A method for predicting fracture network swept volume of compact oil and gas reservoir horizontal well volume fracturing

Abstract

The application discloses a kind of compact oil and gas reservoir horizontal well volume fracturing fracture network swept volume prediction method, including, based on the geology engineering parameter of implemented fracturing horizontal well and microseismic monitoring fracture volume data, the weight factor of each geology and engineering parameter is calculated by establishing multi-factor evaluation model;For the fracturing section to be predicted, the weight factor obtained is combined with the geology parameters of each fracturing cluster perforation position in the fracturing section, and the fracturing fracture efficiency coefficient for characterizing fracturing fluid distribution uniformity is calculated;The original microseismic monitoring fracture volume of the fracturing section is multiplied by the calculated fracturing fracture efficiency coefficient, and the corrected effective fracture network swept volume is obtained;The original microseismic monitoring volume is corrected by introducing the fracture efficiency coefficient in the application, the problem that the prediction result is generally large in the prior art is overcome, and accurate quantitative prediction of effective fracture network swept volume is realized, which provides a scientific basis for fracturing effect evaluation and scheme optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas development, and particularly relates to a method for predicting fracture network swept volume of a horizontal well in a tight oil and gas reservoir. BACKGROUND

[0002] As an important part of unconventional oil and gas resources, the development of tight oil and gas reservoirs has strategic significance for ensuring national energy security. The horizontal well volume fracturing technology is the core means to realize the economic and effective development of tight oil and gas reservoirs, and its goal is to form a complex fracture network system in the reservoir to increase the oil and gas seepage channel. Therefore, accurate evaluation of the fracture network swept volume (SRV) formed after fracturing is the key to optimizing fracturing design, predicting productivity and guiding subsequent development plan adjustment. At present, the evaluation methods of fracture network swept volume mainly include numerical simulation, physical simulation and microseismic monitoring. The numerical simulation method predicts the initiation and expansion of fractures by constructing a complex mathematical and physical model, but the strong heterogeneity of tight reservoirs makes the model simplification assumption too much, resulting in large deviation between the simulation results and the actual situation in the field. Although the physical simulation method can intuitively reveal the fracture expansion mechanism in the laboratory, it is limited by the size effect, and its results are difficult to effectively generalize to the field scale.

[0003] Microseismic monitoring technology, as a geophysical method that can monitor the fracture expansion range in real time and dynamically, has been widely used in the evaluation of field fracturing effect. This technology can delineate the area swept by the fractures by capturing the microseismic events generated by rock rupture, and then estimate the fracture network volume. However, a large number of field practices show that the volume represented by the microseismic event cloud is usually significantly larger than the actual fracture network volume that produces effective conductivity. This is because the microseismic events not only reflect the expansion of the main fractures, but also contain a large number of signals generated by stress adjustment, rock matrix microfracture and other non-effective seepage channels. At the same time, the monitoring results are also affected by the precision of the monitoring instrument and the mechanical properties of the rock formation. Therefore, the fracture network swept volume calculated directly from the microseismic monitoring results is generally larger, which cannot truly reflect the effective transformation effect of the reservoir, leading to an overly optimistic evaluation of the fracturing effect, and thus affecting the continuous optimization of the fracturing scheme and the scientific decision-making of the oil reservoir development.

[0004] CN110738001B discloses a method for calculating the fracturing stimulation area of unconventional reservoirs. The method establishes a two-dimensional stimulation volume calculation equivalent mathematical model, combines the initial and boundary conditions of the mathematical model, iteratively couples the fluid mass conservation equation and the fracture width equation in the fracture system to obtain the fracture fluid pressure and the average fracture width in each grid block, and thus obtains the size of the fracture stimulation volume. Although this method quantitatively calculates the fracture volume by establishing a mathematical model, the unconventional reservoir has strong heterogeneity, and the multi-fracture propagation law is still a difficult problem in the field, making it more difficult to predict the fracture volume. In addition, since this method is a numerical simulation method, there are many assumptions, and the predicted results have a large deviation from the true value.

