Artificial fracture inversion method and device, storage medium and processor

Abstract

The invention relates to the field of geological exploration and development, and discloses an artificial fracture inversion method and device, a storage medium and a processor, and the inversion method comprises the steps: obtaining artificial fracture characteristic parameters of a core of a cored well in a target region, and associating the artificial fracture characteristic parameters with a well trajectory of the cored well; establishing a micro-seismic body mesh model of the target area, importing the association result into the micro-seismic body mesh model to obtain a target mesh, and determining a first association relationship between the artificial fracture in the target mesh and the micro-seismic monitoring data; and based on the first incidence relation, under the constraint of the first inversion constraint condition and the second inversion constraint condition, performing inversion on the artificial fracture of the target area. According to the scheme, the artificial fracture characteristic parameters of the core of the coring well are combined with the microseismic monitoring data, so that the accuracy of artificial fracture inversion is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of geological exploration and development technology, specifically to an artificial fracture inversion method, an artificial fracture inversion device, a machine-readable storage medium, and a processor. Background Technology

[0002] In oil and gas well fracturing operations, high-pressure fluid is typically injected around the wellbore to artificially create fractures, thereby improving oil recovery. Understanding the characteristics of these artificial fractures (such as fracture shape, size, and distribution) allows for the evaluation of fracturing effectiveness, leading to further optimization of the fracturing strategy.

[0003] Currently, the characteristics of artificial fractures in underground rock strata are generally reconstructed through artificial fracture inversion, and this inversion is mostly based on 3D hydraulic fracturing simulation and reconstruction of microseismic monitoring data points. However, comparisons show that the characteristics of artificial fractures inverted using this method deviate significantly from the characteristics of artificial fractures under real-world conditions. In other words, the accuracy of artificial fracture inversion using methods based on 3D hydraulic fracturing simulation and microseismic monitoring data point reconstruction is relatively low. Summary of the Invention

[0004] The purpose of this invention is to overcome the problem that the accuracy of artificial fracture inversion using artificial fracture inversion methods based on three-dimensional fracturing simulation and microseismic monitoring data point reconstruction is low. This invention provides an artificial fracture inversion method, an artificial fracture inversion device, a machine-readable storage medium, and a processor.

[0005] To achieve the above objectives, the present invention provides a method for artificial crack inversion, the method comprising:

[0006] The artificial fracture characteristic parameters of the core samples from the target area are obtained, and the artificial fracture characteristic parameters are correlated with the well trajectory of the core sample.

[0007] Based on the microseismic monitoring data of the target area, a microseismic body grid model corresponding to the target area is established;

[0008] The correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well are imported into the microseismic mesh model to obtain the target mesh, and the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data is determined.

[0009] Obtain the second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as the first inversion constraint condition;

[0010] Based on the microseismic monitoring data, the geometric dimensions of the target fracture are determined, and these geometric dimensions are used as the second inversion constraint.

[0011] Based on the first correlation, and under the constraints of the first inversion constraint and the second inversion constraint, the artificial cracks in the target area are inverted.

[0012] In this embodiment of the application, after obtaining the artificial fracture characteristic parameters of the core samples from the target area, the inversion method further includes:

[0013] The distribution function of the artificial fracture in the rock core is determined based on the characteristic parameters of the artificial fracture.

[0014] In this embodiment of the application, establishing a microseismic body mesh model corresponding to the target area based on the microseismic monitoring data of the target area includes:

[0015] A three-dimensional structural geological model corresponding to the target area is established, and the three-dimensional structural geological model is divided into three-dimensional meshes to obtain a three-dimensional mesh model corresponding to the target area.

[0016] The microseismic monitoring data is correlated with the three-dimensional mesh model to obtain the microseismic body mesh model corresponding to the target area.

[0017] In this embodiment of the application, the microseismic monitoring data includes the energy levels of microseismic monitoring data points;

[0018] The process of importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the cored well into the microseismic mesh model to obtain the target mesh, and determining the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data, includes:

[0019] After importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic mesh model, the target mesh in the microseismic mesh model that intersects with the well trajectory of the coring well is determined;

[0020] Based on the density of artificial fractures corresponding to each target grid and the energy level of microseismic monitoring data points corresponding to each target grid, the correspondence between the density of artificial fractures and the energy level of microseismic monitoring data points is determined, and the correspondence is taken as the first correlation relationship.

[0021] In this embodiment of the application, obtaining the second correlation between the artificial fractures in the target grid and the fractured horizontal wellbore includes: obtaining the second correlation between the density of the artificial fractures in the target grid and the fractured horizontal wellbore.

