Method and device for predicting crack based on microseismic data

Abstract

The invention discloses a method and a device for predicting cracks based on micro-seismic data. The method comprises the following steps: determining a micro-seismic event B value according to the micro-seismic data of each fractured section; determining a natural seismic crack according to the microseismic event B value; determining a main fracture trend surface of a natural seismic fracture of each fracturing section through an earthquake magnitude weighted fitting algorithm, and determining fracture occurrence information of each fracturing section; and according to the fracture density of each fracturing section and the occurrence information, correcting fracture information predicted by adopting seismic data, and determining the fracture density and the fracture direction.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method and device for predicting fractures based on microseismic data. Background Art

[0002] Accelerating the development of shale gas is an important measure to ensure national energy security. Deep shale gas resources have great potential and are the core area of ​​the company's sustained and rapid growth in natural gas production. A large gas area with a reserve of 10 trillion cubic meters and an output of 10 billion cubic meters has been built in southern Sichuan, but the development of Luzhou Lu 203 well area has encountered great difficulties. Mainly affected by casing changes and pressure channeling, the development effect of Luzhou North District was not as expected, which restricted the scale benefit development process.

[0003] The development characteristics of underground fractures are important indicators for the design of fracturing parameters and are also the main basic data for analyzing casing change and pressure channeling in horizontal wells. They are very important for the layout of development platforms (well groups), wellbore trajectory and wellbore structure design, and development plan optimization. At the same time, fractures are also the main mechanism for inducing casing change. Therefore, the ability to accurately predict the distribution of fracture density and development direction is of decisive significance for preventing the risks of pressure channeling and casing change. Summary of the invention

[0004] The inventors of this application discovered that:

[0005] Although deep shale has multi-scale natural fractures that are difficult to characterize in detail, the inventors have found that microseismic data can be used as prior information to constrain the distribution and development direction of fracture density predicted by seismic data, thereby obtaining a more accurate spatial distribution of fractures. This can provide effective guidance for the design of horizontal well groups for shale gas development and the optimization of fracturing parameters in the block, thereby greatly reducing the risks of casing change and pressure channeling.

[0006] The present application provides a method for predicting fractures based on microseismic data, which realizes a method for determining the statistical distribution of fracture density curves and fracture occurrence of each perforation section based on microseismic events, correcting the fracture prediction results of seismic data, and ultimately obtaining more accurate fracture density and fracture direction prediction results.

[0007] In a first aspect, the present application provides a method for predicting cracks based on microseismic data, the method comprising:

[0008] Determine the B value of the microseismic event based on the microseismic data of each fracturing stage;

[0009] Determining natural earthquake cracks according to the B value of the microseismic event;

[0010] The main fracture trend surface of natural earthquake fractures in each fracturing section is determined by using magnitude weighted fitting algorithm, and the occurrence information of fractures in each fracturing section is determined;

[0011] The fracture information predicted by using seismic data is corrected according to the fracture density of each fracturing stage and the occurrence information to determine the fracture density and fracture direction.

[0012] In a second aspect, an embodiment of the present invention provides a device for predicting cracks based on microseismic data, characterized in that the device comprises: a memory and a processor; the memory is used to store a program for a method for predicting cracks based on microseismic data, and the processor is used to read and execute the program for a method for predicting cracks based on microseismic data, and execute any one of the methods described in the above embodiments.

[0013] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a data processing program is stored, and the data processing program is executed by a processor to perform any one of the methods for predicting cracks based on microseismic data.

[0014] Compared with the related art, the present invention provides a method and device for predicting cracks based on microseismic data, the method comprising: determining the B value of microseismic events according to the microseismic data of each fracturing section; determining natural seismic cracks according to the B value of the microseismic events; determining the main fracture trend surface of the natural seismic cracks in each fracturing section by a magnitude weighted fitting algorithm, and determining the occurrence information of the cracks in each fracturing section; correcting the crack information predicted by seismic data according to the crack density of each fracturing section and the occurrence information, and determining the crack density and crack direction. The present application realizes a method of determining the statistical distribution of the crack density curve and the crack occurrence of each perforation section based on microseismic events, correcting the crack prediction results of seismic data, and finally obtaining more accurate crack density and crack direction prediction results.

