A method and device for predicting cracks based on microseismic data

Abstract

A method and device for predicting fractures based on microseismic data, the method comprising: determining a microseismic event B value based on the microseismic data of each fracturing section; determining a natural earthquake fracture based on the microseismic event B value; determining the main fracture trend surface of the natural earthquake fracture in each fracturing section by a magnitude-weighted fitting algorithm, and determining fracture occurrence information of each fracturing section; and correcting fracture information predicted using seismic data based on the fracture density of each fracturing section and the occurrence information to determine the fracture density and fracture direction.

Description

Technical Field

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

[0002] Accelerating shale gas development is a crucial measure for ensuring national energy security. Deep shale gas resources hold immense potential and are a key area for the company's sustained and rapid growth in natural gas production. While large gas fields with reserves of trillions of cubic meters and production of tens of billions of cubic meters have been established in southern Sichuan, development of the Luzhou Lu-203 well area has encountered significant difficulties. Primarily due to casing changes and pressure channeling, development results in the Luzhou North area have fallen short of expectations, hindering the pursuit of scale-based, cost-effective development.

[0003] The development characteristics of underground fractures are important indicators for the design of fracturing parameters and the main basic data for analyzing casing deformation and pressure channeling in horizontal wells. They are also very important for the layout of development platforms (well groups), the design of wellbore trajectories and wellbore structures, and the optimization of development plans. At the same time, fractures are also the main mechanism for inducing casing deformation. Therefore, the ability to accurately predict the distribution and development direction of fracture density is of decisive significance for preventing the risks of pressure channeling and casing deformation. 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 discovered that microseismic data can be used as prior information to constrain the fracture density distribution and development direction 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 of 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 section;

[0009] determining a natural earthquake crack 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 the magnitude weighted fitting algorithm, and the fracture occurrence information of 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 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 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 having a data processing program stored thereon, and the data processing program is executed by a processor to implement any one of the methods for predicting cracks based on microseismic data.

[0014] Compared to related technologies, the present invention provides a method and device for predicting fractures based on microseismic data. The method includes: determining the B value of a microseismic event based on the microseismic data of each fracturing segment; determining natural seismic fractures based on the B value of the microseismic event; determining the main fracture trend surface of the natural seismic fractures in each fracturing segment through a magnitude-weighted fitting algorithm, and determining the fracture occurrence information of each fracturing segment; and correcting the fracture information predicted using seismic data based on the fracture density of each fracturing segment and the occurrence information to determine the fracture density and fracture direction. This application implements a method for determining the statistical distribution of the fracture density curve and fracture occurrence of each perforation segment based on microseismic events, correcting the fracture prediction results of seismic data, and ultimately obtaining more accurate fracture density and fracture direction prediction results.

[0015] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. Other advantages of the present application can be realized and obtained by the solutions 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 This 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 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 Schematic diagram of natural fracture main fracture surface analysis based on natural fracture microseismic event point data in some exemplary embodiments;

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

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

[0023] Figure 7 A plane intersection correlation analysis diagram 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] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that there may be more embodiments and implementations within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, 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] This 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 this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, except for the limitations made according to the appended claims and their equivalents, the embodiments are not subject to other limitations. In addition, various modifications and changes may be made within the scope of protection of the appended 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 in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order 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 performing their steps in the order written, and those skilled in the art can readily understand that these orders 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 based on the microseismic data of each fracturing stage;

[0030] S110: Determine 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 fracture occurrence information of each fracturing section;

[0032] S130: 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.

[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 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 one exemplary embodiment, the number of microseismic events and the magnitude of the microseismic events have a power law relationship. 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 drop rapidly to about 1. The B value estimation comes from classical seismology, which is based on 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 a 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 301 in the Zigong East Block as an example, by statistically analyzing the cumulative distribution and frequency distribution of the magnitude of the microseismic events in Well 301 and calculating the B value, it can be obtained that the magnitude of the microseismic events induced by natural fractures and microseismic events is Mw = -1.2, and its b value is 1.46, indicating that the fracturing microseismic events in this well are affected by natural fractures to a certain extent, but there is no large-scale fault influence, and the fracture rupture mode is an expansion mode.