[0022] In this embodiment of the application, the method further includes:

[0023] Based on the first correlation and the distribution function of artificial fractures in the core, artificial fractures in the target area are inverted under the constraints of the first inversion constraint and the second inversion constraint.

[0024] A second aspect of this application provides an artificial crack inversion device, comprising:

[0025] The acquisition module is used to acquire the artificial fracture feature parameters of the core samples from the target area and associate the artificial fracture feature parameters with the well trajectory of the core sample.

[0026] The model building module is used to build a microseismic body grid model corresponding to the target area based on the microseismic monitoring data of the target area.

[0027] The correlation determination module is used to import the correlation results between the artificial fracture feature parameters and the well trajectory of the coring well into the microseismic mesh model to obtain the target mesh, and determine the first correlation between the artificial fracture in the target mesh and the microseismic monitoring data;

[0028] The constraint determination module is used to obtain a second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as a first inversion constraint; and to determine the geometric dimensions of the fractured target based on the microseismic monitoring data, and use the geometric dimensions as a second inversion constraint.

[0029] The inversion module is used to invert the artificial cracks in the target area based on the first correlation relationship and under the constraints of the first inversion constraint and the second inversion constraint.

[0030] In this embodiment of the application, the microseismic monitoring data includes the energy levels of microseismic monitoring data points;

[0031] The correlation determination module is used to determine the target grids in the microseismic body grid model that intersect with the well trajectory of the cored well after importing the correlation results of the artificial fracture feature parameters and the well trajectory of the cored well into the microseismic body grid model; and to determine the correspondence between the density of artificial fractures and the energy level of microseismic monitoring data points corresponding to each target grid according to the density of artificial fractures and the energy level of microseismic monitoring data points corresponding to each target grid, and to use the correspondence as the first correlation.

[0032] A third aspect of this application provides a processor configured to perform the above-described artificial crack inversion method.

[0033] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned artificial crack inversion method.

[0034] The above technical solution includes: acquiring the characteristic parameters of artificial fractures from core samples taken from a target area, and correlating the characteristic parameters of the artificial fractures with the well trajectory of the core sample; establishing a microseismic mesh model corresponding to the target area based on microseismic monitoring data of the target area; importing the correlation result between the characteristic parameters of the artificial fractures and the well trajectory of the core sample into the microseismic mesh model to obtain a target mesh, and determining a first correlation between the artificial fractures in the target mesh and the microseismic monitoring data; acquiring a second correlation between the artificial fractures in the target mesh and the wellbore of the fractured horizontal well, and using the second correlation as a first inversion constraint; determining the geometric dimensions of the target fracture based on the microseismic monitoring data, and using the geometric dimensions as a second inversion constraint; and inverting the artificial fractures in the target area based on the first correlation, under the constraints of the first and second inversion constraints. The solution provided in this application, by combining the characteristic parameters of artificial fractures from core samples with microseismic monitoring data, greatly improves the accuracy of artificial fracture inversion, thereby enhancing the reliability of fracturing effect evaluation.

[0035] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0037] Figure 1 The schematic diagram illustrates a flow chart of an artificial crack inversion method according to an embodiment of this application;

[0038] Figure 2 The schematic diagram illustrates the orientation of an artificial crack according to an embodiment of this application;

[0039] Figure 3 This illustration schematically shows a visualization diagram of an association according to an embodiment of this application;

[0040] Figure 4 This illustration schematically shows an inversion result diagram according to an embodiment of the present application;

[0041] Figure 5This schematic diagram illustrates a structural block diagram of an artificial crack inversion device according to an embodiment of this application;

[0042] Figure 6 The diagram illustrates the internal structure of a computer device according to an embodiment of this application.

[0043] Explanation of reference numerals in the attached figures

[0044] 210 - Acquisition Module; 220 - Model Building Module; 230 - Relationship Determination Module; 240 - Constraint Determination Module; 250 - Inversion Module; A01 - Processor; A02 - Network Interface; A03 - Internal Memory; A04 - Display Screen; A05 - Input Device; A06 - Non-volatile Storage Medium; B01 - Operating System; B02 - Computer Program. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0046] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0047] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0048] As described in the background section, in oil and gas well fracturing operations, high-pressure fluid is typically injected around the wellbore to artificially create fractures, thereby improving oil recovery. Understanding the characteristics of these artificial fractures (such as fracture shape, size, and distribution) allows for evaluation of fracturing effectiveness and further optimization of fracturing strategies. Currently, the characteristics of artificial fractures in underground rock formations are generally reconstructed through artificial fracture inversion, primarily based on 3D fracturing simulations and microseismic monitoring data point reconstruction. However, comparisons show that the characteristics of artificial fractures inverted using this method deviate significantly from those of real-world artificial fractures. In other words, the accuracy of artificial fracture inversion using 3D fracturing simulations and microseismic monitoring data point reconstruction is relatively low.

