Complexity evaluation method for fracturing fracture network based on microseism

Abstract

The invention discloses a method for evaluating the complexity of a fracturing fracture network based on microseism, and belongs to the technical field of oil and gas reservoir development, and the method comprises the following steps: collecting a plurality of microseism event points in a target fractured well through a microseism monitoring method, and obtaining the monitoring data of the microseism event points; drawing an event point magnitude scatter diagram according to the microseism event point set, and determining a critical magnitude; based on the critical magnitude, dividing the micro-seismic event points into stress relaxation event points and micro-fracture network event points; obtaining the sum of standardized energy values of the stress relaxation event points and the sum of standardized energy values of the microseism event points; the fracturing fracture network complexity of the target fractured well is obtained through calculation; according to the method, the influence of the stress relaxation phenomenon in the natural fracture on the complexity of the fracture net is considered, the relative strength of the stress relaxation phenomenon generated by the evaluation object in fracturing can be effectively reflected, and the complexity of the artificial fracture net is reflected.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas reservoir development, and more specifically, to a method for evaluating the complexity of a fracture network based on microseismic analysis. Background Art

[0002] Shale gas, tight gas, etc. usually require artificial reservoir creation through drilling horizontal wells and combining hydraulic fracturing to achieve industrial exploitation; microseismic monitoring technology is a technical means to monitor the morphology of the fracture network after hydraulic fracturing, and can monitor the spatial morphology of the fracture network generated during fracturing in real time; when the formation ruptures and cracks are generated, elastic fluctuations will be released; an observation system is deployed near the reservoir to collect elastic fluctuation signals, and geophysical methods are used to effectively locate the position of the formation rupture and further analyze the reservoir characteristics after fracturing.

[0003] Complex hydraulic fracturing networks can significantly improve the permeability around the wellbore and in areas far from the wellbore, helping to increase the production of a single well. Therefore, quantitative evaluation of the complexity of the hydraulic fracturing network is of great significance for evaluating the reservoir transformation effects of shale gas and tight gas.

[0004] The influence of natural fractures in horizontal wells on the fracturing effect mainly exists in two aspects; on the one hand, natural fractures are an important guarantee for the enrichment and high yield of shale gas. Natural fractures can provide migration channels and accumulation space for natural gas and formation water, and are conducive to increasing the total gas content of shale gas reservoirs; on the other hand, stress relaxation often exists in the natural fracture development area. The artificial fractures formed by fracturing transition to natural fractures through the stress relaxation area. After the stress relaxation area absorbs the fracturing energy, it is easy to produce elastic deformation, which will affect the formation of complex fracture networks. Therefore, natural fractures have both positive and negative effects on the fracturing effect. Smaller-scale densely distributed natural fractures are conducive to increasing the complexity of the fracture network; while strip-shaped, larger-scale or large-scale natural fractures absorb fracturing energy during fracturing, resulting in poor transformation effect.

[0005] The invention patent with application number CN202210757370.4 discloses a shale oil well fracturing effect evaluation method based on collaborative training, which can evaluate the fracturing effect through block production data and prediction models; the invention patent with application number CN202010189682.0 discloses a method for predicting the complexity of the fracture network formed by shale fracturing, which mainly predicts the complexity of the fracture network by analyzing the brittle mineral content and the number of natural fractures; At present, the horizontal well fracturing transformation effect evaluation method in the relevant technology lacks consideration of the relationship between the stress relaxation factors in the natural fractures and the artificial fracture network of the fracturing, and cannot effectively quantitatively evaluate the complexity of the fracture network produced by fracturing. Summary of the invention

[0006] The purpose of the present invention is to solve the technical problem that the complexity of the fracture network generated by fracturing cannot be effectively and quantitatively evaluated in the relevant fracture network evaluation methods, and to propose a method for evaluating the complexity of the fracture network based on microseismic.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: A method for evaluating the complexity of a fracture network based on microseismic analysis comprises the following steps: S1: collecting a number of microseismic event points in the target fracturing well by a microseismic monitoring method, obtaining monitoring data of the microseismic event points, and establishing a microseismic event point set; S2: drawing a scatter plot of event point magnitudes according to the microseismic event point set, and determining a critical magnitude Mc according to the scatter distribution characteristics of the event point magnitude scatter plot; S3: Based on the critical magnitude Mc, the microseismic event points in the microseismic event point set are divided into stress relaxation event points and micro-fracture network event points; S4: Calculate the energy value q of each microseismic event point at the corresponding magnitude according to the energy relationship calculation formula; S5: using a standardized calculation formula to perform standardization processing on the energy value q, and calculating and obtaining a standardized energy value s of each microseismic event point; S6: Calculate and obtain the sum of the normalized energy values ​​Sr of the stress relaxation event points and the sum of the normalized energy values ​​S of the microseismic event points; S7: Using the artificial fracture complexity evaluation calculation formula, calculate the fracture network complexity Φ of the target fracturing well.