[0015] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by implementing the present application. Other advantages of the present application can be realized and obtained by the schemes described in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to provide an understanding of the technical solution of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the technical solution of the present application and do not constitute a limitation on the technical solution of the present application.

[0017] Figure 1 is a flow chart of a method for predicting cracks based on microseismic data according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of a device for predicting cracks based on microseismic data according to an embodiment of the present application;

[0019] Figure 3Magnitude analysis and B-value analysis diagrams of microseismic event points in some exemplary embodiments;

[0020] Figure 4 A schematic diagram of natural fracture main fracture surface analysis based on natural fracture microseismic event point data in some exemplary embodiments;

[0021] Figure 5 A schematic diagram of multi-crack fracture surface extraction of natural microseismic events based on the RANSAC algorithm in some exemplary embodiments;

[0022] Figure 6 A schematic diagram of statistical analysis of natural fracture parameters in some exemplary embodiments;

[0023] Figure 7 A correlation analysis diagram of plane intersection of microseismic crack intensity and earthquake extraction of multiple fracture attributes in some exemplary embodiments;

[0024] Figure 8 Schematic diagram of random discrete modeling results driven by microseismic fracture data and seismic data in some exemplary embodiments. DETAILED DESCRIPTION

[0025] The present application describes multiple embodiments, but the description is exemplary rather than restrictive, and it is obvious to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described in the present application. Although many possible feature combinations are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0026] The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in the present application may also be combined with any conventional features or elements to form a unique invention scheme defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other invention schemes to form another unique invention scheme defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in the present application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the attached claims and their equivalents, the embodiments are not subject to other restrictions. In addition, various modifications and changes may be made within the scope of protection of the attached claims.

[0027] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps of the specific order described. As will be understood by those of ordinary skill in the art, other sequences of steps are also possible. Therefore, the specific sequence of the steps set forth in the specification should not be interpreted as a limitation to the claims. In addition, the claims for the method and / or process should not be limited to the steps of performing them in the order written, and those skilled in the art can easily understand that these sequences can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0028] The embodiment of the present invention provides a method for predicting cracks based on microseismic data. Figure 1 As shown, the method includes steps S100-S130, which are specifically as follows:

[0029] S100: determining a microseismic event B value according to microseismic data of each fracturing stage;

[0030] S110: determining a natural earthquake crack according to the B value of the microseismic event;

[0031] S120: determining the main fracture trend surface of the natural earthquake fractures in each fracturing section by means of a magnitude weighted fitting algorithm, and determining the occurrence information of the fractures in each fracturing section;

[0032] S130: Correcting the fracture information predicted by using the seismic data according to the fracture density of each fracturing stage and the occurrence information, and determining the fracture density and fracture direction.

[0033] In an exemplary embodiment, determining the B value of a microseismic event based on the microseismic data of each fracturing stage includes:

[0034] By taking the logarithm of the cumulative number of magnitudes of microseismic events;

[0035] A power law relationship is obtained by fitting the determined logarithmic value and the magnitude of the microseismic event, and the B value of the microseismic event is determined based on the obtained power law relationship.

[0036] In an exemplary embodiment, there is a power law relationship between the number of microseismic events and the magnitude of the microseismic events. Studies have shown that standard induced fractures in standard shales often correspond to a B value of about 2. If the events that occur during a construction process correspond to existing fractures or faults, the B value will quickly drop to about 1. B value estimates come from classical seismology, and the basis of this method comes from the fact that the frequency-magnitude relationship of any earthquake sequence follows a power law relationship, namely:

[0037] The power law relationship is:

[0038]

[0039] Among them, the N M It represents the cumulative number of earthquakes or events with magnitude greater than or equal to M; a is the intercept, and b is the B value of the microseismic event.