[0041] In an exemplary embodiment, determining the main fracture trend surface of the natural earthquake fractures in each fracturing section by a magnitude weighted fitting algorithm includes:

[0042] Step 1. Determine the magnitude and 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 and formation properties 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 crack are determined by distinguishing the magnitude, and the main fracture trend surface of the natural cracks 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 in the figure, taking Well 301 in the Zigong East Block as an example, the fracture surfaces of each fracturing section are mainly medium-high angle fractures with a dip of NE-30 degrees and SW-220 degrees. 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 as follows:

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

[0047] Step 2. Establish a magnitude-source distance relationship: Based on known microseismic events, map their magnitude to their source distance. Magnitude can be converted to source distance using empirical equations or seismological models.

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

[0049] Step 4: Determine the spatial location of the microseismic event based on the determined source distance and the source location 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 fitting is performed to obtain the fracture 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 fracture 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 events 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 calculating 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 the microseismic event: This includes X, Y, and Depth information, which is used to determine the horizontal position of the fitted envelope. The area and size of the main fracture surface serve as constraints, limiting the shape and range of the fitted envelope. Using these parameters, an envelope can be fitted, which can be used to estimate the stimulation volume (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 results of fracture interpretation of microseismic events, Figure 6 As shown, a simple statistical analysis 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, abbreviated as RANSAC) is constructed based on microseismic data based on the determined main fracture surface area, size, and SRV data to determine the fracture strength and density of each fracturing segment.

[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 enough to effectively reflect 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 fracture surfaces.

[0060] When constructing a DFN fracture network based on microseismic data, the properties of the fracture network can be inferred using the area and size of the determined main fracture surface and SRV data. 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 can be inferred, which is a parameter that measures the degree of crack opening.

[0063] 3. Calculate 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 intensity 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 using seismic data is corrected based on 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 the Zigong East Block Zi 301 Well as an example, the fracture strength (or fracture density) data obtained by interpreting microseismic data and the earthquake 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 in the figure, the azimuthal coherence ellipse fitting attribute is the most important; the correlation coefficient of the learning results of the test set is above 80%, so the fracture tendency development guidance field obtained is consistent with the microseismic interpretation of the 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 section and the occurrence information to obtain a 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, establishing a fracture development direction guidance field using the azimuthally anisotropic coherent fracture development direction of the seismic data, and performing an on-well orientation correction on the wellside fracture guidance field using the on-well fracture azimuth development parameters obtained in step 1 by setting the well correction radius R.

[0070] Taking Well Zi301 in the eastern Zigong block as an example, the joint fracture orientation modeling process driven by microseismic fractures and seismic data mainly includes three steps: first, the parameters of the microseismic fracture occurrence interpretation results are estimated to obtain the well correction parameters; 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 well correction radius R, 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 the Zigong East Block Zi 301 Well as an example, the results 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 are as follows: Figure 8As shown in the figure, 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 having a data processing program stored thereon, and the data processing program is executed by a processor to implement any one of the methods for predicting cracks based on microseismic data.

[0074] Example 1

[0075] The method flow for 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] Research shows that standard induced fractures in standard shales often correspond to a b-value of approximately 2. If the event occurring during a construction process corresponds to an existing fracture or fault, the b-value will drop rapidly to around 1. The b-value estimate comes from classical seismology, where the frequency-magnitude relationship of any earthquake sequence follows a power law relationship that can be expressed 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 release of induced stress concentration, 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 301 in the Zigong East Block as an example, by statistically analyzing the cumulative distribution and frequency distribution of the magnitude of the microseismic events in Well 301 and calculating the B value, it can be obtained that the magnitude of the microseismic events induced by natural fractures and the microseismic events are distinguished by Mw = -1.2, and its b value is 1.46, indicating that the fracturing microseismic events of this well are affected by natural fractures to a certain extent, but there is no large-scale fracture impact, and the fracture rupture mode is the extensional mode.

