A microseismic event point separation method based on well logging constraints

Abstract

The present invention relates to a method and system for separating microseismic event points based on well logging constraints, comprising: utilizing well logging data to analyze curve energy differentiation characteristics and calculate fracture intensity curves; extracting the energy level, depth, and spatial position of microseismic events to clarify their three-dimensional distribution characteristics; constructing fracture network morphology based on the temporal sequence and spatial position of microseismic events; then, conducting an interactive analysis of the B value of microseismic events and the well logging fracture intensity to perform energy compensation on the microseismic energy level; distinguishing primary and secondary fractures based on the energy conservation principle of fracturing fluid; and evaluating the power law relationship between the number of microseismic events and magnitude to form a systematic statistical analysis. By combining well logging data with microseismic monitoring technology, this method effectively solves the energy attenuation problem in traditional methods, improves the reliability and accuracy of microseismic event separation, provides a scientific basis for fracturing operations, and promotes the efficient development of oil and gas resources.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and in particular to a method for hydraulic fracturing microseismic positioning processing and natural fracture microseismic event point identification and separation. Background Art

[0002] As a clean energy source, the development and utilization of shale gas can help reduce China's dependence on external energy and improve energy security. China's deep shale gas resources hold enormous potential, but their development involves a series of complex engineering and technical challenges, such as horizontal drilling and hydraulic fracturing. However, due to the impacts of casing deformation and fracture channeling, development results have fallen short of expectations, hindering the path to scale-effective development. Accurate imaging of deep shale reservoirs presents significant challenges, as natural fractures exhibit multi-scale characteristics, making prediction accuracy and resolution insufficient for production needs. Furthermore, existing natural fracture prediction methods are limited, lacking a systematic reliability evaluation method for prediction results. The Luzhou deep shale gas development utilizes a "horizontal well drilling + volumetric fracturing" approach. Currently, deep shale gas development in the Luzhou area is facing significant challenges, primarily due to casing deformation and fracture channeling. Consequently, development results in the Luzhou area have fallen short of expectations. The characteristics of natural fracture development are crucial for adjusting fracturing parameters and optimizing development plans. Failure to properly identify and address natural fractures during the fracturing process can lead to risks such as well casing deformation and fracture channeling. Accurate characterization and identification of natural fractures in the reservoir and taking corresponding measures to adjust fracturing parameters and construction plans can reduce development risks, improve fracturing effects, and guide the design of subsequent fracturing plans to better optimize resource utilization and improve development benefits.

[0003] In view of the above resource demands and development characteristics, there is an urgent need for a method that can accurately predict natural fractures and accurately characterize natural fractures. Currently, conventional natural fracture identification technologies mainly include two methods: seismic attribute prediction method and hydraulic fracturing microseismic monitoring method.

[0004] Seismic attribute prediction method: Seismic attributes (curvature, coherence) reflect from different angles the deformation and fracture of the stratum when it is squeezed by tectonic stress. By calculating the curvature and coherence attributes, the development of fractures in the stratum can be predicted. The prediction range is large, but the prediction accuracy is relatively low.

[0005] Hydraulic fracturing microseismic monitoring method: collects microseismic waveform signals generated during the fracturing process, processes and locates the selected microseismic events generated during the fracturing process, and describes the crack morphology and spatial distribution pattern generated by fracturing. This method mainly uses the B-value analysis method to separate artificial fractures and natural fractures based on the energy generated by the microseismic event points, that is, determines the threshold values ​​of different fracture event points, and thus separates the microseismic event points. However, this calculation uses a single data and cannot be verified, and does not consider the energy absorption and loss of the cracks during the propagation of seismic waves, thereby reducing the accuracy of event point separation.

