Shale oil horizontal well volume fracturing segmented productivity prediction method

Abstract

The shale oil horizontal well volume fracturing segmented productivity prediction method of the present application has the following steps: obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period of each fracturing section of the shale oil horizontal well in the same block and the same development layer system in the field; using the analytic hierarchy process to establish a multi-level evaluation system of the shale oil horizontal well volume fracturing productivity influencing factors; establishing a productivity influencing factor evaluation model, calculating the weight factors of different influencing factors and sorting them; coupling the productivity influencing factors, establishing a productivity prediction model, calculating the horizontal well known liquid production profile test section and the prediction section similarity factor, and dynamically predicting the productivity of the prediction section through the known liquid production profile dynamic test result. The productivity prediction method of the present application couples the reservoir capacity, the fracture network complexity and the volume fracturing reconstruction strength factors, establishes a productivity prediction model, quickly and quantitatively predicts the liquid production profile, is accurate and reliable, and is also applicable to the similar oil reservoir horizontal well volume fracturing liquid production profile prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas development, and particularly discloses a shale oil horizontal well volume fracturing segmented productivity prediction method. BACKGROUND

[0002] There are great differences between the shale oil in the basin and the geological characteristics in North America. Directly copying the fracturing mode in North America cannot achieve scale benefit development, and some horizontal wells still face problems such as rapid production decline and short stable production period after volume fracturing. The horizontal well volume fracturing technology that is more matched with the reservoir still needs continuous research and development, and the productivity contribution of each fracturing segment of the horizontal well is an important means to directly evaluate the volume fracturing effect and the process applicability. However, there are many factors affecting the productivity of each fracturing segment and the relationship is complex, which affects the productivity contribution to different extents. At the same time, the long test cycle and high economic cost make the productivity contribution prediction more difficult. Therefore, a faster and more economical productivity contribution prediction method is urgently needed to provide strong support for the parameter optimization of shale oil horizontal well volume fracturing and the benefit development of shale oil.

[0003] At present, the test and prediction methods of the productivity contribution of each fracturing segment of the horizontal well mainly include the following:

[0004] Patent one “A multi-stage fracturing horizontal well liquid production profile test pipe column” (publication number: CN111663929B, publication date: April 7, 2023) tests the oil and water of different perforation layers by designing a liquid production profile test pipe column combined with a tracer. However, since the multi-stage fracturing horizontal well usually has many perforation clusters, a large number of liquid production profile test pipe columns need to be designed, which increases the economic cost. At the same time, the test pipe column needs to be reinserted under the high temperature and high pressure state of the wellbore after hydraulic fracturing, which has a high engineering risk. From the economic and safety point of view, its practicability is limited to a certain extent.

[0005] Patent two “An oilfield horizontal well liquid production profile test pipe column, system and method” (publication number: CN111441763A, publication date: July 24, 2020) also tests the liquid production profile by designing a pipe column. However, this method can only indirectly qualitatively determine the high water cut position of the oil well by testing the wellbore temperature.

[0006] Patent three “A method for testing the liquid production profile of a horizontal well by using quantum dot tracers” (publication number: CN110805432A, publication date: February 18, 2020) uses different quantum dot tracers to follow the injection during the fracturing process to quantitatively test the liquid production profile of oil, gas and water at different times after hydraulic fracturing. This method is feasible in engineering and has strong operability in field application. It realizes the quantitative characterization of oil, gas and water and is a test method worthy of promotion. However, the test cycle is relatively long. SUMMARY

[0007] The shale oil horizontal well volume fracturing segmented productivity prediction method of the present application solves the problems of high cost and indirect qualitative judgment of high water cut position of oil wells in the prior art.

[0008] The technical solution adopted by the present application is a shale oil horizontal well volume fracturing segmented productivity prediction method, which is implemented according to the following steps:

[0009] Step 1, obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period of each fracturing segment of the shale oil horizontal well in the same block and development layer system in the field;

[0010] Step 2, establishing a multi-level evaluation system of the volume fracturing productivity influencing factors of the shale oil horizontal well by using the analytic hierarchy process;

[0011] Step 3, establishing a productivity influencing factor evaluation model according to the multi-level evaluation system of the productivity influencing factors, calculating the weight factors of different influencing factors and sorting them;

[0012] Step 4, coupling the productivity influencing factors according to the weight factor sorting, establishing a productivity prediction model, calculating the similarity factor of the known liquid production profile test segment and the predicted segment of the horizontal well, and dynamically predicting the productivity of the predicted segment through the known liquid production profile dynamic test results.

