A reservoir carbon dioxide migration prediction method, device, equipment and medium

Abstract

This application provides a method, apparatus, equipment, and medium for predicting carbon dioxide migration in reservoirs, relating to the field of carbon dioxide sequestration technology. The method includes: acquiring full-waveform data of various microseismic events generated during the injection of carbon dioxide into the sequestration reservoir; performing source mechanics analysis on the full-waveform data of each microseismic event to obtain analytical results; processing the analytical results to obtain a spatiotemporal trajectory tree of fracture propagation in the sequestration reservoir and dynamic fracture parameters; and predicting the migration characteristics of carbon dioxide in the sequestration reservoir based on the spatiotemporal trajectory tree and dynamic fracture parameters. The reservoir carbon dioxide migration prediction method of this application can significantly improve the precision and reliability of reservoir carbon dioxide migration characteristic prediction.

Description

Technical Field

[0001] This application relates to the field of carbon dioxide sequestration technology, and more specifically, to a method, apparatus, equipment, and medium for predicting carbon dioxide migration in reservoirs. Background Technology

[0002] Against the backdrop of the dual-carbon strategy, carbon dioxide geological sequestration is one of the core technological pathways to achieve carbon emission reduction. By injecting carbon dioxide into deep storage formations, long-term stable sequestration of greenhouse gases can be achieved. The injection process continuously disturbs the original stress field of the formation, inducing microseismic activity in the surrounding rocks. The initiation, expansion, and connectivity of formation fractures directly determine the underground migration path, diffusion range, and sequestration safety of carbon dioxide. Therefore, accurate monitoring of fracture dynamic evolution and fluid migration prediction are crucial to ensuring the stable operation of carbon sequestration projects.

[0003] Existing methods for predicting carbon dioxide migration mostly rely on static geological parameters and macroscopic monitoring data, resulting in insufficient accuracy in predicting reservoir carbon dioxide migration characteristics and failing to provide reliable technical support for risk assessment and long-term management of storage sites. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, device, and medium for predicting carbon dioxide migration in reservoirs, which solves the above-mentioned problems existing in the prior art and can significantly improve the precision and reliability of predicting carbon dioxide migration characteristics in reservoirs.

[0005] Firstly, a method for predicting carbon dioxide transport in reservoirs is provided, which may include: Acquire full waveform data of each microseismic event generated during the injection of carbon dioxide into the sealed storage layer; Source mechanics analysis was performed on the full waveform data of each microseismic event to obtain the analytical results; The analysis results are processed to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; Based on the spatiotemporal trajectory tree and crack dynamic rupture parameters, the migration characteristics of carbon dioxide in the sealed storage layer are predicted.

[0006] In an optional implementation, source mechanics analysis is performed on the full waveform data of each microseismic event to obtain the analytical results, including: For any microseismic event, the source location, occurrence time, and moment tensor of the microseismic event are determined based on the full waveform data of the microseismic event and the observation attribute parameters of the configured sealed storage layer. The moment tensor of the microseismic event is decomposed into eigenvalues ​​to obtain the moment tensor source parameters of the microseismic event. Based on the moment tensor source parameters of each microseismic event, as well as the corresponding source location, time of occurrence, and moment tensor, source mechanics analysis results are generated.

[0007] In an optional implementation, based on the moment tensor source parameters of each microseismic event and the corresponding source location, time of origin, and moment tensor, source mechanics analytical results are generated, including: Microseismic events corresponding to the moment tensor source parameters that meet the configured filtering rules are identified as target microseismic events. For any target microseismic event, the moment tensor of the target microseismic event is decomposed into components and inverted to obtain the geometric parameters of the rupture nodal surface of the target microseismic event. Based on the geometric parameters of the rupture nodal surface, the location of the hypocenter, the time of occurrence, and the moment tensor hypocenter parameters of each target microseismic event, the source mechanics analysis results are generated.

[0008] In an optional implementation, the parsing result is processed to obtain a spatiotemporal trajectory tree of the crack propagation in the sealed storage layer, including: The target microseismic events are sorted according to the order of their occurrence time to obtain the target microseismic event sequence; For any target microseismic event in the target microseismic event sequence, the target microseismic event is taken as the first microseismic event, and the other target microseismic events in the target microseismic event sequence other than the first microseismic event are taken as the second microseismic event. The second micro-earthquake event, whose occurrence time is later than that of the first micro-earthquake event, has the smallest time difference between the occurrence time of the first micro-earthquake event, and whose spatial distance from the first micro-earthquake event is not greater than the configured spatial distance threshold, is regarded as the successor event corresponding to the first micro-earthquake event. Based on the source location and occurrence time of each target microseismic event and its corresponding subsequent events, a spatiotemporal trajectory tree for the dynamic propagation of cracks is constructed.

