Fracturing fracture connectivity evaluation method and system based on microseism

Abstract

The invention discloses a fracturing fracture connectivity evaluation method and system based on microseism, and belongs to the technical field of oil and gas field development, and the method comprises the following steps: S1, collecting microseism event data; s2, correcting the space coordinates of the microseism event; s3, clustering, wherein a cluster corresponds to all microseism events of one crack; s4, fitting a crack plane; s5, calculating a shortest distance between fitting fracture planes, a normal vector included angle and an energy average value of all micro-seismic events, and calculating the energy average value of all micro-seismic events as a fractured fracture connectivity evaluation index; s6, constructing a fracturing fracture connectivity evaluation model, and calculating an evaluation result of the fracturing fracture connectivity evaluation model through the fracturing fracture connectivity evaluation indexes; s7, evaluating the fractured fracture connectivity based on an evaluation result of the fractured fracture connectivity evaluation model; according to the method, the evaluation accuracy of the fracture connectivity can be improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas field development, and in particular relates to a method and system for evaluating connectivity of hydraulic fractures based on microseismic analysis. Background Art

[0002] In the development of oil and gas fields, fracturing is a commonly used production-increasing measure. It forms a fracture network through fracturing, increases the flow channels of oil and gas, and thus increases oil and gas production.

[0003] Fracturing fracture connectivity is a key indicator for oil and gas field development, which can directly affect production efficiency.

[0004] Currently, the connectivity of hydraulic fractures is generally evaluated using a tracer test method. That is, during the fracturing construction process, a tracer is added to the fracturing fluid. As the fracturing fluid flows, the tracer enters the fractures. After fracturing, fluid samples are collected at different locations to detect the concentration and distribution of the tracer to evaluate the connectivity of the fractures. However, this method is greatly affected by the characteristics of the tracer itself, the complexity of the test environment, and the limitations of the test process, and the evaluation results are prone to errors.

[0005] In view of this, a microseismic-based fracturing connectivity evaluation method and system are designed to solve the above problems. Summary of the Invention

[0006] In order to solve the problems raised in the above background technology, the present invention provides a method and system for evaluating the connectivity of hydraulic fractures based on microseismic analysis, which has the characteristic of improving the evaluation accuracy of the connectivity of hydraulic fractures.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for evaluating connectivity of hydraulic fractures based on microseismic analysis, comprising the following steps:

[0008] S1: Acquire microseismic event data, including spatial coordinates, occurrence time, and energy, and simultaneously acquire geophone data, including spatial coordinates and arrival time of detected microseismic events;

[0009] S2: Correct the spatial coordinates of the microseismic event by using the spatial coordinates of the acquired microseismic event, the occurrence time, the spatial coordinates of the acquired geophone, and the arrival time of the microseismic event;

[0010] S3: Clustering microseismic events based on their corrected spatial coordinates, with a cluster corresponding to all microseismic events of a fracture;

[0011] S4: Randomly generate normal vectors and fit the fracture plane to the spatial coordinates of all microseismic events of a fracture within each cluster;

[0012] S5: Calculate the shortest distance between the fitted fracture planes, the normal vector angle, and the average energy of all microseismic events. At the same time, calculate the average energy of all microseismic events based on the energy of the collected microseismic events as an evaluation index for the connectivity of the hydraulic fractures.

[0013] S6: constructing a hydraulic fracture connectivity evaluation model, and calculating an evaluation result of the hydraulic fracture connectivity evaluation model using the hydraulic fracture connectivity evaluation index;

[0014] S7: Evaluate the connectivity of the hydraulic fractures based on the evaluation results of the hydraulic fracture connectivity evaluation model.

[0015] Furthermore, the specific steps of step S2 include:

[0016] The spatial distance from the microseismic event to the geophone is calculated using the spatial coordinates of the microseismic event and the spatial coordinates of the geophone.

[0017] The theoretical propagation time is calculated by the preset seismic wave propagation velocity in the stratum and the spatial distance from the microseismic event to the geophone;

[0018] The arrival time, occurrence time and theoretical propagation time of microseismic events are detected by geophones, and the time residual of geophones is calculated.

