A method, system, apparatus, and medium for predicting the extent of fracture development in volcanic rock

Abstract

The application discloses a method, system, device and medium for predicting the development degree of volcanic rock cracks, and relates to the technical field of volcanic rock oil and gas reservoir exploration. The method comprises the following steps: determining a three-dimensional fusion seismic attribute body of a target area; determining a three-dimensional volcanic rock lithology data body according to the three-dimensional fusion seismic attribute body and a lithology division criterion; determining a crack development rate data body predicted by a thickness factor and a crack development rate data body predicted by a control area fault according to the three-dimensional volcanic rock lithology data body, a statistical relationship curve determined based on a sampling point and a drilling identification result and a well point fusion seismic attribute; and performing weighted calculation on the two crack development rate data bodies to obtain a final crack development rate data body. The application can effectively predict the crack development rate of volcanic rock cracks and provide support for the quantitative evaluation of the crack development degree.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of volcanic rock oil and gas reservoir exploration, and particularly relates to a method, system, device and medium for predicting the development degree of volcanic rock cracks. BACKGROUND

[0002] The cracks are not only important reservoir spaces of the volcanic rock oil and gas reservoirs, but also important channels for oil and gas seepage, and control the migration and accumulation of oil and gas. Meanwhile, the development degree of the natural cracks in the reservoirs provides a key basis for the evaluation of the expansion potential of the crack network space of the volcanic rock oil and gas in the fracturing exploitation process. Therefore, the accurate prediction of the development degree of the cracks in the volcanic rock reservoirs is the key to the success of the volcanic rock oil and gas reservoir exploration, and significantly affects the economic benefits.

[0003] For a long time, the evaluation and prediction of the cracks in the complex volcanic rock reservoirs mainly adopt the core method, the seismic method, the logging method and the numerical simulation method. Among them, the core method mainly analyzes the crack heterogeneity through qualitative description, has high accuracy, but is difficult to be widely researched in the research area. The seismic method focuses on the relative comparison of various three-dimensional seismic data attribute values, and qualitatively divides the crack development zones according to the data interval. The logging method mainly identifies the developed layers, and the crack line density and crack porosity play an important role in the evaluation of the vertical development of the cracks in the single well, but cannot be used for the research on the distribution law, and the correlation between the wells is not enough.

[0004] Based on the above analysis, these commonly used volcanic rock reservoir crack prediction methods have their own advantages, but also have disadvantages and application limitations, resulting in poor application effect, and the prediction accuracy in the actual production still needs to be further improved. The main difficulties are as follows: first, most of the sedimentary basins in China have experienced multiple tectonic movements and volcanic eruptions, resulting in complex crack superposition and combination, and the crack combination form is less discussed. Second, although the cracks in the single well and the wellbore near the stratum are reliable, the development degree and distribution of the cracks in the space of the reservoirs still lack effective and practical methods. Third, most of the prediction methods focus on the cracks themselves, and less consider the crack expansion and communication potential in the fracturing exploitation process. SUMMARY

[0005] The present application aims to provide a method, system, device and medium for predicting the development degree of volcanic rock cracks, so as to effectively predict the crack development rate of the volcanic rock cracks and provide support for the quantitative evaluation of the crack development degree.

[0006] To achieve the above object, the present application provides the following scheme:

[0007] A method for predicting the development degree of volcanic rock cracks, comprising:

[0008] determining a three-dimensional fused seismic attribute volume of a target area; the three-dimensional fused seismic attribute volume of the target area comprises fused seismic attributes of all points in the target area; the fused seismic attributes are obtained by weighted fusion of several set seismic attributes;

[0009] determining a three-dimensional volcanic rock lithology data volume of the target area according to the three-dimensional fused seismic attribute volume of the target area and a lithology classification criterion; the three-dimensional volcanic rock lithology data volume of the target area comprises seismic predicted lithology layers and corresponding lithology layer thicknesses of all points in the target area;

[0010] determining a directly converted crack development rate data volume of the target area according to the three-dimensional volcanic rock lithology data volume of the target area and a second statistical relationship curve; the second statistical relationship curve is a statistical relationship curve of single lithology layer thicknesses of different rock types and crack development rates;