[0008] Furthermore, in step S1, the monitoring data of the microseismic event point at least includes: the fracturing section to which the microseismic event point belongs and magnitude information.

[0009] Furthermore, in step S4, analyzing and processing the monitoring data of the microseismic event point includes: S401: Calculate the energy value q of each microseismic event point at the corresponding magnitude using an energy relationship calculation formula; S402: The energy value q is standardized using a standardized calculation formula to obtain a standardized energy value s of each microseismic event point.

[0010] Furthermore, in step S401, the energy relationship calculation formula is as follows: q=10 （1.5M+4.8） ; Where q is the response energy of the microseismic event point, in joules; M is the magnitude in the original microseismic data.

[0011] Furthermore, in the step S402, the standardized calculation formula is as follows: s=[(q-μ) / σ]+1; Wherein, μ is the average value of the energy value q of the microseismic event point of each fracturing well; σ is the standard deviation of the energy value q of the microseismic event point of each fracturing well.

[0012] Furthermore, in step S5, the artificial seam complexity evaluation calculation formula is as follows: artificial seam complexity Φ=[1-(Sr / S)]×{[(P / P0)+(F / F0)+(C / C0)] / 3}×100%; Among them, P is the average sand addition intensity of the target fracturing well; P0 is the highest average sand addition intensity of the wells in the well area where the target fracturing well is located; F is the average fluid intensity of the target fracturing well; F0 is the highest average fluid intensity of the wells in the well area where the target fracturing well is located; C is the average ceramsite ratio of the target fracturing well; and C0 is the highest average ceramsite ratio of the wells in the well area where the target fracturing well is located.

[0013] Furthermore, according to the fracturing network complexity Φ, an evaluation result for evaluating the complexity of artificial fracturing seams and the fracturing effect is obtained, including: When the fracturing network complexity Φ is less than a first threshold value, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is extremely simple, and the evaluation result of the fracturing effect is extremely poor; When the hydraulic fracture network complexity Φ is greater than or equal to the first threshold value and less than the second threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively simple, and the evaluation result of the hydraulic fracture effect is poor; When the hydraulic fracture network complexity Φ is greater than or equal to the second threshold value and less than the third threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is average, and the evaluation result of the hydraulic fracture effect is average; When the hydraulic fracture network complexity Φ is greater than or equal to the third threshold value and less than the fourth threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively complex, and the evaluation result of the hydraulic fracture effect is relatively good; When the fracturing network complexity Φ is greater than or equal to the fourth threshold, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is complex, and the evaluation result of the fracturing effect is good.

[0014] The beneficial effects of the present invention are: The present application provides a method for evaluating the complexity of a hydraulic fracture network based on microseismicity, which takes into account the influence of stress relaxation in natural fractures on the complexity of the hydraulic fracture network, and calculates the proportion of the total normalized energy value of stress relaxation event points to the total normalized energy values ​​of all microseismic event points. This method can effectively, intuitively and accurately reflect the relative strength of the stress relaxation phenomenon generated in the hydraulic fracture of the evaluation object, thereby reflecting the complexity of the artificial fracture network. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of a method for evaluating the complexity of a fracture network based on microseismic analysis provided by an embodiment of the present invention; Figure 2 It is a scatter plot of the magnitude of the Z1 fracturing well event point in Example 1 of the present invention; Figure 3 A comparison diagram of the sum of the normalized energy values ​​corresponding to the stress relaxation event points of each fracturing stage of the Z1 fracturing well and the sum of the normalized energy values ​​of the microseismic event points in the first embodiment of the present invention; Figure 4 A bar chart showing the calculation results of the artificial fracture complexity of each fracturing stage of the Z1 fracturing well in the first embodiment of the present invention; Figure 5 This is a scatter plot of the magnitude of the Z2 fracturing well event point in Example 2 of the present invention. DETAILED DESCRIPTION

[0016] In the field of oil and gas fields, unconventional oil and gas reservoirs have extremely low permeability and extremely low or no natural production capacity. They can only be increased through large-scale volume fracturing. The complexity of the fracture network directly affects the production capacity of the oil and gas field, because the complexity of the fracture network determines the path and efficiency of oil and gas flowing from the reservoir to the wellbore; a complex and effective fracture network can increase the fluid flow channels in the reservoir, thereby improving the fluidity of oil and gas, and significantly improving the oil and gas recovery rate. The purpose of evaluating the complexity of the fracture network is to evaluate the fracturing effect and provide a reference for optimizing the technical policy for oil and gas field development.