[0040] In an exemplary embodiment, the determination of natural earthquake cracks based on the B value of the microseismic event includes: in the first case, if the B value of the microseismic event is less than 1, the crack is determined to be a natural earthquake crack. In the second case, if the B value of the microseismic event is greater than or equal to 1, the crack is determined to be a non-natural earthquake crack. In this embodiment, the microseismic events induced by natural cracks are distinguished from the microseismic events induced by the fractured reservoir matrix through the magnitude distribution characteristics and B value characteristics of the microseismic data, and the scale of the natural cracks is analyzed. Figure 3 As shown in the figure, taking Well Zi301 in the eastern Zigong block as an example, by statistically analyzing the cumulative distribution and frequency distribution of the magnitude of the microseismic events in Well Zi301 and calculating the B value, it can be obtained that the magnitude Mw = -1.2 of the natural fracture-induced microseismic events and the microseismic events can be distinguished, and its b value is 1.46, indicating that the fracturing microseismic events in the well are affected by the natural fractures to a certain extent, but there is no large-scale fracture impact, and the fracture rupture mode is the expansion mode.

[0041] In an exemplary embodiment, the main fracture trend surface of the natural earthquake fractures in each fracturing stage is determined by a magnitude weighted fitting algorithm, including:

[0042] Step 1. Determine the magnitude of the microseismic event and the spatial location of the microseismic event;

[0043] Step 2. Determine relevant information of the fracturing section; the relevant information of the fracturing section includes information on the size, shape, inclination, geological structure information and formation attribute information of the fracturing section.

[0044] Step 3. According to the magnitude of the microseismic event, the spatial position of the microseismic event and the relevant information of the fracturing section, fitting is performed to determine the main fracture trend surface of the natural earthquake cracks in the fracturing section. In this embodiment, the spatial position and magnitude information of the microseismic event induced by the natural fracture are determined by resolving the magnitude, and the main fracture trend surface of the natural fractures in each fracturing section is fitted by the magnitude weighted fitting algorithm, and the fracture surface occurrence and size trend information of each fracturing section are calculated and analyzed. Figure 4 As shown, taking Well 301 in Zigong East Block as an example, the fracture surfaces of each fracturing section are mainly medium-high angle fractures, with inclinations of NE-30 degrees and SW-220 degrees, and the natural fractures are mainly developed in the middle fracturing section.

[0045] In an exemplary embodiment, the spatial position of the microseismic event is determined by:

[0046] Step 1. Collect microseismic event data: obtain the magnitude data of microseismic events and the corresponding source location data.

[0047] Step 2. Establish the relationship between magnitude and focal distance: According to the known microseismic events, the magnitude is matched with the focal distance. The magnitude can be converted to focal distance using empirical equations or seismological models.

[0048] Step 3. Analyze the distribution of focal distances: According to the magnitudes of multiple microseismic events and the corresponding focal distances, statistics and analysis are performed to observe the distribution characteristics of focal distances.

[0049] Step 4. Determine the spatial position of the microseismic event based on the determined source distance and the source position data.

[0050] In an exemplary embodiment, the process of determining the fracture occurrence information of each fracturing stage is as follows:

[0051] Step 1. Determine the strength of the crack based on the magnitude data of the microseismic event;

[0052] In this step, the spatial position of the microseismic event induced by the natural fracture is determined by resolving the magnitude, and the envelope surface is fitted to obtain the fracturing transformation volume SRV (the transformation volume based on the microseismic event point) as an important indicator for the evaluation of the strength of natural fractures. SRV, also known as the fracturing volume (transformation volume), refers to the shear slip of brittle rocks during hydraulic fracturing, which induces the continuous expansion of natural fractures, and then forms a fracture network, increases the transformation volume, and improves production and ultimate recovery. For the microseismic event of each fracturing section, the three-dimensional convex hull formed by its point set is used as the transformation body of the fracturing section, and the volume of the three-dimensional convex hull is used as the fracturing volume. Taking the Zi 301 well in the Zigong East Block as an example, the crown convex hull algorithm can be used to calculate the SRV volume.