[0049] To address this, one embodiment of this application provides a method for artificial crack inversion, such as... Figure 1 As shown, the artificial crack inversion method may include the following steps:

[0050] Step 101: Obtain the artificial fracture characteristic parameters of the core sample from the target area, and associate the artificial fracture characteristic parameters with the well trajectory of the core sample.

[0051] The target area can be the artificial fracture inversion area. In practical applications, the artificial fracture inversion area can be determined based on the controlled area of ​​the fractured well and the microseismic monitoring range.

[0052] The artificial fractures can be hydraulically engineered fractures. Multiple artificial fractures are typically distributed on the core sample. The characteristic parameters of these artificial fractures can include their location, depth, and attitude. The attitude can specifically include, but is not limited to, azimuth and dip angles.

[0053] The core well is used to drill the rock core. Considering that existing technologies typically use indirect methods such as microseismic analysis and tracers to characterize the extent of artificial fractures in the formation space, which involves significant uncertainty, this embodiment employs core drilling to more intuitively and accurately understand the extent of artificial fractures in the formation space. Furthermore, in specific implementation, the trajectory of the core well can preferably have a large slope, i.e., a larger inclination is preferred for core drilling.

[0054] Correlating the characteristic parameters of artificial fractures with the well trajectory of the core well can be achieved by importing the well trajectory of the core well into geological modeling software, then importing the characteristic parameters of artificial fractures into the geological modeling software, and finally correlating the characteristic parameters of artificial fractures with the well trajectory of the core well.

[0055] In this embodiment of the application, the characteristic parameters of artificial fractures are associated with the well trajectory of the coring well in order to display artificial fractures on the well trajectory, thereby visualizing the spatial distribution of artificial fractures.

[0056] In practice, for any artificial fracture in the core sample, its depth, azimuth, dip angle, and position on the well trajectory can be determined from its characteristic parameters. The artificial fracture is then displayed at the corresponding position on the well trajectory. During display, the well trajectory can be visualized as a curve, and the artificial fracture as a disk. The position of the disk on the curve corresponds to the position of the artificial fracture on the well trajectory, and the tilt of the disk corresponds to the depth, azimuth, and dip angle of the artificial fracture.

[0057] Since most artificial fractures in the formation are caused by horizontal well fracturing, and the embodiments of this application use a large inclination for coring, the artificial fractures on the core often exhibit an approximate "truncation" effect on the core segment along its length. The embodiments of this application use a disc shape to visualize the artificial fractures, which better reflects the distribution of artificial fractures in the core.

[0058] It is understandable that all artificial fractures in the core sample can be correlated and displayed on the well trajectory in the manner described above. That is, in the correlated visualization image, multiple disks with different inclinations are distributed along the curve corresponding to the well trajectory.

[0059] In this embodiment of the application, after obtaining the characteristic parameters of artificial fractures in the core samples from the target area, the distribution function of artificial fractures in the core can be determined based on these characteristic parameters. Specifically, after obtaining the characteristic parameters of artificial fractures in the core samples from the target area, the average azimuth, average dip, aggregation, density, etc., of the artificial fractures in the core can be calculated, thereby determining the distribution function of artificial fractures in the core.

[0060] Step 102: Based on the microseismic monitoring data of the target area, establish a microseismic body grid model corresponding to the target area.

[0061] In this embodiment of the application, step 102, based on the microseismic monitoring data of the target area, establishes a microseismic body grid model corresponding to the target area, which may include steps (1) and (2), as follows:

[0062] Step (1): Establish a three-dimensional structural geological model corresponding to the target area, and perform three-dimensional meshing on the three-dimensional structural geological model to obtain a three-dimensional mesh model corresponding to the target area.

[0063] Specifically, a three-dimensional structural geological model corresponding to the target area can be established based on each microseismic monitoring data point. Then, based on the accuracy of microseismic monitoring, the three-dimensional structural geological model is divided into three-dimensional meshes to obtain a three-dimensional mesh model corresponding to the target area.

[0064] Specifically, the microseismic monitoring range can be determined based on each microseismic monitoring data point, the target area can be determined according to the controlled area of ​​the fractured well and the microseismic monitoring range, and then a three-dimensional structural geological model corresponding to the target area can be established.

[0065] When determining the target area, it is also necessary to determine the boundary and longitudinal depth range of the target area, and to establish a three-dimensional structural geological model corresponding to the target area based on the boundary and longitudinal depth range of the target area.

