Method, system, medium and equipment for describing communication path of karst fracture-vug type reservoir

Abstract

The invention provides a karst fracture-cavity type reservoir communication path description method and system, a medium and equipment, and belongs to the technical field of geophysical exploration of petroleum. The method comprises the following steps: step 1, carrying out classification attribute optimization and depiction on a fracture-cavity type reservoir body; step 2, connectivity degree attribute characterization; 3, tracking paths in the dynamically connected well groups by using an automatic tracking method based on waveform similarity; and 4, quantitatively depicting the communication path of the karst fracture-vug type reservoir. According to the method, different types of seepage paths, heterogeneity of the seepage paths and high-flow-guiding channels in the karst fracture-cavity type oil reservoir can be tracked, communication paths formed by all corrosion holes, cracks and karst ancient river channels and flow guiding capacity are included, and the method has the advantages of being high in speed and visual in display; compared with dynamic connectivity analysis, the method is not limited by whether dynamic data are comprehensive or not, and compared with current static connectivity analysis, the connectivity path is analyzed more quantitatively.

Description

Technical Field

[0001] The invention belongs to the technical field of petroleum geophysical exploration, and in particular relates to a method, system, medium and equipment for depicting the connection path of a karst fracture-cave reservoir. Background Art

[0002] The Tahe Oilfield is an unconventional oil and gas reservoir formed by multiple phases of tectonic movement and paleokarst superposition and transformation, with caves and fractures as the main storage space and seepage channels. Due to the strong reservoir heterogeneity, how to characterize the well-to-well connectivity has become a difficult problem.

[0003] In order to guide the efficient development of karst fracture-vuggy reservoirs, it is crucial to correctly understand the connectivity of underground reservoirs and the flow laws of fluids.

[0004] At present, the connectivity between wells is mainly studied from two aspects: static and dynamic. The main research method of static connectivity is to analyze connectivity through geological models, and to judge the connectivity between wells by whether there is a connecting channel in the static characterization results of the reservoir. However, there is still a gap between static connectivity and dynamic connectivity, and there are several main problems: 1. The current static characterization of fracture-cavity reservoirs can only answer the question of "whether there is" the possibility of connectivity, but cannot meet the fine requirements for the characterization of static connectivity paths in current development, so it is necessary to characterize the heterogeneity of fractures and caves; 2. The current static connectivity does not use the prior information of dynamic connectivity, resulting in a large difference between static connectivity and dynamic connectivity, and there are many cases where dynamic and static do not match; 3. The current static connectivity analysis still remains at the perspective of qualitative analysis, without intuitive characterization of the connectivity path and graded quantitative evaluation.

[0005] The dynamic connectivity research method mainly uses the different production characteristics between single wells and well groups with connectivity, and uses pressure trend analysis, production interference well testing, injection and production effectiveness and other methods to analyze the dynamic connectivity between wells. The dynamic method has its constraints and requires sufficient dynamic data and corresponding measures, but the lack of data often makes it difficult to study the dynamic connectivity between wells.

[0006] In addition, water drive characteristics and numerical simulations have certain reference significance for well connectivity analysis, but due to the complex and changeable fluid flow characteristics in carbonate reservoirs, the current methods still have certain uncertainties. Summary of the invention

[0007] The purpose of the present invention is to solve the difficulties existing in the above-mentioned prior art, and to provide a method, system, medium and equipment for characterizing the connection paths of karst fracture-cave reservoirs. In view of the characteristics of karst fracture-cave oil reservoirs with superimposed multiple periods of karstification and structural transformation, strong heterogeneity and complex connection paths, how to identify the seepage paths of caves, underground rivers, cracks, etc., quantitatively characterize their heterogeneity, track the dominant connection paths and conduct quantitative graded evaluation, so as to achieve the purpose of characterizing the dominant connection channels between fractures and caves, and provide reliable technical support for the efficient development of karst fracture-cave oil reservoirs.