[0053] When obtaining the fracturing stimulation volume SRV by fitting the envelope surface, the envelope surface can be fitted according to the following parameters:

[0054] 1. Magnitude data of microseismic events: used to represent the activity and distribution of the underground fracture system as the height of the fitted envelope surface.

[0055] 2. Spatial location data of microseismic events: including X, Y and Depth information, used to determine the horizontal position of the fitted envelope. The area and size of the main fracture surface: used as constraints to limit the shape and range of the fitted envelope. By using the above parameters, an envelope can be fitted, which can be used to estimate the hydraulic stimulation volume, i.e., SRV.

[0056] Step 2. The number of microseismic events and their spatial distribution are used to determine the density of fractures.

[0057] According to the above-mentioned fracture interpretation results of microseismic events, Figure 6 As shown, a simple statistics is performed on the fracture parameters of each fracturing section to obtain the fracture density and strength parameter values.

[0058] In an exemplary embodiment, a random discrete fracture modeling algorithm based on conditional constraint RANSAC (Random Sample Consensus, referred to as RANSAC) is used to construct a DFN fracture network based on microseismic data according to the determined main fracture surface area, size, and SRV data to determine the fracture strength and density of each fracturing section.

[0059] The complexity index of the middle fracturing section of Well Zi301 is relatively high, showing the development of a network of fractures, in which there are multiple groups of fractures with different occurrences. In order to better reflect the fracture network structure, a random discrete fracture modeling algorithm based on conditional constraint RANSAC (Random Sample Consensus, referred to as RANSAC) is constructed to construct a DFN fracture network based on microseismic data. Taking Well Zi301 in the Zigong East Block as an example, in actual situations, there may be multiple groups of fractures with different occurrences in the fracturing section. Therefore, simply extracting the main fault surface is not effective for the fracture network structure inside the fracturing section. In this study, a robust three-dimensional fracture network reconstruction method (RFM3D) was developed using random simulation consistency. Figure 5 As shown in the figure, the dark surface is the main fracture surface, and the rest are secondary main fracture surfaces.

[0060] When constructing a DFN fracture network based on microseismic data, the properties of the fracture network can be derived using the area, size, and SRV data of the determined main fracture surface. The specific process is as follows:

[0061] 1. Analyze microseismic data: By analyzing microseismic data, including information such as magnitude and spatial location, the fracture development of each microseismic event can be determined.

[0062] 2. Determine the strength of the crack: Based on indicators such as the magnitude of the microseismic event, the strength of the crack, which is a parameter that measures the degree of crack opening, can be inferred.

[0063] 3. Calculate the fracture occurrence: By calculating the distribution density of microseismic events, the fracture occurrence distribution of each fracturing stage can be obtained. This can be determined by considering the number of microseismic events and their spatial distribution.

[0064] Through the above steps, the fracture strength and density of each fracture stage can be determined, providing information about the DFN fracture network.

[0065] In an exemplary embodiment, the fracture information predicted by using seismic data is corrected according to the fracture density of each fracturing stage and the occurrence information to obtain a fracture development degree and orientation model of the work area, including:

[0066] Performing training based on the intensity of the crack determined by the microseismic event and the earthquake fracture attribute to determine the earthquake fracture attribute in the earthquake fracture attribute;

[0067] The earthquake fracture attributes are used to predict cracks in earthquake data.

[0068] In this embodiment, taking Well 301 in the Zigong East Block as an example, the fracture strength (or fracture density) data obtained by interpreting microseismic data and seismic fracture attributes (Attributes) are used to form a training sample set, and the machine learning random forest algorithm is used to train the model, and the attribute importance ranking is obtained, such as Figure 7 As shown, the azimuthal coherence ellipse fitting attribute is of the highest importance; the correlation coefficient of the learning results of the test set is above 80%, so the fracture tendency development guidance field is obtained, which is consistent with the microseismic interpretation of fracture data, providing a basis for subsequent DFN random simulation.