[0006] This patent adopts multidisciplinary technical ideas and means, proposes the basic principles and calculation methods of the method, and shows through theoretical calculations and error analysis that the method is reasonable and effective. At the same time, it uses multi-information fusion to interpret the natural fracture characteristics of logging data, and then performs energy compensation on the microseismic event points before separating the microseismic event points. This method makes up for the technical problem that traditional separation methods do not consider energy loss, thereby affecting the separation accuracy. This method has the technical advantages of strong practicality and high prediction accuracy. Summary of the Invention

[0007] The present invention addresses the shortcomings of the prior art and provides a method for separating microseismic event points based on well logging constraints. Based on microseismic data collected in real time during hydraulic fracturing, the method extracts and analyzes effective information, groups microseismic events that are located or relocated in real time according to space, time, and energy level, and uses the energy differentiation characteristics of the well logging data analysis curve to clearly identify the spatial location of the fracture development section. On this basis, an interactive analysis is conducted using the microseismic event B value and the well logging fracture intensity curve to ultimately determine a specific microseismic separation scheme. This method fully utilizes the advantages of microseismic data attributes in predicting a wide range of fractures and the high accuracy of well logging data, improving the reliability and accuracy of microseismic data separation and natural fracture identification, thereby increasing the accuracy of natural fracture prediction in reservoirs, which is beneficial for guiding fracturing operations and improving fracturing results.

[0008] In order to achieve the above object of the invention, the technical solution adopted by the present invention is as follows:

[0009] A microseismic event point separation method based on well logging constraints includes the following steps:

[0010] (a) Using well logging data, we analyze the energy differentiation characteristics of the logging data curve and calculate the fracture strength curve, which serves as the condition constraint for subsequent microseismic data separation;

[0011] (b) Extract the energy level, depth, and spatial location information of microseismic event data to clarify the distribution patterns and characteristics of microseismic event points in three-dimensional space;

[0012] (c) Based on the temporal and spatial locations of microseismic events, the fracture network morphology is constructed, assuming that new fractures are likely to extend from existing fractures;

[0013] (d) Interactive analysis of the B value of microseismic events and the well logging fracture intensity curve is performed, and energy compensation of the microseismic energy level is performed based on the fracture development intensity at different depths;

[0014] (e) Based on the energy conservation principle of fracturing fluid, distinguish between primary and secondary fractures and analyze the relationship between the number of fracture branches and the width, conductivity and pressure transmission of the main fracture;

[0015] (f) Evaluate the power law relationship between the number of microseismic events and their magnitude, calculate the B values ​​for different well sections, and form a systematic statistical analysis;

[0016] (g) Based on the energy-compensated microseismic information data, the separation scheme of microseismic event points is finally determined to achieve effective classification of natural fractures and reservoir matrix.

[0017] Furthermore, the calculation of the fracture strength curve is based on the anisotropic characteristics of the logging data and the fracture porosity analysis to provide a more accurate fracture strength assessment.

[0018] Furthermore, the energy compensation is achieved by correcting the microseismic energy level to accurately reflect the effect of crack strength on microseismic energy.

[0019] Furthermore, the power law relationship is derived by statistically analyzing the frequency and magnitude distribution of microseismic events to form a linear model to support the prediction of microseismic events.

[0020] Furthermore, the separation scheme has been verified multiple times using different test data to ensure the accuracy and reliability of the results.

[0021] The present invention also discloses a microseismic event point separation system, which can be used to implement the above-mentioned microseismic event point separation method, specifically comprising:

[0022] Data acquisition module: responsible for collecting well logging data and microseismic event data, including energy level, depth and spatial location information.

[0023] Data preprocessing module: performs noise filtering, standardization and format conversion on the collected logging data and microseismic event data for subsequent analysis.

[0024] Energy analysis module: Analyzes the energy differentiation characteristics of logging data curves, calculates fracture strength curves, and provides conditional constraints for microseismic data separation.

[0025] Spatial distribution analysis module: extracts the spatial distribution patterns of microseismic events and constructs the network morphology of microseismic events in three-dimensional space.

[0026] Interactive analysis module: Interactive analysis of the B value of microseismic events and the logging fracture intensity curve is carried out, and energy compensation of microseismic energy levels is performed based on the fracture development intensity at different depths.