[0011] performing well-seismic scale conversion error correction on the directly converted crack development rate data volume of the target area according to a third statistical relationship curve to obtain a crack development rate data volume predicted by thickness factors; the third statistical relationship curve is a statistical relationship curve of directly converted crack development rates of different rock types and enhanced weighted crack development rates;

[0012] determining a crack development rate data volume predicted by control area faults according to the three-dimensional volcanic rock lithology data volume of the target area and a first statistical relationship curve; the first statistical relationship curve is a statistical relationship curve of vertical distances from control area faults and crack development rates of different rock types; the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on drilling identification results of sampling points and well point fused seismic attributes;

[0013] performing weighted calculation on the crack development rate data volume predicted by thickness factors and the crack development rate data volume predicted by control area faults to obtain a final crack development rate data volume; the final crack development rate data volume comprises crack development rate prediction values of all points in the target area.

[0014] A system for predicting the development degree of volcanic rock cracks, comprising:

[0015] The three-dimensional fused seismic attribute volume determination module is configured to determine a three-dimensional fused seismic attribute volume of a target region, wherein the three-dimensional fused seismic attribute volume of the target region comprises fused seismic attributes of all points in the target region, and the fused seismic attributes are obtained by weighting and fusing a plurality of set seismic attributes.

[0016] The three-dimensional volcanic rock lithology data volume determination module is configured to determine a three-dimensional volcanic rock lithology data volume of the target region according to the three-dimensional fused seismic attribute volume of the target region and a lithology division criterion, wherein the three-dimensional volcanic rock lithology data volume of the target region comprises seismic predicted lithology layers and corresponding lithology layer thicknesses of all points in the target region.

[0017] The directly converted fracture development rate data volume determination module is configured to determine a directly converted fracture development rate data volume of the target region according to the three-dimensional volcanic rock lithology data volume of the target region and a second statistical relationship curve, wherein the second statistical relationship curve is a statistical relationship curve of single lithology layer thicknesses of different rock types and fracture development rates.

[0018] The well-seismic scale conversion error correction module is configured to correct well-seismic scale conversion errors of the directly converted fracture development rate data volume of the target region according to a third statistical relationship curve, to obtain a fracture development rate data volume predicted by thickness factors, wherein the third statistical relationship curve is a statistical relationship curve of directly converted fracture development rates of different rock types and enhanced weighted fracture development rates.

[0019] The fault-controlled fracture development rate data volume determination module is configured to determine a fracture development rate data volume predicted by fault-controlled fractures of the target region according to the three-dimensional volcanic rock lithology data volume of the target region and a first statistical relationship curve, wherein the first statistical relationship curve is a statistical relationship curve of vertical distances from fault-controlled fractures of different rock types and fracture development rates, and the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on drilling identification results of sampling points and well point fused seismic attributes.

[0020] The fracture development rate data volume calculation module is configured to perform weighted calculation on the fracture development rate data volume predicted by thickness factors and the fracture development rate data volume predicted by fault-controlled fractures, to obtain a final fracture development rate data volume, wherein the final fracture development rate data volume comprises fracture development rate prediction values of all points in the target region.

[0021] An electronic device includes a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the method for predicting a volcanic rock fracture development degree.

[0022] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method for predicting the development degree of volcanic rock cracks.

[0023] According to the specific embodiments of the present application, the following technical effects are disclosed:

[0024] The method for predicting the development degree of volcanic rock cracks provided by the present application determines the statistical relationship curves of the vertical distance of different rock types from the controlling area fracture and the crack development rate, the statistical relationship curves of the single lithology sublayer thickness of different rock types and the crack development rate, and the statistical relationship curves of the directly converted crack development rate and the enhanced weighted crack development rate of different rock types, according to the drilling identification results of the sampling points and the well point fusion seismic attribute, determines the crack development rate data body predicted by the thickness factor and the crack development rate data body predicted by the controlling area fracture according to the above statistical relationship curves and the three-dimensional fusion seismic attribute body, and further determines the final crack development rate data body. Since the present application combines the longitudinal resolution of logging information and the plane data dense and continuous research advantages of seismic data, it can overcome the defect that seismic data can only predict the crack development zone, thereby effectively predicting the crack development rate of volcanic rock cracks and providing support for the quantitative evaluation of the crack development degree. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0026] Figure 1 The flowchart of the method for predicting the development degree of volcanic rock cracks provided by the present application;