[0066] Generally speaking, the higher the accuracy of microseismic monitoring, the smaller the size of a single 3D grid and the more 3D grids in the 3D grid model.

[0067] The above modeling process can be completed on a geological software modeling platform.

[0068] Step (2) involves associating the microseismic monitoring data with the three-dimensional mesh model to obtain the microseismic body mesh model corresponding to the target area.

[0069] Specifically, associating the microseismic monitoring data with the three-dimensional mesh model can include associating each microseismic monitoring data point and the energy level corresponding to each microseismic monitoring data point with the three-dimensional mesh model.

[0070] Associating each microseismic monitoring data point with the three-dimensional mesh model can include: organizing each microseismic monitoring data point into scattered data with spatial coordinates, and then importing this scattered data into the three-dimensional mesh model for association. In specific implementation, the scattered data corresponding to each microseismic monitoring data point can be displayed as dots in the three-dimensional mesh model. It is understood that by associating each microseismic monitoring data point with the three-dimensional mesh model, the discrete microseismic monitoring data points can be meshed.

[0071] Associating the energy levels corresponding to each microseismic monitoring data point with the three-dimensional mesh model can include: calculating the energy level (also called magnitude) of each microseismic monitoring data point and assigning the energy level to the corresponding microseismic monitoring data point in the three-dimensional mesh model. The energy level of each microseismic monitoring data point can be calculated by inputting a custom formula. In practice, the magnitude of the energy levels in the three-dimensional mesh model can be represented by the size of the dots, or by the color of the dots, etc.

[0072] Step 103: Import the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic body grid model to obtain the target grid, and determine the first correlation between the artificial fractures in the target grid and the microseismic monitoring data.

[0073] Specifically, the correlation results between the artificial fracture feature parameters and the well trajectory of the core well are imported into the microseismic mesh model, that is, the correlation results between the artificial fracture feature parameters and the well trajectory of the core well are mapped to the microseismic mesh model.

[0074] In this embodiment, the microseismic monitoring data includes the energy levels of microseismic monitoring data points. Therefore, step 103, which imports the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic body grid model to obtain a target grid, and determines the first correlation relationship between the artificial fractures in the target grid and the microseismic monitoring data, may include: after importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic body grid model, determining the target grids in the microseismic body grid model that intersect with the well trajectory of the coring well; and determining the correspondence between the density of artificial fractures and the energy levels of the microseismic monitoring data points corresponding to each target grid, based on the density of artificial fractures corresponding to each target grid and the energy levels of the microseismic monitoring data points corresponding to each target grid, and using this correspondence as the first correlation relationship.

[0075] The target grid can also be called the effective grid, that is, the grid in the microseismic grid model that intersects with the well trajectory of the coring well is considered the effective grid. Correspondingly, the well segment corresponding to the well trajectory segment that intersects with the grid in the microseismic grid model can be called the effective well segment.

[0076] In practical implementation, based on the density of artificial fractures corresponding to each target grid and the energy level of the microseismic monitoring data points corresponding to each target grid, the correspondence between the density of artificial fractures and the energy level of the microseismic monitoring data points is determined. This can include: for any target grid, determining the density of artificial fractures corresponding to that target grid and the average energy level of the microseismic monitoring data points within it; then, constructing the coordinate points of that target grid with the artificial fracture density as the abscissa and the average energy level of the microseismic monitoring data points within it as the ordinate; the coordinate points of other target grids are obtained in the same way; then, plotting the coordinate points corresponding to each target grid in a rectangular coordinate system and performing curve fitting to obtain the fitting formula, which is the correspondence between the density of artificial fractures and the energy level of the microseismic monitoring data points.

[0077] The density of artificial fractures corresponding to the target grid can be obtained by dividing the number of artificial fractures in the target grid by the length of the well trajectory segment corresponding to the target grid. The process of determining the average energy level of microseismic monitoring data points in the target grid may include: determining the microseismic monitoring data points included in the target grid, summing the energy levels of each microseismic monitoring data point in the target grid, and dividing the summation result by the number of microseismic monitoring data points in the target grid.

[0078] Step 104: Obtain the second correlation between the artificial fractures in the target grid and the fractured horizontal wellbore, and use the second correlation as the first inversion constraint condition.

[0079] In this embodiment of the application, obtaining the second correlation between the artificial fractures in the target grid and the wellbore of the fractured horizontal well may include: obtaining the first distance between the target grid and the wellbore of the fractured horizontal well, and obtaining the second distance between the target grid and the perforations on the wellbore of the fractured horizontal well, and determining the second correlation between the artificial fractures in the target grid and the first distance and the second distance.