[0005] CN109507723A discloses a method and system for calculating the fracturing volume of a microseismic fracturing fracture model. The method projects the triangular faces in the triangular face polyhedral model to obtain the fracturing volume of the microseismic fracturing fracture model. However, in reality, the mechanical properties of microseismic monitoring events and rock are usually related to the monitoring accuracy of the equipment, and a large number of practices have proved that the microseismic monitoring range is much larger than the hydraulic fracture range, and thus the method has a large fracture volume and is not ideal. SUMMARY

[0006] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0007] In view of the above-mentioned existing problems, the present application is proposed. Therefore, the present application provides a method for predicting the swept volume of a fracture network of a horizontal well in a tight oil and gas reservoir to solve the problems in the background art.

[0008] To solve the above technical problems, the present application provides the following technical scheme: a method for predicting the swept volume of a fracture network of a horizontal well in a tight oil and gas reservoir, comprising:

[0009] Based on the geologic engineering parameters and the microseismic monitoring fracture volume data of at least one horizontal well that has been fractured, a multi-factor evaluation model is established, and the weight factors of each geologic parameter and engineering parameter in the geologic engineering parameters on the microseismic monitoring fracture volume are calculated;

[0010] For any fracturing stimulation section of the horizontal well to be predicted, the fracturing fracture efficiency coefficient of the fracturing stimulation section is calculated according to the geologic parameters of the fracturing cluster perforation positions in the fracturing stimulation section and the weight factors.

[0011] Based on the microseismic monitoring fracture volume of the fracturing section and the fracturing fracture efficiency coefficient, an effective fracture network swept volume of the fracturing section is calculated.

[0012] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the geological engineering parameters include:

[0013] Reservoir geological parameters, geomechanical parameters and fracturing parameters;

[0014] The reservoir geological parameters include porosity, permeability and oil saturation, the geomechanical parameter is brittleness index, and the fracturing parameters include fracturing fluid volume and sand volume.

[0015] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the weight factor includes:

[0016] A multi-factor evaluation model matrix composed of the geological engineering parameters is established;

[0017] An evaluation matrix reference column composed of the microseismic monitoring fracture volumes corresponding to each fracturing section is established;

[0018] The evaluation model matrix and the evaluation matrix reference column are normalized;

[0019] Based on the normalized data, a correlation coefficient between each geological engineering parameter and the microseismic monitoring fracture volume is calculated;

[0020] According to the correlation coefficient, a weight factor of each geological engineering parameter is normalized.

[0021] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the correlation coefficient includes:

[0022] The absolute difference between each element in the normalized evaluation model matrix and the corresponding element in the evaluation matrix reference column is determined, and based on the maximum value, the minimum value and a preset calculation coefficient of the absolute difference, the correlation coefficient is determined.

[0023] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the fracturing fracture efficiency coefficient includes:

[0024] Geological parameters of each fracturing cluster perforation position in a fracturing section to be predicted are obtained, and the geological parameters are normalized;

[0025] The geological quality indexes of each fracturing cluster are obtained by multiplying the normalized geological parameters by the weight factors of the corresponding geological parameters and summing them up.

[0026] The fracturing fracture-creating efficiency coefficient of the fracturing reconstruction section is calculated based on the geological quality indexes.

[0027] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the fracturing fracture-creating efficiency coefficient based on the geological quality indexes comprises:

[0028] The liquid intake coefficient of the fracturing cluster is calculated by dividing the geological quality index of the single fracturing cluster by the sum of the geological quality indexes of all the fracturing clusters in the fracturing reconstruction section.

[0029] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the fracturing fracture-creating efficiency coefficient based on the geological quality indexes further comprises:

[0030] The liquid intake amount of each fracturing cluster is obtained by multiplying the total fracturing fluid amount of the fracturing reconstruction section by the liquid intake coefficient of each fracturing cluster.

[0031] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the fracturing fracture-creating efficiency coefficient based on the geological quality indexes further comprises:

[0032] The standard deviation of the liquid intake amount of each fracturing cluster in the fracturing reconstruction section is calculated, and the fracturing fracture-creating efficiency coefficient is determined based on the standard deviation.