[0009] In an optional implementation, a spatiotemporal trajectory tree for dynamic crack propagation is constructed based on the source location and occurrence time of each target microseismic event and its corresponding subsequent events, including: Based on the focal location and occurrence time of each target microseismic event and its corresponding subsequent events, calculate the rupture propagation velocity and rupture propagation direction angle of each target microseismic event and its corresponding subsequent events. Taking any target microseismic event as a node, the hypocenter location and occurrence time of the target microseismic event are used as the attributes of the corresponding node; each target microseismic event and its corresponding subsequent event are connected by directed edges, and the rupture propagation velocity and rupture propagation direction angle are used as the attributes of the corresponding directed edges to generate a spatiotemporal trajectory tree for dynamic crack propagation.

[0010] In an optional implementation, the method includes: processing the parsed results to obtain dynamic fracture parameters for crack propagation in the storage layer, including: Based on the geometric parameters of the rupture nodal surface of each target microseismic event and the principal stress direction of the configured storage layer, the dominant azimuth and dip angle of the fractures for each target microseismic event are determined. Based on the moment tensor source parameters of each target microseismic event, the crack type of each target microseismic event is determined; The dominant azimuth, dominant dip, and crack type of each target microseismic event are used as the dynamic rupture parameters of the cracks in each target microseismic event.

[0011] In an optional implementation, the transport characteristics of carbon dioxide in the sealed reservoir are predicted based on the spatiotemporal trajectory tree and crack dynamic rupture parameters, including: Using the configured carbon dioxide injection points as the source points for carbon dioxide propagation, the spatiotemporal trajectory tree is transformed into a topology map of carbon dioxide transport channels. Based on the crack type and crack propagation speed of each node in the carbon dioxide transport channel topology diagram, determine the carbon dioxide propagation speed of each directed edge in the carbon dioxide transport channel topology diagram; Based on the carbon dioxide propagation speed of each directed edge, the topology of the carbon dioxide transport channel is iteratively deduced and predicted to obtain the predicted arrival time of carbon dioxide for each node. Based on the predicted arrival time of carbon dioxide for each node, a carbon dioxide transport time series isosurface is generated as a transport feature of carbon dioxide in the sealed storage layer.

[0012] Secondly, a reservoir carbon dioxide migration prediction device is provided, the device comprising: The acquisition unit is used to acquire full waveform data of each microseismic event generated during the injection of carbon dioxide into the sealed storage layer; The analytical unit is used to perform source mechanics analysis on the full waveform data of each microseismic event and obtain analytical results; The extraction unit is used to process the analysis results to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; The prediction unit is used to predict the migration characteristics of carbon dioxide in the sealed storage layer based on the spatiotemporal trajectory tree and crack dynamic rupture parameters.

[0013] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the method steps in the first aspect described above.

[0014] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements any of the method steps of the first aspect described above.

[0015] This application utilizes full-waveform data from microseismic events to conduct source mechanics analysis, fully exploring the detailed information on formation rupture contained in microseismic signals, thus improving the comprehensiveness and accuracy of source response analysis. Through source sequence reconstruction and rupture feature extraction, a precise spatiotemporal trajectory tree of fracture propagation is constructed, and dynamic rupture parameters are obtained, completely reconstructing the entire evolutionary process of reservoir fracture development and dynamic propagation under gas injection disturbance. Based on the correlation analysis of fracture spatiotemporal evolution laws, the carbon dioxide migration mechanism is effectively overcome, overcoming the shortcomings of traditional prediction methods that rely on static geological data and have weak dynamic characterization capabilities. This significantly improves the precision and reliability of reservoir carbon dioxide migration characteristic prediction, providing precise data support and technical basis for the stability assessment of carbon dioxide geological storage sites, disaster risk early warning, and long-term safety management of storage projects. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 An architecture diagram of a reservoir carbon dioxide migration prediction system provided in this application embodiment; Figure 2 A schematic flowchart of a reservoir carbon dioxide migration prediction method provided for an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a reservoir carbon dioxide migration prediction device provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] The reservoir carbon dioxide migration prediction method provided in this application embodiment can be applied to... Figure 1 In the system architecture shown, such as Figure 1 As shown, the system may include a server and a microseismic monitoring component. The server may be a physical server, a server cluster consisting of multiple physical servers, or a distributed system. It may also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] In one embodiment, the microseismic monitoring component includes a three-component seismic detector array and seismic detectors; the three-component seismic detector array is arranged in several monitoring wells within a 1-3 km radius around the carbon dioxide injection well, and is a multi-level three-component seismic detector array (e.g., 10-30 meters between each level, for a total of 10-50 levels); it can be a piezoelectric accelerometer or a fiber Bragg grating accelerometer; multiple seismic detectors are deployed on the surface in a grid pattern (e.g., 200 m × 200 m).

