Method, device, storage medium and computer device for dynamic crack identification

Abstract

The present invention provides a method, apparatus, storage medium and computer device for dynamically identifying fractures. The method includes: transforming the production dynamic data of a target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages; for the production spectrogram of each production stage, performing quantitative analysis on the production spectrogram, and determining the fracture-vug types below the target well at this production stage and the volume ratio between various fracture-vug types according to the analysis results; for each production stage, determining the volume of each fracture-vug type below the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug types below the target well and the volume ratio between various fracture-vug types, so as to obtain the volume of each fracture-vug type of the target well at each production stage. This method provides a basis for fracture-vug reservoir description, geological modeling and water flooding treatment of oil wells.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir development, and particularly relates to a method and device for dynamically identifying fractures, a storage medium, and a computer device. Background Art

[0002] In fractured-vuggy oil and gas reservoirs, fractures are not only the flow channels of carbonate oil and gas reservoirs but also one of the main reservoir space types. Therefore, it is crucial to identify the fracture distribution in the reservoir as much as possible for the exploration and development of carbonate oil and gas resources.

[0003] Like other types of reservoirs, the fracture identification results of fractured-vuggy reservoirs can be used in all aspects of reservoir description and reservoir development. The fracture identification results can not only directly affect the accuracy of the reservoir geological model and the reliability of reservoir numerical simulation calculations but also be the main basis for formulating reasonable working systems and development adjustment countermeasures for oil wells. However, quantitative fracture identification is still a recognized difficult problem internationally and is also one of the hot issues in reservoir description.

[0004] There are many conventional means for fracture identification and description. For example, they include core observation method, imaging logging method, seismic inversion method, tracer monitoring method, water absorption profile analysis method, etc. Among them, the core observation method and the imaging logging method are limited to observing the fractures around the wellbore; the seismic inversion method not only has low identification accuracy but also is difficult to identify whether the water breakthrough in the oil well caused by fractures is bottom water or injected water. These methods belong to static identification and cannot identify the changes in fractures between different development stages. Moreover, the content and conclusions of these methods mostly belong to qualitative identification with low quantification degree, which directly affects the application effect of fracture identification results.

[0005] The burial depth of the reservoir of the fault-karst body oil reservoir in the Shunbei area of the Tahe Oilfield mostly reaches more than 5000 meters, and the deepest can reach about 8000 - 9000 meters. For example, the designed well depth of Well Shunbei Peng 1 is 8593 meters. The research results of the fault-karst body oil reservoir show that a large number of vertical fractures are developed in the deep part of the reservoir. These vertical fractures communicate with the bottom water. Due to the high energy and large volume of the bottom water, water channeling along the high-angle fractures is serious, resulting in rapid water breakthrough in the oil well, which directly affects the development effect of the fault-karst body oil reservoir. In the middle and late stages of the development of the fault-karst body oil reservoir, in order to maintain the formation energy, it is necessary to convert some oil wells with poor development effects and high water cut into injection wells, which results in the situation of water fingering from the injection wells along the transverse fractures to the oil wells.

[0006] The channeling of bottom water and injection water along fractures can lead to rapid water flooding in oil wells. How to identify the water flooding caused by injection water and bottom water and treat different water inlets separately to improve the pertinence and effectiveness of water flooding treatment methods is an urgent task. A large number of oilfield development practice experiences have proved that clear dynamic development characteristics of fractures can provide a direct basis for the methods of treating water flooding in oil wells. Therefore, the dynamic identification of fracture development has high application value. From the perspective of the production process, fractures have a great impact on the laws of oil-water movement, the law of water cut increase, water flooding recovery rate, etc., and can even be a restrictive factor. The fracture development degrees in different directions are different, the water cut change processes are different, and the corresponding treatment measures are also different. Only by determining the fracture development conditions in different directions and analyzing the dynamic change laws of fractures during the production process can we guide geological modeling, plan preparation, treatment measures, etc.

[0007] From the needs of production and scientific research analysis, fracture identification is one of the core tasks in the description of fracture-vuggy reservoirs. However, the existing methods for fracture identification and description are a world-class problem. Especially for fracture-cavernous reservoirs with developed bottom water, the development time is short and the experience is scarce. There are a large number of key problems in fracture identification, including methods for dynamic fracture identification, differential analysis of fracture development degrees, verification of identification results, quantitative characterization of identification methods, etc. These problems have brought great troubles to the identification and development of fracture-vuggy reservoir formations to varying degrees. Therefore, a new dynamic identification method is urgently needed to identify fractures. Summary of the Invention

[0008] The main objective of the present invention is to provide a method, device, storage medium, and computer equipment for dynamic fracture identification to achieve quantitative identification of the fracture-vug development degree in a reservoir.