[0069] In an exemplary embodiment, the fracture information predicted by seismic data is corrected according to the fracture density of each fracturing stage and the occurrence information to obtain the fracture development degree and orientation model of the work area, including: performing parameter estimation on the microseismic fracture occurrence interpretation results to obtain well correction parameters, using the azimuthally anisotropic coherent fracture development direction of the seismic data to establish a fracture development direction guidance field, and by setting the radius R of the well correction, using the wellbore fracture azimuth development parameters obtained in step 1 to perform wellbore orientation correction on the wellside fracture guidance field.

[0070] Taking Well Zi301 in the eastern Zigong block as an example, the joint fracture orientation modeling process based on microseismic fractures and seismic data driving mainly includes three steps: first, the parameters of the microseismic fracture occurrence interpretation results are estimated to obtain the well correction parameters, and then the fracture development direction guidance field is established using the azimuthally anisotropic coherent fracture development direction of the seismic data. Finally, by setting the radius R of the well correction, the wellbore fracture orientation development parameters obtained in the first step are used to perform wellbore orientation correction on the wellside fracture guidance field.

[0071] Taking Well 301 in the Zigong East Block as an example, the result of fracture prediction in the Zigong East Block obtained by using the method for predicting fractures based on microseismic data implemented in the embodiment of the present application is as follows: Figure 8As shown, it can be seen that the distribution of the microseismic target scale fracture network is consistent with the microseismic distribution. On this basis, the shortest path algorithm is used to connect the discrete fracture networks to form a continuous fracture network.

[0072] In a second aspect, an embodiment of the present invention provides a device for predicting cracks based on microseismic data, such as Figure 2 As shown, the device includes: a memory 200 and a processor 210; the memory is used to store a program for a method for predicting cracks based on microseismic data, and the processor is used to read and execute the program for a method for predicting cracks based on microseismic data, and execute any one of the methods in the above embodiments.

[0073] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a data processing program is stored, and the data processing program is executed by a processor to perform any one of the methods for predicting cracks based on microseismic data.

[0074] Example 1

[0075] The method flow of predicting fractures based on microseismic data includes:

[0076] The first step is to determine the B value of the microseismic event based on the microseismic data of each fracturing section.

[0077] Studies have shown that standard induced fractures in standard shales often correspond to a b value of about 2. If the event that occurs during a construction process corresponds to an existing fracture or fault, the b value will drop rapidly to about 1. The b value estimate comes from classical seismology. The frequency-magnitude relationship of any earthquake sequence follows a power law relationship that can be written as follows:

[0078]

[0079] N M is the cumulative number of earthquakes or events with a magnitude greater than or equal to M; the cumulative magnitude distribution data can be obtained based on the magnitude data corresponding to each event.

[0080] a is the intercept and b is the slope of the straight line, which represents the B value of the microseismic event.

[0081] Step 2: Determine the natural earthquake cracks based on the B value of the microseismic event

[0082] After the natural fractures (small faults) are connected, the number of large-magnitude event points will increase abnormally due to the concentrated release of induced stress, and the natural fracture connection is evaluated based on the expansion law of microseismic events: the fracturing microseismic event points extend on a large scale along the natural fractures, and the density of microseismic events is high.

[0083] If the B value of the microseismic event is less than 1, the crack is determined to be a natural earthquake crack.

[0084] If the B value of the microseismic event is greater than or equal to 1, the crack is determined to be an earthquake crack.

[0085] like Figure 2 As shown in the figure, taking Well Zi301 in the eastern Zigong block as an example, by statistically analyzing the cumulative distribution and frequency distribution of the magnitude of the microseismic events in Well Zi301 and calculating the B value, it can be obtained that the magnitude Mw = -1.2 of the natural fracture-induced microseismic events and the microseismic events can be distinguished, and its b value is 1.46, indicating that the fracturing microseismic events of the well are affected by the natural fractures to a certain extent, but there is no large-scale fracture impact, and the fracture rupture mode is the extensional mode (Extensional).