[0021] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0022] Figure 2 This is a flowchart illustrating a reservoir carbon dioxide migration prediction method provided in an embodiment of this application. Figure 2 As shown, the method may include: Step S210: Obtain full waveform data of each microseismic event generated during the process of injecting carbon dioxide into the sealed storage layer.

[0023] In practice, initial full waveform data for each initial microseismic event is continuously acquired from the start of carbon dioxide injection into the sealed storage layer until the injection ends, with a sampling rate typically between 1000-2000 Hz. The full waveform data can include: the time sequence of P-wave and S-wave waveforms from each detector, the initial arrival times of the P-wave and S-wave, and the initial motion polarity of the P-wave. The initial motion polarity of the P-wave is the sign direction of the first amplitude peak when the P-wave reaches the detector, and can include positive and negative polarities. When positive, the first vibration direction is away from the source (appearing as upward motion in the vertical component, recorded as a positive amplitude); when negative, the first vibration direction is towards the source (appearing as downward motion in the vertical component, recorded as a negative amplitude). The initial full waveform data of each initial microseismic event are filtered and denoised using the configured baseline microseismic background noise to obtain the full waveform data of each microseismic event. Specifically, the baseline microseismic background noise is acquired; the root mean square amplitude and power spectral density of the baseline microseismic background noise are calculated; the root mean square amplitude of the full waveform data of each initial microseismic event is calculated; the signal-to-noise ratio (SNR) is calculated based on the root mean square amplitude of the baseline microseismic background noise and the root mean square amplitude of the full waveform data of each initial microseismic event; if the SNR is not greater than a preset SNR threshold, the initial microseismic event is discarded and not proceeded to subsequent processing; if the SNR is greater than the preset SNR threshold, the initial microseismic event is identified as a microseismic event, and adaptive filtering or noise template subtraction is performed on the initial full waveform data of each microseismic event based on the baseline noise to obtain the denoised waveform time sequence, which is the full waveform data of each microseismic event.

[0024] In one embodiment of this application, the method for obtaining baseline microseismic background noise may include: Before CO2 injection begins, ground or well vibration data are continuously collected for at least 24-72 hours to obtain baseline microseismic background noise.

[0025] Step S220: Perform source mechanics analysis on the full waveform data of each microseismic event to obtain the analysis results.