[0009] In a first aspect, the present application provides a method for dynamic fracture identification, including the following steps: transforming the production dynamic data of a target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages, where the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well; for the production spectrogram of each production stage, performing quantitative analysis on the production spectrogram, and determining the fracture-vug types below the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis results; for each production stage, determining the volume of each fracture-vug type below the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug types below the target well and the volume ratio between each fracture-vug type, so as to obtain the volume of each fracture-vug type of the target well at each production stage.

[0010] In one embodiment, the production dynamic data includes dynamic data of production volume, pressure or water cut; the quantitative analysis of the production spectrogram includes quantitative analysis of the spectral width, density, amplitude, power or phase of the production spectrogram.

[0011] In one embodiment, the production dynamic data includes dynamic data of water cut, and the production spectrogram is a water cut spectrogram; when the production spectrogram is a water cut spectrogram, the amplitude of the production spectrogram is quantitatively analyzed, and the fracture-vug types below the target well at this production stage and the volume ratio between each fracture-vug type are determined according to the analysis results, including: analyzing the average amplitude of each different frequency band in the water cut spectrogram of the target well, and for each frequency band of the water cut spectrogram, determining the fracture-vug type corresponding to this frequency band according to its average amplitude; analyzing the proportional relationship between the average amplitudes of each different frequency band in the water cut spectrogram, and based on the proportional relationship and the fracture-vug type corresponding to each frequency band in the water cut spectrogram, determining the volume ratio between each fracture-vug type below the target well.

[0012] In one embodiment, when the target well is a production well of a multi-well fracture-vug unit, the production dynamic data of the target well at different production stages is transformed from the time domain to the frequency domain to obtain the production spectrogram of the target well at each production stage, including: obtaining the production dynamic data of the target well before and after water injection at each production stage; transforming the production dynamic data of the target well before and after water injection at each production stage from the time domain to the frequency domain to obtain the production spectrogram of the target well before and after water injection at each production stage; for each production stage, determining the difference spectrogram obtained by subtracting the production spectrogram of the target well after water injection from the production spectrogram before water injection, and using the difference spectrogram as the production spectrogram of the target well at this production stage, so as to obtain the production spectrogram of the target well at each production stage.

[0013] In one embodiment, after obtaining the production dynamic data of the target well before water injection and before obtaining the production dynamic data of the target well after water injection, the method further includes: performing connectivity analysis on the multi-well fracture-vug unit to determine the injection wells connected to the target well, and injecting water into the injection wells; after the target well is affected by the water injection of the injection wells, obtaining the production dynamic data of the target well at this production stage.

[0014] In one embodiment, each different frequency band in the water cut spectrogram of the target well includes three frequency bands with frequencies of 0 - 0.02, 0.02 - 0.1, and frequencies greater than 0.1; the fracture-vug type corresponding to the frequency range of 0 - 0.02 is a large karst cave, the fracture-vug type corresponding to the frequency range of 0.02 - 0.1 is a large fracture, and the fracture-vug type corresponding to the frequency range greater than 0.1 is a small fracture.

[0015] In one embodiment, the fracture-vug types below the target well include large karst caves, large-scale fractures, and medium- and small-scale fractures. Among them, the large karst cave is a cave with a diameter greater than 500 mm, the large-scale fracture is a fracture with a width greater than 1 mm, and the medium- and small-scale fracture is a fracture with a width less than or equal to 1 mm.

[0016] In a second aspect, the present application provides a fracture dynamic identification device, including: a data transformation module, configured to transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages, where the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well; a data analysis module, configured to perform quantitative analysis on the production spectrograms for each production stage, and determine the fracture-vug types below the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis results; a volume calculation module, configured to, for each production stage, determine the volume of each fracture-vug type below the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug types below the target well and the volume ratio between each fracture-vug type, so as to obtain the volume of each fracture-vug type of the target well at each production stage.

[0017] In a third aspect, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the fracture dynamic identification method as described above.

[0018] In a fourth aspect, the present application provides a computer device including a processor and a storage medium storing program code, which, when executed by the processor, implements the steps of the fracture dynamic identification method as described above.