[0019] An objective function is constructed with the optimization goal of minimizing the sum of squares of the time residuals of the detectors. The spatial coordinates of the microseismic events are updated by the gradient descent method to obtain the final spatial coordinates of the microseismic events.

[0020] Furthermore, the specific steps of step S3 include:

[0021] Preset neighborhood radius and minimum number of points;

[0022] For each microseismic event, the number of neighboring points within the radius is calculated;

[0023] If the number of neighborhood points is greater than or equal to the minimum number of points, the microseismic event is the core point;

[0024] Starting from the core point, the density-reachable points are clustered into different clusters, and each cluster corresponds to all microseismic events of a fracture.

[0025] Furthermore, the specific steps of step S4 include:

[0026] Generate three random numbers for initializing the normal vector of the crack surface through a random number generator to form a vector;

[0027] Calculate the modulus of the component vectors;

[0028] Divide each component of the vector by the modulus to obtain the unit vector, which is the initial normal vector;

[0029] Calculate the average spatial coordinates of all microseismic events of a fracture within each cluster;

[0030] The average value is used as the initial center point of a crack in each cluster;

[0031] The initial crack surface is constructed by the initial normal vector and the initial center point of a crack in the cluster;

[0032] Calculate the distance between the spatial coordinates of all microseismic events of a crack in each cluster and the initial crack surface;

[0033] Construct an objective function with the optimization goal of minimizing the sum of squared distances. Update the initial normal vector and the initial center point of the crack using the gradient descent method to obtain the final normal vector and the final center point of the crack.

[0034] The crack surface is constructed using the final normal vector and the final crack center point.

[0035] Furthermore, the expression of the fracturing crack connectivity evaluation model in step S6 is:

[0036]

[0037] Where: d i,j represents the shortest distance between crack surfaces i and j; L0 represents the characteristic length, which is a preset value; θ i,j represents the angle between the normal vectors of crack surfaces i and j; E i,j represents all microseismic events s on fracture surfaces i and j i The average energy of all microseismic events s i Energy E i The average value of ; γ represents the energy weight coefficient.

[0038] The microseismic-based fracture connectivity evaluation system comprises:

[0039] The data acquisition module collects microseismic event data, including spatial coordinates, occurrence time, and energy, and simultaneously collects geophone data, including spatial coordinates and arrival time of detected microseismic events;

[0040] A data correction module corrects the spatial coordinates of the microseismic event by using the spatial coordinates of the collected microseismic event, the occurrence time, the spatial coordinates of the collected geophone, and the arrival time of the microseismic event;

[0041] Data clustering module, clustering microseismic events by their corrected spatial coordinates, with a cluster corresponding to all microseismic events along a fracture;

[0042] The fracture surface construction module randomly generates normal vectors and fits the fracture plane to the spatial coordinates of all microseismic events of a fracture in each cluster;

[0043] The fracture connectivity index calculation model calculates the shortest distance between the fitted fracture planes, the normal vector angle, and the average energy of all microseismic events. At the same time, the average energy of all microseismic events is calculated based on the energy of the collected microseismic events as the evaluation index of the hydraulic fracture connectivity.

[0044] Model building module, building a fracture connectivity evaluation model for hydraulic fracturing;

[0045] The model evaluation module calculates the evaluation result of the hydraulic fracture connectivity evaluation model through the hydraulic fracture connectivity evaluation index, and evaluates the hydraulic fracture connectivity based on the evaluation result of the hydraulic fracture connectivity evaluation model.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention fits a fracture plane by randomly generating normal vectors and the spatial coordinates of all microseismic events of a fracture after clustering. The shortest distance between the fitted fracture planes, the angle between the normal vectors, the energy average of all microseismic events, and the energy average of all microseismic events are used as fracturing crack connectivity evaluation indicators. A fracturing crack connectivity evaluation model is constructed, and the evaluation results of the fracturing crack connectivity evaluation model are used to evaluate the fracturing crack connectivity. Compared with the existing technology, the evaluation accuracy of the fracturing crack connectivity can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Flow chart of the method of the present invention;