[0026] In practice, for any microseismic event, the source location, origin time, and moment tensor of the microseismic event are determined based on the full waveform data of the microseismic event and the configured observation attribute parameters of the sealed reservoir. The observation attribute parameters may include the spatial coordinates of each detector used to acquire the corresponding full waveform data, as well as the reservoir P-wave velocity, S-wave velocity, and density of the sealed reservoir. Specifically, the P-wave first arrival time and S-wave first arrival time of each detector are extracted from the full waveform data of the microseismic event. Based on the extracted P-wave first arrival time, S-wave first arrival time, detector spatial coordinates, reservoir P-wave velocity, and reservoir S-wave velocity, the P-wave travel time equation and S-wave travel time equation for each detector are constructed. The initial source location and initial occurrence time of the microseismic event are determined based on the P-wave and S-wave travel time equations of each geophone. Specifically, the P-wave and S-wave travel time equations of each geophone are simultaneously solved to generate a set of travel time equations. Solving the set of travel time equations yields the source location and occurrence time values ​​that minimize the sum of squared residuals of all equations, which are used as the initial source location and initial occurrence time of the microseismic event. Using the initial source location, reservoir velocity and density, as well as the P-wave and S-wave waveform time series and geophone spatial coordinates, the source location, occurrence time, and moment tensor of the microseismic event are optimized through three-dimensional grid search and moment tensor inversion. The moment tensor of the microseismic event is decomposed into eigenvalues ​​to obtain the moment tensor source parameters. These parameters include: the bicouple component, the isotropic component, and their ratios and percentages. Specifically, the moment tensor of the microseismic event is decomposed into eigenvalues, yielding three eigenvalues ​​ordered from largest to smallest: the first eigenvalue, the second eigenvalue, and the third eigenvalue. The isotropic component is calculated based on the arithmetic mean of the three eigenvalues, and its sign is determined by the sign of the value (positive indicates volume expansion, negative indicates volume contraction). The absolute value of the first eigenvalue is subtracted from the absolute value of the third eigenvalue, and then divided by two to calculate... The values ​​of the bicouple components; the values ​​of the compensated linear vector dipole components are calculated by subtracting the energy of the bicouple and isotropic components from the total energy; the percentage of the bicouple component in the total energy is calculated by dividing the absolute value of the bicouple component by the sum of the absolute values ​​of the bicouple, isotropic, and compensated linear vector dipole components, and then multiplying by 100%; the percentage of the isotropic component in the total energy is calculated by dividing the absolute value of the isotropic component by the sum of the absolute values ​​of the three components, and then multiplying by 100%; the ratio of the bicouple component to the isotropic component is calculated by dividing the absolute value of the bicouple component by the absolute value of the isotropic component and adding a very small positive number. Microseismic events corresponding to moment tensor source parameters that meet the configured screening rules are identified as target microseismic events. The screening rules may include a two-couple component percentage threshold and an isotropic component percentage threshold. The two-couple component percentage threshold can be 70%, and the isotropic component percentage threshold can be 20%. Specifically, if the percentage of the two-couple component in any microseismic event is greater than the two-couple component percentage threshold and the percentage of the isotropic component is less than the isotropic component percentage threshold, then the microseismic event is identified as a target microseismic event; otherwise, it is marked as a non-shear rupture event and is not used in subsequent steps. The target microseismic event is a microseismic event dominated by pure shear rupture.

[0027] The moment tensor of the target microseismic event is decomposed into components and inverted to obtain the geometric parameters of the rupture nodal surface. These parameters include the strike, dip, and slip angle of the two nodal surfaces. Specifically, for any target microseismic event, the double-couple component is separated from the moment tensor, yielding its moment tensor. The moment tensor of the double-couple component is then decomposed into eigenvectors to obtain the fault plane normal vector and the slip direction vector. Based on these vectors, two nodal surface solutions are calculated, each containing three parameters: strike, dip, and slip angle. The strike is the azimuth angle of the intersection line between the nodal surface and the horizontal plane, calculated from the horizontal component of the nodal surface normal vector. The dip is the angle between the nodal surface and the horizontal plane, calculated from the vertical component of the nodal surface normal vector. The slip angle is the pointing angle of the slip direction on the nodal surface, calculated from the projection of the slip direction vector onto the nodal surface. Based on the geometric parameters of the rupture nodal surface, the location of the hypocenter, the time of occurrence, and the moment tensor hypocenter parameters of each target microseismic event, the source mechanics analysis results are generated.

[0028] In one embodiment of this application, using the initial source location, reservoir velocity and density, and the time series sequences of P-wave and S-wave waveforms of each detector, as well as the spatial coordinates of the detectors, a three-dimensional grid search and moment tensor inversion are performed, including: Taking the initial hypocenter location as the center, an equally spaced grid is established in three-dimensional space. The grid range covers the initial hypocenter location and extends a predetermined distance in the east-west, north-south, and vertical directions, with a predetermined grid step size. For each grid node, the three-dimensional coordinates of that node are used as a candidate hypocenter location. Using the known reservoir P-wave velocity, S-wave velocity, density values, and the spatial coordinates of each detector, the Green's function value of each detector at the candidate hypocenter location is calculated. The P-wave and S-wave waveform time series of each detector are discretized according to time sampling points to obtain the observed waveform vector. For the candidate hypocenter location... Select the hypocenter location and construct the observation equation using the Green's function value and the observed waveform vector: the observed waveform vector is equal to the Green's function matrix multiplied by six unknown moment tensor components; solve the observation equation using the least squares method to obtain the six moment tensor components corresponding to the candidate hypocenter location; synthesize the theoretical waveform based on the six moment tensor components and calculate the fitting residual between the theoretical waveform and the observed waveform; after traversing all grid nodes, select the grid node with the smallest fitting residual as the final hypocenter location, use the moment tensor component of this node as the moment tensor of the microseismic event, and determine the time of occurrence based on the waveform time shift of this node.