[0019] The fracture dynamic identification method provided by the present invention performs time-frequency domain transformation processing on the oilfield production dynamic data to obtain the production spectrograms in the frequency domain, and analyzes the obtained production spectrograms to achieve quantitative analysis of the fracture-vug development degree in the fracture-vug reservoir, realize dynamic differential and quantitative identification of the fracture-vug reservoir, and provide a basis for fracture-vug reservoir description, geological modeling, and oil well water flooding treatment. The method of the present invention is simple, reliable, and has high accuracy. The required data are all based on on-site production, with a wide source and low cost of obtaining data. This method is not only applicable to carbonate rock reservoirs, especially to fault-karst reservoirs, but also applicable to the description of other bottom water type oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The specification drawings forming a part of the present application are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0021] Figure 1Flow chart of a method for dynamically identifying fractures according to an exemplary embodiment of the present application;

[0022] Figure 2 Flow chart of a method for dynamically identifying fractures according to a specific embodiment of the present application;

[0023] Figure 3 Production curve of Well TH10433H according to a specific embodiment of the present application;

[0024] Figure 4 Water cut change curve of Well TH10433H according to a specific embodiment of the present application;

[0025] Figure 5 Water cut change spectrum of Well TH10433 according to a specific embodiment of the present application;

[0026] Figure 6 Water cut change spectrum of Well TH10433H when only fractures are included according to a specific embodiment of the present application;

[0027] Figure 7 Seismic processing result of Well TH10433H according to a specific embodiment of the present application;

[0028] Figure 8 Production curve of Well S86 according to a specific embodiment of the present application;

[0029] Figure 9 Water cut spectrum of Well S86 before water injection effectiveness according to a specific embodiment of the present application;

[0030] Figure 10 Water cut spectrum of Well S86 when only fractures are included before water injection effectiveness according to a specific embodiment of the present application;

[0031] Figure 11 Water cut spectrum of Well S86 after water injection effectiveness according to a specific embodiment of the present application;

[0032] Figure 12 Seismic processing result of Well S86 according to a specific embodiment of the present application. Specific Embodiments

[0033] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0034] Embodiment 1

[0035] This embodiment provides a method for dynamically identifying fractures, Figure 1 which is the flow chart of a method for dynamically identifying fractures according to an exemplary embodiment of the present application. AsFigure 1 As shown in Figure 1 , the method for dynamically identifying fractures may include the following steps:

[0036] S100: Transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages, where the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well;

[0037] S200: For the production spectrogram of each production stage, perform quantitative analysis on the production spectrogram, and determine the fracture-vug types below the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis results;

[0038] S300: For each production stage, determine the volumes of each fracture-vug type below the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug types below the target well and the volume ratio between each fracture-vug type, so as to obtain the volumes of each fracture-vug type of the target well at each production stage.

[0039] Embodiment 2

[0040] This embodiment provides a method for dynamically identifying fractures, including the following steps:

[0041] First step, transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages, where the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well, and may include, for example, the dynamic data of production, the dynamic data of pressure, or the dynamic data of water cut.

[0042] When the target well is a production well of a single-well fracture-vug unit, transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at each production stage, including: obtaining the production dynamic data of the target well before water injection at each production stage; transforming the production dynamic data of the target well before water injection at each production stage from the time domain to the frequency domain to obtain the production spectrograms of the target well before water injection at each production stage; for each production stage, using the production spectrogram of the target well before water injection as the production spectrogram of the target well at this production stage, so as to obtain the production spectrograms of the target well at each production stage.

[0043] When the target well is a production well of a multi - well fracture - cave unit, transform the production performance data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at each production stage, including: obtaining the production performance data of the target well before and after water injection at each production stage; transforming the production performance data of the target well before and after water injection at each production stage from the time domain to the frequency domain to obtain the production spectrograms of the target well before and after water injection at each production stage; for each production stage, determine the difference spectrogram obtained by subtracting the production spectrogram of the target well after water injection from that before water injection, and use the difference spectrogram as the production spectrogram of the target well at this production stage, so as to obtain the production spectrograms of the target well at each production stage.

[0044] Wherein, when the target well is a production well of a multi - well fracture - cave unit, after obtaining the production performance data of the target well before water injection and before obtaining the production performance data of the target well after water injection, the method further includes: performing connectivity analysis on the multi - well fracture - cave unit, determining the injection wells connected to the target well, and injecting water into the injection wells; after the target well is affected by the water injection of the injection wells, obtaining the production performance data of the target well at this production stage.

[0045] The production performance data of the target well at different production stages can be transformed from the time domain to the frequency domain by various methods. For example, it can be done by the method of Fourier transform.

[0046] As a mathematical processing method and means, Fourier transform is a very effective analysis method in the field of digital signal processing. In complex signal processing, the typical role of Fourier transform is to decompose a signal into a frequency spectrum - showing the magnitude corresponding to the frequency. It can transform the signal in the time domain to the frequency domain for analysis (forward transform), and can also transform the signal in the frequency domain to the time domain for analysis (inverse transform).