[0049] Figure 2 This is a system framework diagram of the present invention;

[0050] In the figure: 1. Data acquisition module; 2. Data correction module; 3. Data clustering module; 4. Fracture surface construction module; 5. Fracture connectivity index calculation model; 6. Model construction module; 7. Model evaluation module. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] The present invention provides the following technical solution: a method for evaluating connectivity of hydraulic fractures based on microseismic analysis, comprising the following steps:

[0053] S1: Acquire microseismic event data, including spatial coordinates, occurrence time, and energy, and simultaneously acquire geophone data, including spatial coordinates and arrival time of detected microseismic events;

[0054] S2: Correct the spatial coordinates of the microseismic event by using the spatial coordinates of the acquired microseismic event, the occurrence time, the spatial coordinates of the acquired geophone, and the arrival time of the microseismic event;

[0055] S3: Clustering microseismic events based on their corrected spatial coordinates, with a cluster corresponding to all microseismic events of a fracture;

[0056] S4: Randomly generate normal vectors and fit the fracture plane to the spatial coordinates of all microseismic events of a fracture within each cluster;

[0057] S5: Calculate the shortest distance between the fitted fracture planes, the normal vector angle, and the average energy of all microseismic events. At the same time, calculate the average energy of all microseismic events based on the energy of the collected microseismic events as an evaluation index for the connectivity of the hydraulic fractures.

[0058] S6: constructing a hydraulic fracture connectivity evaluation model, and calculating an evaluation result of the hydraulic fracture connectivity evaluation model using the hydraulic fracture connectivity evaluation index;

[0059] S7: Evaluate the connectivity of the hydraulic fractures based on the evaluation results of the hydraulic fracture connectivity evaluation model.

[0060] Specifically, the steps of step S1 include:

[0061] Collect microseismic event data, including spatial coordinates, occurrence time, and energy;

[0062] Microseismic events S = {s i |i=1,2,…,n}, where microseismic events s i =(x i ,y i ,z i ,t i ,E i), (x i ,y i ,z i ) represents the microseismic event s i The spatial coordinates of t i represents the microseismic event s i The occurrence time, E i represents the microseismic event s i energy;

[0063] Acquisition of geophone data, including spatial coordinates and arrival times of detected microseismic events;

[0064] Detector R = {r j |j=1,2,…,n}, where detector r j =(x j ,y j ,z j ), (x j ,y j ,z j ) represents the detector r j The spatial coordinates of

[0065] The microseismic event S is detected by the geophone R i |i=1,2,…,n}’s arrival time t.

[0066] Specifically, the specific steps of step S2 include:

[0067] Through microseismic events i The spatial coordinates (x i ,y i ,z i ) and detector r j The spatial coordinates (x j ,y j ,z j ), calculate the microseismic events s i To detector r j The spatial distance d j,i :

[0068]

[0069] Where: (x j ,y j ,z j ) represents the detector r j The spatial coordinates of (x i ,y i ,z i ) represents the microseismic event s i The spatial coordinates of

[0070] By pre-setting the seismic wave propagation velocity v in the stratum and the microseismic event s i To detector r j The spatial distance d j,i , calculate the theoretical propagation time Δt j,i :

[0071]

[0072] Where: d j,i represents the microseismic event s i To detector r j v represents the propagation speed of seismic waves in the stratum;

[0073] Through the detector r j Detection of microseismic events i Arrival time t i ', microseismic events i The occurrence time t i and microseismic events i Theoretical propagation time Δt j,i , calculate the detector r j The time residual ∈ j,i :

[0074] ∈ j,i =t i '-(t i +Δt j,i )

[0075] Where: t i ' represents the detector r j Detection of microseismic events i Arrival time; t i represents the microseismic event s i The time of occurrence; Δt j,i represents the microseismic event s i Theoretical propagation time;

[0076] Construct the objective function to minimize the detector r j The time residual ∈ j,i The square sum of the microseismic events s is optimized by gradient descent method. i The spatial coordinates (x i ,y i ,z i ) is updated to get (x i ',y i ',z i '):

[0077]

[0078] Where: ∈j,i Represents the detector r j The time residual of n is the detector r j quantity.