[0080] Specifically, the first distance between the target grid and the wellbore of the fractured horizontal well is obtained, that is, the first distance from each target grid to the wellbore of the fractured horizontal well is obtained. The first distance can be the vertical distance to the wellbore of the fractured horizontal well.

[0081] Obtain the second distance between the target grid and the perforation on the fracturing horizontal wellbore; that is, obtain the second distance from each target grid to the perforation. In practical applications, the location of the perforation can be determined based on the segmented and clustered data in the fracturing design report.

[0082] In this embodiment, obtaining the second correlation between the artificial fractures in the target grid and the fractured horizontal wellbore specifically involves obtaining the second correlation between the density of the artificial fractures in the target grid and the fractured horizontal wellbore. That is, determining the second correlation between the artificial fractures in the target grid and the first distance and the second distance specifically involves determining the second correlation between the density of the artificial fractures in the target grid and the first distance and the second distance. In specific implementation, the second correlation between the density of the artificial fractures and the first and second distances can be determined based on the density of the artificial fractures corresponding to each target grid, the first distance from each target grid to the wellbore of the fractured horizontal wellbore, and the second distance from each target grid to the perforation on the fractured horizontal wellbore. By determining the second correlation between the density of the artificial fractures and the first and second distances, the distribution pattern of the artificial fractures with respect to the first and second distances can be obtained, i.e., the distribution pattern of the artificial fractures at different distances from the fractured horizontal wellbore and at different distances from the perforation.

[0083] Step 105: Based on the microseismic monitoring data, determine the geometric dimensions of the target fracture and use the geometric dimensions as the second inversion constraint.

[0084] Specifically, the target fracturing fracture is a single cluster of hydraulic fracturing main fractures. In this embodiment, the geometric dimensions of the main fracturing fracture can be determined based on the spatial distribution shape of each microseismic monitoring data point. In practical implementation, convex inclusions of each microseismic monitoring data point can be generated in geological modeling software, and then the geometric dimensions of the main fracturing fracture can be determined based on the range of the convex inclusions.

[0085] By using the geometry of the main fracturing fracture as a second inversion constraint, the size and extent of the fracture model can be constrained as a whole.

[0086] Step 106: Based on the first correlation, and under the constraints of the first inversion constraint and the second inversion constraint, the artificial cracks in the target area are inverted.

[0087] In practice, artificial fractures in the target area can be inverted based on the first correlation and the distribution function of artificial fractures in the rock core, under the constraints of the first inversion constraint and the second inversion constraint.

[0088] In practical applications, the above inversion can be performed using geological grid modeling methods.

[0089] It is understood that the artificial fracture inversion method provided in this application includes: obtaining artificial fracture characteristic parameters of core samples from a target area, and associating the artificial fracture characteristic parameters with the well trajectory of the core sample; establishing a microseismic body grid model corresponding to the target area based on microseismic monitoring data of the target area; importing the association result between the artificial fracture characteristic parameters and the well trajectory of the core sample into the microseismic body grid model to obtain a target grid, and determining a first association relationship between the artificial fractures in the target grid and the microseismic monitoring data; obtaining a second association relationship between the artificial fractures in the target grid and the fractured horizontal wellbore, and using the second association relationship as a first inversion constraint; determining the geometric dimensions of the target fracture based on the microseismic monitoring data, and using the geometric dimensions as a second inversion constraint; and inverting the artificial fractures in the target area based on the first association relationship and under the constraints of the first and second inversion constraints. The solution provided in this application combines the characteristic parameters of artificial fractures from core samples with microseismic monitoring data, which greatly improves the accuracy of artificial fracture inversion and thus enhances the reliability of fracturing effect evaluation.

[0090] Figure 1 This is a flowchart illustrating an artificial crack inversion method. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders. For example, step 102 can be executed first, followed by step 101, or steps 101 and 102 can be executed simultaneously. As another example, step 105 can be executed first, followed by step 104, or steps 104 and 105 can be executed simultaneously.

[0091] The following will illustrate the solutions provided in this application with specific examples. It should be understood that the following examples are merely specific implementation methods and do not imply an undue limitation on the solutions of this application.

[0092] The reservoir in this example is located in the Baikouquan Formation demonstration area of ​​the Ma131 fault block in the Ma131 well area. This reservoir is a tight conglomerate oil reservoir from Mahu, and a three-dimensional well network was used for development. During fracturing, microseismic monitoring was used to monitor the hydraulic fracture propagation process, and the obtained data consisted of discrete points. After a period of fracturing production, the core well MaJ02 was deployed in the central part of the demonstration area to systematically describe the artificial fractures in the core. The artificial fractures were inverted using the microseismic monitoring data and the characteristic parameters of the artificial fractures in the MaJ02 core, including the following steps:

[0093] Step 1: Collect the geological model of the artificial fracture inversion area, extract the boundary and vertical depth range of the artificial fracture inversion area, load it into the geological modeling software, and perform three-dimensional mesh generation to obtain the three-dimensional mesh model corresponding to the artificial fracture inversion area.