[0047] Among them, the expression of the Fourier forward transform is:

[0048]

[0049] The expression of the Fourier inverse transform is:

[0050]

[0051] Regarding the production performance data of the oil well drilled through the fracture - cave reservoir body as a reflection of the reservoir production signal, performing Fourier transform on it can obtain the distribution of different frequencies. Analyzing the frequency changes of the dynamic indicators at different development stages can obtain the dynamic indicators of fractures and karst caves.

[0052] In the second step, for the production spectrogram of each production stage, perform quantitative analysis on the production spectrogram, and determine the fracture - cave types below the target well at this production stage and the volume ratio between each fracture - cave type according to the analysis results.

[0053] Among them, the quantitative analysis of the production spectrogram includes the quantitative analysis of the spectral width, density, amplitude, power or phase of the production spectrogram. When the production dynamic data includes the dynamic data of water content, the production spectrogram is the water cut spectrogram. When the production spectrogram is the water cut spectrogram, the amplitude of the production spectrogram is quantitatively analyzed, and the fracture-cavity type below the target well in this production stage and the volume ratio between each fracture-cavity type are determined according to the analysis results, including: analyzing the average amplitude of each different frequency band in the water cut spectrogram of the target well, and for each frequency band of the water cut spectrogram, determining the fracture-cavity type corresponding to this frequency band according to its average amplitude; analyzing the proportional relationship between the average amplitudes of each different frequency band in the water cut spectrogram, and according to the proportional relationship, based on the fracture-cavity type corresponding to each frequency band in the water cut spectrogram, determining the volume ratio between each fracture-cavity type below the target well.

[0054] The fracture-cavity types below the target well may include large karst caves, large-scale fractures and medium-small scale fractures. Among them, the large karst cave is a cave with a diameter greater than 500 mm, the large-scale fracture is a fracture with a fracture width greater than 1 mm, and the medium-small scale fracture is a fracture with a fracture width less than or equal to 1 mm.

[0055] Each different frequency band in the water cut spectrogram of the target well may include three frequency bands with frequencies of 0 - 0.02, 0.02 - 0.1, and frequencies greater than 0.1. Among them, the fracture-cavity type corresponding to the frequency range of 0 - 0.02 is a large karst cave, the fracture-cavity type corresponding to the frequency range of 0.02 - 0.1 is a large-scale fracture, and the fracture-cavity type corresponding to the frequency range greater than 0.1 is a small-scale fracture.

[0056] In the third step, for each production stage, according to the total production of the target well in this production stage, the fracture-cavity type below the target well and the volume ratio between each fracture-cavity type, determine the volume of each fracture-cavity type below the target well in this production stage, so as to obtain the volume of each fracture-cavity type of the target well in each production stage.

[0057] Among them, the total production may include total oil production, total gas production, etc.

[0058] The crack dynamic identification method provided by the present invention performs time-frequency domain transformation on the oilfield production dynamic data to obtain a production spectrogram in the frequency domain, and analyzes the obtained production spectrogram to achieve quantitative analysis of the fracture and cave development degree in the fracture-cave reservoir, realize the dynamic differential and quantitative identification of the fracture-cave reservoir, and provide a basis for fracture-cave reservoir description, geological modeling and oil well water flooding treatment. The method of the present invention is simple, reliable, and has high accuracy. All the required data are based on on-site production, with a wide range of sources and low cost for obtaining data. This method is not only applicable to carbonate rock reservoirs, especially fracture-cavity reservoirs, but also applicable to the description of other bottom water type oil and gas reservoirs.

[0059] Embodiment III

[0060] This embodiment introduces the crack dynamic identification method of the present application through a specific example.

[0061] Through numerical simulation calculations, it is found that the development characteristics of a fracture-cave reservoir with developed bottom water are completely different from those of a sandstone reservoir. In a fracture-cave reservoir with developed bottom water, the bottom water and the injected water rapidly advance along the fractures, causing serious water flooding in the oil wells. Moreover, the fracture aperture varies greatly with the water breakthrough time and water breakthrough intensity. Once the fracture sees water, the fracture will produce very little oil.

