2,…,m (2)

[0150] Where: X is the evaluation matrix; Xi (j) is the evaluation matrix element; m is the number of horizontal well fracturing stages; n is the number of productivity influencing factors; X0 is the evaluation standard column;

[0151] Step 3.3, standardization of evaluation matrix data: Use the maximum value method to standardize the evaluation matrix and evaluation criteria column. The calculation formula is as follows:

[0152]

[0153] Where: is the standardized element of the evaluation matrix; (X i (j)) max is the maximum value of the jth influencing factor parameter set;

[0154] Step 3.4, gray correlation calculation: Calculate the gray correlation between different influencing factors and the evaluation criteria column according to the standardized evaluation matrix and the evaluation criteria column. The calculation expression is as follows:

[0155]

[0156] Where: r j is the grey correlation degree; in The data were normalized for the evaluation reference column; is the data after the evaluation matrix elements are standardized; ρ is the resolution coefficient;

[0157] Step 3.5, weight factor calculation: According to the gray correlation between each capacity influencing factor and the evaluation standard column, calculate the weight factors of different capacity influencing factors to quantitatively characterize the degree of impact on capacity, and rank the weight factors of each capacity influencing factor. The larger the value, the greater the impact on capacity.

[0158] The calculation formula of the weight factor of factors affecting production capacity is as follows:

[0159]

[0160] Where: c j is the weight factor of the factors affecting production capacity; r j is the grey correlation degree.

[0161] Step 4 is implemented as follows:

[0162] Step 4.1, coupling the factors affecting capacity, establish the capacity prediction matrix B,

[0163]

[0164] Where: B capacity forecast matrix; B i(j) is the element of the production capacity prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k; h is the number of measured fracturing stages; k is the number of parameters affecting the production capacity of the measured fracturing stages;

[0165] The elements of the capacity prediction matrix are the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set corresponding to the measured fracturing section, and the prediction reference series is the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set of the predicted section;

[0166] Step 4.2: Based on the establishment of the capacity forecast matrix, standardize the elements of the capacity forecast matrix. The specific contents are as follows:

[0167] According to the distribution range of the parameter values ​​of the capacity influencing factors, the elements of the capacity forecast matrix are standardized, and the calculation formula is as follows:

[0168]

[0169]

[0170] Where: is the mean of each parameter set of the capacity prediction matrix column vector; Standardize elements for the capacity forecast matrix;

[0171] The elements of the capacity forecast matrix are standardized using formulas (7) and (8):

[0172]

[0173] Where: B * Normalize the matrix for capacity forecasting.

[0174] Step 4.3: Calculate the similarity factor between the test section and the predicted section of the known production profile of the horizontal well according to the standardized matrix of capacity prediction. First, calculate the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section in the prediction matrix, and then calculate the similarity factor in combination with the standardized matrix of capacity prediction. The calculation formula is as follows:

[0175]

[0176]

[0177] Where: SI i is the similarity factor; D is the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section;

[0178] Step 4.4, sort the similarity factors between the known fracturing sections and the predicted fracturing sections. The larger the value, the greater the similarity. The fluid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the predicted section. The dynamic change results of the fluid production profile of the predicted section at different stages are obtained based on the dynamic test results of the fluid production profile of the measured fracturing section.

Claims

1. A method for predicting the production capacity of shale oil horizontal well volume fracturing stages, characterized in that: Follow the steps below to implement it: Step 1: obtaining the dynamic distribution of the average production profile of the fracturing fluid flowback period and the oil-discovering stable production period of each fracturing stage of the shale oil horizontal well in the same block and the same development layer on site; Step 2: Establish a multi-level evaluation system for factors affecting shale oil horizontal well volume fracturing productivity using the analytic hierarchy process; Step 3: According to the multi-level evaluation system of capacity influencing factors, establish a capacity influencing factor evaluation model, calculate the weight factors of different influencing factors and rank them; Step 4: Sort the coupled production capacity influencing factors according to the weight factors, establish a production capacity prediction model, calculate the similarity factor between the test section and the prediction section of the known production profile of the horizontal well, and dynamically predict the production capacity of the prediction section based on the dynamic test results of the known production profile.

2. The method for predicting the production capacity of shale oil horizontal well volume fracturing by segmentation according to claim 1, characterized in that: The step 1 specifically comprises: using quantum dot tracers to test the fluid production profile of each fracturing section of shale oil horizontal wells in the same block and development formation, and obtaining the average fluid production profile dynamic distribution during the fracturing fluid flowback period and the oil-seeking stable production period after volume fracturing.

3. The method for predicting the production capacity of shale oil horizontal well volume fracturing by segmentation according to claim 1 or 2, characterized in that: In step 2, the productivity influencing factors include reservoir capacity factor, fracture network complexity factor and volume fracturing intensity factor.