[0017] s Example 1: See Figure 1 As shown, the present invention provides a method for evaluating the complexity of a fracture network based on microseismic analysis. In practical applications, the method is used to evaluate the fracturing effect and the complexity of the fracture network of different fracturing sections in the same fracturing well, so as to reflect the effect of fracturing technology on improving the production of a single well. The method mainly includes the following steps: S1: A number of microseismic event points are collected in the target fracturing well by a microseismic monitoring method, and monitoring data of the microseismic event points are obtained to establish a microseismic event point set.

[0018] In step S1, a horizontal well is hydraulically fractured to form a fracture network, and elastic fluctuation data generated by rock fracture is collected by a microseismic monitoring method; several elastic fluctuations in the elastic fluctuation data are located respectively, so as to obtain several microseismic event points in the target fractured well and the monitoring data of each microseismic event point, and establish a microseismic event point set of the target fractured well; The monitoring data of the microseismic event point at least includes: the fracturing section to which the microseismic event point belongs and the magnitude information.

[0019] S2: drawing a scatter plot of event point magnitudes according to the microseismic event point set, and determining a critical magnitude Mc according to the scatter distribution characteristics of the event point magnitude scatter plot; In step S2, the vertical axis of the event point magnitude scatter plot is the magnitude, the magnitude of each microseismic event point corresponds to the magnitude on the vertical axis of the event point magnitude scatter plot, and each microseismic event point is randomly arranged in the horizontal axis direction; the purpose of adopting this technical solution is to weaken the association between the fracturing section attribute and the magnitude attribute of the microseismic event point in a single well in the event point magnitude scatter plot, highlight the distribution characteristics of all microseismic event points in a single well in different magnitude ranges, and avoid the microseismic event points being too densely or dispersedly distributed on the magnitude scatter plot, making it difficult to identify their distribution characteristics; Based on the three-stage distribution characteristics of the scatter points in the event point magnitude scatter diagram, the event point magnitude scatter diagram can be divided into three major sections according to the magnitude from large to small, and the three major sections are: large-scale natural fracture response section, relatively large-scale natural fracture response section and fine natural fracture + hydraulic fracture artificial fracture section; and the boundary magnitude between the fine natural fracture + hydraulic fracture artificial fracture section and the relatively large natural fracture section is used as the critical magnitude Mc; it should be noted that when technical personnel in this field determine the critical magnitude Mc according to the scatter point distribution characteristics of the event point magnitude scatter diagram, the difference between the critical magnitudes Mc obtained by different technical personnel should not exceed 0.1 magnitude.

[0020] S3: Based on the critical magnitude Mc, the microseismic event points in the microseismic event point set are divided into stress relaxation event points and micro-fracture network event points; It should be noted that since large-scale natural fractures and relatively large-scale natural fractures often experience stress relaxation during fracturing, the above-mentioned natural fractures provide a larger energy release space for microseismic events, resulting in more intense microseismic events detected in areas with natural fracture development; while the micro-fracture network is mainly composed of artificial fractures caused by fracturing, which contains a small number of micro-natural fractures, mainly caused by artificial fracturing, and the micro-seismic events detected in the area where they are located are more moderate; therefore, based on the magnitude of the micro-seismic event point, it can be judged whether the fracture where it is located has experienced stress relaxation and whether a micro-fracture network has been formed, and then the complexity of the artificial fracture network in the target fracturing well can be judged; In step S3, the microseismic event points within the large-scale natural fracture response section and the relatively large-scale natural fracture response section (magnitude ≥ critical magnitude Mc) are designated as stress relaxation event points, and the microseismic event points within the fine natural fracture + hydraulic fracture section (magnitude < critical magnitude Mc) are designated as micro-fracture network event points.