[0062] Through production practice, it is found that the oil well communicates with the bottom water and the injection well through fractures. Under the action of the pressure difference, the occurrence time and intensity change of water production in different fractures show an irregular spectrogram distribution. The more evenly the fractures are developed, the wider the spectrogram width. On the contrary, the more concentrated the fracture development degree is, the narrower the spectrogram width is, and the higher the spectral peak is. The height of the spectrogram, that is, the amplitude size, reflects the volume size of the fracture. Therefore, it can be said that the water breakthrough distribution spectrogram of the fracture directly reflects the development degree of the fracture-cave reservoir body in the fracture-cavity reservoir. Using statistical analysis and with the help of the frequency spectrum analysis method, quantitative analysis of a large number of spectrograms can obtain the cut-off values of different reservoir body types, and based on this, quantitative identification and description of the fracture and cave development degree can be established.

[0063] According to the basic theory of geophysics and the research results of seismic attributes of fracture-cave reservoirs in the later stage, there is a direct relationship between the frequency-divided signal of seismic waves and the size of fractures. The larger the fracture, the lower the frequency of the seismic reflection wave, and vice versa. In the process of oil reservoir seismic inversion, Fourier transform is introduced to process the seismic reflection wave signal to obtain different reflection wave frequency distributions. The seismic imaging obtained according to different reflection wave frequency distributions can be used to identify fracture-cave structures of different scales, such as the "beaded" seismic reflection characteristics of pore-type reservoirs. Therefore, the size of the seismic wave frequency reflects the size distribution of the fractures. Based on this principle, the oil well production data can be regarded as the production signal output by the oil reservoir system, and mathematical processing of the production signal can obtain the production frequency spectrum, providing conditions for the later quantitative description of fractures.

[0064] The dynamic properties of production data determine that fracture identification based on spectral analysis of production data is a dynamic identification method. In this embodiment, starting from the mathematical processing method, using the fast Fourier transform as a means, we calculate the variation characteristics of the water cut in oil wells to achieve dynamic fracture identification, providing a basis for geological modeling of fault-karst reservoirs and water flooding treatment of oil wells.

[0065] The fracture dynamic identification method of this embodiment includes:

[0066] (1) Production spectrum calculation: Perform Fourier transform on the production dynamic data at different development stages, convert the production curve from the time domain to the frequency domain distribution, and form a production spectrum, generally including three spectrum types: production, pressure, and water cut.

[0067] (2) Quantitative analysis of fracture development degree: Use spectral analysis technology to quantitatively analyze the spectrum, obtain quantization indexes such as spectral width, density, amplitude, power, and phase, and achieve quantization calculation.

[0068] (3) Analysis of fracture development degree in different orientations: For a single-well fracture-vug unit, the production spectrum obtained is the fracture information communicating with the bottom water, and most of these fractures belong to high-angle fractures; for a multi-well fracture-vug unit, during the period of water injection response, use the spectrum to reflect the fracture-vugs communicating with the bottom water and the injected water, and obtain the fracture system developed at medium and low angles.

[0069] (4) Quantitative analysis of the development degree of different types of reservoir spaces: Distinguish different types of reservoir spaces according to frequency differences. Among them, the part with a frequency of 0 - 0.02 corresponds to large karst cave bodies, the part with a frequency of 0.02 - 0.1 corresponds to large fractures, and the part with a frequency greater than 0.1 corresponds to small and medium-scale fractures.

[0070] The following takes the change in the water cut of a production well in a fault-karst reservoir as an example to illustrate the technical solution of this application. For different fracture-vug units, the process of fracture dynamic identification includes the following steps (where, for a single-well fracture-vug unit, only steps (1), (2), and (6) need to be executed, and for a multi-well fracture-vug unit, steps (1) to (6) need to be executed (as Figure 2 shown)):

[0071] (1) Select an oil well in the geological background of a fault-karst reservoir, collect the production dynamic data of this well, and screen the production dynamic data to delete the abnormal points caused by changes in work systems and measures;

[0072] (2) Analyze the water injection of the production well, perform Fourier transform processing on the changing part of the water cut before water injection in the water injection well, and obtain the spectral curve of high-angle fractures communicating with the bottom water;

[0073] (3) Conduct connectivity analysis to obtain the connectivity between oil and water wells, determine the injection wells connected to the production wells for water injection, and analyze the water cut curves before and after the production wells are affected respectively to determine whether the water source is bottom water or injected water.

[0074] (4) Perform Fourier transform processing on the part of the water cut change after the production well is affected by water injection to obtain the frequency spectrum curve of the fracture distribution communicating with the bottom water and the injection wells;

[0075] (5) Process the two frequency spectrum curves of the production well before and after water injection to obtain the dynamic distribution results of high-angle fractures communicating with the bottom water and medium-low angle fractures communicating with the injection wells respectively.