[0033] The more uneven the distribution of the liquid intake amount of each fracturing cluster is, the larger the standard deviation is, and the smaller the fracturing fracture-creating efficiency coefficient is.

[0034] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the step of calculating the effective fracture network swept volume comprises:

[0035] The effective fracture network swept volume is obtained by multiplying the microseismic monitoring fracture volume of the fracturing reconstruction section by the fracturing fracture-creating efficiency coefficient corresponding to the fracturing reconstruction section.

[0036] As a preferred scheme of the method for predicting the fracture network swept volume of the horizontal well volume fracturing in the tight oil and gas reservoir, the steps of calculating the fracturing fracture-creating efficiency coefficient of the fracturing reconstruction section and calculating the effective fracture network swept volume are repeatedly performed to obtain the effective fracture network swept volumes of all the fracturing reconstruction sections of the horizontal well to be predicted.

[0037] Compared with the prior art, the beneficial effects of the scheme of the present application are:

[0038] 1. Based on the implementation of well field geology engineering parameters and microseismic monitoring volume big data, the quantitative evaluation of the effective fracture network swept volume of each fracturing section of the horizontal well is realized by calculating the fracture efficiency coefficient of different fracturing sections, and the problem of the large result of the fracture network swept volume of the microseismic monitoring on site is overcome.

[0039] 2. By establishing a multi-factor evaluation model, calculating the weight factor of different geology engineering parameters, calculating the geology quality index according to the geology parameters at the perforation position of the fracturing reconstruction section, and further calculating the liquid influx index of each cluster of the horizontal well fracturing section, the quantitative evaluation of the liquid influx and fracture efficiency of different fracturing clusters of the fracturing reconstruction section is realized, and the disadvantages of the traditional method of general evaluation are overcome. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical scheme of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work. Among them:

[0041] Figure 1 The general flow chart of the fracture network swept volume prediction method of the tight oil and gas reservoir horizontal well volume fracturing for an embodiment of the present application;

[0042] Figure 2 The horizontal well different fracturing section geology engineering parameter correlation coefficient calculation comparison chart of the fracture network swept volume prediction method of the tight oil and gas reservoir horizontal well volume fracturing for an embodiment of the present application;

[0043] Figure 3 The horizontal well different fracturing section geology engineering parameter weight factor calculation comparison chart of the fracture network swept volume prediction method of the tight oil and gas reservoir horizontal well volume fracturing for an embodiment of the present application;

[0044] Figure 4 The horizontal well F1 fracturing section different cluster geology quality index comparison chart of the fracture network swept volume prediction method of the tight oil and gas reservoir horizontal well volume fracturing for an embodiment of the present application;

[0045] Figure 5 The horizontal well F1 fracturing section different cluster liquid influx index comparison chart of the fracture network swept volume prediction method of the tight oil and gas reservoir horizontal well volume fracturing for an embodiment of the present application;

[0046] Figure 6 The horizontal well F1 fracturing section different cluster liquid influx amount comparison chart for the correlation coefficient calculation comparison chart of the different fracturing section geology engineering parameters in the example well in the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the above objectives, features and advantages of the present application more clear and comprehensible, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of embodiments of the present application, rather than all embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the protection scope of the present application.

[0048] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced without the specific details. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure aspects of the present application. Accordingly, it will be apparent to one of ordinary skill in the art that the present application can be practiced with embodiments other than those described in detail.

[0049] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or selective embodiment mutually exclusive with other embodiments.

[0050] The present application is described in detail with reference to the accompanying drawings. In the detailed description of the embodiments of the present application, the sectional view of the device structure is partially enlarged without the general proportion for the convenience of description, and the schematic view is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual manufacture.