[0008] The present invention is achieved through the following technical solutions:

[0009] A first aspect of the present invention provides a method for characterizing a communication path of a karst fracture-cave reservoir, comprising the following steps:

[0010] Step 1, classify and optimize the attributes of the fracture-cavity reservoir and characterize it;

[0011] Step 2: Characterization of connectivity attributes;

[0012] Step 3, using an automatic tracking method based on waveform similarity to track the path within the dynamically connected well group;

[0013] Step 4: quantitatively characterize the connectivity paths of karst fracture-vuggy reservoirs.

[0014] A further improvement of the present invention is:

[0015] In step 1, post-stack wave impedance inversion and fault are used to classify and optimize the fracture-cavity reservoir, specifically:

[0016] Through layer calibration and wavelet extraction, the initial wave impedance model is constructed, and sparse pulse inversion is performed to obtain the final wave impedance result, which is used to characterize cave-hole type reservoirs; ant tracking is performed based on coherence, AFE or structural tensor attributes, and parameters are adjusted to optimize ant body attributes to characterize primary and secondary faults.

[0017] A further improvement of the present invention is:

[0018] In step 1, based on the statistics of the attribute range of the well emptying and leakage section, the porosity-impedance quantity plate and the ant body attribute threshold value are constructed to classify and characterize the fracture-cavity reservoir:

[0019] The porosity of caves is greater than 5%, and that of holes is 2-5%. Reservoirs are graded and characterized based on the porosity-wave impedance scale. Fault and fracture reservoirs are characterized based on the ant body threshold calibration results.

[0020] A further improvement of the present invention is:

[0021] The connectivity attribute characterization in step 2 specifically includes:

[0022] Using inversion, frequency-divided coherence gradient and curvature attributes, the drilling data representing the porosity and permeability encountered on the well are used as learning samples to train the neural network.

[0023] The porosity, fault scale indicator factor, and fracture density attributes are integrated to comprehensively characterize the seepage capacity or connectivity of the fracture-cavity body.

[0024] A further improvement of the present invention is:

[0025] The drilling data representing the porosity and permeability encountered in the well are used as learning samples to train the neural network. Specifically, the reservoir interpretation type, leakage velocity or porosity data are used as learning samples. First, the well data is coarsened to make it consistent with the resolution of the attribute model. Then, the coarsened well data and porosity, fault scale indicator factor, and curvature attributes are used as input to train the neural network.

[0026] The neural network is based on BP (back propagation) neural network.

[0027] A further improvement of the present invention is:

[0028] In step 3, the dynamic connected well group is tracked using an automatic tracking method based on waveform similarity, and the specific operations include:

[0029] Taking the dynamic data of the connected well groups that have been constructed in the work area as the priori basis, on the profiles along the cross-sections, underground river pipelines, and weathering crust characterization results, the automatic tracking algorithm based on waveform similarity is used to track the peaks on the two-dimensional profiles of the fusion attributes of the connectivity degree between wells, as the dominant connection path in the well-connected medium along the cross-section;

[0030] Select 1-2 connected well groups to carry out the automatic tracking method based on waveform similarity, and then use the well group as the starting point to carry out three-dimensional automatic tracking of the entire work area for the fusion attributes to obtain the global advantageous connection path.

[0031] A further improvement of the present invention is:

[0032] In the step 4, the connectivity paths of the karst fracture-cave reservoirs are quantitatively described. Specifically, the operations include: assigning values ​​to the global dominant connectivity paths according to the fusion attributes, calibrating the attribute thresholds of the fusion attributes, porosity, fracture scale indicator factor, and fracture density in combination with the injection-production relationship, dividing the dominant connectivity paths into high, medium, and low connectivity segments, and quantitatively characterizing the dominant connectivity paths in the entire area.

[0033] A second aspect of the present invention provides a karst fracture-cave reservoir communication path characterization system, comprising:

[0034] Attribute optimization and characterization unit, used for classifying and optimizing fracture-cavity reservoirs;

[0035] An attribute representation unit, used for representing the attribute of connectivity degree;

[0036] An intra-group path tracking unit, used for tracking the intra-well path of the dynamically connected well group using an automatic tracking method based on waveform similarity;

[0037] The reservoir connection path characterization unit is used to quantitatively characterize the connection path of karst fracture-cavity reservoirs.