[0079] Specifically, the specific steps of step S3 include:

[0080] Preset neighborhood radius r and minimum number of points N min ;

[0081] For each microseismic event s j , calculate the number of neighboring points N within the radius r:

[0082]

[0083] Where: (x i ,y i ,z i ) represents the spatial coordinates of the i-th microseismic event; (x j ,y j ,z j ) represents the current microseismic event s j The spatial coordinates of; n represents the total number of microseismic events;

[0084] If N≥N min , then the microseismic event s j As the core point;

[0085] From the core point j Starting from this, the density-reachable points are clustered into different clusters, each cluster corresponds to all microseismic events of a fracture;

[0086] Density can be achieved when a microseismic event s j Connect to another microseismic event through the core point s i , then the microseismic event s j It is believed that the microseismic events i Density can be achieved;

[0087] The clusters have a high density in space, suggesting that they may have occurred in the formation process of the same crack.

[0088] Specifically, the specific steps of step S4 include:

[0089] Generate three random numbers a to initialize the crack surface normal vector through a random number generator i 、b i and c i , forming a vector (a i ,b i ,c i );

[0090] Calculate the component vector (a i ,b i ,c i )'s modulus length |n i |:

[0091]

[0092] Where: (a i ,b i ,c i ) represents the component vector;

[0093] The vector (a i ,b i ,c i ) divided by the modulus |n i |, get the unit vector, that is, the initial normal vector n i :

[0094]

[0095] Where: (a i ,b i ,c i ) represents the component vector; |n i | indicates the module length;

[0096] Calculate all microseismic events s for a crack in each cluster i The spatial coordinates (x i ',y i ',z i ')'s average value:

[0097]

[0098] Where: (x i ',y i ',z i ') represents the microseismic event s i The spatial coordinates of n represent the microseismic event s i quantity;

[0099] Take (x, y, z) as the initial center point of a crack in each cluster;

[0100] By the initial normal vector n i =(a i ',b i ',c i ') and the initial center point (x, y, z) of a crack within the cluster to construct the initial crack surface;

[0101] Calculate all microseismic events s for a crack in each cluster iThe spatial coordinates (x i ',y i ',z i ') The distance L from the initial crack surface i :

[0102] L i =|a i '(x i '-x)+b i '(y i '-y)+c i '(z i '-z)|

[0103] Where: (a i ',b i ',c i ') represents the initial normal vector; (x i ',y i ',z i ') represents the microseismic event s i The spatial coordinates of (x, y, z) represent the initial center point of the crack;

[0104] Construct the objective function, minimize the sum of squared distances as the optimization goal, and use the gradient descent method to optimize the initial normal vector (a i ',b i ',c i ') and the initial center point of the crack (x, y, z), and update to get (a i ”,b i ”,c i ”) and (x',y',z'):

[0105]

[0106] Where: L i represents all microseismic events s of a crack within the cluster i The spatial coordinates (x i ',y i ',z i ') The distance from the initial crack surface;

[0107] By updating the normal vector (a i ”,b i ”,c i ”) and the crack center point (x', y', z') to construct the crack surface.

[0108] Specifically, the specific steps of step S5 include:

[0109] Calculate the shortest distance d between crack surfaces i and j i,j :

[0110] d i,j =|a i ”x j '+b i ”y j '+c i ”z j +d i |

[0111] Where: (a i ”,b i ”,c i ”) represents the normal vector of crack i; (x j ',y j ',z j ') represents the center point of crack j; d i represents the constant term of crack i;

[0112] The smaller the distance, the closer the two cracks are in space and the greater the potential for connectivity;

[0113] Calculate the angle cosθ between the normal vectors of the crack surfaces i and j i,j :

[0114] cosθ i,j =n i ·n j =a i ”a' j '+b i ”b' j '+c i ”c' j '

[0115] Where: (a i ”,b i ”,c i ”) represents the normal vector of crack i; (a″ j ,b″ j ,c″ j ) represents the crack j normal vector;

[0116] The closer the normal vector angle is to 1, the more consistent the two fractures are in strike and the greater the potential for connectivity.