[0027] Figure 2 The schematic diagram of calculating the logging curve value of each depth point by using the N-point area method provided by the present application;

[0028] Figure 3 The statistical relationship curve diagram of the vertical distance of basic lava from the controlling area fracture and the crack development rate provided by the present application;

[0029] Figure 4 The statistical relationship curve diagram of the vertical distance of acid lava from the controlling area fracture and the crack development rate provided by the present application;

[0030] Figure 5 The statistical relationship curve diagram of the vertical distance of volcanic breccia from the controlling area fracture and the crack development rate provided by the present application;

[0031] Figure 6 A statistical relation curve of the vertical distance of tuff from the fault control area to the fracture development rate provided by the present application;

[0032] Figure 7 A longitudinal schematic diagram of the distance change of the wellbore to the fracture provided by the present application;

[0033] Figure 8 A planar schematic diagram of the distance change of the wellbore to the fracture provided by the present application;

[0034] Figure 9 A statistical relation curve of the single lithology layer thickness of basic lava to the fracture development rate provided by the present application;

[0035] Figure 10 A statistical relation curve of the single lithology layer thickness of acidic intrusive rock to the fracture development rate provided by the present application;

[0036] Figure 11 A statistical relation curve of the single lithology layer thickness of volcanic breccia to the fracture development rate provided by the present application;

[0037] Figure 12 A statistical relation curve of the single lithology layer thickness of tuff to the fracture development rate provided by the present application;

[0038] Figure 13 A schematic diagram of the acquisition range of seismic attributes provided by the present application;

[0039] Figure 14 A comparison schematic diagram of the seismic predicted lithology and the logging interpreted lithology provided by the present application;

[0040] Figure 15 A linear relation diagram of the directly converted fracture development rate and the enhanced weighted fracture development rate of basic lava provided by the present application;

[0041] Figure 16 A linear relation diagram of the directly converted fracture development rate and the enhanced weighted fracture development rate of acidic intrusive rock provided by the present application;

[0042] Figure 17 A linear relation diagram of the directly converted fracture development rate and the enhanced weighted fracture development rate of volcanic breccia provided by the present application;

[0043] Figure 18 A linear relation diagram of the directly converted fracture development rate and the enhanced weighted fracture development rate of tuff provided by the present application. DETAILED DESCRIPTION

[0044] Clearly and completely, the technical solutions in the embodiments of the present application will be described below with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] The present application aims to provide a method, system, device and medium for predicting the development degree of volcanic rock cracks, so as to effectively predict the crack development rate of volcanic rock cracks and provide support for quantitative evaluation of the development degree of cracks.

[0046] The volcanic rock reservoir is affected by multiple modes and multiple stages of eruption, and is also affected by the superimposed influence of multiple tectonic movements. The size and duration of tectonic stress in different periods are seriously heterogeneous, resulting in complex and diverse cracks in the volcanic rock reservoir, which presents extremely strong heterogeneity, mainly in the diversity of crack types, commonly intersecting cracks, network cracks and vertical cracks, and the diversity of crack combination forms. At present, the crack line density, crack porosity and crack width obtained by logging angle can better evaluate the development degree and effectiveness of intersecting cracks, but cannot reflect the heterogeneous development and distribution of network cracks in the volcanic rock reservoir. Although the seismic angle has good recognition effect on the macro crack development zone, this research based on large-scale data information has the disadvantages of low resolution and strong multi-solution, and still mainly relies on the relative size comparison in terms of crack development degree, and cannot obtain the real crack development degree. Therefore, in order to meet the production needs and technical needs of volcanic rock reservoir crack prediction, by combining the longitudinal resolution of logging information and the plane data dense and continuous research advantages of seismic data, the present application proposes a method, system, device and medium for predicting the development degree of volcanic rock cracks by phase-controlled thickness.

[0047] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with the drawings and specific embodiments.