[0013] The present application has the following characteristics:

[0014] Step 1 is specifically: using quantum dot tracers to test the liquid production profile of each fracturing segment of the shale oil horizontal well in the same block and development layer system, and obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period after volume fracturing.

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

[0016] The reservoir capacity factors include the reconstruction length, porosity, permeability, oil saturation and shale content of the horizontal segment oil layer; the fracture network complexity factors include the brittleness index, horizontal stress difference and breakdown pressure; and the volume fracturing reconstruction intensity factors include the fracture density, liquid injection intensity, sand addition intensity and construction displacement.

[0017] Step 3 is implemented according to the following steps:

[0018] Step 3.1, parameter set acquisition: collecting the productivity influencing factor parameter set of each fracturing segment of the shale oil horizontal well according to the logging interpretation results, and the calculation method is the average value method;

[0019] Step 3.2, establishing the productivity influence factor evaluation matrix: according to the productivity influence factor parameter set and the measured fracture section liquid production profile of the horizontal well of the shale oil, an evaluation matrix X and an evaluation reference column X0 are respectively established, wherein the elements of the evaluation matrix are the measured productivity influence factors of the fracture section, and the evaluation standard column is the measured liquid production profile of the fracture section;

[0020]

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

[0022] In the formula, X is the evaluation matrix; X i (j) is the element of the evaluation matrix; m is the number of the fracture sections of the horizontal well; n is the number of the productivity influence factors; X0 is the evaluation standard column;

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

[0024]

[0025] In the formula, is the standardized element of the evaluation matrix; (X i (j)) max is the maximum value in the jth influence factor parameter set;

[0026] Step 3.4, grey correlation degree calculation: the grey correlation degree between different influence factors and the evaluation standard column is calculated according to the standardized evaluation matrix and the evaluation standard column, and the calculation expression is as follows:

[0027]

[0028] In the formula, r j is the grey correlation degree; wherein is the standardized data of the evaluation reference column; is the standardized data of the evaluation matrix element; and p is the resolution coefficient;

[0029] Step 3.5, weight factor calculation: the weight factors of different productivity influence factors are calculated according to the grey correlation degrees between each productivity influence factor and the evaluation standard column to quantitatively represent the influence degree on the productivity, and the weight factors of each productivity influence factor are sorted, and the greater the value is, the greater the influence degree on the productivity is;

[0030] The calculation formula of the productivity influence factor weight factor is as follows:

[0031]

[0032] In the formula: c j r is the weight factor of the energy production influencing factor; r j is the grey correlation degree.

[0033] Step 4 is specifically implemented according to the following steps:

[0034] Step 4.1, coupling the energy production influencing factors, establishing an energy production prediction matrix B,

[0035]

[0036] In the formula: B is the energy production prediction matrix; B i (j) is the energy production prediction matrix element, i = 0, 1, 2, …, h; j = 1, 2, …, k; h is the number of measured fracturing sections; k is the number of measured fracturing section influencing energy production factor parameters;

[0037] The energy production prediction matrix element is the parameter set of the measured fracturing section corresponding reservoir capacity, fracture network complexity, and volume fracturing reconstruction strength, and the prediction reference series is the parameter set of the reservoir capacity, fracture network complexity, and volume fracturing reconstruction strength of the predicted section;

[0038] Step 4.2, on the basis of the establishment of the energy production prediction matrix, the energy production prediction matrix elements are standardized to obtain an energy production prediction standardized matrix;

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

[0040] Step 4.4, the similarity factor between the known fracturing section and the predicted fracturing section is sorted, the greater the value, the greater the similarity, and the liquid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the predicted section. According to the measured fracturing section liquid production profile dynamic test result, the dynamic change result of the predicted section liquid production profile at different stages is obtained.

[0041] Step 4.2 is specifically:

[0042] According to the value distribution range of the energy production influencing factor parameters, the energy production prediction matrix elements are standardized, and the calculation formula is as follows:

[0043]

[0044]

[0045] In the formula: is the mean value of each parameter set of the energy production prediction matrix column vector; is the energy production prediction matrix standardized element;

[0046] The formula (7) and (8) are used to normalize the matrix elements of the productivity prediction:

[0047]

[0048] In the formula, B * is the normalized matrix of the productivity prediction.