[0051] Meanwhile, in the description of the present application, it should be noted that the terms "upper, lower, inner and outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0052] In the present application, unless otherwise specifically defined and limited, the terms "mounting, connecting, connection" should be understood broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the communication inside two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0053] Example 1

[0054] Referring to Figures 1 to 6 For the first embodiment of the present application, the embodiment provides a method for predicting fracture network swept volume of a horizontal well in a tight oil and gas reservoir, comprising:

[0055] In this embodiment, a typical tight oil reservoir (QC) in China is used, the reservoir burial depth is 2000 m, the main development layer is C6~C8 small layer, and the horizontal well volume fracturing development is used. QC1 is a typical horizontal well in a block, the development layer is C6 small layer, the well spacing is 350 m, the horizontal well section length is 1700 m, the oil layer drilling rate is 79.4%, 18 segments are designed for fracturing, numbered as F1~F18. In order to evaluate the fracturing effect of reservoir reconstruction, 18 segments of downhole microseismic are matched. Taking the basic data of F1~F5 segments as an example, the effective fracture network swept volume of F1 fracturing segment is calculated, which provides a basis for fracturing scheme optimization and effect evaluation;

[0056] S1, based on the geology engineering parameters of at least one horizontal well with implemented fracturing and the microseismic monitoring fracture volume data, a multi-factor evaluation model is established, and the weight factors of each geology parameter and engineering parameter in the geology engineering parameters on the microseismic monitoring fracture volume are calculated;

[0057] It should be noted that this step aims to quantitatively identify the key geology and engineering factors affecting the fracture network volume and their importance by analyzing historical data;

[0058] Further, at least one horizontal well with complete fracturing construction data and microseismic monitoring data in the study area is selected as a sample well, a three-dimensional geology model is established according to the logging fine interpretation results of the sample well, and the average reservoir geology parameters and geomechanics parameters of each fracturing reconstruction segment are extracted from the three-dimensional geology model. At the same time, the fracturing reconstruction parameters corresponding to each fracturing segment and the fracture volume data obtained by microseismic monitoring are collected, as shown in Table 1. Among them, the reservoir geology parameters, geomechanics parameters and fracturing reconstruction parameters jointly constitute the geology engineering parameters;

[0059] Specifically, the reservoir geology parameters include porosity, permeability and oil saturation;

[0060] Specifically, the geomechanics parameters include brittleness index;

[0061] Specifically, the fracturing reconstruction parameters include single segment fracturing fluid volume and single segment sand volume;

[0062] Table 1 Basic parameter table of F1~F5 fracturing segments of horizontal well QC1

[0063]

[0064] Further, all the above parameters are one-to-one corresponding to the microseismic monitoring fracture volume of each fracturing segment to form a complete basic data set;

[0065] Further, based on the collected basic data set, an evaluation model matrix for correlation analysis is constructed with a reference column, and a multi-factor evaluation model matrix composed of various geological engineering parameters is established:

[0066]

[0067] wherein, is a microseismic monitoring fracture volume evaluation model; is the number of horizontal well fracturing sections; is the corresponding basic parameter of horizontal well fracturing;

[0068] Further, at the same time, an evaluation matrix reference column composed of the microseismic monitoring fracture volume corresponding to each fracturing section is established:

[0069]

[0070] wherein, is the microseismic monitoring fracture volume of each fracturing section of the horizontal well, with the unit of square meter (m 3 );

[0071] In addition, in order to eliminate the influence of the dimension and numerical range difference of different geological engineering parameters on the analysis results, the evaluation model matrix and the evaluation matrix reference column need to be standardized. The mean value, initial value or range value method can be used to map all data to a comparable interval (such as [0, 1]);

[0072] Further, based on the standardized data, the grey correlation analysis method is used to calculate the correlation coefficient between each geological engineering parameter and the microseismic monitoring fracture volume (reference Figure 2 ). The correlation coefficient represents the correlation degree of each influencing factor sequence and the reference sequence at each time (i.e. different fracturing sections):

[0073]

[0074] wherein, is the correlation coefficient, which is a dimensionless quantity; is the calculation coefficient, which is 0.5; is the standardized element of the geological engineering parameter evaluation matrix of each fracturing section of the horizontal well; is the standardized element of the microseismic monitoring fracture volume of each fracturing section of the horizontal well;