[0048] Embodiment one

[0049] The present application provides a method for predicting the development degree of volcanic rock cracks, as shown in Figure 1 The method comprises the following steps:

[0050] Step S1: determining a three-dimensional fusion seismic attribute volume of a target area; the three-dimensional fusion seismic attribute volume of the target area comprises fusion seismic attributes of all point positions of the target area; the fusion seismic attribute is obtained by weighted fusion of a plurality of set seismic attributes.

[0051] Step S2: determining a three-dimensional volcanic rock lithology data volume of the target region according to the three-dimensional fused seismic attribute volume of the target region and a lithology classification criterion; the three-dimensional volcanic rock lithology data volume of the target region comprises seismic predicted lithology layers and corresponding lithology layer thicknesses of all point positions in the target region.

[0052] Step S3: determining a directly converted fracture development rate data volume of the target region according to the three-dimensional volcanic rock lithology data volume of the target region and a second statistical relationship curve; the second statistical relationship curve is a statistical relationship curve of single lithology layer thicknesses of different rock types and fracture development rates.

[0053] Step S4: performing well-seismic scale conversion error correction on the directly converted fracture development rate data volume of the target region according to a third statistical relationship curve to obtain a fracture development rate data volume predicted by a thickness factor; the third statistical relationship curve is a statistical relationship curve of directly converted fracture development rates of different rock types and enhanced weighted fracture development rates.

[0054] Step S5: determining a fracture development rate data volume predicted by a control area fault according to the three-dimensional volcanic rock lithology data volume of the target region and a first statistical relationship curve; the first statistical relationship curve is a statistical relationship curve of vertical distances from the control area fault and fracture development rates of different rock types; the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on drilling identification results of sampling points and well point fused seismic attributes.

[0055] Step S6: performing weighted calculation on the fracture development rate data volume predicted by the thickness factor and the fracture development rate data volume predicted by the control area fault to obtain a final fracture development rate data volume; the final fracture development rate data volume comprises fracture development rate prediction values of all point positions in the target region.

[0056] Further, the above method further comprises:

[0057] Step S0: obtaining drilling identification results of sampling points and well point fused seismic attributes; the drilling identification results are drilling coring and thin section identification results; determining a first statistical relationship curve and a second statistical relationship curve according to the drilling identification results; determining a third statistical relationship curve according to the drilling identification results, the well point fused seismic attributes and the second statistical relationship curve.

[0058] The above steps will be described in detail below.

[0059] Firstly, in step S0, the first statistical relationship curve and the second statistical relationship curve are determined according to the drilling identification results, specifically comprising:

[0060] Step S011: determining the rock type of the sampling point according to the drilling identification result.

[0061] In practical application, the rock types of the volcanic rock in the research area are determined by drilling coring and thin section identification data, and the rock types that need to be interpreted are determined.

[0062] Step S012: depth homing of the drilling identification result is performed, and logging data of each depth point is calculated; the logging data includes area average results of natural gamma curve value, acoustic curve value, neutron curve value, density curve value and deep resistivity curve value. Specifically, the depth homing of the drilling identification result is performed to obtain the logging curve of each depth point; the N-point area method is used to calculate the logging data of each depth point according to the logging curve.

[0063] In practical application, as shown in FIG. Figure 2 The logging curve values of the homed depth points are obtained by homing the drilling identification result, including natural gamma, acoustic, neutron, density and deep resistivity curves. The statistical method of the logging curve values of the homed depth points uses the N-point area method, and N is usually 5, 7 or 9. The area average result is more consistent with the basic principle model of the logging curve response, i.e. the rock volume physical model, so the result is more reasonable and reliable.

[0064] The specific formula for calculating the logging curve value by using the N-point area method is as follows:

[0065]

[0066] Wherein, x represents the area average result of the logging curve value, x represents the logging curve value on the logging curve, x s represents the logging curve value of the s-th sampling point on the logging curve, s represents the serial number of the sampling point, N represents the total number of the sampling points on the logging curve, and Rlev represents the sampling interval of the logging curve, which is generally 0.125 m. Figure 2 S1-S6 in the formula respectively represent the areas formed by the adjacent two sampling points and the logging curve.