[0038] The third object of the present invention is to provide a computer-readable storage medium, which stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for characterizing the connection path of karst fracture-cave reservoirs as described above.

[0039] A fourth aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in the method for characterizing the connectivity path of karst fracture-cave reservoirs as described above.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The method of the present invention mainly uses data such as well logging, seismic, and production dynamics, and is efficient and economical;

[0042] Compared with dynamic connectivity analysis, the method of the present invention is not limited by whether the dynamic data is comprehensive, and compared with the current static connectivity analysis, it can analyze the connectivity path more quantitatively;

[0043] The present invention can track different types of seepage paths, heterogeneity of seepage paths and high conductivity channels in karst fracture-vuggy oil reservoirs, including the connection paths and conductivity formed by all dissolved holes, cracks and karst ancient river channels, and has the characteristics of fast speed and intuitive display. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of the method of the present invention;

[0045] Figure 2 It is the characterization of the fault zone of a fracture-cave unit in Tahe Oilfield and the fusion of its connectivity attributes;

[0046] Figure 3 It is the qualitative identification of the dominant connection path on the main section;

[0047] Figure 4 It is the dynamic connectivity relationship between wells A, B, and C and the static fracture-cavity characterization;

[0048] Figure 5 It is the dynamic connectivity relationship between wells B and D and the static fracture-cavity characterization;

[0049] Figure 6 It is a two-dimensional tracking of the dominant connection paths between wells in a dynamically connected well group;

[0050] Figure 7 It is the dynamic connection of well group fractures and holes and the three-dimensional connection path characterization;

[0051] Figure 8 It is the result of characterizing the advantageous connected paths of the entire work area in three dimensions. DETAILED DESCRIPTION

[0052] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0053] This method first classifies and characterizes the fracture-cavity reservoir based on the well calibration, and uses inversion, frequency-divided coherence gradient, curvature and other attributes to construct a neural network with the well reservoir type or porosity data as learning samples, and fuses multiple attributes to characterize the degree of connectivity and assign values ​​to the fracture-cavity model; then, the dynamic connected well group uses an automatic tracking method based on waveform similarity to track the path within the well group, and then uses this path as the initial point for global tracking to obtain the global path, and assigns it according to the degree of connectivity attribute, and quantifies and grades the networked connected path according to the strength of the attribute. Compared with the previous static connectivity analysis, the method of the present invention has the advantages of high efficiency, high resolution, and quantification, providing technical support for the next step of the development of karst fracture-cavity reservoirs.

[0054] [Example 1]

[0055] The embodiment of the present invention provides a method for describing the connection paths of karst fracture-cavity reservoirs. Figure 1 As shown, the specific steps include:

[0056] Step 1, classify and optimize the attributes of the fracture-cavity reservoir and characterize it;

[0057] Specific operations include:

[0058] Post-stack wave impedance inversion and fault classification are used to select the optimal attributes of fracture-cavity reservoirs, specifically:

[0059] Through layer calibration and wavelet extraction, the initial wave impedance model is constructed, and sparse pulse inversion is performed to obtain the final wave impedance result, which is used to characterize cave-hole type reservoirs; ant tracking is performed based on coherence, AFE or structural tensor attributes, and parameters are adjusted to optimize ant body attributes to characterize primary and secondary faults.

[0060] Based on the statistics of the attribute range of well emptying and leakage sections, the porosity-impedance scale and ant body attribute threshold are constructed to classify and characterize the fracture-cavity reservoir:

[0061] The porosity of caves is greater than 5%, and that of holes is 2-5%. Reservoirs are graded and characterized based on the porosity-wave impedance scale. Fault and fracture reservoirs are characterized based on the ant body threshold calibration results.

[0062] Step 2: Connectivity attribute representation

[0063] By using inversion, frequency-divided coherence gradient, curvature and other attributes, the drilling data (such as reservoir interpretation type, leakage velocity, porosity, etc.) on the well that characterizes the porosity and permeability encountered are used as learning samples to train the neural network. The porosity, fault scale indicator factor, and fracture development density (obtained by curvature or prestack AVAZ) attributes are integrated to comprehensively characterize the seepage capacity or connectivity of the fracture-cavity body.