[0075] Further, according to the correlation coefficients of each fracturing section and each parameter calculated above, the average value of the correlation coefficient of each parameter in all fracturing sections is calculated, and normalized processing is performed, and finally the weight factor of each geological engineering parameter on the microseismic monitoring fracture volume is obtained (reference Figure 3:

[0076]

[0077] wherein, is a weight factor of different parameters, is a dimensionless quantity, and the sum of weight factors of all parameters is 1;

[0078] It should be noted that the weight factor quantitatively reflects the contribution degree of different parameters to the formation of fracture network volume, and provides a basis for subsequent calculation of the fracturing fracture efficiency coefficient;

[0079] S2, for any fracturing section of the horizontal well to be predicted, according to the geological parameters of the perforation positions of each fracturing cluster in the fracturing section and the weight factor, the fracturing fracture efficiency coefficient of the fracturing section is calculated;

[0080] It should be noted that the core of this step is to evaluate the uniformity of the distribution of fracturing fluid between different fracturing clusters inside a single fracturing section. It needs to be explained that the more uniform the distribution, the more ideal the fracturing effect, and the higher the fracture efficiency. The present application quantitatively characterizes this efficiency by introducing the "fracturing fracture efficiency coefficient", which will be used to correct the fracture network swept volume directly derived from microseismic monitoring, which is usually large;

[0081] Further, taking any fracturing section of the horizontal well to be predicted as an example, the fracturing fracture efficiency coefficient thereof is calculated;

[0082] Specifically, the detailed geological parameters at each perforation position of each fracturing cluster in the fracturing section to be predicted are obtained, which are different from the average parameters of the fracturing section obtained in step S1. What is needed here is more detailed data corresponding to each cluster position. These geological parameters mainly include porosity, permeability, oil saturation and brittleness index. Then, the obtained geological parameters of each cluster are standardized to eliminate the dimension influence. Finally, the standardized geological parameter values of each fracturing cluster are multiplied by the weight factor of the corresponding geological parameter calculated in step S1 and summed up to obtain the geological quality index (reference Figure 4 ) of each fracturing cluster, which comprehensively reflects the degree of good or bad of the geological conditions at the position of the cluster. Its calculation formula is as follows:

[0083]

[0084] wherein, is the geological quality index of different perforation positions of the fracturing section, and is a dimensionless quantity; is the standardized value of the kth reservoir geological parameter, and is a dimensionless quantity; is the weight factor of the kth geological parameter, and is a dimensionless quantity;

[0085] It should be noted that fracturing clusters with higher geological quality indices have rock physical and mechanical properties more conducive to fracture formation and propagation, and therefore theoretically will absorb more fracturing fluid. Based on this, the fluid inflow coefficient of a fracturing cluster can be calculated by normalizing the geological quality index of a single fracturing cluster by dividing it by the sum of the geological quality indices of all fracturing clusters within the fracturing section (see reference). Figure 5 This coefficient represents the proportion of the total liquid volume that the cluster is expected to receive:

[0086]

[0087] in, is the fluid injection coefficient of the lth cluster in the horizontal well fracturing section, and is a dimensionless quantity; L represents the cluster number of the fracturing section in the horizontal well; L is the number of clusters in the fracturing section of the horizontal well, in units of clusters.

[0088] Furthermore, by multiplying the total planned fracturing fluid volume for the fracturing stimulation section by the fluid injection coefficient of each fracturing cluster calculated in the previous step, the specific fracturing fluid volume allocated to each fracturing cluster can be predicted (see reference). Figure 6 ):

[0089]

[0090] in, For the horizontal well fracturing and stimulation section Cluster injection volume, in cubic meters; Q is the total fracturing fluid volume of the horizontal well fracturing section, in cubic meters;

[0091] It should be noted that, since ideal volumetric fracturing requires fracturing fluid to enter uniformly among the clusters in order to form a balanced fracture network, the dispersion of the predicted fluid inflow of each cluster reflects the efficiency of the fracturing operation. Based on this, we also need to calculate the standard deviation of the predicted fluid inflow of each fracturing cluster within the fracturing stimulation section. The larger the standard deviation, the more uneven the fluid inflow of each cluster.