[0021] S4: analyzing and processing the monitoring data of the microseismic event points to obtain the normalized energy value sum Sr of the stress relaxation event points in the target fracturing section and the normalized energy value sum S of the microseismic event points in the target fracturing section; In order to quantitatively evaluate the complexity of the fracture network numerically, rather than fuzzily judging the complexity of the fracture network based on the number of stress relaxation event points, in step S4, the monitoring data of the microseismic event points are analyzed and processed, including: S401: using an energy relationship calculation formula to calculate the energy value q of the microseismic event point at the corresponding magnitude in the target fracture section; In this step, the energy relationship calculation formula can adopt the Richter magnitude-energy relationship calculation formula, and the energy relationship calculation formula is as follows: q=10 （1.5M+4.8） ; Among them, q is the response energy of the microseismic event point, in joules; M is the magnitude in the original microseismic data, generally distributed between -3 and 3, dimensionless; S402: Standardizing the energy value q using a standardized calculation formula to calculate and obtain a standardized energy value s of the microseismic event point in the target fracturing section; S403: Add the standardized energy values ​​s of the microseismic event points classified as stress relaxation event points in the target fracturing section to obtain the total standardized energy value Sr of the stress relaxation event points in the target fracturing section; add the standardized energy values ​​s of all microseismic event points in the target fracturing section to obtain the total standardized energy value S of the microseismic event points in the target fracturing section.

[0022] Since the differences between the energy values ​​q of the microseismic event points at the corresponding magnitudes are too large, it is difficult to reflect the characteristics of the microseismic event points at the corresponding magnitudes. Therefore, the energy values ​​q are standardized to obtain the standardized energy values ​​s of each microseismic event point. This can more intuitively reflect the energy of the event point and facilitate further processing.

[0023] In this step, the standardized calculation formula is as follows: s=[(q-μ) / σ]+1; Wherein, μ is the average value of the energy value q of the microseismic event point of each fracturing well; σ is the standard deviation of the energy value q of the microseismic event point of each fracturing well.

[0024] S5: Using the artificial fracture complexity evaluation calculation formula, calculate the fracture network complexity Φ of the target fracturing section; In step S5: the artificial seam complexity evaluation calculation formula is as follows: artificial seam complexity Φ=[1-(Sr / S)]×{[(P / P0)+(F / F0)+(C / C0)] / 3}×100%; Among them, P is the average sand addition intensity of the target fracturing well; P0 is the highest average sand addition intensity of the wells in the well area where the target fracturing well is located; F is the average fluid intensity of the target fracturing well; F0 is the highest average fluid intensity of the wells in the well area where the target fracturing well is located; C is the average ceramsite ratio of the target fracturing well; and C0 is the highest average ceramsite ratio of the wells in the well area where the target fracturing well is located.

[0025] As an implementation mode of the present application, when there is no suitable maximum average sand addition intensity of wells in the well area where the target fracturing well is located as a reference (for example: there is only a single well group of target fracturing wells in the well area where the target fracturing well is located, at this time, the value of "P / P0" in the formula is always 1), P0 in the formula can be determined as the maximum sand addition intensity of the wells in the adjacent area.

[0026] According to the fracturing network complexity Φ obtained in step S5, an evaluation result for evaluating the complexity of artificial fracturing seams and the fracturing effect is obtained, including: When the fracturing network complexity Φ is less than a first threshold value, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is extremely simple, and the evaluation result of the fracturing effect is extremely poor; When the hydraulic fracture network complexity Φ is greater than or equal to the first threshold value and less than the second threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively simple, and the evaluation result of the hydraulic fracture effect is poor; When the hydraulic fracture network complexity Φ is greater than or equal to the second threshold value and less than the third threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is average, and the evaluation result of the hydraulic fracture effect is average; When the hydraulic fracture network complexity Φ is greater than or equal to the third threshold value and less than the fourth threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively complex, and the evaluation result of the hydraulic fracture effect is relatively good; When the fracturing network complexity Φ is greater than or equal to the fourth threshold, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is complex, and the evaluation result of the fracturing effect is good.

[0027] The present application provides a method for evaluating the complexity of a fracture network based on microseismic events. By calculating the ratio of the total standardized energy value of a stress relaxation event point to the total standardized energy value of all microseismic event points, the ratio can effectively, intuitively and accurately reflect the relative strength of the stress relaxation phenomenon generated by the evaluation object during fracturing, thereby reflecting the complexity of the artificial fracture network. The stronger the stress relaxation phenomenon generated, the weaker the complexity of the artificial fracture network of the fracture. Conversely, the stronger the complexity of the artificial fracture network of the fracture, the better the fracturing effect.