[0029] Step S230: Process the analysis results to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic rupture parameters of the crack.

[0030] The dynamic fracture parameters of the crack may include: the dominant azimuth angle of crack propagation, the dominant dip angle of crack propagation, and the crack type; the crack type may include: tensile initiation and shear slip.

[0031] In practice, the target microseismic events are sorted according to the order of their occurrence time to obtain the target microseismic event sequence. Based on the occurrence time and focal location of each target microseismic event in the target microseismic event sequence, the successor events of each target microseismic event are determined. Specifically, for any target microseismic event in the target microseismic event sequence, this target microseismic event is designated as the first microseismic event; other target microseismic events with occurrence times greater than the occurrence time of the first microseismic event are designated as second microseismic events; the spatial distance from the focal location of the first microseismic event to the focal location of each second microseismic event is calculated; if the spatial distance corresponding to any second microseismic event is not greater than the configured spatial distance threshold, then the corresponding second microseismic event is determined as a candidate successor event; the candidate successor event with the smallest difference between its occurrence time and the occurrence time of the first microseismic event is selected as the successor event of the first microseismic event; if there are multiple candidate successor events with the same and smallest time difference from the first microseismic event, then all of these candidate successor events are selected as the successor events of the first microseismic event, forming multiple branches. Based on the focal locations and occurrence times of each target microseismic event and its corresponding subsequent events, the rupture propagation velocity and rupture propagation direction angle of each target microseismic event and its corresponding subsequent events are calculated. Specifically, for each determined directed edge from the first microseismic event to the subsequent event, the focal locations of the first and subsequent events are obtained, and the spatial distance between the two points is calculated. The occurrence times of the first and subsequent events are obtained, and the time difference between the occurrence times of the subsequent and first events is calculated. The spatial distance is divided by the time difference to obtain the rupture propagation velocity of the directed edge. The source locations of a microseismic event and subsequent events are determined. The source location of the subsequent event is subtracted from the source location of the first microseismic event by the differences in the east-west, north-south, and vertical directions. Based on the east-west and north-south differences, the azimuth angle is calculated using the arctangent function, representing the angle from true north to the rupture propagation direction. Based on the vertical difference and the horizontal distance (the square root of the sum of the squares of the east-west and north-south differences), the dip angle is calculated using the arctangent function, representing the angle between the rupture propagation direction and the horizontal plane (positive values ​​indicate upward, negative values ​​indicate downward). Each target microseismic event is considered a node, with attributes including the epicenter location and the time of origin. Each directed edge connecting the first microseismic event to its successor is considered a connection, with attributes including the calculated rupture propagation velocity and orientation angle (azimuth and dip). All nodes and edges are combined into a tree structure, with the event with the smallest origin time serving as the root node. Each node points to its successor event, forming a spatiotemporal trajectory tree for dynamic crack propagation.

[0032] Based on the geometric parameters of the rupture surfaces of each target microseismic event and the principal stress directions of the configured storage layer, the dominant azimuth and dip angles of the fractures for each target microseismic event are determined. Specifically, for any target microseismic event, the directions of the maximum and minimum principal stresses in the region where the configured storage layer is located are obtained. For any surface of the target microseismic event, the slip direction vector on the surface is calculated based on the strike, dip, and slip angle of the surface. The direction of the maximum shear stress is calculated based on the directions of the maximum and minimum principal stresses. The angles between the slip direction vectors of the two surfaces and the direction of the maximum shear stress are calculated respectively. The surface with the smaller angle is selected as the actual rupture surface. The strike value of the actual rupture surface is used as the dominant azimuth of fracture propagation for this event, and the dip value of the actual rupture surface is used as the dominant dip angle of fracture propagation for this target microseismic event.

[0033] Based on the moment tensor source parameters of the target microseismic event, the crack type of the target microseismic event is determined; specifically, based on the sign of the isotropic components of the target microseismic event; if the sign of the isotropic components is positive, the crack type of the event is identified as tensile opening; if the sign of the isotropic components is negative or close to zero, the crack type of the event is identified as shear slip.