[0092] Specifically, the formula for calculating this standard deviation is as follows:

[0093]

[0094] Where D is the standard deviation of the predicted fluid inflow of each fracturing cluster within the fracturing modification section;

[0095] Furthermore, the final fracturing efficiency coefficient is determined based on this standard deviation. It should be noted that the more uneven the fluid injection distribution among the fracturing clusters, the larger the standard deviation, and the smaller the corresponding fracturing efficiency coefficient.

[0096]

[0097] in, is a fracturing fracture efficiency coefficient, is a dimensionless quantity;

[0098] It should be noted that the fracturing fracture efficiency coefficient directly quantifies the degree of effective and balanced reconstruction of the fracture network in the section;

[0099] S3, based on the microseismic monitoring fracture volume of the fracturing reconstruction section and the fracturing fracture efficiency coefficient, the effective fracture network swept volume of the fracturing reconstruction section is calculated;

[0100] It should be noted that the purpose of this step is to solve the core problem of the microseismic monitoring volume mentioned in the background art being generally large. By taking the "fracturing fracture efficiency coefficient" calculated in step S2, which can reflect the uniformity of the section reconstruction, as a correction factor, the original fracture network swept volume obtained by field monitoring is corrected, so that the effective fracture network swept volume closer to the real oil and gas production contribution is obtained;

[0101] Further, the effective fracture network swept volume of a single fracturing reconstruction section is calculated;

[0102] Specifically, the microseismic monitoring fracture volume of the fracturing reconstruction section to be predicted is multiplied by the fracturing fracture efficiency coefficient of the fracturing reconstruction section calculated in step S2. The product is the effective fracture network swept volume of the fracturing reconstruction section:

[0103]

[0104] wherein, is the horizontal well volume fracturing fracture network swept volume, and the unit is ten thousand cubic meters; is the horizontal well t-th fracturing section microseismic monitoring fracture volume, the unit is ten thousand cubic meters; T is the number of horizontal well fracturing sections, the unit is section; t is the horizontal well fracturing reconstruction section number;

[0105] It should be noted that the effective fracture network swept volume obtained by the above calculation eliminates the reconstruction efficiency loss caused by uneven distribution of fracturing fluid between clusters in the section. The result is more practically meaningful than the original microseismic monitoring volume, and can more accurately reflect the reservoir reconstruction area with effective conductivity;

[0106] In addition, in order to obtain the reconstruction effect evaluation of the whole horizontal well to be predicted, steps S2 and S3 can be repeatedly executed to calculate the effective fracture network swept volume of all fracturing reconstruction sections of the horizontal well in turn. The effective reconstruction volume profile along the horizontal wellbore can be obtained, which provides a more scientific and quantitative basis for the productivity prediction, subsequent fracturing scheme optimization and development strategy adjustment of the whole block.

[0107] Those skilled in the art will appreciate that embodiments of the application can be readily used as software, hardware, or a combination of software and hardware. In a software embodiment, various software modules are stored in memory (such as RAM, flash or disc storage), and executed by one or more general purpose or special purpose processors. In these embodiments, certain embodiments of the application provide software instructions stored in non-transitory computer-readable storage medium (media) and / or computer program product including a serial program, a diskette, or hard disk, or (in time sharing single or multi-processor computers) a communications network, all of which are read and executed by one or more processors to perform the steps described herein. As

[0108] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 means for performing each one or more functions specified in the flowchart block or blocks.

[0109] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 means for performing each one or more functions specified in the flowchart block or blocks.

[0110] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more functions specified in the flowchart block or blocks. Figure 1 means for performing each one or more functions specified in the flowchart block or blocks.