[0094] Step Two: Collect data including the trajectory of the fractured horizontal well, perforation location, microseismic monitoring data points, core well trajectory, and characteristic parameters of artificial fractures from the core samples. Among these, the orientation of the artificial fractures, as shown in the characteristic parameters, can be... Figure 2 As shown. In Figure 2 In this model, the concentric circular grid represents a polar coordinate system; the circumferential range is 0–360° (intervals of 30°), representing the inclination of the artificial crack normal vector; the radial range is 0–90° (intervals of 10°), representing the inclination angle of the artificial crack normal vector. Each dot represents an artificial crack, and contour maps are drawn based on the density of these dots; larger values ​​correspond to higher gray levels, i.e., darker colors. Furthermore, Figure 2 The diagram composed of arrows is a rose diagram, where the arrows indicate the direction of the artificial cracks, and the thickness and length of the arrows reflect the number of artificial cracks. Further, the distribution function of the artificial cracks is determined based on the parameters of the artificial cracks.

[0095] Step three involves loading the data from step two into the geological modeling software from step one, and correlating each microseismic monitoring data point and its corresponding energy level with the 3D mesh model. It also involves correlating the artificial fracture characteristic parameters of the core samples with the well trajectory, and determining a fitting formula to characterize the correlation between the density of artificial fractures and the energy levels of the microseismic monitoring data points. Furthermore, the results of the above correlation are visualized, as shown in the visualization below. Figure 3 As shown. In Figure 3 In the diagram, A represents the well trajectory of the core well, which is represented by a curve; B represents the artificial fracture in the core, which is represented by a disk; C represents the inversion area of ​​the artificial fracture, which is represented by a cuboid block; and D represents the microseismic monitoring data point, which is represented by a dot.

[0096] Step 4: Determine the first and second inversion constraints.

[0097] Step 5: Using the fitting formula and the distribution function of the artificial fractures, and under the constraints of the first and second inversion conditions, the artificial fractures in the inversion area are inverted based on the geological grid modeling method. The inversion results are as follows: Figure 4 As shown, in Figure 4 In the diagram, each irregular polygon E represents the inversion result of artificial fractures, curve A represents the well trajectory of the core well, disk B (partially obscured by irregular polygon E) represents the artificial fractures in the core, and C represents the inversion area of ​​artificial fractures, which has been divided into three-dimensional meshes.

[0098] Based on the same inventive concept, such as Figure 5 As shown, Figure 5 The schematic diagram illustrates a structural block diagram of an artificial crack inversion device according to an embodiment of this application. In one embodiment, an artificial crack inversion device 200 is provided, including an acquisition module 210, a model building module 220, an association determination module 230, a constraint determination module 240, and an inversion module 250, wherein:

[0099] The acquisition module 210 is used to acquire the artificial fracture feature parameters of the core sample from the target area and associate the artificial fracture feature parameters with the well trajectory of the core sample.

[0100] The model building module 220 is used to build a microseismic body grid model corresponding to the target area based on the microseismic monitoring data of the target area.

[0101] The correlation determination module 230 is used to import the correlation results between the artificial fracture feature parameters and the well trajectory of the coring well into the microseismic mesh model to obtain the target mesh, and determine the first correlation between the artificial fracture in the target mesh and the microseismic monitoring data;

[0102] The constraint determination module 240 is used to obtain a second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as a first inversion constraint; and to determine the geometric dimensions of the fractured target fracture based on the microseismic monitoring data, and use the geometric dimensions as a second inversion constraint.

[0103] The inversion module 250 is used to invert the artificial cracks in the target area based on the first correlation relationship and under the constraints of the first inversion constraint and the second inversion constraint.

[0104] In one embodiment, the artificial fracture inversion device 200 further includes a distribution function determination module, which is used to determine the distribution function of the artificial fracture in the core according to the artificial fracture characteristic parameters obtained by the acquisition module 210 from the core sample of the target area.

[0105] In one embodiment, the model building module 220 is used to build a three-dimensional structural geological model corresponding to the target area, and to perform three-dimensional meshing on the three-dimensional structural geological model to obtain a three-dimensional mesh model corresponding to the target area.

[0106] The microseismic monitoring data is correlated with the three-dimensional mesh model to obtain the microseismic body mesh model corresponding to the target area.