[0034] Step S240: Based on the spatiotemporal trajectory tree and crack dynamic rupture parameters, predict the migration characteristics of carbon dioxide in the sealed storage layer.

[0035] In practice, the spatiotemporal trajectory tree is transformed into a topological graph of carbon dioxide transport channels. Specifically, the spatiotemporal trajectory tree is used as a graph structure for priority channels of carbon dioxide, where nodes represent key locations in the fracture network and directed edges represent possible paths for carbon dioxide propagation. The three-dimensional coordinates of the injection well perforation point are set as the source point for carbon dioxide propagation. If the source point is not in the nodes of the trajectory tree, it is added to the graph as a new node, and the source point is connected to the earliest event node in the trajectory tree with a directed edge. The initial direction angle of this edge is calculated based on the spatial coordinate difference between the source point and the earliest event node. Based on the fracture type and rupture propagation velocity of each node in the carbon dioxide migration channel topology diagram, the carbon dioxide propagation velocity of each directed edge in the topology diagram is determined. Specifically, for each directed edge in the carbon dioxide migration channel topology diagram, the first microseismic event node and subsequent event node corresponding to that edge are obtained. If the fracture type is tensile opening, the fracture aperture increment is estimated using the positive amplitude of the isotropic component (the larger the amplitude, the larger the aperture increment). Then, based on the configured CO2 viscosity value and reservoir pressure gradient value, the theoretical propagation velocity of the carbon dioxide front is calculated using the cubic law. If the fracture type is shear slip, the carbon dioxide propagation velocity is approximately equal to the rupture propagation velocity value multiplied by a preset velocity scaling factor (this factor ranges from 1.0 to 1.5, indicating that the carbon dioxide front is slightly faster than the rupture front). The calculated carbon dioxide propagation velocity value is assigned to the directed edge. Specifically, if the fracture type is tensile opening, the carbon dioxide propagation velocity calculation formula is as follows: ;in, Indicates the speed of carbon dioxide propagation; This represents the dynamic crack aperture, which is the sum of the configured initial crack aperture and the aperture increment obtained by converting the positive amplitude of the isotropic component. The CO2 viscosity value is defined by the user based on the sealing layer. This represents the reservoir pressure gradient value, which is defined by the user based on the sealed reservoir. This represents the crack porosity, with a value of 1.0 for fresh cracks without filling. If the crack type is shear slip, the carbon dioxide propagation velocity is calculated using the following formula: ; The speed proportional coefficient is shown in the table. Indicates the speed of rupture propagation; Based on the carbon dioxide propagation speed of each directed edge, the shortest path time propagation algorithm is used to iteratively deduce and predict the topology of the carbon dioxide transport channel to obtain the predicted arrival time of carbon dioxide for each node. Specifically, the arrival time of the source node is set to zero; the arrival times of all other nodes are initialized to infinity; a priority queue is maintained, where each element contains a node number and its current arrival time, sorted in ascending order of arrival time; the source node is added to the priority queue; the following operations are repeated until the queue is empty: the node with the smallest arrival time is taken from the priority queue and designated as the current node; all subsequent directed edges of the current node are traversed, and the time increment required for carbon dioxide to propagate from the previous node to the subsequent node is calculated based on the length of the directed edge (i.e., the spatial distance between two event nodes) and the carbon dioxide propagation speed value; the arrival time of the current node is added to the time increment to obtain the arrival time of the candidate successor node; if the candidate arrival time is less than the current recorded arrival time of the successor node, the arrival time of the successor node is updated to the candidate value, and the successor node is added to the priority queue. Based on any location point in the sealed storage layer, the predicted arrival time of carbon dioxide at that location point is determined according to its spatial coordinates, the topology of the carbon dioxide migration channel, and the predicted arrival time of carbon dioxide corresponding to each node. Specifically, for any specified spatial location in the sealed storage layer (e.g., the coordinates of a monitoring well), if the location is not on a node, linear interpolation is performed based on the nearest edge in the graph structure to obtain the predicted arrival time of that location. The arrival times of all nodes are interpolated in three-dimensional space to draw an isochronous surface of the carbon dioxide front, showing the migration position of the front at different times. Based on the predicted arrival time of carbon dioxide at each node and the predicted arrival time of carbon dioxide at different locations in the sealed storage layer, a carbon dioxide transport time series isosurface is generated to obtain the transport characteristics of carbon dioxide in the sealed storage layer.

