CN106154334B - Underground micro-seismic event real time inversion localization method based on grid search - Google Patents

Abstract

The present invention provides a real-time inversion positioning method for downhole microseismic events based on grid search. The real-time inversion positioning method for downhole microseismic events based on grid search includes: establishing a velocity model in the space containing microseismic sources under the wellhead ;Pick up the first arrival time of P wave and S wave for microseismic events; calculate the degree of polarization and polarization direction of the first arrival of P wave for each record of microseismic events; establish the inversion objective function, and calculate the grid point to observation by wave field forward modeling The theoretical first arrival travel time of the system; calculate the objective function value, determine the source position; calculate the three-dimensional spatial position coordinates of the source. The real-time inversion positioning method for downhole microseismic events based on grid search is fast in calculation speed and high in efficiency; it has strong noise resistance, and can perform more accurate microseismic source positioning for microseismic events with low signal-to-noise ratio; The results are more accurate and reliable, and the interpretation accuracy of microseismic monitoring results is improved.

Description

Real-time inversion location method for downhole microseismic events based on grid search

technical field

The invention relates to the technical field of oilfield development, in particular to a grid search-based real-time inversion positioning method for downhole microseismic events.

Background technique

Hydraulic fracturing is one of the main means used to increase production and injection in the production and development of oil and gas fields. Since the 1970s, large-scale hydraulic fracturing has been widely used in low-permeability and ultra-low-permeability oil and gas reservoirs. Studying the development process of fractures, predicting the effect of hydraulic fracturing, and determining the best plan have important guiding significance for fracturing construction. The earliest research on fracturing process simulation can be traced back to the 1960s. At that time, the research was limited to completely ignoring the formula of rock mechanics analysis and some simple two-dimensional models developed later, but these models did not conform to the general field actual fracturing. conditions of. In the 1980s, great progress was made in the analysis and research of the fracturing process, but there is still a certain distance from the fully mature application in actual production. To establish a complete fracturing analysis method, it is an important subject to study a reliable fracture diagnosis method.

In the process of hydraulic fracturing and oil and gas extraction, it is found that a large number of microseisms with recordable levels will occur underground during the process of water injection and pressurization to the bottom of the well, which naturally arouses people's thinking about using these microseisms for fracturing detection. Later, with the continuous deepening of the research on the acoustic emission phenomenon of rock fractures and the wide application of microseismic detection in the mining industry, the petroleum industry has gradually recognized the idea of describing the development of underground fractures by detecting the microseismic vibrations generated during hydraulic fracturing. method. Since the 1970s, a series of experiments on microseismic monitoring of hydraulic fracturing have been carried out. These tests have proved that hydraulic fracturing can indeed induce a large number of microseismic events, and the use of these microseismic events for fracturing monitoring has also achieved initial success. . With the continuous improvement of microseismic monitoring equipment and data processing methods, the quality of recorded microseismic data is getting higher and higher, people's research on microseismic is also more in-depth, and a large number of research results that have important guiding significance for actual production have been obtained. . Today, microseismic has developed into an important technical means for hydraulic fracturing monitoring, and has achieved great success in the development of unconventional oil and gas resources such as shale oil and gas and tight sandstone oil and gas.

In the application of microseismic, it is necessary to invert the exact position of the source, and how to accurately invert the coordinates of the source position is a key technology for microseismic application. With the rapid development of microseismic technology, the microseismic positioning algorithm and its implementation technology are constantly innovating and improving. Among the initial seismic source positioning methods, the inversion method based on the first arrival of the direct wave and the stratigraphic model is mainly used. The positioning methods can be basically divided into two categories: direct positioning and relative positioning. The basic idea of the first type of method is basically based on the forward and reverse iterative inversion of the model, using the picked up direct P and S wave first arrivals to invert the source position or the time of the earthquake, so that the simulated first arrival and the actual first arrival This method is the most widely used method at present; the other method is the relative positioning method, which considers that seismic events do not occur singly but in clusters, and the waveforms are similar. Among the microseismic events that appear in clusters, there will be one or more seismic events with strong energy. For these strong events, the location of the source is obtained by inversion by direct positioning method, and then other microseismic events with weaker energy Earthquake events are located.

At present, most of the commonly used hypocenter location algorithms need to use the arrival time information of P-wave and S-wave of microseismic events. Due to the low signal-to-noise ratio of some microseismic events, there is usually a large error in the picked-up first arrival time. Therefore, It is very necessary to study a seismic source location method with strong noise resistance. For this reason, we invented a new real-time inversion and positioning method for downhole microseismic events based on grid search, which solved the above technical problems.

Contents of the invention

The purpose of the present invention is to provide a real-time inversion positioning method for downhole micro-seismic events based on grid search for the micro-seismic source positioning technology.