[0027] Primary and secondary fracture analysis module: Based on the energy conservation principle of fracturing fluid, it distinguishes primary and secondary fractures and analyzes the relationship between the number of fracture branches and the width, conductivity and pressure transmission of the main fracture.

[0028] Statistical analysis module: evaluates the power law relationship between the number of microseismic events and magnitude, counts the B values ​​of different well sections, and forms a systematic statistical analysis.

[0029] Event point separation module: Based on the energy-compensated microseismic information data, the microseismic event point separation scheme is finally determined to achieve effective classification of natural fractures and reservoir matrix.

[0030] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned microseismic event point separation method is implemented.

[0031] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned microseismic event point separation method is implemented.

[0032] Compared with the prior art, the advantages of the present invention are:

[0033] The present invention gives full play to the wide range of fracture prediction capabilities of microseismic data and the high precision advantages of well logging data, organically combines microseismic monitoring technology with high-precision well logging interpretation schemes, and solves the problem that traditional methods do not consider the energy attenuation of seismic waves during propagation in different media. By combining the quantitative interpretation of natural fractures with well logging data and performing energy compensation on microseismic event points, the recognition of artificial fractures and natural fractures is significantly improved. The energy level intensity and data correlation after compensation are improved, realizing the complementary advantages of data from different disciplines, and reasonably and objectively improving the separation of microseismic events and the reliability and accuracy of the identification of natural fractures. In addition, the accuracy of the model is verified by actual fracturing data, which further guarantees the accuracy of the model and helps to guide fracturing construction and improve fracturing effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a method for separating microseismic event points according to an embodiment of the present invention;

[0035] Figure 2 This is a result diagram of the interpretation of the fracture strength curve of the well logging data according to an embodiment of the present invention;

[0036] Figure 3 is a statistical histogram of effective information energy levels of microseismic event points according to an embodiment of the present invention;

[0037] Figure 4 This is a statistical analysis diagram of the B value of microseismic events and the fracture strength interpreted by well logging according to an embodiment of the present invention;

[0038] Figure 5 This is a diagram showing the real-time positioning or post-processing positioning results of a microseismic event according to an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of microseismic energy compensation according to an embodiment of the present invention;

[0040] Figure 7 This is a B-value analysis diagram of a microseismic event according to an embodiment of the present invention (without energy compensation);

[0041] Figure 8 This is a B-value analysis diagram of a microseismic event according to an embodiment of the present invention (energy compensation has been performed);

[0042] Figure 9 This is a comparison chart of the microseismic energy supplementation and well logging identification results in an embodiment of the present invention;

[0043] Figure 10 This is a comparison chart of the earthquake prediction results before and after microseismic energy replenishment according to an embodiment of the present invention;

[0044] Figure 11 is a microseismic event point separation result diagram (cracks) according to an embodiment of the present invention;

[0045] Figure 12 is a microseismic event point separation result map (matrix) according to an embodiment of the present invention;

[0046] Figure 13 This is a construction parameter verification diagram of an embodiment of the present invention (H56 platform);

[0047] Figure 14 This is a construction parameter verification diagram of an embodiment of the present invention (H58 platform);

[0048] Figure 15 This is a comparison chart of the coincidence rates before and after microseismic energy compensation according to an embodiment of the present invention (and the logging interpretation results). DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and examples.

[0050] like Figure 1 As shown, this embodiment mainly includes six steps. First, the energy differentiation characteristics of the well logging data analysis curve are used to calculate the fracture intensity curve, which is used as a constraint for the subsequent microseismic data separation condition. Then, effective information such as the energy level, depth, and spatial location of the microseismic event data is extracted to clarify the distribution pattern and characteristics of the microseismic event points in three-dimensional space. Based on the timing and location of the microseismic events, the fracture network morphology is constructed, following the high probability of new fractures extending from existing fractures. On this basis, the microseismic event B value and the well logging fracture intensity curve are interactively analyzed. Combined with the fracture development intensity at different depths, energy compensation is performed on the microseismic energy level. Based on the fracturing fluid energy conservation principle, primary and secondary fractures are distinguished. The more fracture branches, the wider the primary fracture, the stronger the conductivity and pressure transmission. Then, the natural fracture connectivity is evaluated based on the microseismic event B value. The number of microseismic events and their magnitudes have a power law relationship. Statistical analysis of microseismic data was performed to establish B values ​​for different well sections. Based on the energy-compensated microseismic information data, a microseismic event point separation scheme was ultimately determined to achieve the classification of natural fractures and reservoir matrix. The separation results were finally verified using fracturing production data.