[0107] In one embodiment, the microseismic monitoring data includes the energy levels of microseismic monitoring data points; the correlation determination module 230 is used to determine the target grids in the microseismic body grid model that intersect with the well trajectory of the cored well after importing the correlation results of the artificial fracture characteristic parameters and the well trajectory of the cored well into the microseismic body grid model; and to determine the correspondence between the density of artificial fractures and the energy levels of microseismic monitoring data points according to the density of artificial fractures corresponding to each target grid and the energy levels of microseismic monitoring data points corresponding to each target grid, and to use the correspondence as the first correlation relationship.

[0108] In one embodiment, the correlation determination module 230 is used to obtain a second correlation between the density of artificial fractures in the target grid and the wellbore of the fractured horizontal well.

[0109] In one embodiment, the inversion module 250 is used to invert the artificial fractures in the target area based on the first correlation and the distribution function of the artificial fractures in the core, under the constraints of the first inversion constraint and the second inversion constraint.

[0110] The artificial crack inversion device includes a processor and a memory. The aforementioned acquisition module 210, model building module 220, correlation determination module 230, constraint determination module 240, and inversion module 250 are all stored as program units in the memory. The processor executes the aforementioned program modules stored in the memory to implement the corresponding functions.

[0111] The processor contains a core, which retrieves the corresponding program unit from memory. One or more cores can be configured, and by adjusting the core parameters, fast and efficient modeling and computation can be achieved at the entire chip scale.

[0112] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0113] This application provides a machine-readable storage medium storing a program that, when executed by a processor, implements the aforementioned artificial crack inversion method.

[0114] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown. The computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements an artificial crack inversion method. The display screen A04 can be a liquid crystal display or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0115] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0116] In one embodiment, the artificial crack inversion device provided in this application can be implemented as a computer program, which can be implemented in the form of, for example... Figure 6 The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the intelligent scheduling device for this construction task, for example... Figure 5 The diagram shows an acquisition module 210, a model building module 220, a correlation determination module 230, a constraint determination module 240, and an inversion module 250. The computer program comprised of these modules causes the processor to execute the steps in the artificial crack inversion methods of the various embodiments of this application described in this specification.

[0117] Figure 6 The computer equipment shown can be used as follows Figure 5 The execution method of the acquisition module 210, model building module 220, correlation determination module 230, constraint determination module 240 and inversion module 250 in the artificial crack inversion device shown.

[0118] This application provides a device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps:

[0119] The artificial fracture characteristic parameters of the core samples from the target area are obtained, and the artificial fracture characteristic parameters are correlated with the well trajectory of the core sample.

[0120] Based on the microseismic monitoring data of the target area, a microseismic body grid model corresponding to the target area is established;

[0121] The correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well are imported into the microseismic mesh model to obtain the target mesh, and the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data is determined.

[0122] Obtain the second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as the first inversion constraint condition;

[0123] Based on the microseismic monitoring data, the geometric dimensions of the target fracture are determined, and these geometric dimensions are used as the second inversion constraint.

[0124] Based on the first correlation, and under the constraints of the first inversion constraint and the second inversion constraint, the artificial cracks in the target area are inverted.

[0125] In one embodiment, after obtaining the artificial fracture characteristic parameters of the core samples from the target area, the inversion method further includes:

[0126] The distribution function of the artificial fracture in the rock core is determined based on the characteristic parameters of the artificial fracture.

[0127] In one embodiment, establishing a microseismic body grid model corresponding to the target area based on microseismic monitoring data of the target area includes:

[0128] A three-dimensional structural geological model corresponding to the target area is established, and the three-dimensional structural geological model is divided into three-dimensional meshes to obtain a three-dimensional mesh model corresponding to the target area.

[0129] The microseismic monitoring data is correlated with the three-dimensional mesh model to obtain the microseismic body mesh model corresponding to the target area.

[0130] In one embodiment, the microseismic monitoring data includes the energy levels of microseismic monitoring data points;

[0131] The process of importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the cored well into the microseismic mesh model to obtain the target mesh, and determining the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data, includes:

[0132] After importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic mesh model, the target mesh in the microseismic mesh model that intersects with the well trajectory of the coring well is determined;

[0133] Based on the density of artificial fractures corresponding to each target grid and the average energy level of microseismic monitoring data points corresponding to each target grid, the correspondence between the density of artificial fractures and the energy level of microseismic monitoring data points is determined, and the correspondence is taken as the first correlation relationship.

[0134] In one embodiment, obtaining the second correlation between artificial fractures in the target grid and the fractured horizontal wellbore includes: obtaining the second correlation between the density of artificial fractures in the target grid and the fractured horizontal wellbore.