[0117] Calculate all microseismic events s between fracture surfaces i and j i Energy E i The average value E i,j :

[0118]

[0119] Where: E i represents the microseismic event s i The energy of the microseismic event s ithe number of

[0120] The higher the energy, the more intense the rock fracture between fractures, the more complete the fracture development, and the greater the potential for connectivity;

[0121] Calculate all microseismic events s i Energy E i The average value E0:

[0122]

[0123] Where: E i represents the microseismic event s i The energy of the microseismic event s i The number of

[0124] Specifically, the specific steps of step S6 include:

[0125] The shortest distance d between crack surfaces i and j i,j , preset characteristic length L0, normal vector angle cosθ i,j and all microseismic events s i The average value of the energy E i,j and all microseismic events s i Energy E i The average value E0 of the fracturing crack connectivity evaluation index is calculated:

[0126]

[0127] Where: d i,j represents the shortest distance between crack surfaces i and j; L0 represents the characteristic length; θ i,j represents the angle between the normal vectors of crack surfaces i and j; E i,j represents all microseismic events s on fracture surfaces i and j i The average energy of all microseismic events s i Energy E i The average value of ; γ represents the energy weight coefficient.

[0128] A microseismic-based fracture connectivity evaluation system, comprising:

[0129] Data acquisition module 1, which collects microseismic event data, including spatial coordinates, occurrence time and energy, and simultaneously collects detector data, including spatial coordinates and arrival time of detected microseismic events;

[0130] Data correction module 2, corrects the spatial coordinates of the microseismic event by using the spatial coordinates of the collected microseismic event, the occurrence time, the spatial coordinates of the collected geophone, and the arrival time of the microseismic event;

[0131] Data clustering module 3, clustering microseismic events according to their corrected spatial coordinates, where a cluster corresponds to all microseismic events of a fracture;

[0132] Fracture surface construction module 4 randomly generates normal vectors and fits the fracture plane to the spatial coordinates of all microseismic events of a fracture in each cluster;

[0133] Fracture connectivity index calculation model 5 calculates the shortest distance between the fitted fracture planes, the normal vector angle, and the average energy of all microseismic events. At the same time, the average energy of all microseismic events is calculated based on the energy of the collected microseismic events as the evaluation index of the hydraulic fracture connectivity.

[0134] Model building module 6, building a fracturing fracture connectivity evaluation model;

[0135] The model evaluation module 7 calculates the evaluation result of the hydraulic fracture connectivity evaluation model through the hydraulic fracture connectivity evaluation index, and evaluates the hydraulic fracture connectivity based on the evaluation result of the hydraulic fracture connectivity evaluation model.

[0136] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for evaluating connectivity of hydraulic fractures based on microseismic analysis, characterized in that: The following steps are involved: S1: Acquire microseismic event data, including spatial coordinates, occurrence time, and energy, and simultaneously acquire geophone data, including spatial coordinates and arrival time of detected microseismic events; S2: Correct the spatial coordinates of the microseismic event by using the spatial coordinates of the acquired microseismic event, the occurrence time, the spatial coordinates of the acquired geophone, and the arrival time of the microseismic event; S3: Clustering microseismic events based on their corrected spatial coordinates, with a cluster corresponding to all microseismic events of a fracture; S4: Randomly generate normal vectors and fit the fracture plane to the spatial coordinates of all microseismic events of a fracture within each cluster; S5: Calculate the shortest distance between the fitted fracture planes, the normal vector angle, and the average energy of all microseismic events. At the same time, calculate the average energy of all microseismic events based on the energy of the collected microseismic events as an evaluation index for the connectivity of the hydraulic fractures. S6: constructing a hydraulic fracture connectivity evaluation model, and calculating an evaluation result of the hydraulic fracture connectivity evaluation model using the hydraulic fracture connectivity evaluation index; S7: Evaluate the connectivity of the hydraulic fractures based on the evaluation results of the hydraulic fracture connectivity evaluation model.