[0076] Before water injection, the production well communicates with the bottom water, and the frequency spectrum curve reflects the distribution of high-angle fractures below the production well. After water injection, the production well communicates with the bottom water and the injected water, and the frequency spectrum curve reflects the overall situation of high-angle fractures and medium-low angle fractures below the production well. Subtracting the frequency spectrum curve after water injection from the frequency spectrum curve before water injection can obtain the frequency spectrum curve reflecting the medium-low angle fractures below the production well.

[0077] (6) Classify and evaluate the fracture-cavity development degree of the fault-karst reservoir, and conduct a detailed breakdown according to the transformed frequency distribution. The amplitude value corresponding to the part with a frequency of 0-0.02 is high, indicating that the water cut change is very small. This part corresponds to large karst cavities. When the frequency increases to 0.1, the overall amplitude value decreases, but the change accelerates. The part with a frequency of 0.02-0.1 corresponds to large fractures, and the part with a frequency greater than 0.1 corresponds to small and medium-scale fractures.

[0078] According to the various fracture-cavity types and the proportional relationship between them, combined with the total production information such as the total oil production or total gas production of the production well, the volume of each fracture-cavity type below the production well can be determined.

[0079] As an important means, Fourier transform brings new ideas and means for reservoir engineers to identify the fracture development situation. Through Fourier transform, complex and disordered production data can be logically integrated to obtain frequency spectrum analysis indicators such as frequency spectrum, amplitude, power, and density. Comparing the fracture dynamic identification results of this application with the frequency division results of seismic reflection signals further verifies the effectiveness and reliability of the technical solution of this application.

[0080] Example 4

[0081] For a single-well fracture-cavity unit, take the single-well fracture-cavity unit of TH10433H in the fault-karst reservoir as an example to illustrate the fracture dynamic identification method of this application.

[0082] Well TH10433H is located in the tenth area of Tahe Oilfield. It is a horizontal well under the geological background of fault-karst bodies. Well TH10433H was drilled to TD on January 3, 2014, with a total depth of 6375.00 (slant) / 6083.00 (vertical), and the horizon is O2yj. The 7-5 / 8″ casing is directly below the wellhead, and it is completed by testing with 149.2mm open-hole tubing. This well has successively undergone measures such as flowing naturally, artificial lift conversion, acidizing, wellhead replacement, and directional sandblasting perforation completion.

[0083] The connectivity study of this well shows that it is not connected to the surrounding wells and belongs to an isolated fracture-cave single-well unit, reflecting the development of high-angle fractures in this area. Considering that there have been many changes in the working systems and measures of this well from 2015 to 2017, it is necessary to discard the data from 2015 to 2017 and specifically study the production data changes before October 2015. Then, spectral processing and analysis are carried out on the production data changes. The results are shown in Figures 3 - 6 .

[0084] From the distribution results of the water cut frequency spectrum of this well, the part with a frequency of 0 corresponds to the cave signal. According to the analysis results, the reservoir space type corresponding to the frequency band of 0 - 0.02 is large caves, with an average amplitude of 26.28. The reservoir space type corresponding to the frequency band between 0.02 and 0.1 is fracture zones, i.e., large fractures, with an average amplitude of 1.64. The remaining fractures are medium and small-scale fractures, with an average amplitude value of 0.47. It can be seen that the volume of the cave is about 55.9 times that of the fractures and about 16 times that of the fracture zone part. Combining with the total production information of this well, the volumes of large caves, large fractures, and medium and small-scale fractures below this production well can be determined.

[0085] Compared with the seismic data as shown in Figure 7 , it can be seen that the seismic reflection image shows that there are bead-like reflection characteristics near the bottom of Well TH10433H. Moreover, there was a mud loss of 1355 cubic meters during the drilling process. All these information indicate that there are a certain number of caves and fracture systems developed near this well.

[0086] For multi-well fracture-cave units, taking the S86 multi-well fracture-cave unit in the fault-karst reservoir as an example, the fracture dynamic identification method of this application is described.

[0087] Basic situation: Well S86 was drilled to TD on August 21, 2001, with a total depth of 5881.50m, and the horizon is O1-2y. Then, it was completed by open-hole acid fracturing. This well has successively undergone measures such as flowing naturally, artificial lift conversion, acidizing, and tubing string replacement.

[0088] Geological background analysis: This well is located in the eighth area of Tahe Oilfield, with a geological background of fault-karst bodies, and it is the main oil-producing well in the S86 multi-well fracture-cave unit.

[0089] Water production analysis: The well started to be affected in October 2010. Therefore, the water cut before that came from bottom water, and the subsequent change in water cut mainly came from the injection well.