[0135] In one embodiment, the method further includes:

[0136] Based on the first correlation and the distribution function of artificial fractures in the core, artificial fractures in the target area are inverted under the constraints of the first inversion constraint and the second inversion constraint.

[0137] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0143] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0145] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for artificial crack inversion, characterized in that, The method includes: The artificial fracture characteristic parameters of the core samples from the target area are obtained, and the artificial fracture characteristic parameters are correlated with the well trajectory of the core sample. Based on the microseismic monitoring data of the target area, a microseismic body grid model corresponding to the target area is established; The correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well are imported into the microseismic mesh model to obtain the target mesh, and the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data is determined. Obtain the second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as the first inversion constraint condition; Based on the microseismic monitoring data, the geometric dimensions of the target fracture are determined, and these geometric dimensions are used as the second inversion constraint. Based on the first correlation, and under the constraints of the first inversion constraint and the second inversion constraint, the artificial cracks in the target area are inverted.

2. The artificial crack inversion method according to claim 1, characterized in that, After obtaining the artificial fracture characteristic parameters of the core samples from the target area, the inversion method further includes: The distribution function of the artificial fracture in the rock core is determined based on the characteristic parameters of the artificial fracture.

3. The artificial crack inversion method according to claim 1, characterized in that, The step of establishing a microseismic body grid model corresponding to the target area based on the microseismic monitoring data of the target area includes: A three-dimensional structural geological model corresponding to the target area is established, and the three-dimensional structural geological model is divided into three-dimensional meshes to obtain a three-dimensional mesh model corresponding to the target area. The microseismic monitoring data is correlated with the three-dimensional mesh model to obtain the microseismic body mesh model corresponding to the target area.

4. The artificial crack inversion method according to claim 1, characterized in that, The microseismic monitoring data includes the energy levels of microseismic monitoring data points; the process of importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic body mesh model to obtain the target mesh, and determining the first correlation between the artificial fractures in the target mesh and the microseismic monitoring data, includes: After importing the correlation results between the artificial fracture characteristic parameters and the well trajectory of the coring well into the microseismic mesh model, the target mesh in the microseismic mesh model that intersects with the well trajectory of the coring well is determined. Based on the density of artificial fractures corresponding to each target grid and the energy level of microseismic monitoring data points corresponding to each target grid, the correspondence between the density of artificial fractures and the energy level of microseismic monitoring data points is determined, and the correspondence is taken as the first correlation relationship.

5. The artificial crack inversion method according to claim 1, characterized in that, The step of obtaining the second correlation between artificial fractures in the target grid and the fractured horizontal wellbore includes: obtaining the second correlation between the density of artificial fractures in the target grid and the fractured horizontal wellbore.

6. The artificial crack inversion method according to claim 2, characterized in that, The method further includes: Based on the first correlation and the distribution function of artificial fractures in the core, artificial fractures in the target area are inverted under the constraints of the first inversion constraint and the second inversion constraint.

7. An artificial crack inversion device, characterized in that, include: The acquisition module is used to acquire the artificial fracture feature parameters of the core samples from the target area and associate the artificial fracture feature parameters with the well trajectory of the core sample. The model building module is used to build a microseismic body grid model corresponding to the target area based on the microseismic monitoring data of the target area. The correlation determination module is used to import the correlation results between the artificial fracture feature parameters and the well trajectory of the coring well into the microseismic mesh model to obtain the target mesh, and determine the first correlation between the artificial fracture in the target mesh and the microseismic monitoring data; The constraint determination module is used to obtain the second correlation between artificial fractures in the target grid and the wellbore of the fractured horizontal well, and use the second correlation as the first inversion constraint. And, based on the microseismic monitoring data, to determine the geometric dimensions of the target fracture for hydraulic fracturing, using the geometric dimensions as a second inversion constraint condition; The inversion module is used to invert the artificial cracks in the target area based on the first correlation relationship and under the constraints of the first inversion constraint and the second inversion constraint.

8. The artificial crack inversion device according to claim 7, characterized in that, The microseismic monitoring data includes the energy levels of the microseismic monitoring data points; The correlation determination module is used to determine the target grids in the microseismic body grid model that intersect with the well trajectory of the cored well after importing the correlation results of the artificial fracture feature parameters and the well trajectory of the cored well into the microseismic body grid model; and to determine the correspondence between the density of artificial fractures and the energy level of microseismic monitoring data points corresponding to each target grid according to the density of artificial fractures and the energy level of microseismic monitoring data points corresponding to each target grid, and to use the correspondence as the first correlation.

9. A processor, characterized in that, It is configured to perform the artificial crack inversion method according to any one of claims 1 to 6.

10. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the artificial fracture inversion method according to any one of claims 1 to 6.

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