[0049] The step 4.3 is specifically: firstly, the maximum distance between the parameter sets of the known fracturing sections and the parameter sets of the predicted section in the prediction matrix is calculated, and then the similarity factor is calculated in combination with the normalized matrix of the productivity prediction, and the calculation formula is as follows:

[0050]

[0051]

[0052] In the formula, SI i is the similarity factor; D is the maximum distance between the parameter sets of the known fracturing sections and the parameter sets of the predicted section.

[0053] The shale oil horizontal well volume fracturing segmented productivity prediction method has the beneficial effects that:

[0054] The shale oil horizontal well volume fracturing segmented productivity prediction method can simultaneously couple the reservoir capacity, the fracture network complexity and the volume fracturing reconstruction strength factors affecting the productivity, and the main control factors are determined, and the productivity prediction model is established, the liquid production profile is quickly and quantitatively predicted, the calculation method is accurate and reliable, the problems of large construction risk, long test period and high cost are solved, the time and economic cost are greatly saved, the method is also applicable to the liquid production profile prediction of the volume fracturing of the similar oil reservoir horizontal well, has a good application prospect, and can provide strong support for the post-fracturing effect evaluation and fracturing parameter optimization of the shale oil horizontal well. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 is the oil and water production profile of the measured fracturing section of the horizontal well H1-1 in the flowback period of the embodiment 1 of the present application;

[0056] Figure 2 is the oil and water production profile of the measured fracturing section of the horizontal well H1-1 in the production period of the embodiment 1 of the present application;

[0057] Figure 3 is the liquid production profile of the measured fracturing section of the horizontal well H1-1 in the flowback period of the embodiment 1 of the present application;

[0058] Figure 4 is the liquid production profile of the measured fracturing section of the horizontal well H1-1 in the production period of the embodiment 1 of the present application;

[0059] Figure 5It is a multi-level evaluation system graph of influencing factors of productivity for shale oil horizontal well volume fracturing segmented productivity prediction method of the present application;

[0060] Figure 6 It is a productivity influencing factor ranking graph in the shale oil horizontal well volume fracturing segmented productivity prediction method of the present application;

[0061] Figure 7 It is a similarity factor ranking graph between the measured fracturing section and the predicted section Fr14 of the horizontal well H1-1 in embodiment 1 of the present application;

[0062] Figure 8 It is a similarity factor ranking graph between the measured fracturing section and the predicted section Fr15 of the horizontal well H1-1 in embodiment 1 of the present application;

[0063] Figure 9 It is a liquid production profile graph of the predicted sections Fr14 and Fr15 of the horizontal well H1-1 in the flowback period in embodiment 1 of the present application;

[0064] Figure 10 It is a liquid production profile graph of the predicted sections Fr14 and Fr15 of the horizontal well H1-1 in the production period in embodiment 1 of the present application. DETAILED DESCRIPTION

[0065] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0066] The shale oil horizontal well volume fracturing segmented productivity prediction method of the present application is specifically implemented according to the following steps:

[0067] Step 1, obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period of each fracturing section of the shale oil horizontal well in the same block and the same development layer system of the field.

[0068] Using quantum dot tracers to test the liquid production profile of each fracturing section of the shale oil horizontal well in the same block and the same development layer system, the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period after volume fracturing is obtained.

[0069] Step 2, using the analytic hierarchy process to establish a multi-level evaluation system of the productivity influencing factors of the shale oil horizontal well volume fracturing, including reservoir capacity factors, fracture network complexity factors, and volume fracturing reconstruction intensity factors;

[0070] Among them, the reservoir capacity factors include the reconstruction length, porosity, permeability, oil saturation and shale content of the horizontal section oil layer; the fracture network complexity factors include the brittleness index, horizontal stress difference and breakdown pressure; the volume fracturing reconstruction intensity factors include the fracture density, liquid injection intensity, sand addition intensity and construction displacement.

[0071] Step 3, according to the multi-level evaluation system of the capacity influencing factors, an evaluation model of the capacity influencing factors is established, and the weight factors of different influencing factors are calculated and sorted, and the greater the value is, the greater the contribution to the capacity is.

[0072] In step 3, according to the multi-level evaluation system of the capacity influencing factors, an evaluation model of the capacity influencing factors is established, and the weight factors of different influencing factors are calculated and sorted, and the main control factors affecting the capacity are determined. The specific contents include the following:

[0073] Step 3.1, parameter set acquisition: according to the logging interpretation results, the parameter set of the capacity influencing factors of each fracturing section of the shale oil horizontal well is collected, and the average method is used to calculate.