[0090] Connectivity analysis: The injection-production relationship curve method was used to analyze the connectivity. The results showed that the well was connected to Well TK836CH and was affected by bottom water and Well TK836CH injection well. It belonged to a fracture-cavity unit with multi-well connectivity, reflecting the development and distribution of medium-low angle fractures between wells in this area.

[0091] Injection effectiveness analysis: Well S86 was affected by the injection well TK836CH. The water cut change data from October 2, 2010 to August 9, 2011 were selected for processing. The results are shown in Figures 8 - 11 .

[0092] From the distribution result of the water cut frequency spectrum of this well, the part near frequency 0 corresponds to the cave signal. According to the analysis result, the reservoir space type corresponding to the frequency band of 0 - 0.02 is large caves, with an average amplitude of 31.88. The reservoir space type corresponding to the frequency band of 0.02 - 0.1 is fracture zones, i.e., large fractures, with an average amplitude of 2.93. The remaining frequency bands correspond to medium and small scale fractures, with an average amplitude value of 0.64. It can be seen that the volume of the cave is about 49.8 times that of the fracture volume and about 10.88 times that of the fracture zone. Combining with the total production data of this well, the volumes of large caves, large fractures and medium and small scale fractures below this well can be obtained.

[0093] Compared with the seismic data as shown in Figure 12 , it can be seen that there are bead-like reflection characteristics in the seismic reflection image near the bottom of Well S86. Moreover, there was a mud loss of 3024 cubic meters during the drilling process. All these information indicate that there are a certain number of caves and fracture systems developed near this well.

[0094] In this embodiment, the fracture-cavity development situation obtained based on production dynamics is compared with the frequency division interpretation result in seismic interpretation to achieve mutual verification of the two methods. Based on a large amount of research, a quantitative method for dynamic identification and description of fractures and caves in this application is finally formed.

[0095] The fracture dynamic identification method of this application was applied to 208 oil wells in 39 fracture-cavity units of the fault-karst reservoir in Tahe Oilfield. Taking the water cut distribution of oil wells as the application object, the water cut frequency spectrum and fracture-cavity dynamic identification results were obtained and compared with the seismic data processing results. The coincidence rate was over 92%, showing a high consistency. This method can better reflect the dynamic changes of fractures and lay a solid foundation for the dynamic identification of fractures in fault-karst reservoirs and the water flooding treatment of oil wells.

[0096] Example 5

[0097] This embodiment provides a device for dynamically identifying fractures, including: a data transformation module, configured to transform the production dynamic data of a target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages, where the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well; a data analysis module, configured to perform quantitative analysis on the production spectrograms for each production stage, and determine the fracture-vug types below the target well and the volume ratio between each fracture-vug type according to the analysis results; a volume calculation module, configured to determine the volume of each fracture-vug type below the target well at each production stage according to the total production of the target well at this production stage and the fracture-vug types below the target well and the volume ratio between each fracture-vug type, so as to obtain the volume of each fracture-vug type of the target well at each production stage.

[0098] In another example of this embodiment, the device for dynamically identifying fractures further includes a memory and a processor, where the processor executes the following program modules stored in the memory: a data transformation module, a data analysis module, and a volume calculation module to implement the dynamic identification of the fracture-vug reservoir type and volume below the production well.

[0099] Embodiment Six

[0100] This embodiment provides a storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the steps of the fracture dynamic identification method as described above are implemented.

[0101] The storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory, or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD), or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage, or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0102] Embodiment Seven

[0103] This embodiment provides a computer device, including a processor and a storage medium storing program code, where when the program code is executed by the processor, the steps of the fracture dynamic identification method as described above are implemented.

[0104] In one example, a computer device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0105] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (FLASH RAM). The memory is an example of computer-readable media.

[0106] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. When the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0107] It should be understood that the exemplary embodiments in this specification can be implemented in many different forms and should not be construed as being limited only to the embodiments set forth herein. These embodiments are provided to make the disclosure of the present application thorough and complete, and to fully convey the concept of these exemplary embodiments to those of ordinary skill in the art, and should not be construed as a limitation of the present invention.