[0036] In one embodiment of this application, after constructing the spatiotemporal trajectory tree, the method may further include: For each path in the spatiotemporal trajectory tree consisting of continuous directed edges, the number of microseismic events on the path, the median spatial distance between adjacent microseismic events, and the median time interval are counted. The variance of the rupture propagation velocity of the path is calculated. If the velocity variance exceeds a preset velocity threshold, or the number of events on the path is lower than a preset minimum number of events, the path is marked as a low-reliability branch and assigned a lower confidence level in subsequent carbon dioxide migration predictions or removed from the topology graph.

[0037] In one embodiment of this application, the method may further include: After determining the carbon dioxide propagation velocity of each directed edge, the fluctuation amplitude and frequency of the injection pressure are calculated in a fixed time window (e.g., every 10 minutes). If the pressure fluctuation amplitude exceeds a preset threshold, it is determined that a new crack has opened or the aperture of an existing crack has changed. Based on the statistical relationship between the pressure fluctuation amplitude and the positive amplitude of the isotropic component, the crack aperture increment conversion coefficient of the tensile opening crack segment is updated online, and the carbon dioxide propagation velocity of the corresponding directed edge is recalculated. The updated velocity field is used for subsequent iterative deduction.

[0038] Corresponding to the above method, embodiments of this application also provide a reservoir carbon dioxide migration prediction device, such as... Figure 3 As shown, the device includes: Acquisition unit 310 is used to acquire full waveform data of each microseismic event generated during the process of injecting carbon dioxide into the sealed storage layer; The analytical unit 320 is used to perform source mechanics analysis on the full waveform data of each microseismic event to obtain analytical results; Extraction unit 330 is used to process the analysis results to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; Prediction unit 340 is used to predict the migration characteristics of carbon dioxide in the sealed storage layer based on the spatiotemporal trajectory tree and crack dynamic rupture parameters.

[0039] The functions of each functional unit of the reservoir carbon dioxide migration prediction device provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the reservoir carbon dioxide migration prediction device provided in the embodiments of this application will not be repeated here.

[0040] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440.

[0041] Memory 430 is used to store computer programs; When the processor 410 executes the program stored in the memory 430, it performs the following steps: Acquire full waveform data of each microseismic event generated during the injection of carbon dioxide into the sealed storage layer; Source mechanics analysis was performed on the full waveform data of each microseismic event to obtain the analytical results; The analysis results are processed to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; Based on the spatiotemporal trajectory tree and crack dynamic rupture parameters, the migration characteristics of carbon dioxide in the sealed storage layer are predicted.

[0042] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0043] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0044] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0045] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0046] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 2 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0047] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform any of the reservoir carbon dioxide migration prediction methods described in the above embodiments.

[0048] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the reservoir carbon dioxide migration prediction methods in the above embodiments.

[0049] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented 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.

[0050] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. 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 illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] 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.

[0052] 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.

[0053] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0054] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of this application and its equivalents, then these modifications and variations are also intended to be included in the embodiments of this application.

Claims

1. A method for predicting carbon dioxide migration in reservoirs, characterized in that the method... include: Acquire full waveform data of each microseismic event generated during the injection of carbon dioxide into the sealed storage layer; Source mechanics analysis was performed on the full waveform data of each microseismic event to obtain the analytical results; The analysis results are processed to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; Based on the spatiotemporal trajectory tree and crack dynamic rupture parameters, the migration characteristics of carbon dioxide in the sealed storage layer are predicted.

2. The method as described in claim 1, characterized in that, Source mechanics analysis was performed on the full waveform data of each microseismic event, and the analytical results were obtained, including: For any microseismic event, the source location, occurrence time, and moment tensor of the microseismic event are determined based on the full waveform data of the microseismic event and the observation attribute parameters of the configured sealed storage layer. The moment tensor of the microseismic event is decomposed into eigenvalues ​​to obtain the moment tensor source parameters of the microseismic event. Based on the moment tensor source parameters of each microseismic event, as well as the corresponding source location, time of occurrence, and moment tensor, source mechanics analysis results are generated.