2. The microseismic-based fracture connectivity evaluation method according to claim 1, characterized in that: The specific steps of step S2 include: The spatial distance from the microseismic event to the geophone is calculated using the spatial coordinates of the microseismic event and the spatial coordinates of the geophone. The theoretical propagation time is calculated by the preset seismic wave propagation velocity in the stratum and the spatial distance from the microseismic event to the geophone; The arrival time, occurrence time and theoretical propagation time of microseismic events are detected by geophones, and the time residual of geophones is calculated. An objective function is constructed with the optimization goal of minimizing the sum of squares of the time residuals of the detectors. The spatial coordinates of the microseismic events are updated by the gradient descent method to obtain the final spatial coordinates of the microseismic events.

3. The microseismic-based fracture connectivity evaluation method according to claim 2, characterized in that: The specific steps of step S3 include: Preset neighborhood radius and minimum number of points; For each microseismic event, the number of neighboring points within the radius is calculated; If the number of neighborhood points is greater than or equal to the minimum number of points, the microseismic event is the core point; Starting from the core point, the density-reachable points are clustered into different clusters, and each cluster corresponds to all microseismic events of a fracture.

4. The microseismic-based fracture connectivity evaluation method according to claim 3, characterized in that: The specific steps of step S4 include: Generate three random numbers for initializing the normal vector of the crack surface through a random number generator to form a vector; Calculate the modulus of the component vectors; Divide each component of the vector by the modulus to obtain the unit vector, which is the initial normal vector; Calculate the average spatial coordinates of all microseismic events of a fracture within each cluster; The average value is used as the initial center point of a crack in each cluster; The initial crack surface is constructed by the initial normal vector and the initial center point of a crack in the cluster; Calculate the distance between the spatial coordinates of all microseismic events of a crack in each cluster and the initial crack surface; Construct an objective function with the optimization goal of minimizing the sum of squared distances. Update the initial normal vector and the initial center point of the crack using the gradient descent method to obtain the final normal vector and the final center point of the crack. The crack surface is constructed using the final normal vector and the final crack center point.

5. The microseismic-based fracture connectivity evaluation method according to claim 4, characterized in that: The expression of the fracturing crack connectivity evaluation model in step S6 is: Where: d i,j represents the shortest distance between crack surfaces i and j; L0 represents the characteristic length, which is a preset value; θ i,j represents the angle between the normal vectors of crack surfaces i and j; E i,j represents all microseismic events s on fracture surfaces i and j i The average energy of all microseismic events s i Energy E i The average value of ; γ represents the energy weight coefficient.

6. The microseismic-based fracturing connectivity evaluation system according to claim 5, characterized in that: include: A data acquisition module (1) collects microseismic event data, including spatial coordinates, occurrence time and energy, and simultaneously collects detector data, including spatial coordinates and arrival time of the detected microseismic event; A data correction module (2) corrects the spatial coordinates of the microseismic event by using the spatial coordinates of the collected microseismic event, the occurrence time, the spatial coordinates of the collected geophone, and the arrival time of the microseismic event; Data clustering module (3) clusters microseismic events using their corrected spatial coordinates, with a cluster corresponding to all microseismic events of a fracture; The fracture surface construction module (4) randomly generates normal vectors and fits the fracture plane to the spatial coordinates of all microseismic events of a fracture in each cluster; Fracture connectivity index calculation model (5) calculates the shortest distance between the fitted fracture planes, the normal vector angle and the average energy of all microseismic events. At the same time, the average energy of all microseismic events is calculated based on the energy of the collected microseismic events as the evaluation index of fracture connectivity; Model building module (6), constructing a fracture connectivity evaluation model for hydraulic fracturing; The model evaluation module (7) calculates the evaluation result of the hydraulic fracture connectivity evaluation model through the hydraulic fracture connectivity evaluation index, and evaluates the hydraulic fracture connectivity based on the evaluation result of the hydraulic fracture connectivity evaluation model.

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