[0051] The energy and velocity differences between fast and slow shear waves are usually expressed as percent anisotropy:

[0052]

[0053] Where: P e ——is the energy percentage anisotropy;

[0054] E q ——is the energy of fast transverse waves;

[0055] E sl ——The energy of slow transverse waves.

[0056]

[0057] Where: Pt is the percentage anisotropy of time difference;

[0058] dt ssl —— is the time difference of slow shear waves;

[0059] dt sq ──It is the time difference of fast transverse waves.

[0060] The main factors contributing to shear wave anisotropy include uneven horizontal stress, open fractures, faults, high-angle formation bedding, and the influence of elliptical wellbores. In these cases, the fast shear wave azimuth corresponds to the direction of maximum horizontal principal stress or the strike of faults / fractures and formation bedding, respectively. A comprehensive fracture indication curve (FVPA_PRED) was constructed using a neural network approach by combining dual lateral resistivity fracture porosity, Stoneley wave attenuation fracture indication curves, and fast and slow shear wave anisotropy fracture indication characteristics with electrical imaging interpretation of fracture porosity (FVPA).

[0061] Based on the effective signals from microseismic events, the spatial location, onset time, and magnitude of the earthquake are located. The inversion process and characteristics of the formation and development of the hydraulic fractures and fracture network are then solved, and the length, width, height, and orientation of the fracture network formed by reservoir stimulation are determined. The waveform signals recorded by the geophones are processed through screening, noise suppression, and spatial positioning before being converted into a microseismic event point. The position of the microseismic event point represents the location of the vibration, and the size of the microseismic event point represents the magnitude of the vibration. The top view of the microseismic event is the projection of the microseismic event point onto the XY plane. The extension of the microseismic event point perpendicular to the well trajectory on the top view is defined as the length of the artificial fracture network, the extension of the microseismic event point along the well trajectory is defined as the width of the artificial fracture network, and the strike direction of the artificial fracture network is defined as the distribution direction of the microseismic event point. The side view of the microseismic event is the projection of the microseismic event point onto the Z plane, and its height on the Z plane is defined as the height of the artificial fracture network.

[0062] Typically, hydraulic fracturing-induced microseismic waves have low energy levels, ranging from -3 to -0.5. These microseismic waves experience significant energy attenuation with increasing observation distance. Some attenuation is caused by normal energy loss due to reservoir rock vibration during propagation, while others are due to abnormal attenuation caused by geological factors, such as passage through natural fractures and different lithologic interfaces. It is important to note that if the received microseismic signal energy is abnormal, it is necessary to analyze the seismic wave energy attenuation between the observation system and the signal's origin. This paper analyzes the relationship between energy attenuation and natural fractures during microseismic energy level propagation at different observation locations and proposes a precise evaluation method for compensating for seismic wave energy attenuation. By compensating for seismic wave energy attenuation, this method avoids prediction errors caused by low signal recognition accuracy for microseismic events and achieves precise processing of microseismic events. The method analyzes the fracture strength, artificial fracture complexity index, and B value of different wells, statistically analyzes the relationship between fracture strength, artificial fracture complexity index, and B value of different fracturing sections, and uses fracture strength interpreted from well logging to compensate for microseismic energy.

[0063] M=M0*e f

[0064] In the above formula, the microseismic energy level without energy compensation is M0, the crack strength is f, and M is the microseismic energy after energy compensation.