[0028] The evaluation method of the present application also provides a calculation formula for the complexity of the fracture network. By combining the sand addition intensity, liquid intensity, and expanded clay ratio of the evaluation object, the complexity of the fracture network of each evaluation object can be calculated, thereby achieving a quantitative evaluation of the complexity of the fracture network.

[0029] Based on the above-mentioned calculation formula for the complexity of the fracture network, this evaluation method also provides a fracture network complexity evaluation standard, which divides the complexity of the fracture network into several levels. It can intuitively evaluate the complexity of the fracture network of each fracturing section in the same horizontal well, and can also realize the inter-well comparison of the complexity of the fracture network of different wells.

[0030] In this embodiment, the microseismic monitoring data of each fracturing stage of the Z1 fracturing well is taken as the analysis object, the fracturing stage (Stage) and the corresponding magnitude (Magnitude) to which all event points of the Z1 fracturing well belong are sorted out, and a set of microseismic event points of the Z1 fracturing well is established, as shown in Table 1 (comparison of original analysis data table of the complexity evaluation of the fracturing network of different fracturing stages of the Z1 fracturing well); , Table 1

[0031] See also Figure 2 , based on the monitoring data of all microseismic event points in the Z1 fracturing well microseismic event point set, a scatter plot of the event point magnitude is drawn, such as Figure 2 (Magnitude scatter plot of event points of Z1 fracturing well), wherein the magnitude of the microseismic event points in Z1 fracturing well corresponds to the magnitude on the vertical axis of the magnitude scatter plot, and the microseismic event points in Z1 fracturing well are randomly arranged in the horizontal axis direction; According to the scatter distribution characteristics of the magnitude scatter plot of the Z1 fracturing well event point, the fractures reflected by the microseismic event points are divided into large-scale natural fracture response segments, relatively large-scale natural fracture response segments, and fine natural fractures + artificial fractures segments. The boundary magnitude between the fine natural fractures + artificial fractures segments and the relatively large natural fracture segments is taken as the critical magnitude Mc. In this example, the critical magnitude Mc of the Z1 fracturing well is -1.0.

[0032] All microseismic event points in each fracturing section of the Z1 fracturing well are divided into stress relaxation event points (magnitude ≥ critical magnitude Mc) and micro-fracture network event points (magnitude < critical magnitude Mc) based on the critical magnitude Mc. The results are shown in Table 2 (Classification table of microseismic event points in different fracturing sections of the Z1 fracturing well); , Table 2 According to the energy relationship calculation formula: q=10 （1.5M+4.8） The energy values ​​q corresponding to the magnitudes of all microseismic event points in each fracturing section of the Z1 fracturing well are calculated and sorted out. The sorting results are shown in Table 3 (Energy value calculation table of microseismic event points in each fracturing section of the Z1 fracturing well); , Table 3 The energy value q corresponding to the magnitude of the microseismic event point in each fracturing section in the Z1 fracturing well is standardized, and the standardized energy value s of all microseismic event points in each fracturing section is calculated using the formula: s=[(q-μ) / σ]+1. The calculation results are shown in Table 4 (Calculation table of standardized energy values ​​of microseismic event points in each fracturing section of the Z1 fracturing well); Where: μ is the average value of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing section (μ=11388359 in this example); σ is the standard deviation of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing section (σ=25158557 in this example); , Table 4 See also Figure 3 According to the standardized energy values ​​s of all microseismic event points in each fracturing section of the Z1 fracturing well, the sum of the standardized energy values ​​Sr corresponding to the stress relaxation event points (magnitude ≥ critical magnitude Mc) in each fracturing section of the Z1 fracturing well and the sum of the standardized energy values ​​S of the microseismic event points are calculated respectively; the results are summarized as follows: Figure 3 (Comparison of the sum of the normalized energy values ​​corresponding to the stress relaxation event points of each fracturing section of the Z1 fracturing well and the sum of the normalized energy values ​​of the microseismic event points); See also Figure 4 Based on the sum of the standardized energy values ​​of the stress relaxation event points in each fracturing stage Sr and the sum of the standardized energy values ​​of the microseismic event points in each fracturing stage S, the complexity of the artificial fractures Φ = [1-(Sr / S)] × {[(P / P0) + (F / F0) + (C / C0)] / 3} × 100% is used to calculate the complexity of the artificial fractures Φ of each fracturing stage of the Z1 fracturing well; in this example: the highest average sand addition intensity P0 of the well area where the Z1 fracturing well is located is 4.5t / m, and the highest average fluid intensity F0 is 45m 3 / m, the highest average ceramsite proportion C0 is 55%, and the average sand addition strength P of Z1 fracturing well isz1 The average liquid strength is 3.92t / m Z1 The average ceramsite content is 43.4m3 / m. Z1 is 48.2%; finally, the results of the complexity of artificial fractures Φ in each fracturing section of Well Z1 are as follows Figure 4 (The calculation results of the complexity of artificial fractures Φ in each fracturing section of Z1 fracturing well) are shown in the bar graph; By comparing the calculated artificial fracture complexity Φ of each fracturing section of the Z1 fracturing well with the standard table of artificial fracture complexity experience (as shown in Table 5 below), the fracturing effect between different fracturing sections in the Z1 fracturing well can be obtained, and the complexity of artificial fractures in the Z1 fracturing well can be evaluated; among them: the overall artificial fracture complexity of the Z1 fracturing well is low, and the fracturing effect is poor, the artificial fracture complexity of the 3rd fracturing section is relatively the highest, and the fracturing effect is relatively the best, and the artificial fracture complexity of the 14th fracturing section is relatively the lowest, and the fracturing effect is relatively the worst; , Table 5 It can be understood that the complexity of the artificial sewing net = 30% corresponds to the first threshold value in the above technical scheme; the complexity of the artificial sewing net = 40% corresponds to the second threshold value in the above technical scheme; the complexity of the artificial sewing net = 40% corresponds to the second threshold value in the above technical scheme; the complexity of the artificial sewing net = 60% corresponds to the third threshold value in the above technical scheme; the complexity of the artificial sewing net = 80% corresponds to the fourth threshold value in the above technical scheme.