4. The method for predicting the production capacity of shale oil horizontal well volume fracturing by segmentation according to claim 3 is characterized in that: The reservoir capacity factors include: the transformation length of the horizontal oil layer, porosity, permeability, oil saturation, and mud content; the fracture network complexity factors include: brittleness index, horizontal stress difference, and fracture pressure; the volume fracturing transformation strength factors include: fracture density, liquid injection strength, sand addition strength, and construction displacement.

5. The method for predicting the production capacity of shale oil horizontal well volume fracturing by segmentation according to claim 3, characterized in that: The step 3 is specifically implemented according to the following steps: Step 3.1, parameter set acquisition: According to the logging interpretation results, the parameter set of the factors affecting the productivity of each fracturing stage of the shale oil horizontal well is collected, and the method of obtaining is the average method; Step 3.2, establish an evaluation matrix of production capacity influencing factors: according to the parameter set of production capacity influencing factors and the measured fluid production profile of the fracturing section of the shale oil horizontal well, establish an evaluation matrix X and an evaluation reference column X0 respectively, wherein the evaluation matrix elements are the production capacity influencing factors of the measured fracturing section, and the evaluation standard column is the measured fluid production profile of the fracturing section; X0=(X1(0),…,X i (0),…,X m (0)) T i=1,2,…,m (2) Where: X is the evaluation matrix; X i (j) is the evaluation matrix element; m is the number of horizontal well fracturing stages; n is the number of productivity influencing factors; X0 is the evaluation standard column; Step 3.3, standardization of evaluation matrix data: Use the maximum value method to standardize the evaluation matrix and evaluation criteria column. The calculation formula is as follows: Where: is the standardized element of the evaluation matrix; (X i (j)) max is the maximum value of the jth influencing factor parameter set; Step 3.4, gray correlation calculation: Calculate the gray correlation between different influencing factors and the evaluation criteria column according to the standardized evaluation matrix and the evaluation criteria column. The calculation expression is as follows: Where: r j is the grey correlation degree; in The data were normalized for the evaluation reference column; The data after normalization of the evaluation matrix elements; ρ is the resolution coefficient; Step 3.5, weight factor calculation: According to the gray correlation between each capacity influencing factor and the evaluation standard column, calculate the weight factors of different capacity influencing factors to quantitatively characterize the degree of impact on capacity, and rank the weight factors of each capacity influencing factor. The larger the value, the greater the impact on capacity. The calculation formula of the weight factor of factors affecting production capacity is as follows: Where: c j is the weight factor of the factors affecting production capacity; r j is the grey correlation degree.

6. The method for predicting the production capacity of shale oil horizontal well volume fracturing by segmentation according to claim 5, characterized in that: The step 4 is specifically implemented according to the following steps: Step 4.1, coupling the factors affecting capacity, establish the capacity prediction matrix B, Where: B capacity forecast matrix; B i (j) is the element of the production capacity prediction matrix, i = 0, 1, 2, ..., h; j = 1, 2, ..., k; h is the number of measured fracturing stages; k is the number of parameters affecting the production capacity of the measured fracturing stages; The elements of the capacity prediction matrix are the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set corresponding to the measured fracturing section, and the prediction reference series is the reservoir capacity, fracture network complexity, and volume fracturing intensity parameter set of the predicted section; Step 4.2: Based on the established capacity forecast matrix, standardize the elements of the capacity forecast matrix to obtain a standardized capacity forecast matrix; Step 4.3, according to the standardized matrix of production capacity prediction, calculate the similarity factor between the test section and the predicted section of the known production profile of the horizontal well; Step 4.4, sort the similarity factors between the known fracturing sections and the predicted fracturing sections. The larger the value, the greater the similarity. The fluid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the predicted section. The dynamic change results of the fluid production profile of the predicted section at different stages are obtained based on the dynamic test results of the fluid production profile of the measured fracturing section.

7. The method for predicting the production capacity of shale oil horizontal well volume fracturing by stages according to claim 6, characterized in that: The step 4.2 is specifically as follows: standardize the elements of the capacity prediction matrix according to the distribution range of the parameter values ​​of the capacity influencing factors, and the calculation formula is as follows: Where: is the mean value of each parameter set of the capacity prediction matrix column vector; Standardize elements for the capacity forecast matrix; The elements of the capacity forecast matrix are standardized using formulas (7) and (8): Where: B * Normalize the matrix for capacity forecasting.

8. The method for predicting the production capacity of shale oil horizontal well volume fracturing by stages according to claim 7, characterized in that: The step 4.3 is specifically as follows: firstly, the maximum distance between each parameter set of the known fracturing stage and each parameter set of the prediction stage in the prediction matrix is ​​calculated, and then the similarity factor is calculated in combination with the production capacity prediction standardization matrix. The calculation formula is as follows: Where: SI i is the similarity factor; D is the maximum distance between each parameter set of the known fracturing section and each parameter set of the predicted section.

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