[0065] Microseismic events can be described by the b-value of their frequency-magnitude distribution. The Gutenberg-Richer relationship is as follows:

[0066] log N(m>M)=a-bM

[0067] In the above formula, N is the number of events with magnitude greater than M. The b value is the slope of the linear portion of the log(N) vs. M plot. This plot has a negative curvature for small values ​​of M due to the oversampling caused by the detection threshold. The resulting linear break is called the absolute minimum magnitude, M c .

[0068] On this basis, a microseismic event point separation scheme is determined to guide the prediction of natural fractures, and the actual fracturing construction curve is used to verify and analyze the prediction results.

[0069] like Figure 2 As shown in Figure 2, the primary purpose of the orthogonal dipole (BCR) measurement method is to measure shear wave anisotropy in formations. In fractured formations caused by tectonic stress or other geological factors, shear wave velocities typically exhibit azimuthal anisotropy. Shear waves caused by particles vibrating parallel to the fracture strike and propagating upward along the wellbore axis are more azimuthal than those caused by particles vibrating perpendicular to the fracture strike and propagating upward along the wellbore axis. If the particle vibration caused by the shear wave is at an angle to the fracture strike, the incident shear wave splits into fast and slow shear waves propagating parallel and perpendicular to the fracture strike, respectively, and propagating upward along the wellbore axis at different velocities. This phenomenon is known as shear wave splitting. It is worth noting that this shear wave splitting phenomenon occurs not only in fractured formations but also in unfractured formations with unbalanced in-situ stress, i.e., in formations with strong anisotropy. Strong anisotropy is also observed when the formation dips at a large angle and has well-developed bedding. Indicators for measuring formation anisotropy include the percent energy difference between fast and slow shear waves, anisotropy indicators based on time difference, and anisotropy indicators based on time. Based on these theories, a single-well fracture intensity curve can be calculated, which, combined with microseismic B values, can be used to develop natural fracture and reservoir matrix separation strategies.

[0070] like Figure 3 As shown in the figure, a statistical analysis of the energy levels of natural cracks generated by hydraulic fracturing was conducted, and a total of 5770 event points were analyzed. The energy level distribution range of the platform microseismic event points was: -2.434 to 1.635. The analysis results showed that the platform had a high proportion of large-magnitude events and was greatly affected by natural cracks.

[0071] like Figure 4As shown in the figure, the fracture intensity, artificial fracture complexity index and B value of different wells were analyzed, and the relationship between the fracture intensity of different fracturing sections of horizontal wells and the artificial fracture complexity index and B value was statistically analyzed. When the fracture intensity is greater than 0.02, the fracture complexity index and natural fracture B value increase significantly.

[0072] like Figure 5 As shown in the figure, using the microseismic monitoring data generated by actual fracturing construction, the spatial position of the event point and the time of earthquake occurrence can be clearly located, and the energy level corresponding to different event points can be characterized, providing a data basis for event point separation.

[0073] exist Figure 6 In the figure, microseismic waves propagate from the earthquake source to three detectors. After the earthquake source ruptures, the microseismic waves reach detectors G1, G2, and G3 according to their respective propagation times. The microseismic signals received at the detectors show varying degrees of attenuation. However, there are no natural cracks on the path from the microseismic event point to the G1 detector, which is a normal attenuation of energy with propagation distance. Natural cracks exist between the earthquake source and G2 and G3. Our method is to reversely compensate the energy of the received signals of each detector along the propagation path to compensate for the additional energy loss caused by the existence of natural cracks.

[0074] like Figure 7 As shown in the figure, the B-value analysis technique is used to calculate the B-value of a microseismic event point. The horizontal axis represents the energy level, and the vertical axis represents the number of microseismic data corresponding to that energy level, expressed logarithmically. A tangent line is drawn at a relatively smooth position on the statistical curve, and its slope is the B-value.

[0075] like Figure 8 As shown in the figure, energy compensation is performed on the collected microseismic time points in combination with the logging interpretation results. After compensation, the slope of the B value increases significantly, reflecting that there are obvious differences in energy strength at different event points. This shows that the energy compensation scheme can improve the recognition of artificial fractures and natural fractures. The absolute minimum magnitude increases from 0.82 to 1.04, and the energy compensation increment is 26.8%. At the same time, the data correlation also increases from 0.9974 to 0.9985. Figure 8 As shown in the figure, combined with the single well fracture prediction curve, the seismic fracture prediction model is adjusted to Figure 8 It can be seen that the fracture prediction accuracy is significantly improved after single well disturbance, and the matching rate is increased by more than 30%.