[0111] While the preferred embodiments of the application have been described, additional variations and modifications can be employed. Therefore, the terms and expressions

[0112] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for predicting fracture network swept volume of compact oil and gas reservoir horizontal well volume fracturing, characterized in that, The method comprises the following steps: Based on the geologic engineering parameters of at least one horizontal well with implemented fracturing and the microseismic monitoring fracture volume data, a multi-factor evaluation model is established, and the weight factors of each geologic parameter and engineering parameter in the geologic engineering parameters on the microseismic monitoring fracture volume are calculated; The geologic engineering parameters comprise: Reservoir geologic parameters, geologic mechanics parameters and fracturing reconstruction parameters; The reservoir geologic parameters comprise porosity, permeability and oil saturation, the geologic mechanics parameter is brittleness index, and the fracturing reconstruction parameters comprise fracturing fluid volume and sand volume; For any fracturing reconstruction section of a horizontal well to be predicted, the fracturing fracture forming efficiency coefficient of the fracturing reconstruction section is calculated according to the geologic parameters of each fracturing cluster perforation position in the fracturing reconstruction section and the weight factors of the corresponding geologic parameters; The step of calculating the fracturing fracture forming efficiency coefficient comprises: The geologic parameters of each fracturing cluster perforation position in the fracturing reconstruction section to be predicted are obtained, and the geologic parameters are standardized; The standardized geologic parameters are multiplied by the weight factors of the corresponding geologic parameters and summed to obtain the geologic quality index of each fracturing cluster; Based on the geologic quality index, the fracturing fracture forming efficiency coefficient of the fracturing reconstruction section is calculated; Based on the microseismic monitoring fracture volume of the fracturing reconstruction section and the fracturing fracture forming efficiency coefficient, the effective fracture network swept volume of the fracturing reconstruction section is calculated.

2. The method of claim 1, wherein, The step of calculating the weight factors comprises: A multi-factor evaluation model matrix composed of the geologic engineering parameters is established; An evaluation matrix reference column composed of the microseismic monitoring fracture volumes corresponding to each fracturing section is established; The evaluation model matrix and the evaluation matrix reference column are standardized; Based on the standardized data, the correlation coefficients between each geologic engineering parameter and the microseismic monitoring fracture volume are calculated; According to the correlation coefficients, the weight factors of each geologic engineering parameter are normalized.

3. The method of claim 2, wherein, The step of calculating the correlation coefficients comprises: The absolute difference between each element in the standardized evaluation model matrix and the corresponding element in the evaluation matrix reference column is determined, and based on the maximum value, the minimum value and the preset calculation coefficient of the absolute difference, the correlation coefficients are determined.

4. The method of claim 1, wherein, The step of calculating the fracturing fracture forming efficiency coefficient based on the geologic quality index comprises: The geologic quality index of a single fracturing cluster is divided by the sum of the geologic quality indexes of all fracturing clusters in the fracturing reconstruction section to calculate the liquid inlet coefficient of the fracturing cluster.

5. The method of claim 4, wherein, The step of calculating the fracturing fracture forming efficiency coefficient based on the geologic quality index further comprises: The total fracturing fluid volume of the fracturing reconstruction section is multiplied by the liquid inlet coefficient of each fracturing cluster to obtain the liquid inlet volume of each fracturing cluster.

6. The method of claim 5, wherein, The step of calculating the fracturing fracture forming efficiency coefficient based on the geologic quality index further comprises: The standard deviation of the liquid inlet volume of each fracturing cluster in the fracturing reconstruction section is calculated, and the fracturing fracture forming efficiency coefficient is determined based on the standard deviation; The more uneven the distribution of the liquid inlet volume of each fracturing cluster is, the larger the standard deviation is, and the smaller the fracturing fracture forming efficiency coefficient is.

7. The method of claim 1, wherein, The step of calculating the effective fracture network swept volume comprises: The microseismic monitoring fracture volume of the fracturing reconstruction section is multiplied by the fracturing fracture efficiency coefficient corresponding to the fracturing reconstruction section to obtain the effective fracture network swept volume.

8. The method of claim 1, wherein, The steps of calculating the fracturing fracture efficiency coefficient of the fracturing reconstruction section and calculating the effective fracture network swept volume are repeatedly performed to calculate the effective fracture network swept volumes of all fracturing reconstruction sections of the horizontal well to be predicted.

Images