[0086] The third step is to determine the main fracture trend surface of the natural earthquake cracks in each fracturing section through 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 and corresponding source location data of microseismic events;

[0091] Step 312: Convert the magnitude data of the microseismic event into the earthquake source 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 relevant information about the fracturing section, wherein the relevant information about the fracturing section includes information about the size, shape, inclination, geological structure, and formation properties of the fracturing section. This relevant information is obtained through measurement.

[0095] Step 33: Fitting is performed based on the magnitude of the microseismic event, the spatial location 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 intensity 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 in the figure, taking Well 301 in the Zigong East Block as an example, the fracture surfaces of each fracturing section are mainly medium-high angle fractures with a dip of NE-30 degrees and SW-220 degrees. The natural fractures are mainly developed in the middle fracturing section.

[0100] Step 5: Calculate the hydraulic fracturing volume (SRV) by fitting the envelope surface, and determine the natural fracture strength based on the fitted hydraulic fracturing volume (SRV).

[0101] Step 6: Construct a 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 determine 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 others are secondary main fracture surfaces.

[0103] Taking Well 301 in the Zigong East 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 301, 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, as shown in the figure, Figure 6 As shown in the figure, a simple statistical analysis of the fracture parameters of each fracturing section is performed to obtain statistical parameter tables such as fracture density and strength. The main fracture surface area, size, SRV, and RANSAC-DFN are used to obtain indicators such as the number of fractures in each fracturing section. The fracture strength, density, and occurrence distribution information of each fracturing section are constructed to provide a basis for further earthquake 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 intensity (or fracture density) data obtained by interpreting microseismic data and seismic fracture attributes (Attributes) are used to form a training sample set. The machine learning random forest algorithm is used to train the model, and the attribute importance ranking is obtained. Among them, the azimuth coherence ellipse fitting attribute is the most important; the azimuth coherence ellipse fitting attribute is used to predict fractures in seismic data.

[0106] Step 8: Correct the fracture prediction results of seismic data

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

[0108] like Figure 8 As shown in the figure, taking Well 301 in the Zigong East Block as an example, the modeling results of the Zigong East Block using random discrete fracture modeling show that the distribution of the fracture network at the microseismic target scale 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 is well 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 tape, 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 skilled in the art that communication media generally embodies 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 section; determining a natural earthquake crack 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 the magnitude weighted fitting algorithm, and the fracture occurrence information of each fracturing section is determined; Correcting the fracture information predicted by seismic data according to the fracture density of each fracturing stage and the occurrence information to determine the fracture density and fracture direction; The method of determining the main fracture trend surface of the natural earthquake cracks in each fracturing section by using a magnitude weighted fitting algorithm includes: Determine the magnitude of microseismic events and the spatial location of microseismic events; Determine relevant information about the fracturing stage; Determine the main fracture trend surface of the natural earthquake crack in the fracturing section by fitting the magnitude of the microseismic event, the spatial location of the microseismic event and the relevant information of the fracturing section; The step of correcting the fracture information predicted by 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 cracks determined by the microseismic event and the earthquake fracture attributes to determine the best earthquake fracture attribute among the earthquake fracture attributes; The optimal earthquake fracture attributes are used to predict fractures in earthquake data.

2. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: Determining the microseismic event B value based on the microseismic data of each fracturing stage includes: By taking the logarithm of the cumulative number 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 a 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 a 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 relevant information of the fracturing section includes information on the size, shape, inclination, geological structure and formation attribute of the fracturing section.

6. The method for predicting cracks based on microseismic data according to claim 1, 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.

7. The method for predicting cracks based on microseismic data according to claim 1, characterized in that: The process of determining the fracture occurrence information of each fracturing stage is as follows: determining the intensity of the crack based on the magnitude data of the microseismic event; The number of microseismic events and their spatial distribution determine the density of fractures.

8. 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 performing a method for predicting cracks based on microseismic data, and the processor is used to read and execute the program for performing a method for predicting cracks based on microseismic data, and execute the method described in any one of claims 1-7.

9. A computer-readable storage medium having a data processing program stored thereon, wherein a processor executes the method for predicting fractures based on microseismic data according to any one of claims 1 to 7.

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