[0076] like Figure 9 As shown in the figure, a comparison is made between the single well fracture prediction curve and the natural fracture strength predicted by microseismic analysis. The comparison results show that the correlation coefficient is increased from 0.6419 to 0.8341 after energy compensation, providing an accurate data basis for subsequent attribute disturbance.

[0077] like Figure 10 As shown in the figure, the 3D seismic fracture prediction model is rotated by combining the fracture prediction curve after energy compensation. Figure 9 It can be seen that the fracture prediction accuracy is significantly improved after single well disturbance, and the matching rate is increased by more than 30%.

[0078] like Figure 11 As shown in the figure, a separation scheme was developed in combination with the logging interpretation results to carry out the division of response characteristics of different types of microseismic event points on the platform, completing the separation of microseismic events. Events with magnitudes greater than the threshold are basically microseismic events caused by natural fractures, showing an irregular trend of extending along the direction of natural fractures. For large-scale faults, the microseismic response characteristics are: the fracture energy level is relatively high, indicating a violent energy release, and the time and location of the earthquake are not fixed, and the extension distance is greater than 1000m.

[0079] like Figure 12 As shown in the figure, events with magnitudes less than the threshold are basically caused by the expansion of artificial fracture networks. The microseismic event points formed in the matrix reservoir are generally symmetrically distributed at both ends of the wellbore. The energy level event points are the smallest, and the lateral distance of spatial extension is also the shortest. The data distribution is relatively neat.

[0080] like Figure 13 As shown in the figure, due to the serious filtration loss of large-scale natural fractures, when the fracturing fluid enters the fracture zone, the pump pressure will be significantly reduced. The four wells of the Lu 203H56 platform have obvious pressure drop characteristics when carrying out fracturing operations at the location of large-scale development. The pressure drop time is positively correlated with the distance between the spatial location of the fracture development and the wellbore. The relevant well section construction parameters are selected to verify it, and the accuracy of the Lu 203H56 platform model is verified by engineering parameters.

[0081] like Figure 14 As shown in the figure, the fracturing operation data shows that the five wells of the Lu 203H58 platform all have obvious pressure drop characteristics at the location of large-scale fracture development. Due to the serious filtration loss of large-scale natural fractures, when the fracturing fluid enters the fracture zone, the pump pressure will be significantly reduced. The accuracy of the Lu 203H58 platform model is verified by engineering parameters.

[0082] like Figure 15 As shown in the figure, based on the comparison of the agreement rate between the natural fracture interpretation before and after microseismic energy compensation and the well logging interpretation, it can be seen that: before energy compensation, the agreement rate between the natural fractures identified by microseismic and the well logging interpretation is only 70%, green indicates agreement, and red indicates disagreement; before energy compensation, the agreement rate is increased to 83.3%, indicating that this technology can effectively improve the agreement rate.

[0083] In another embodiment of the present invention, a microseismic event point separation system is provided. The system can be used to implement the above-mentioned microseismic event point separation method, specifically comprising:

[0084] Data acquisition module: responsible for collecting well logging data and microseismic event data, including energy level, depth and spatial location information.

[0085] Data preprocessing module: performs noise filtering, standardization and format conversion on the collected logging data and microseismic event data for subsequent analysis.

[0086] Energy analysis module: Analyzes the energy differentiation characteristics of logging data curves, calculates fracture strength curves, and provides conditional constraints for microseismic data separation.

[0087] Spatial distribution analysis module: extracts the spatial distribution patterns of microseismic events and constructs the network morphology of microseismic events in three-dimensional space.

[0088] Interactive analysis module: Interactive analysis of the B value of microseismic events and the logging fracture intensity curve is carried out, and energy compensation of microseismic energy levels is performed based on the fracture development intensity at different depths.