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

[0075]

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

[0077] In the formula, X is the evaluation matrix, X i (j) is the element of the evaluation matrix, m is the number of the fracturing sections of the horizontal well, n is the number of the capacity influencing factors, and X0 is the evaluation standard column.

[0078] Step 3.3, data standardization of the evaluation matrix: since the dimensions and physical meanings of different influencing parameters are quite different, the evaluation matrix and the evaluation standard column need to be standardized. The maximum value method is used, and the calculation formula is as follows:

[0079]

[0080] In the formula, is the standardized element of the evaluation matrix, (X i (j)) max is the maximum value in the jth parameter set of the influencing factors.

[0081] Step 3.4, calculation of the grey correlation degree: according to the standardized evaluation matrix and the evaluation standard column, the grey correlation degree between different influencing factors and the evaluation standard column is calculated, and the calculation expression is as follows:

[0082]

[0083] In the formula: rj is the grey correlation degree; wherein is the normalized data of the evaluation reference column; is the normalized data of the evaluation matrix element; and p is the resolution coefficient, usually taken as 0.5.

[0084] Step 3.5, weight factor calculation: according to the grey correlation degree between each productivity influencing factor and the evaluation standard column, further calculate the weight factor of different influencing factors to quantitatively characterize the degree of influence on productivity, the calculation formula is shown as formula (5). And sort the weight factor of each productivity influencing factor, the greater the value, the greatest degree of influence on productivity.

[0085]

[0086] In the formula: c j is the weight factor of the productivity influencing factor; r j is the grey correlation degree.

[0087] Step 4, according to the weight factor sorting coupling productivity influencing factors, establish a productivity prediction model, calculate the similarity factor of the known production profile test section and the prediction section of the horizontal well, the greater the value, the higher the similarity, the production profile is equivalent, further dynamically predict the productivity of the prediction section through the dynamic test results of the known production profile.

[0088] Specifically: according to the weight factor sorting coupling productivity influencing factors, establish a productivity prediction model, calculate the similarity factor of the known production profile test section and the prediction section of the horizontal well, the greater the value, the higher the similarity, the production profile is equivalent, further dynamically predict the production profile of the prediction section through the dynamic test results of the known production profile. Specifically includes the following contents:

[0089] Step 4.1, coupling productivity influencing factors, establish a productivity prediction matrix B, wherein the prediction matrix element is the measured fracture section corresponding to the reservoir capacity, fracture network complexity, volume fracturing reconstruction intensity parameter set, and the prediction reference series is the reservoir capacity, fracture network complexity, volume fracturing reconstruction intensity parameter set of the prediction section, as shown in expression (6).

[0090]

[0091] In the formula: B is the productivity prediction matrix; B i (j) is the productivity prediction matrix element, i=0, 1, 2, …, h; j=1, 2, …, k; h is the number of measured fracture sections; k is the number of measured fracture section influencing productivity factor parameters.

[0092] Because of many factors affecting the capacity, the dimensional difference of different factors is large, and it is difficult to compare the absolute value. Therefore, on the basis of the capacity prediction matrix, the matrix elements are further standardized, and the specific content is as follows:

[0093] Step 4.2, according to the parameter value distribution range of the capacity influencing factor, the capacity prediction matrix elements are standardized, and the calculation formula is as follows:

[0094]

[0095]

[0096] In the formula: is the mean value of the parameter set of the column vector of the capacity prediction matrix; is the standardized element of the capacity prediction matrix.

[0097] The formula (7) and formula (8) are used to standardize the elements of the capacity prediction matrix, as follows:

[0098]

[0099] In the formula: B * is the capacity prediction standardized matrix.

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

[0101]

[0102] Further combined with the similarity factor calculated by the capacity prediction standardized matrix, the calculation formula is as follows:

[0103]

[0104] In the formula: SI i is the similarity factor; D is the maximum distance between the parameter set of the known fracturing section and the parameter set of the prediction section.

[0105] Step 4.4, the similarity factor between the known fracturing section and the prediction fracturing section is sorted, the greater the value, the greater the similarity, and the liquid production profile of the fracturing section corresponding to the maximum similarity factor is taken as the prediction section, and then the dynamic test result of the liquid production profile of the prediction section at different stages is obtained according to the dynamic test result of the liquid production profile of the measured fracturing section.