[0033] Embodiment 2: The present invention provides another implementation method based on Embodiment 1. In practical applications, the method is used to evaluate the fracturing effect and the complexity of the fracture network between different fracturing well groups. The evaluation method provided in this embodiment further includes the following steps based on Steps S1 to S3 of Embodiment 1: S'4: Analyze and process the monitoring data of the microseismic event points to obtain the sum of the standardized energy values ​​Sr of all stress relaxation event points in the target fracturing well and the sum of the standardized energy values ​​S of all microseismic event points; In step S'4, analyzing and processing the monitoring data of the microseismic event points includes: S'401: calculating and obtaining the energy value q of each microseismic event point at the corresponding magnitude according to the energy relationship calculation formula; In this step, the energy relationship calculation formula can adopt the Richter magnitude energy relationship formula, and the energy relationship calculation formula is as follows: q=10 （1.5M+4.8） ; Among them, q is the response energy of the microseismic event point, in joules; M is the magnitude in the original microseismic data, generally distributed between -3 and 3, dimensionless; S'402: Standardizing the energy value q using a standardized calculation formula to obtain a standardized energy value s of each microseismic event point; S'403: Add the standardized energy values ​​s of the microseismic event points classified as stress relaxation event points in the target fracturing well to obtain the standardized energy value sum Sr of all stress relaxation event points in the target fracturing well; add the standardized energy values ​​s of all microseismic event points in the target fracturing well to obtain the standardized energy value sum S of all microseismic event points in the target fracturing well; In this step, the standardized calculation formula is as follows: s=[(q-μ) / σ]+1; Among them, μ is the average value of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing well; σ is the standard deviation of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing well.

[0034] S'5: Using the artificial fracture complexity evaluation formula, calculate the fracture network complexity Φ of the target fracture well; In step S5: the artificial seam complexity evaluation calculation formula is as follows: artificial seam complexity Φ=[1-(Sr / S)]×{[(P / P0)+(F / F0)+(C / C0)] / 3}×100%; Where: P is the average sand addition intensity of the target fracturing well, P0 is the highest average sand addition intensity of the wells in the well area where the target fracturing well is located, F is the average fluid intensity of the target fracturing well, F0 is the highest average fluid intensity of the wells in the well area where the target fracturing well is located, C is the average ceramsite proportion of the target fracturing well, and C0 is the highest average ceramsite proportion of the wells in the well area where the target fracturing well is located.