[0086] The third step is to determine the main fracture trend surface of the natural earthquake cracks in each fracturing section by using the magnitude weighted fitting algorithm;

[0087] In this step, the process of determining the main fracture trend surface of the natural earthquake fractures in each fracturing section includes:

[0088] Step 31. Determine the magnitude of the microseismic event and the spatial location of the microseismic event;

[0089] The process of determining the spatial position of the microseismic event is as follows:

[0090] Step 311. Obtaining magnitude data of microseismic events and corresponding source location data;

[0091] Step 312: Convert the magnitude data of the microseismic event into the epicenter distance using a seismological model;

[0092] Based on known microseismic events, their magnitudes are matched with the focal distances, and a seismological model is established. The magnitudes are converted into focal distances using the established seismological model.

[0093] Step 313: Determine the spatial position of the microseismic event based on the determined source distance and the source location data.

[0094] Step 32: Determine the relevant information of the fracturing section, wherein the relevant information of the fracturing section includes the size, shape, inclination, geological structure and formation attribute information of the fracturing section. These relevant information are obtained through measurement.

[0095] Step 33: Fitting is performed based on the magnitude of the microseismic event, the spatial position of the microseismic event and the relevant information of the fracturing section to determine the main fracture trend surface of the natural earthquake cracks in the fracturing section.

[0096] The fourth step is to determine the occurrence information of the cracks in each fracturing section; the occurrence information includes the density and direction of the cracks.

[0097] wherein the strength of the crack is determined based on the magnitude data of the microseismic event;

[0098] The number of microseismic events and their spatial distribution determine the density of fractures.

[0099] like Figure 4 As shown, taking Well 301 in Zigong East Block as an example, the fracture surfaces of each fracturing section are mainly medium-high angle fractures, with inclinations of NE-30 degrees and SW-220 degrees, and the natural fractures are mainly developed in the middle fracturing section.

[0100] The fifth step is to obtain the fracturing stimulation volume SRV by fitting the envelope surface, and determine the natural fracture strength based on the fracturing stimulation volume SRV obtained by fitting.

[0101] Step 6: Construct DFN fracture network based on microseismic data

[0102] like Figure 5 As shown in the figure, taking Well 301 in the Zigong East Block as an example, there may be a fracture network composed of multiple groups of fractures with different occurrences in the fracturing section. Therefore, only extracting the main fracture surface cannot effectively extract the fracture network structure inside the fracturing section. In this step, a robust three-dimensional fracture network reconstruction method (RFM3D) is implemented using random simulation consistency. The dark surface in the figure is the main fracture surface, and the rest are secondary main fracture surfaces.

[0103] Taking Well Zi301 in the eastern Zigong block as an example, the DFN discrete fracture network is constructed using the constrained RANSAC algorithm. The fracture network analysis of the 11th fracturing section of Well Zi301, where fractures are well developed, shows that the red main fault surface conducts several horizontal fractures. Through the above-mentioned fracture interpretation results of microseismic events, such as Figure 6 As shown in the figure, a simple statistics of the fracture parameters of each fracturing section is performed to obtain the statistical parameter table of fracture density, strength, etc. The main fracture rupture surface area, size, SRV, RANSAC-DFN are used to obtain the number of fractures in each fracturing section and other indicators, and the fracture strength, density, and occurrence distribution information of each fracturing section are constructed to provide a basis for further seismic fracture attribute analysis and fracture modeling.

[0104] Step 7: Determine the sensitive attributes of seismic data fracture prediction based on the determined fracture density

[0105] like Figure 7As shown in the figure, taking Well 301 in the Zigong East Block as an example, the fracture strength (or fracture density) data obtained by interpreting microseismic data and seismic fracture attributes (Attributes) are used to form a training sample set. The model is trained using the machine learning random forest algorithm, and the attribute importance ranking is obtained, among which the azimuth coherent ellipse fitting attribute is the most important; the azimuth coherent ellipse fitting attribute is used to predict fractures in seismic data.