[0106] The shale oil horizontal well volume fracturing segmented productivity prediction method of the present application firstly acquires the dynamic distribution of liquid production profile of each fracturing segment of the shale oil horizontal well at different production times; secondly, a multi-level evaluation system of factors influencing the productivity of the shale oil horizontal well is established by using the analytic hierarchy process, including the factors of reservoir capacity, fracture network complexity and volume fracturing reconstruction intensity. Then, an evaluation model of the factors influencing the productivity is established, the weight factors of different influencing factors are calculated, and they are sorted, the greater the value is, the greater the contribution to the productivity is. Finally, the factors influencing the productivity are coupled according to the weight factor sorting, a productivity prediction model is established, the similarity factor of the known liquid production profile test segment and the predicted segment of the horizontal well is calculated, the greater the value is, the higher the similarity is, and the productivity contribution is equivalent, so as to achieve the purpose of dynamically predicting the volume fracturing segmented productivity of the shale oil horizontal well.

[0107] Example 1

[0108] The liquid production profile prediction of a shale oil horizontal well in the Ordos Basin is taken as an example for productivity prediction.

[0109] The basin shale oil has the characteristics of low rock brittleness index, low oil layer pressure coefficient and low single well production, and the advanced energy storage subdivision cutting volume fracturing technology of long horizontal well is the key technology for the benefit development of shale oil, and the liquid production profile test is one of the important means for evaluating the effect of horizontal well after fracturing. The horizontal well H1 in the main development test area of the basin shale oil is selected, and the long horizontal well is subdivided and cut by volume fracturing reconstruction, a total of 15 fracturing segments are numbered as Fr1-Fr15, the average liquid production profile distribution of 13 segments (F1-F13) after volume fracturing by using quantum dot tracer method is obtained, the liquid production profile of the fracturing segments Fr14-Fr15 is predicted by using the shale oil horizontal well volume fracturing segmented productivity prediction method of the present application, and the specific steps are as follows:

[0110] Step 1, the dynamic distribution of liquid production profile of each fracturing segment of the example well H1-1 at different production times after volume fracturing is obtained, including the fracturing fluid flowback period and the oil production stable production period, wherein the water production profile and the oil production profile are as shown in Figure 1 and Figure 2 , and the liquid production profile is as shown in Figure 3 and Figure 4 .

[0111] Step 2, a multi-level evaluation system of factors influencing the volume fracturing productivity of the shale oil horizontal well is established by using the analytic hierarchy process, including the factors of reservoir capacity, fracture network complexity and volume fracturing reconstruction intensity. The reservoir capacity factor includes the reconstruction length of the horizontal segment, porosity, permeability, oil saturation and shale content; the fracture network complexity factor includes brittleness index, horizontal stress difference and breakdown pressure; the volume fracturing reconstruction intensity factor includes the fracture density, liquid injection intensity, sanding intensity and construction displacement, as shown in Figure 5 .

[0112] Step 3, according to the multi-level evaluation system of productivity factors, the productivity factor evaluation model is established, the weight factors of different influence factors are calculated, and the main control factors affecting productivity are sorted out. It includes the following contents:

[0113] 1) According to the logging interpretation results, the productivity factor parameter set of each fracturing section of shale oil horizontal well is collected, and the method is average method, as shown in table 1 and table 2.

[0114] Table 1 Geomechanical parameter table of each fracturing section of horizontal well H1-1

[0115]

[0116]

[0117] Table 2 Fracturing reconstruction and liquid production profile parameter table of each fracturing section of horizontal well H1-1

[0118]

[0119] 2) According to the productivity influence factor parameter set and the measured liquid production profile of each fracturing section of shale oil horizontal well, the evaluation matrix and evaluation standard column are established respectively, as shown in expression (12) and expression (13).

[0120]

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

[0122] 3) The data of evaluation matrix and evaluation standard column are standardized by formula (3), and the grey correlation degree and weight factor between productivity different influence factors and evaluation standard column are calculated by formula (4) and formula (5) respectively, as shown in table 3.

[0123] Table 3 Weight factor calculation table of productivity influence factors

[0124]

[0125]

[0126] 4) According to the contribution degree of weight factor different influence factors to productivity, the sequence from large to small is as follows: reconstruction length, sanding strength, fracture density, permeability, liquid inlet strength, construction displacement, porosity, horizontal stress difference, brittleness index, fracture pressure, oil saturation and shale content, as shown in Figure 6 .