[0035] Based on the fracture network complexity Φ of the target fracturing well obtained in step S'5, the artificial fracture complexity empirical classification standard table (Table 5) is compared to obtain the evaluation results for evaluating the complexity of artificial fractures and the fracturing effect of the target fracturing well. It can be understood that by adopting the above scheme, the evaluation results of the complexity of artificial fractures and the fracturing effect of any single well can be obtained; and the fracturing effects and the complexity of artificial fractures between different well groups can be further compared.

[0036] In this embodiment, the monitoring data of microseismic event points of Z1 and Z2 fracturing wells are taken as the analysis object, the fracturing sections and magnitudes to which each event point of Z1 and Z2 fracturing wells belongs are sorted out, and a set of microseismic event points of Z1 and Z2 fracturing wells is established, as shown in Table 6 below; , Table 6 See also Figure 2 and Figure 5According to the monitoring data of all microseismic event points in the microseismic event point set of Z1 and Z2 fracturing wells, the event point magnitude scatter plots are drawn respectively; and the critical magnitude Mc of each well group is determined; in this embodiment, the critical magnitude Mc1 of Z1 fracturing well is -1.0, and the critical magnitude Mc2 of Z2 fracturing well is -0.8.

[0037] The microseismic event points of Z1 and Z2 fracturing wells are divided into stress relaxation event points (magnitude ≥ critical magnitude Mc) and micro fracture network event points (magnitude < critical magnitude Mc) based on the critical magnitude Mc. The results are shown in Table 7 (Classification table of event points of Z1 and Z2 fracturing wells). , Table 7 According to the energy relationship calculation formula: q=10 （1.5M+4.8） The energy values ​​q corresponding to the magnitudes of the microseismic event points in the Z1 and Z2 fracturing wells are calculated and sorted respectively, and the sorting results are shown in Table 8 (Z1 and Z2 fracturing well microseismic event point monitoring energy value calculation table); , Table 8 The energy values ​​q of the microseismic event points in the Z1 and Z2 fracturing wells were standardized respectively, and the standardized energy values ​​s of all microseismic event points were calculated using the formula: s=[(q-μ) / σ]+1. The calculation results are shown in Table 9 (Calculation table of standardized energy values ​​for monitoring microseismic event points in Z1 and Z2 fracturing wells); Where: μ is the average value of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing well; σ is the standard deviation of the energy value q corresponding to the magnitude of all microseismic event points in each fracturing well; In this example: the average value μ1 of the Z1 fracturing well is 11388359 joules, the standard deviation σ1 is 25158557 joules, the average value μ2 of the Z2 fracturing well is 324364 joules, and the standard deviation σ2 is 4290962 joules; , Table 9 According to the standardized energy values ​​s of the microseismic event points of the Z1 and Z2 fracturing wells, the sum Sr of the standardized energy values ​​corresponding to the stress relaxation event points (magnitude ≥ Mc) of the Z1 and Z2 fracturing wells and the sum S of the standardized energies of all microseismic event points are calculated respectively; Among them: the total standardized energy S1 of the stress relaxation event point in the Z1 fracturing well is 951 joules, and the total standardized energy S 总1 is 1606 joules, the total standardized energy S2 of the stress relaxation event point in the Z2 fracturing well is 486 joules, and the total standardized energy S 总2 is 3593 joules; Based on the above-obtained calculation data, the artificial fracture complexity Φ of Z1 and Z2 fracturing wells was calculated using the artificial fracture complexity Φ=[1-(Sr / S)]×{[(P / P0)+(F / F0)+(C / C0)] / 3}×100%; Where: P is the average sand addition intensity of the target fracturing well, P0 is the highest average sand addition intensity of the wells in the well area where the target fracturing well is located, F is the average fluid intensity of the target fracturing well, F0 is the highest average fluid intensity of the wells in the well area where the target fracturing well is located, C is the average ceramsite proportion of the target fracturing well, and C0 is the highest average ceramsite proportion of the wells in the well area where the target fracturing well is located.