[0064] The drilling data (such as reservoir interpretation type, leakage velocity, porosity, etc.) representing the porosity and permeability encountered on the well are used as learning samples to train the neural network. Specifically, the reservoir interpretation type, leakage velocity or porosity data are used as learning samples. First, the well data is resampled (coarsened) to make it consistent with the resolution of the attribute model; then the coarsened well data and attributes such as porosity, fault scale indicator factor, curvature, etc. are used as input to carry out principal component analysis, and parameters such as the number of iterations and error range are set to train the neural network;

[0065] The neural network is based on BP (back propagation) neural network.

[0066] Comprehensively characterize the seepage capacity or connectivity of the fracture-cavity body, specifically:

[0067] In clastic reservoirs, porosity or reservoir type has a certain correlation with permeability or connectivity, but the target work area is carbonate rock. Therefore, not only porosity is selected as the attribute to characterize permeability, but also fault scale indicator factors, prestack fracture detection, curvature and other attributes characterizing fracture development density and ground stress are introduced to comprehensively characterize the seepage capacity of carbonate fracture-vuggy reservoirs.

[0068] Step 3, using an automatic tracking method based on waveform similarity to track the path within the dynamically connected well group;

[0069] Taking the dynamic data of the connected well groups that have been constructed in the work area as the priori basis, on the profiles of the characterization results such as cross-sections, underground river pipelines, and weathering crusts, the automatic tracking algorithm based on waveform similarity is used to track the peaks on the two-dimensional profiles of the connectivity fusion attributes between the wells, as the dominant connectivity path along the cross-sections and other well-connected media.

[0070] Select 1-2 connected well groups to carry out the automatic tracking method based on waveform similarity, and then use the well group as the starting point to carry out three-dimensional automatic tracking of the entire work area for the fusion attributes to obtain the global advantageous connection path.

[0071] Step 4: Quantitatively characterize the connection paths of karst fracture-cave reservoirs

[0072] The specific operations include: assigning values ​​to the global dominant connectivity paths according to the fusion attributes, calibrating the attribute thresholds of the fusion attributes, porosity, fracture scale indicator factor, and fracture density in combination with the injection-production relationship, dividing the dominant connectivity paths into high, medium, and low connectivity segments, and quantitatively characterizing the dominant connectivity paths in the entire area.

[0073] Compared with the previous static connectivity analysis, the method of the present invention has the advantages of high efficiency, high resolution and quantification, and provides technical support for the next development of karst fracture-cave reservoirs.

[0074] [Example 2]

[0075] The embodiments of the method of the present invention are as follows:

[0076] (1) Fracture-cavity reservoir characterization and attribute fusion

[0077] Taking a fracture-cave unit in Tahe Oilfield as an example, pre-stack depth migration, post-stack impedance inversion, tensor, ANT, etc. are used as the data basis for fracture-cave characterization. The corresponding attribute values ​​of drilling emptying and leakage sections are extracted along the well trajectory. The porosity-impedance quantity plate is established and the threshold value of fault fracture carving is determined. The fracture-cave reservoir is classified and characterized.

[0078] In order to characterize the heterogeneity of physical properties in karst caves, caves, underground rivers, weathering crusts, and multi-scale faults, and to trace the dominant connectivity paths on this basis, it is necessary to use well data as hard data and multiple seismic attributes as soft data to characterize the heterogeneity of physical properties.

[0079] When multi-attribute fusion is performed, it should be noted that when selecting well data and multiple seismic attributes, data and parameters that have a corresponding relationship with porosity, permeability or connectivity should be selected. This time, a fracture-cave unit in Block 12 of the Tahe Oilfield is taken as an example. The interpretation results of reservoir types of 5 wells are selected as learning samples. The seismic attributes selected are porosity, maximum positive curvature (characterizing the density of fracture development), and prestack frequency coherence gradient attribute (characterizing the degree of fracture fragmentation) to train the neural network. The attribute fusion process is as follows: Figure 2 As shown in Figure 2, the horizontal and vertical connection paths are qualitatively characterized based on the fusion attributes that characterize the degree of connectivity along the cross section ( Figure 3 ).