The purpose of the present invention can be achieved by the following technical measures: a real-time inversion positioning method for downhole microseismic events based on grid search, the real time inversion positioning method for downhole microseismic events based on grid search comprises: Step 1, Establish a velocity model in the space containing the microseismic source; step 2, pick up the first arrival time of P wave and S wave for the microseismic event; step 3, calculate the degree of polarization and polarization direction of the first arrival of P wave for each record of the microseismic event; Step 4, establish the inversion objective function, and calculate the theoretical first arrival travel time from the grid point to the observation system through wave field forward modeling; Step 5, calculate the value of the objective function, and determine the location of the seismic source; Step 6, calculate the three-dimensional spatial position coordinates of the seismic source.

The purpose of the present invention can also be achieved through the following technical measures:

Step 1 includes:

Based on acoustic logging data and other velocity data, establish an initial velocity model in the space below the wellhead containing microseismic sources;

Accurate velocity models can be obtained by performing inversion on the basis of the initial velocity model by using the first arrival time difference of perforation records or other positioning records and known receiving and firing position information.

Step 2 includes:

Identify microseismic events from microseismic monitoring data;

The first arrivals of P-wave and S-wave are picked up for the identified microseismic events.

In step 3, use the polarization analysis method to calculate the degree of polarization P and polarization direction α of the first arrival of P wave in each record, and the calculation formulas of P and α are respectively:

M is the covariance matrix, where XYZ corresponds to the three spatial directions, i is the sampling space number, E(X) refers to the mean value of the variable, and λ ₁ , λ ₂ , λ ₃ are the three eigenvalues of the covariance matrix M , and λ ₁ ＞λ ₂ ＞λ ₃ , u ₁ =[u _x ,u _y ,u _z ] is the eigenvector corresponding to the largest eigenvalue of M, and the definition of the covariance matrix M is:

The degree of polarization P reflects the degree of polarization, and its value ranges from 0 to 1, wherein P=1 indicates that the signal is linearly polarized, such as a seismic signal; P=0 indicates that the signal is circularly polarized, such as random noise.

Step 4 includes:

Establish a three-dimensional coordinate system in space;

The inversion objective function is established by using the P-wave and S-wave first arrival times extracted from the microseismic events;

The three-dimensional target area is divided into grids, assuming that each grid point is the possible location of the seismic source, and the theoretical first arrival travel time from each point in the grid to the observation system is calculated by using the wave field forward modeling method.

In step 4, the following inversion objective function is established by using the first arrival of P wave and S wave extracted from microseismic events:

in

are the first arrival times of the picked-up P wave and S wave, respectively, are the P-wave and S-wave travel times obtained through the forward modeling of the wave field, γ is the weight coefficient between 0 and 1, F _obj is the objective function of the inversion, i is the number of downhole instruments at all levels, and there are M Level, this objective function equation means that the difference between the theoretical arrival time and the actual arrival time is calculated at each level, and then brought into the equation for averaging.

In step 5, input the theoretical first arrival travel time into the above objective function to calculate the objective function value of each point, select the minimum value of the objective function as the real position of the seismic source, and thus obtain the azimuth, distance and depth information of the seismic source .

In step 6, after obtaining the azimuth, distance, and depth information of the seismic source, the position coordinates of the seismic source can be obtained through conversion using the three-dimensional coordinate system.

The grid search-based real-time inversion positioning method for downhole microseismic events in the present invention has fast calculation speed and high efficiency; strong noise resistance, and can perform relatively accurate microseismic source positioning for microseismic events with low signal-to-noise ratio; The microseismic source positioning result is more accurate and reliable, and the interpretation accuracy of the microseismic monitoring result is improved.

Description of drawings

Fig. 1 is the flowchart of a specific embodiment of the downhole microseismic event real-time inversion positioning method based on grid search of the present invention;

Fig. 2 is the schematic diagram of the laying position of actual microseismic source and observation system in a specific embodiment of the present invention;

Fig. 3 is the schematic diagram of synthesizing arrival time data in a specific embodiment of the present invention;

Fig. 4 is a schematic diagram of the seismic source positioning results in a specific embodiment of the present invention.

detailed description

In order to make the above and other objects, features and advantages of the present invention more comprehensible, the preferred embodiments are listed below and shown in the accompanying drawings in detail as follows.

As shown in FIG. 1 , FIG. 1 is a flow chart of the grid search-based real-time inversion and positioning method for downhole microseismic events of the present invention.

Referring to FIG. 1 , at step 110 , a velocity model is established in the space below the wellhead containing microseismic sources. Here velocity models can be modeled in the space below the wellhead containing microseismic sources. Steps may include: modeling initial velocities in the space below the wellhead containing microseismic sources based on sonic logging and other velocity data; The time difference and the known receiving and firing position information are inverted on the basis of the initial velocity model to obtain an accurate velocity model. The velocity model building technology here belongs to the prior art, and will not be described in detail here in order not to obscure the subject of the present invention.