[0038] In this embodiment, the Z1 and Z2 fracturing wells are located in the same well area, and the highest average fracturing sand intensity P0 is 4.5t / m, and the highest average fracturing fluid intensity F0 is 45m 3 / m, the highest average ceramsite proportion C0 is 55%, and the average sand addition intensity P of the target well is z1 , P z2 They are 3.92t / m and 4.07t / m respectively, and the average liquid strength F Z1 、F Z2 43.4m respectively 3 / m and 45.0m 3 / m, average ceramsite proportion C Z1 , C Z2 The final calculation shows that the complexity of artificial fractures in Z1 fracturing well is 36.87%, and the complexity of artificial fractures in Z2 fracturing well is 81.31%; The calculated artificial fracture complexity of Z1 and Z2 fracturing wells is compared with the artificial fracture complexity empirical classification standard table (Table 5) provided in Example 1, so as to compare the fracturing effects between different well groups and evaluate the complexity of artificial fractures in the target wells. Among them, the artificial fracture complexity of Z1 fracturing well is relatively simple and the fracturing effect is poor, while the artificial fracture complexity of Z2 fracturing well is complex and the fracturing effect is good.

[0039] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating the complexity of a fracture network based on microseismic analysis, characterized in that: The method comprises the following steps: S1: Collect a number of microseismic event points in the target fracturing well through a microseismic monitoring method, obtain monitoring data of the microseismic event points, and establish a microseismic event point set; S2: drawing a scatter plot of event point magnitudes according to the microseismic event point set, and determining a critical magnitude Mc according to the scatter distribution characteristics of the event point magnitude scatter plot; S3: Based on the critical magnitude Mc, the microseismic event points in the microseismic event point set are divided into stress relaxation event points and micro-fracture network event points; S4: analyzing and processing the monitoring data of the microseismic event points to obtain the normalized energy value sum Sr of the stress relaxation event points and the normalized energy value sum S of the microseismic event points; S5: Using the artificial fracture complexity evaluation calculation formula, calculate the fracture network complexity Φ of the target fracturing well.

2. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 1, characterized in that: In the step S1, the monitoring data of the microseismic event point at least includes: the fracturing section to which the microseismic event point belongs and the magnitude information.

3. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 1, characterized in that: In step S4, analyzing and processing the monitoring data of the microseismic event point includes: S401: Calculate the energy value q of each microseismic event point at the corresponding magnitude using an energy relationship calculation formula; S402: The energy value q is standardized using a standardized calculation formula to obtain a standardized energy value s of each microseismic event point.

4. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 3, characterized in that: In step S401, the energy relationship calculation formula is as follows: q=10 （1.5M+4.8） ; Where q is the response energy of the microseismic event point, in joules; M is the magnitude in the original microseismic data.

5. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 3, characterized in that: In the step S402, the normalized calculation formula is as follows: s=[(q-μ) / σ]+1; Wherein, μ is the average value of the energy value q of the microseismic event point of each fracturing well; σ is the standard deviation of the energy value q of the microseismic event point of each fracturing well.

6. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 1, characterized in that: In step S5, the artificial seam complexity evaluation calculation formula is as follows: artificial seam complexity Φ=[1-(Sr / S)]×{[(P / P0)+(F / F0)+(C / C0)] / 3}×100%; Among them, P is the average sand addition intensity of the target fracturing well; P0 is the highest average sand addition intensity of the wells in the well area where the target fracturing well is located; F is the average fluid intensity of the target fracturing well; F0 is the highest average fluid intensity of the wells in the well area where the target fracturing well is located; C is the average ceramsite ratio of the target fracturing well; and C0 is the highest average ceramsite ratio of the wells in the well area where the target fracturing well is located.

7. The method for evaluating the complexity of a fracture network based on microseismic analysis according to claim 1, characterized in that: According to the fracturing network complexity Φ, an evaluation result for evaluating the complexity of artificial fracturing seams and the fracturing effect is obtained, including: When the fracturing network complexity Φ is less than a first threshold value, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is extremely simple, and the evaluation result of the fracturing effect is extremely poor; When the hydraulic fracture network complexity Φ is greater than or equal to the first threshold value and less than the second threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively simple, and the evaluation result of the hydraulic fracture effect is poor; When the hydraulic fracture network complexity Φ is greater than or equal to the second threshold value and less than the third threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is average, and the evaluation result of the hydraulic fracture effect is average; When the hydraulic fracture network complexity Φ is greater than or equal to the third threshold value and less than the fourth threshold value, it is determined that the evaluation result of the complexity of the artificial fractures of the target hydraulic fracture well is relatively complex, and the evaluation result of the hydraulic fracture effect is relatively good; When the fracturing network complexity Φ is greater than or equal to the fourth threshold, it is determined that the evaluation result of the complexity of the artificial fracturing seams of the target fracturing well is complex, and the evaluation result of the fracturing effect is good.

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