3. The method as described in claim 2, characterized in that, Based on the moment tensor source parameters of each microseismic event, as well as the corresponding source location, time of occurrence, and moment tensor, source mechanics analytical results are generated, including: Microseismic events corresponding to the moment tensor source parameters that meet the configured filtering rules are identified as target microseismic events. For any target microseismic event, the moment tensor of the target microseismic event is decomposed into components and inverted to obtain the geometric parameters of the rupture nodal surface of the target microseismic event. Based on the geometric parameters of the rupture nodal surface, the location of the hypocenter, the time of occurrence, and the moment tensor hypocenter parameters of each target microseismic event, the source mechanics analysis results are generated.

4. The method as described in claim 2, characterized in that, The analysis results are processed to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer, including: The target microseismic events are sorted according to the order of their occurrence time to obtain the target microseismic event sequence; For any target microseismic event in the target microseismic event sequence, the target microseismic event is taken as the first microseismic event, and the other target microseismic events in the target microseismic event sequence other than the first microseismic event are taken as the second microseismic event. The second micro-earthquake event, whose occurrence time is later than that of the first micro-earthquake event, has the smallest time difference between the occurrence time of the first micro-earthquake event, and whose spatial distance from the first micro-earthquake event is not greater than the configured spatial distance threshold, is regarded as the successor event corresponding to the first micro-earthquake event. Based on the source location and occurrence time of each target microseismic event and its corresponding subsequent events, a spatiotemporal trajectory tree for the dynamic propagation of cracks is constructed.

5. The method as described in claim 4, characterized in that, Based on the focal locations and occurrence times of each target microseismic event and its corresponding subsequent events, a spatiotemporal trajectory tree for crack dynamic propagation is constructed, including: Based on the focal location and occurrence time of each target microseismic event and its corresponding subsequent events, calculate the rupture propagation velocity and rupture propagation direction angle of each target microseismic event and its corresponding subsequent events. Taking any target microseismic event as a node, the hypocenter location and occurrence time of the target microseismic event are used as the attributes of the corresponding node; each target microseismic event and its corresponding subsequent event are connected by directed edges, and the rupture propagation velocity and rupture propagation direction angle are used as the attributes of the corresponding directed edges to generate a spatiotemporal trajectory tree for dynamic crack propagation.

6. The method as described in claim 3, characterized in that, include: The analysis results are processed to obtain the dynamic fracture parameters of the storage layer crack propagation, including: Based on the geometric parameters of the rupture nodal surface of each target microseismic event and the principal stress direction of the configured storage layer, the dominant azimuth and dip angle of the fractures for each target microseismic event are determined. Based on the moment tensor source parameters of each target microseismic event, the crack type of each target microseismic event is determined; The dominant azimuth, dominant dip, and crack type of each target microseismic event are used as the dynamic rupture parameters of the cracks in each target microseismic event.

7. The method as described in claim 6, characterized in that, Based on spatiotemporal trajectory trees and crack dynamic rupture parameters, the transport characteristics of carbon dioxide in the sealed reservoir are predicted, including: Using the configured carbon dioxide injection points as the source points for carbon dioxide propagation, the spatiotemporal trajectory tree is transformed into a topology map of carbon dioxide transport channels. Based on the crack type and crack propagation speed of each node in the carbon dioxide transport channel topology diagram, determine the carbon dioxide propagation speed of each directed edge in the carbon dioxide transport channel topology diagram; Based on the carbon dioxide propagation speed of each directed edge, the topology of the carbon dioxide transport channel is iteratively deduced and predicted to obtain the predicted arrival time of carbon dioxide for each node. Based on the predicted arrival time of carbon dioxide for each node, a carbon dioxide transport time series isosurface is generated as a transport feature of carbon dioxide in the sealed storage layer.

8. A reservoir carbon dioxide migration prediction device, characterized in that, The device includes: The acquisition unit is used to acquire full waveform data of each microseismic event generated during the injection of carbon dioxide into the sealed storage layer; The analytical unit is used to perform source mechanics analysis on the full waveform data of each microseismic event and obtain analytical results; The extraction unit is used to process the analysis results to obtain the spatiotemporal trajectory tree of the crack propagation in the sealed storage layer and the dynamic crack rupture parameters; The prediction unit is used to predict the migration characteristics of carbon dioxide in the sealed storage layer based on the spatiotemporal trajectory tree and crack dynamic rupture parameters.

9. An electronic device, characterized in that, Electronic devices include a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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