[0106] Step 8: Correction of fracture prediction results from seismic data

[0107] Step 9: Use the shortest path algorithm to connect the discrete fracture networks to form a continuous fracture network.

[0108] like Figure 8 As shown in the figure, taking Well 301 in the Zigong East Block as an example, the random discrete fracture modeling is used to model the Zigong East Block. It can be seen that the distribution of the microseismic target scale fracture network is consistent with the microseismic distribution. On this basis, the shortest path algorithm is used to connect the discrete fracture networks to form a continuous fracture network.

[0109] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A method for predicting cracks based on microseismic data, characterized in that: The method comprises: Determine the B value of the microseismic event based on the microseismic data of each fracturing stage; Determining natural earthquake cracks according to the B value of the microseismic event; The main fracture trend surface of natural earthquake fractures in each fracturing section is determined by using magnitude weighted fitting algorithm, and the occurrence information of fractures in each fracturing section is determined; The fracture information predicted by using seismic data is corrected according to the fracture density of each fracturing stage and the occurrence information to determine the fracture density and fracture direction.

2. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: Determining the microseismic event B value according to the microseismic data of each fracturing stage includes: By taking the logarithm of the cumulative number of magnitudes of microseismic events; A power law relationship is obtained by fitting the determined logarithmic value and the magnitude of the microseismic event, and the B value of the microseismic event is determined based on the obtained power law relationship.

3. The method for predicting cracks based on microseismic data according to claim 2, characterized in that: The power law relationship is: Among them, the N M It represents the cumulative number of earthquakes or events with magnitude greater than or equal to M; a is the intercept, and b is the B value of the microseismic event.

4. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: Determining the natural earthquake crack according to the B value of the microseismic event includes: If the B value of the microseismic event is less than 1, the crack is determined to be a natural earthquake crack. If the B value of the microseismic event is greater than or equal to 1, it is determined that the crack is a non-natural earthquake crack.

5. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: The method of determining the main fracture trend surface of the natural earthquake cracks in each fracturing section by means of a magnitude weighted fitting algorithm comprises: Determine the magnitude of microseismic events and the spatial location of microseismic events; Determine relevant information of the fracturing stage; Fitting is performed based on the magnitude of the microseismic event, the spatial position of the microseismic event and the relevant information of the fracturing section to determine the main fracture trend surface of the natural earthquake cracks in the fracturing section.

6. The method for predicting cracks based on microseismic data according to claim 5, characterized in that: The relevant information of the fracturing section includes the size, shape, inclination, geological structure and formation attribute information of the fracturing section.

7. The method for predicting cracks based on microseismic data according to claim 5, characterized in that: The process of determining the spatial position of the microseismic event is as follows: Obtain the magnitude data of microseismic events and the corresponding source location data; Converting the magnitude data of the microseismic event into epicenter distance using a seismological model; The spatial position of the microseismic event is determined based on the determined source distance and the source position data.

8. The method for predicting cracks based on microseismic data according to claim 7, characterized in that: The process of determining the fracture occurrence information of each fracturing stage is as follows: determining the intensity of the fracture based on the magnitude data of the microseismic event; The number of microseismic events and their spatial distribution determine the density of fractures.

9. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: The method of correcting the fracture information predicted by using seismic data according to the fracture density of each fracturing stage and the occurrence information to determine the fracture density and fracture direction includes: Performing training based on the intensity of the crack determined by the microseismic event and the earthquake fracture attribute to determine the earthquake fracture attribute in the earthquake fracture attribute; The earthquake fracture attributes are used to predict cracks in earthquake data.

10. A device for predicting cracks based on microseismic data, characterized in that: The device includes: a memory and a processor; the memory is used to store a program for a method for predicting cracks based on microseismic data, and the processor is used to read and execute the program for a method for predicting cracks based on microseismic data, and execute the method described in any one of claims 1-9.

11. A computer-readable storage medium having a data processing program stored thereon, wherein the data processing program is executed by a processor to implement the method for predicting fractures based on microseismic data as described in any one of claims 1 to 9.

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