In step 120, the step of picking up the first arrival time of P wave and S wave to the microseismic event may include: identifying the microseismic event from the microseismic monitoring data; performing P wave and S wave first arrival on the identified microseismic event pick up.

In step 130, calculate the degree of polarization and polarization direction of the first arrival of the P wave in each record of the microseismic event, and the step of determining the azimuth angle of the source may include: using the polarization analysis method to calculate the degree of polarization of the first arrival of the P wave in each record of the microseismic event and polarization direction.

The degree of polarization P and polarization direction α of the first arrival of each recorded P wave were calculated using the polarization analysis method. The calculation formulas of P and α are respectively:

M is the covariance matrix, which is a standard definition in probability, where XYZ corresponds to three spatial directions, i is the sampling space number, and E(X) refers to the mean value of the variable. Among them, λ ₁ , λ ₂ , λ ₃ (λ ₁ >λ ₂ >λ ₃ ) are the three eigenvalues of the covariance matrix M, and u ₁ =[u _x ,u _y ,u _z ] is the corresponding value of the largest eigenvalue of M Eigenvector, the definition of the covariance matrix M is:

The degree of polarization P reflects the degree of polarization, and its value ranges from 0 to 1, wherein P=1 indicates that the signal is linearly polarized, such as a seismic signal; P=0 indicates that the signal is circularly polarized, such as random noise.

In step 140, the target inversion function is established, and the wavefield forward modeling calculation of the theoretical first arrival travel time from the grid point to the observation system may include: establishing a spatial three-dimensional coordinate system; using the P wave and S wave first arrival extracted from microseismic events The inversion objective function is established at the same time; the three-dimensional target area is divided into grids, assuming that each grid point is the possible location of the seismic source, and the theoretical initial distance from each point in the grid to the observation system is calculated by using the wave field forward modeling method. to go.

Using the P-wave and S-wave first arrival times extracted from microseismic events, the following inversion objective function is established:

in

are the first arrival times of the picked-up P wave and S wave, respectively, are the P-wave and S-wave travel times obtained through wave field forward modeling, respectively, and γ is a weight coefficient between 0 and 1. F _obj refers to the objective function of the inversion. i refers to the number of downhole instruments at all levels, and there are M levels in total. This objective function equation means that the difference between the theoretical arrival time and the actual arrival time is calculated at each level, and then brought into the equation for averaging.

In step 150, the objective function value is calculated, and the step of determining the source location may include: inputting the travel time information into the above objective function to calculate the objective function value of each point, and selecting the minimum value of the objective function as the real location of the seismic source.

In step 160, calculating the three-dimensional spatial position coordinates of the seismic source may include: after obtaining the azimuth, distance and depth information of the seismic source, using a three-dimensional coordinate system to obtain the position coordinates of the seismic source through conversion.

In a specific embodiment of the application of the present invention, the physical model adopted is a one-dimensional horizontal layered model, and the model parameters are shown in Table 1.

Table 1 Formation model parameters

Assuming that the actual microseismic source location is [50m, 2250m], Figure 2 shows the layout of the microseismic source and observation system. Firstly, the synthesized arrival data is obtained through forward calculation using ray tracing algorithm, and a group of random numbers are added to represent the first arrival picking error, as shown in Figure 3, where a shows the arrival time of P wave, and picture b shows the arrival time of S wave When. When using the grid search algorithm for positioning, the search range we choose is a horizontal distance of 0-100m, a vertical distance of 2200-2300m, and a grid size of 1m×1m. Figure 4 shows the source location results obtained from the search based on the objective function proposed in this paper, where the circle in the center indicates the real source location, the asterisk indicates the source location obtained by inversion, and the depth of the color indicates different objective function values. It can be seen from the figure that the source position obtained by inversion is [49m, 2247m], and the difference between the inversion result and the real microseismic source position is only less than 4m, which proves that the method of this paper can be used to invert to obtain a more accurate source position.