[0127] Step 4, the capacity influencing factors are coupled according to the weight factor ranking to establish a capacity prediction model, and a similarity factor between the known liquid production profile test section and the predicted section of the horizontal well is calculated, the greater the value, the higher the similarity, the liquid production profile is equivalent, and the dynamic prediction of the liquid production profile of the predicted section is further carried out through the dynamic test result of the known liquid production profile.

[0128] 1) According to the weight factor ranking, the capacity influencing factors are coupled, and the capacity prediction model is established, taking the predicted section F14 as an example, as follows:

[0129]

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

[0131] Table 4 Similarity factor calculation table of measured fracturing section and predicted fracturing section Fr14 and Fr15

[0132]

[0133]

[0134] 3) The similarity factor between the known fracturing section and the predicted section Fr14 is ranked, as shown in Figure 7 and Figure 8 , the greater the value, the greater the similarity, wherein the similarity factor between the fracturing section Fr8 and the predicted section Fr14 is the largest, which is 0.9999, so the liquid production profile change rule after volume fracturing of the two sections is consistent, and according to the liquid production profile test result of the fracturing section Fr8 in step 1, the average liquid production profile of the fracturing fluid flowback period and the oil-bearing stable production period of the predicted section Fr14 is 2.1 and 3% respectively. The same method is used to repeat steps 4) of 1) to 4) in step 4, so that the similarity factor between the fracturing section Fr9 and the predicted section Fr15 is the largest, which is 0.9649, so the liquid production profile change rule after volume fracturing of the two sections is consistent, and the average liquid production profile of the fracturing fluid flowback period and the oil-bearing stable production period of the predicted section Fr15 is 0.8 and 13 respectively, as shown in Figure 9 and Figure 10 .

[0135] Example 2

[0136] The shale oil horizontal well volume fracturing segmented capacity prediction method is specifically implemented according to the following steps:

[0137] Step 1, obtaining the dynamic distribution of the average liquid production profile of the fracturing fluid flowback period and the oil-bearing stable production period of each fracturing section of the shale oil horizontal well in the same block and the same development layer series in the field;

[0138] Step 2, a multi-level evaluation system of the shale oil horizontal well volume fracturing productivity influencing factors is established by using the analytic hierarchy process;

[0139] Step 3, according to the multi-level evaluation system of the productivity influencing factors, an evaluation model of the productivity influencing factors is established, the weight factors of different influencing factors are calculated and sorted;

[0140] Step 4, according to the weight factor sorting and the productivity influencing factors, a productivity prediction model is established, the similarity factor of the known liquid production profile test section and the predicted section of the horizontal well is calculated, and the productivity of the predicted section is dynamically predicted through the known liquid production profile dynamic test result.

[0141] Example 3

[0142] On the basis of Example 2, step 1 is specifically: using quantum dot tracer to test the liquid production profile of each fracturing section of the shale oil horizontal well in the same block and the same development layer system, and obtaining the average liquid production profile dynamic distribution of the fracturing fluid flowback period and the oil-bearing stable production period after volume fracturing.

[0143] In step 2, the productivity influencing factors include the reservoir capacity factor, the fracture network complexity factor and the volume fracturing reconstruction intensity factor; the reservoir capacity factor includes the reconstruction length of the horizontal section oil layer, the porosity, the permeability, the oil saturation and the shale content; the fracture network complexity factor includes the brittleness index, the horizontal stress difference and the breakdown pressure; the volume fracturing reconstruction intensity factor includes the fracture density, the liquid inlet intensity, the sanding intensity and the construction displacement.

[0144] Example 4

[0145] On the basis of 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 productivity influencing factors of each fracturing section of the shale oil horizontal well is collected, and the average method is used for calculation;

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

[0148]

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

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

[0151] Step 3.3, evaluation matrix data standardization: the maximum value method is used to standardize the evaluation matrix and the evaluation standard column, and the calculation formula is as follows:

[0152]

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

[0154] Step 3.4, grey correlation degree calculation: according to the standardized evaluation matrix and the evaluation standard column, the grey correlation degree between different influencing factors and the evaluation standard column is calculated, and the calculation expression is as follows:

[0155]

[0156] In the formula: r j is the grey correlation degree; wherein is the standardized data of the evaluation reference column; is the standardized data of the evaluation matrix element; p is the resolution coefficient;

[0157] Step 3.5, weight factor calculation: according to the grey correlation degree between each productivity influencing factor and the evaluation standard column, the weight factor of different productivity influencing factors is calculated to quantitatively characterize the degree of influence on productivity, and each productivity influencing factor weight factor is sorted, and the greater the value, the greatest degree of influence on productivity;

[0158] The calculation formula of the productivity influencing factor weight factor is as follows:

[0159]

[0160] In the formula: c j is the productivity influencing factor weight factor; r j is the grey correlation degree.