[0080] (2) Tracking the dominant path of dynamically connected well groups

[0081] In order to ensure the stability of static path characterization, it is necessary to characterize the paths between wells in the well group of the constructed connected well network as the basis for tracing the global dominant connected path.

[0082] First, a qualitative analysis was conducted on the paths connecting the well network in the study area to identify the types of connection paths: Well A in the study area injected water into Well B and Well C. From the static characterization of the fracture-cavity reservoir, the types of inter-well connection paths were mainly northwest-oriented and east-west-oriented faults ( Figure 4 ); D water injection B negative effect, static reservoir engraving shows that the inter-well connection path types are mainly faults and dark river sections ( Figure 5 ).

[0083] On the interpretation section intercepted along the fault and underground river, the waveform similarity algorithm is used to perform two-dimensional dominant path tracking from the bottom hole fracture body, and finally the dominant seepage channel along the section and the underground river pipeline is obtained, and represented by the interpretation line along the section ( Figure 6 ).

[0084] The fusion attribute representing the degree of connectivity is assigned to the path line, calibrated with dynamic information such as effective time, quantified attribute threshold value and divided the dominant path into high, medium and low connectivity segments ( Figure 7 ).

[0085] (3) Tracking of the optimal connectivity paths across the entire region

[0086] By tracking the dominant connection paths within 1-2 connected well groups in the study area, these paths are used as basic sample points to carry out three-dimensional path tracking in the entire area ( Figure 8 ). The initial tracking results did not cover the entire area, so the tracking results were interpolated and smoothed as a whole to fill in the blank areas and obtain the connected level. Based on the fracture-hole characterization model, this connected level was phase-controlled, and only the dominant connected paths within the fracture-hole contours were retained, while it was considered that there were no or few connected paths in the bedrock.

[0087] Using the connectivity threshold in step (2) as a limiting condition, the network connectivity paths in the entire area are quantified into high, medium, and low connectivity segments.

[0088] In short, through the fusion of seismic attributes guided by well data, the reservoir heterogeneity of fracture-cave reservoirs is characterized to differentiate the physical properties or connectivity. The injection-production response and pressure response in production dynamics are integrated into the static connectivity analysis in the form of prior information, reducing the uncertainty of path characterization and quantifying the segmented characteristics of the path; starting from the connected well group, the dominant path of the whole area is tracked, all possible connected paths are characterized, and quantitative evaluation is performed separately, which has certain guiding significance for the development of karst fracture-cave reservoirs.

[0089] [Example 3]

[0090] An embodiment of the present invention provides a karst fracture-cave reservoir communication path characterization system, comprising:

[0091] Attribute optimization and characterization unit, used for classifying and optimizing fracture-cavity reservoirs;

[0092] An attribute representation unit, used for representing the attribute of connectivity degree;

[0093] An intra-group path tracking unit, used for tracking the intra-well path of the dynamically connected well group using an automatic tracking method based on waveform similarity;

[0094] The reservoir connection path characterization unit is used to quantitatively characterize the connection path of karst fracture-cavity reservoirs.

[0095] [Example 4]

[0096] An embodiment of the present invention provides a computer-readable storage medium, which stores at least one computer-executable program. When the at least one program is executed by the computer, the computer executes the steps in the method for characterizing the connection path of the karst fracture-cave reservoir as described in Example 1.

[0097] [Example 5]

[0098] An embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in the method for characterizing the connection path of karst fracture-cave reservoirs as described in Example 1.

[0099] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0100] The above technical solution is only one implementation mode of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the principles disclosed in the present invention, and it is not limited to the technical solution described in the above specific embodiments of the present invention. Therefore, the above description is only preferred and does not have a restrictive meaning.

Claims

1. A method for describing the connection paths of karst fracture-cavity reservoirs, characterized in that: The following steps are involved: Step 1, classify and optimize the attributes of the fracture-cavity reservoir and characterize it; Step 2: Characterization of connectivity attributes; Step 3, using an automatic tracking method based on waveform similarity to track the path within the dynamically connected well group; Step 4: quantitatively characterize the connectivity paths of karst fracture-vuggy reservoirs.