Claims (6)

1. The real-time inversion positioning method for downhole microseismic events based on grid search, characterized in that the real-time inversion positioning method for downhole microseismic events based on grid search comprises: Step 1, establishing a velocity model in the space containing the microseismic source under the wellhead; Step 2, pick up the first arrival time of P wave and S wave for microseismic events; Step 3, calculating the degree of polarization and the direction of polarization of the first arrival of the P wave in each trace of the microseismic event; Step 4, establish the inversion objective function, and calculate the theoretical first arrival travel time from the grid point to the observation system through wave field forward modeling; Step 5, calculating the objective function value and determining the source location; Step 6, calculating the three-dimensional spatial position coordinates of the seismic source; In step 4, the following inversion objective function is established by using the first arrival of P wave and S wave extracted from microseismic events: <mfenced open=""close=""><mtable><mtr><mtd><mrow><msub><mi>F</mi><mrow><mi>o</mi><mi>b</mi><mi>j</mi></mrow></msub><mo>=</mo><mi>&amp;gamma;</mi><munderover><mo>&amp;Sigma;</mi>mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><mo>&amp;lsqb;</mo><msup><mrow><mo>(</mo><msubsup><mi>T</mi><mi>p</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>p</mi><mi>i</mi></msubsup><mo>-</mo><msub><mi>T</mi><mi>o</mi></msub><mo>)</mo></mrow><mn>2</mn></msup><mo>+</mo><msup><mrow><mo>(</mo><msubsup><mi>T</mi><mi>s</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>s</mi><mi>i</mi></msubsup><mo>-</mo><msub><mi>T</mi><mi>o</mi></msub><mo>)</mo></mrow><mn>2</mn></msup><mo>&amp;rsqb;</mo><mo>/</mo><mn>2</mn></mrow></mtd></mtr><mtr><mtd><mrow><mo>+</mo><mrow><mo>(</mo><mn>1</mn><mo>-</mo><mi>&amp;gamma;</mi><mo>)</mo></mrow><munderover><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><msup><mrow><mo>&amp;lsqb;</mo><mrow><mo>(</mo><msubsup><mi>T</mi><mi>p</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>T</mi><mi>s</mi><mi>i</mi></msubsup><mo>)</mo></mrow><mo>-</mo><mrow><mo>(</mo><msubsup><mi>t</mi><mi>p</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>s</mi><mi>i</mi></msubsup><mo>)</mo></mrow><mo>&amp;rsqb;</mo></mrow><mn>2</mn></msup></mrow></mtd></mtr></mtable></mfenced> in <mrow><msub><mi>T</mi><mi>o</mi></msub><mo>=</mo><mfrac><mn>1</mn><mi>M</mi></mfrac><munderover><mo>&amp;Sigma;</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>M</mi></munderover><mo>&amp;lsqb;</mo><mrow><mo>(</mo><msubsup><mi>T</mi><mi>p</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>p</mi><mi>i</mi></msubsup><mo>)</mo></mrow><mo>+</mo><mrow><mo>(</mo><msubsup><mi>T</mi><mi>s</mi><mi>i</mi></msubsup><mo>-</mo><msubsup><mi>t</mi><mi>s</mi><mi>i</mi></msubsup><mo>)</mo></mrow><mo>&amp;rsqb;</mo><mo>/</mo><mn>2</mn></mrow> are the first arrival times of the picked-up P wave and S wave, respectively, are the P-wave and S-wave travel times obtained through the forward modeling of the wave field, γ is the weight coefficient between 0 and 1, F _obj is the objective function of the inversion, i is the number of downhole instruments at all levels, and there are M Level, this objective function equation means that the difference between the theoretical arrival time and the actual arrival time is calculated at each level, and then brought into the equation for averaging. 2. the downhole microseismic event real-time inversion location method based on grid search according to claim 1, is characterized in that, step 1 comprises: Based on acoustic logging data and other velocity data, establish an initial velocity model in the space below the wellhead containing microseismic sources; Accurate velocity models can be obtained by performing inversion on the basis of the initial velocity model by using the first arrival time difference of perforation records or other positioning records and known receiving and firing position information. 3. the downhole microseismic event real-time inversion location method based on grid search according to claim 1, is characterized in that, step 2 comprises: Identify microseismic events from microseismic monitoring data; The first arrivals of P-wave and S-wave are picked up for the identified microseismic events. 4. the downhole microseismic event real-time inversion location method based on grid search according to claim 1, is characterized in that, step 4 comprises: Establish a three-dimensional coordinate system in space; The inversion objective function is established by using the P-wave and S-wave first arrival times extracted from the microseismic events; The three-dimensional target area is divided into grids, assuming that each grid point is the possible location of the seismic source, and the theoretical first arrival travel time from each point in the grid to the observation system is calculated by using the wave field forward modeling method. 5. the real-time inversion positioning method for downhole microseismic events based on grid search according to claim 1, characterized in that, in step 5, the theoretical first arrival travel time is input into the above-mentioned objective function to calculate the time of each point The objective function value, the minimum value of the objective function is selected as the real position of the seismic source, and thus the azimuth, distance and depth information of the seismic source are obtained. 6. the downhole microseismic event real-time inversion location method based on grid search according to claim 5, is characterized in that, in step 6, after obtaining the azimuth and distance, depth information of source, utilize three-dimensional coordinate system The location coordinates of the seismic source can be obtained through conversion.