[0161] Step 4 is implemented according to the following steps:

[0162] Step 4.1, coupling productivity influencing factors, establishing a productivity prediction matrix B,

[0163]

[0164] In the formula: B is the productivity prediction matrix; B i(j) is the productivity prediction matrix element, i = 0, 1, 2, …, h; j = 1, 2, …, k; h is the number of measured fracturing sections; k is the number of measured fracturing section influencing productivity factor parameters;

[0165] The productivity prediction matrix element is the parameter set of reservoir capacity, fracture network complexity and volume fracturing reconstruction strength corresponding to the measured fracturing section, and the prediction reference series is the parameter set of reservoir capacity, fracture network complexity and volume fracturing reconstruction strength of the prediction section;

[0166] Step 4.2, on the basis of the establishment of the productivity prediction matrix, the productivity prediction matrix element is standardized, the specific content is as follows:

[0167] According to the value distribution range of the productivity influencing factor parameters, the productivity prediction matrix element is standardized, and the calculation formula is as follows:

[0168]

[0169]

[0170] In the formula: is the mean value of each parameter set of the productivity prediction matrix vector; is the productivity prediction matrix standardized element;

[0171] The productivity prediction matrix element is standardized by using formula (7) and (8):

[0172]

[0173] In the formula: B * is the productivity prediction standardized matrix.

[0174] Step 4.3, according to the productivity prediction standardized matrix, the similarity factor of the known liquid production profile test section and the prediction section of the horizontal well is calculated, 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, then the similarity factor is calculated combined with the productivity prediction standardized matrix, the calculation formula is as follows:

[0175]

[0176]

[0177] In the formula: 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 prediction section;

[0178] Step 4.4, sort the similarity factors between the known fracturing sections and the predicted fracturing sections, the greater the value, the greater the similarity, take the maximum similarity factor corresponding to the fracturing section liquid production profile as the predicted section, and obtain the dynamic change results of the liquid production profile of the predicted section at different stages according to the dynamic test results of the measured fracturing section liquid production profile.

Claims

1. A method for predicting the production capacity of shale oil horizontal wells through volumetric fracturing, characterized in that... The specific steps are as follows: Step 1: Obtain the dynamic distribution of average production fluid profiles for each fracturing section of horizontal shale oil wells in the same block and development strata in the field during the flowback period of fracturing fluid and the stable production period after oil breakthrough. Step 2: Establish a multi-level evaluation system for factors affecting the volumetric fracturing production capacity of shale oil horizontal wells using the analytic hierarchy process (AHP). Step 3: Based on the multi-level evaluation system of factors affecting production capacity, establish an evaluation model for factors affecting production capacity, calculate the weight factors of different influencing factors and rank them. Step 4: Based on the weighted factor ranking and coupling of production capacity influencing 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 through the dynamic test results of the known production profile. In step 2, the factors affecting production capacity include storage capacity, fracture network complexity, and volumetric fracturing intensity. The reservoir capacity factors include: the length of the horizontal section of the oil layer, porosity, permeability, oil saturation, and clay content; the fracture network complexity factors include: brittleness index, horizontal stress difference, and fracturing pressure; the volumetric fracturing intensity factors include: fracture density, fluid injection intensity, sand addition intensity, and discharge rate.

2. The method for predicting the staged production capacity of shale oil horizontal well volumetric fracturing according to claim 1, characterized in that, Step 1 specifically involves: using quantum dot tracers to conduct production profile tests on each fractured section of a horizontal shale oil well in the same block and development strata, and obtaining the average dynamic distribution of production profile during the flowback period of fracturing fluid after volumetric fracturing and the stable production period after oil breakthrough.