[0089] Primary and secondary fracture analysis module: Based on the energy conservation principle of fracturing fluid, it distinguishes primary and secondary fractures and analyzes the relationship between the number of fracture branches and the width, conductivity and pressure transmission of the main fracture.

[0090] Statistical analysis module: evaluates the power law relationship between the number of microseismic events and magnitude, counts the B values ​​of different well sections, and forms a systematic statistical analysis.

[0091] Event point separation module: Based on the energy-compensated microseismic information data, the microseismic event point separation scheme is finally determined to achieve effective classification of natural fractures and reservoir matrix.

[0092] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the microseismic event point separation method.

[0093] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.

[0094] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the microseismic event point separation method in the above embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0095] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A microseismic event point separation method based on well logging constraints, characterized in that: The following steps are involved: (a) Using well logging data, we analyze the energy differentiation characteristics of the logging data curve and calculate the fracture strength curve, which serves as the condition constraint for subsequent microseismic data separation; (b) Extract the energy level, depth, and spatial location information of microseismic event data to clarify the distribution patterns and characteristics of microseismic event points in three-dimensional space; (c) Based on the temporal and spatial locations of microseismic events, the fracture network morphology is constructed, assuming that new fractures are likely to extend from existing fractures; (d) Interactive analysis of the B value of microseismic events and the well logging fracture intensity curve is performed, and energy compensation of the microseismic energy level is performed based on the fracture development intensity at different depths; (e) Based on the energy conservation principle of fracturing fluid, distinguish between primary and secondary fractures and analyze the relationship between the number of fracture branches and the width, conductivity and pressure transmission of the main fracture; (f) Evaluate the power law relationship between the number of microseismic events and their magnitude, calculate the B values ​​for different well sections, and form a systematic statistical analysis; (g) Based on the energy-compensated microseismic information data, the separation scheme of microseismic event points is finally determined to achieve effective classification of natural fractures and reservoir matrix.

2. The method according to claim 1, wherein The calculation of the fracture strength curve is based on the anisotropic characteristics of well logging data and fracture porosity analysis.

3. The method according to claim 1, wherein The energy compensation is achieved by correcting the microseismic energy level.

4. The method according to any one of claims 1, wherein The power law relationship is derived by statistically analyzing the frequency and magnitude distribution of microseismic events, forming a linear model to support the prediction of microseismic events.

5. The method according to any one of claims 1, wherein The separation scheme has been verified multiple times using different test data to ensure the accuracy and reliability of the results.

6. A microseismic event point separation system, characterized by: The system can be used to implement the microseismic event point separation method according to any one of claims 1 to 5, specifically comprising: Data acquisition module: responsible for collecting well logging data and microseismic event data, including energy level, depth and spatial location information; Data preprocessing module: performs noise filtering, standardization, and format conversion on the collected well logging data and microseismic event data for subsequent analysis; Energy analysis module: Analyzes the energy differentiation characteristics of logging data curves, calculates fracture strength curves, and provides conditional constraints for microseismic data separation; Spatial distribution analysis module: extracts the spatial distribution patterns of microseismic events and constructs the network morphology of microseismic events in three-dimensional space; Interactive analysis module: Interactive analysis of the B value of microseismic events and the logging fracture intensity curve, combining the fracture development intensity at different depths to perform energy compensation for the microseismic energy level; Primary and secondary fracture analysis module: Based on the energy conservation principle of fracturing fluid, it distinguishes primary and secondary fractures and analyzes the relationship between the number of fracture branches and the width, conductivity and pressure transmission of the main fracture; Statistical analysis module: evaluates the power law relationship between the number of microseismic events and magnitude, calculates the B value of different well sections, and forms a systematic statistical analysis; Event point separation module: Based on the energy-compensated microseismic information data, the microseismic event point separation scheme is finally determined to achieve effective classification of natural fractures and reservoir matrix.

7. A computer device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the microseismic event point separation method according to one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the microseismic event point separation method according to any one of claims 1 to 5 is implemented.

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