[0108] Any equivalent structural or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0109] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a readable storage medium as described above (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a system device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

Claims

1. A method for dynamically identifying cracks, characterized in that, Including the following steps: Transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at different production stages. Wherein, the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well, and the production dynamic data includes the dynamic data of production rate, the dynamic data of pressure, or the dynamic data of water cut; For the production spectrogram of each production stage, perform quantitative analysis on the production spectrogram, and determine the fracture-vug types under the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis results. The quantitative analysis of the production spectrogram includes quantitative analysis of the spectral width, density, amplitude, power, or phase of the production spectrogram; For each production stage, determine the volume of each fracture-vug type under the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug types under the target well and the volume ratio between each fracture-vug type, so as to obtain the volume of each fracture-vug type of the target well at each production stage; Among them, the fracture-vug types under the target well include: large karst cavities, large-scale fractures, and medium-small scale fractures. Among them, the large karst cavity is a karst cavity with a diameter greater than 500 mm, the large-scale fracture is a fracture with a fracture width greater than 1 mm, and the medium-small scale fracture is a fracture with a fracture width less than or equal to 1 mm.

2. The crack dynamic identification method according to claim 1, wherein The production dynamic data includes the dynamic data of water cut, and the production spectrogram is the water cut spectrogram; When the production spectrogram is the water cut spectrogram, perform quantitative analysis on the amplitude of the production spectrogram; Determining the fracture-vug types under the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis results includes: Analyze the average amplitude of each different frequency band in the water cut spectrogram of the target well. For each frequency band of the water cut spectrogram, determine the fracture-vug type corresponding to this frequency band according to its average amplitude; Analyze the proportional relationship between the average amplitudes of each different frequency band in the water cut spectrogram, and determine the volume ratio between each fracture-vug type under the target well based on the fracture-vug type corresponding to each frequency band in the water cut spectrogram according to the proportional relationship.

3. The crack dynamic identification method according to claim 1, characterized in that, When the target well is a production well of a multi-well fracture-vug unit, transforming the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrograms of the target well at each production stage includes: Obtain the production dynamic data of the target well before and after water injection at each production stage; Transform the production dynamic data of the target well before and after water injection at each production stage from the time domain to the frequency domain to obtain the production spectrograms of the target well before and after water injection at each production stage; For each production stage, determine the difference spectrogram obtained by subtracting the production spectrogram of the target well after water injection from the production spectrogram before water injection, and use the difference spectrogram as the production spectrogram of the target well at this production stage, so as to obtain the production spectrograms of the target well at each production stage.

4. The crack dynamic identification method according to claim 3, characterized in that, After obtaining the production dynamic data of the target well before water injection and before obtaining the production dynamic data of the target well after water injection, the method further includes: Perform connectivity analysis on the multi-well fracture-vug unit, determine the injection wells connected to the target well, and inject water into the injection wells; After the target well is affected by the water injection of the water injection well, obtain the production dynamic data of the target well at this production stage.

5. The crack dynamic identification method according to claim 2, characterized in that Each different frequency band in the water cut spectrogram of the target well includes three frequency bands with frequencies of 0 to 0.02, 0.02 to 0.1, and frequencies greater than 0.1; When the frequency range is 0 to 0.02, the corresponding fracture-vug type is a large karst cave. When the frequency range is 0.02 to 0.1, the corresponding fracture-vug type is a large fracture. When the frequency range is greater than 0.1, the corresponding fracture-vug type is a small fracture.

6. A device for dynamically identifying cracks, characterized in that, It includes: A data transformation module, configured to transform the production dynamic data of the target well at different production stages from the time domain to the frequency domain to obtain the production spectrogram of the target well at different production stages. Among them, the production dynamic data is the dynamic data of a parameter characterizing the production capacity of the target well, and the production dynamic data includes the dynamic data of production, the dynamic data of pressure, or the dynamic data of water cut; A data analysis module, configured to perform quantitative analysis on the production spectrogram for each production stage, and determine the fracture-vug type below the target well at this production stage and the volume ratio between each fracture-vug type according to the analysis result. The quantitative analysis of the production spectrogram includes quantitative analysis of the spectral width, density, amplitude, power, or phase of the production spectrogram; A volume calculation module, configured to, for each production stage, determine the volume of each fracture-vug type below the target well at this production stage according to the total production of the target well at this production stage and the fracture-vug type below the target well and the volume ratio between each fracture-vug type, so as to obtain the volume of each fracture-vug type of the target well at each production stage. Among them, the fracture-vug types below the target well include: large karst cave bodies, large-scale fractures, and medium and small-scale fractures. Among them, the large karst cave body is a karst cave with a diameter greater than 500 mm, the large-scale fracture is a fracture with a width greater than 1 mm, and the medium and small-scale fracture is a fracture with a width less than or equal to 1 mm.

7. A storage medium stores a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the fracture dynamic identification method as described in any one of claims 1-5.

8. A computer device, including a processor and a storage medium storing program codes. When the program codes are executed by the processor, it implements the steps of the fracture dynamic identification method as described in any one of claims 1-5.

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