3. The method for predicting the staged production capacity of shale oil horizontal well volumetric fracturing according to claim 1, characterized in that, Step 3 is implemented in the following steps: Step 3.1, Parameter Set Acquisition: Based on the well logging interpretation results, collect the parameter set of factors affecting the productivity of each fracturing section of the shale oil horizontal well, and the method used is the average value method; Step 3.2: Establish an evaluation matrix for factors affecting production capacity: Based on the parameter set of factors affecting production capacity and the measured production profile of the fractured section of shale oil horizontal wells, establish evaluation matrices respectively. and evaluation reference column The evaluation matrix elements are the factors affecting the production capacity of the measured fracturing section, and the evaluation criteria are the fluid production profile of the measured fracturing section. （1） （2） In the formula: For evaluation matrix; To evaluate matrix elements; This refers to the number of fracturing stages in a horizontal well. The number of factors affecting production capacity; List the evaluation criteria; Step 3.3, Evaluation Matrix Data Standardization: The evaluation matrix and evaluation criterion columns are standardized using the maximum value method. The calculation formula is as follows: （3） In the formula: To evaluate the standardized elements of the matrix; For the first The maximum value in the set of influencing factor parameters; Step 3.4, Grey Relation Degree Calculation: Based on the standardized evaluation matrix and evaluation criterion columns, calculate the grey relation degree between different influencing factors and the evaluation criterion columns. The calculation expression is as follows: （4） In the formula: Grey relational degree; ,in To evaluate the standardized data in the reference column; To evaluate the data after the matrix elements have been standardized; The resolution coefficient; Step 3.5, Calculation of weighting factors: Based on the grey relational degree between each capacity influencing factor and the evaluation criteria column, calculate the weighting factors of different capacity influencing factors to quantitatively characterize the degree of influence on capacity, and rank the weighting factors of each capacity influencing factor. The larger the value, the greater the degree of influence on capacity. The formula for calculating the weighting factor of factors affecting production capacity is as follows: （5） In the formula: Weighting factors for factors affecting production capacity; This represents the degree of grey relational correlation.

4. The method for predicting the staged production capacity of shale oil horizontal well volumetric fracturing according to claim 3, characterized in that, Step 4 is implemented in the following steps: Step 4.1: Couple the factors affecting production capacity and establish a production capacity forecast matrix. , （6） In the formula: Capacity forecast matrix; For the elements of the capacity forecast matrix, ; This represents the number of fracturing stages that have been measured. This represents the number of parameters affecting production capacity in the measured fracturing section. The capacity prediction matrix elements are the set of parameters corresponding to the measured fracturing section, including reservoir capacity, fracture network complexity, and volumetric fracturing intensity. The prediction reference series is the set of parameters corresponding to the predicted section, including reservoir capacity, fracture network complexity, and volumetric fracturing intensity. Step 4.2: Based on the established capacity forecast matrix, standardize the elements of the capacity forecast matrix to obtain the standardized capacity forecast matrix. Step 4.3: Calculate the similarity factor between the test section and the predicted section of the known production profile of the horizontal well based on the standardized matrix of production prediction; Step 4.4: Sort the similarity factors between the known fracturing segments and the predicted fracturing segments. The larger the value, the greater the similarity. Take the production profile of the fracturing segment corresponding to the largest similarity factor as the predicted segment. Based on the dynamic test results of the production profile of the measured fracturing segments, obtain the dynamic change results of the production profile of the predicted segment at different stages.

5. The method for predicting the staged production capacity of shale oil horizontal well volumetric fracturing according to claim 4, characterized in that, Step 4.2 specifically involves standardizing the elements of the capacity prediction matrix based on the distribution range of the parameters affecting capacity. The calculation formula is as follows: （7） （8） In the formula: This represents the mean of the parameter sets in the column vectors of the capacity forecast matrix; Standardized elements for the capacity forecasting matrix; The elements of the capacity forecast matrix are standardized using formulas (7) and (8): （9） In the formula: A standardized matrix for capacity forecasting.

6. The method for predicting the staged production capacity of shale oil horizontal well volumetric fracturing according to claim 5, characterized in that, Step 4.3 specifically involves: first, calculating the maximum distance between the known parameter sets of the fracturing section and the parameter sets of the predicted section in the prediction matrix; then, calculating the similarity factor in conjunction with the standardized production capacity prediction matrix. The calculation formula is as follows: （10） （11） In the formula: Similarity factor; This represents the maximum distance between the known parameter sets of the fracturing section and the predicted parameter sets of the fracturing section.

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