2. The method for describing the connection paths of karst fracture-cavity reservoirs according to claim 1, characterized in that: In step 1, post-stack wave impedance inversion and fault are used to classify and optimize the fracture-cavity reservoir, specifically: Through layer calibration and wavelet extraction, the initial wave impedance model is constructed, and sparse pulse inversion is performed to obtain the final wave impedance result, which is used to characterize cave-hole type reservoirs; ant tracking is performed based on coherence, AFE or structural tensor attributes, and parameters are adjusted to optimize ant body attributes to characterize primary and secondary faults.

3. The method for describing the connection paths of karst fracture-cavity reservoirs according to claim 2, characterized in that: In step 1, based on the statistics of the attribute range of the well emptying and leakage section, the porosity-impedance quantity plate and the ant body attribute threshold value are constructed to classify and characterize the fracture-cavity reservoir: The porosity of caves is greater than 5%, and that of holes is 2-5%. Reservoirs are graded and characterized based on the porosity-wave impedance scale. Fault and fracture reservoirs are characterized based on the ant body threshold calibration results.

4. The method for describing the connection paths of karst fracture-cavity reservoirs according to claim 3, characterized in that: The connectivity attribute characterization in step 2 specifically includes: Using inversion, frequency-divided coherence gradient and curvature attributes, the drilling data representing the porosity and permeability encountered on the well are used as learning samples to train the neural network. The porosity, fault scale indicator factor, and fracture density attributes are integrated to comprehensively characterize the seepage capacity or connectivity of the fracture-cavity body.

5. The method for describing the connection paths of karst fracture-cave reservoirs according to claim 4, characterized in that: The drilling data representing the porosity and permeability encountered in the well are used as learning samples to train the neural network. Specifically, the reservoir interpretation type, leakage velocity or porosity data are used as learning samples. First, the well data is coarsened to make it consistent with the resolution of the attribute model. Then, the coarsened well data and porosity, fault scale indicator factor, and curvature attributes are used as input to train the neural network. The neural network is based on a back-propagation neural network.

6. The method for describing the connection paths of karst fracture-cavity reservoirs according to claim 5, characterized in that: In step 3, the dynamic connected well group is tracked using an automatic tracking method based on waveform similarity, and the specific operations include: Taking the dynamic data of the connected well groups that have been constructed in the work area as the priori basis, on the profiles along the cross-sections, underground river pipelines, and weathering crust characterization results, the automatic tracking algorithm based on waveform similarity is used to track the peaks on the two-dimensional profiles of the fusion attributes of the connectivity degree between wells, as the dominant connection path in the well-connected medium along the cross-section; Select 1-2 connected well groups to carry out the automatic tracking method based on waveform similarity, and then use the well group as the starting point to carry out three-dimensional automatic tracking of the entire work area for the fusion attributes to obtain the global advantageous connection path.

7. The method for describing the connection paths of karst fracture-cavity reservoirs according to claim 6, characterized in that: In the step 4, the connectivity paths of the karst fracture-cave reservoirs are quantitatively described. Specifically, the operations include: assigning values ​​to the global dominant connectivity paths according to the fusion attributes, calibrating the attribute thresholds of the fusion attributes, porosity, fracture scale indicator factor, and fracture density in combination with the injection-production relationship, dividing the dominant connectivity paths into high, medium, and low connectivity segments, and quantitatively characterizing the dominant connectivity paths in the entire area.

8. A karst fracture-cave reservoir connection path characterization system, characterized in that: include: Attribute optimization and characterization unit, used for classifying and optimizing fracture-cavity reservoirs; An attribute representation unit, used for representing the attribute of connectivity degree; An intra-group path tracking unit, used for tracking the intra-well path of the dynamically connected well group using an automatic tracking method based on waveform similarity; The reservoir connection path characterization unit is used to quantitatively characterize the connection path of karst fracture-cavity reservoirs.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer-executable program, and when the at least one program is executed by the computer, the computer executes the steps in the method for characterizing the communication path of the karst fracture-cave reservoir as described in any one of claims 1-7.

10. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps in the method for characterizing the connection path of the karst fracture-cave reservoir as described in any one of claims 1 to 7.

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