[0067] Step S013: determining the thickness of a single lithologic layer according to the rock type of the sampling point and the logging data of each depth point, and determining the thickness of each fracture segment in the single lithologic layer by using the micro-resistivity scanning imaging logging image.

[0068] In practical application, the lithology identification result of the above-mentioned slice is combined with the logging curve value of the depth point thereof to make a lithology identification chart for point-by-point interpretation of single-well volcanic rock lithology, and a single-well continuous lithology sublayer is obtained. The thickness of a single lithology sublayer is the bottom depth of the lithology layer minus the top depth, and each lithology sublayer has a thickness of at least 0.5 m, and if the thickness is less than 0.5 m, the single lithology sublayer is combined into the upper continuous single lithology sublayer.

[0069] In the above-mentioned single lithology sublayer, the micro-resistivity scanning imaging logging image is used to identify the oblique fractures and network fractures, and the fracture segment thicknesses, including the oblique fracture segment thickness and the network fracture segment thickness, are further obtained. For a single oblique fracture, the fracture segment thickness is the distance defined by the peak and the valley of the fracture curve identified on the imaging logging image; for multiple oblique fractures, the fracture segment thickness is the distance defined by the top peak and the bottom valley; and for a network fracture, the fracture segment thickness is the distance defined by the top depth and the bottom depth of the fracture phenomenon identified on the imaging logging image. In the single lithology sublayer, the interval distance between each fracture segment is not less than 1 m, and the cumulative sum of the thicknesses of each fracture segment is not greater than the thickness of the lithology sublayer.

[0070] Step S014: determining a logging interpretation development rate parameter of the single lithology sublayer according to the thickness of the single lithology sublayer and the thicknesses of each fracture segment in the single lithology sublayer. Specifically, the thicknesses of each fracture segment in the single lithology sublayer are summed and divided by the thickness of the single lithology sublayer to obtain the logging interpretation development rate parameter of the single lithology sublayer.

[0071] In practical application, the logging interpretation fracture development rate parameter of the single lithology sublayer is obtained in combination with the thickness of the single lithology sublayer and the thicknesses of each fracture segment thereof. The calculation method is that the cumulative sum of the thicknesses of each fracture segment in the single lithology sublayer is multiplied by 100% of the ratio of the thickness of the lithology sublayer, and the fracture development rate of the lithology sublayer is not greater than 100%.

[0072] Step S015: determining a first statistical relationship curve and a second statistical relationship curve according to the rock type of the sampling point, the thickness of the single lithology sublayer, the logging interpretation development rate parameter of the single lithology sublayer, and the vertical distance of the single lithology sublayer from the control area fault. The vertical distance of the single lithology sublayer from the control area fault can be directly calculated from the coordinates thereof.

[0073] Specifically, based on the rock type obtained by logging, the thickness of the single lithology sublayer and the fracture development rate thereof, the logging interpretation fracture development rate of each lithology sublayer, and the vertical distance of the control area major fault at the same depth, the influence characteristics of the tectonic fault on the fracture development degree are analyzed, the mathematical statistical relationship curve among the three is determined, and the first statistical relationship curve and the second statistical relationship curve are obtained as follows: Figure 3 、 Figure 4 、 Figure 5 and Figure 6The first statistical relationship curve shown.

[0074] Wherein, the fault throw of the control area fault is usually large, and the cut-off layer is more. In the embodiment, the fault throw is up to 650m, and the Carboniferous, Permian and Triassic systems are cut off. For the control area fault with large fault throw, the plane position of the fault at different depth points will change obviously, such as Figure 7 and Figure 8 The longitudinal and plane position changes of the fault A from the HT depth to the HB depth are shown, and the distance from the borehole to the fault changes from d1 to d2; the longitudinal depth HT and HB of the lithologic layer are usually the middle depth value of the lithologic layer.

[0075] Further, based on the rock types, the thickness of the single lithologic layer and the fracture development rate obtained by the logging, the influence characteristics of the rock types and the thickness on the fracture development degree are analyzed, and the mathematical statistical relationship curve among the three is determined, and the second statistical relationship curve shown in Figure 9 , Figure 10 , Figure 11 and Figure 12 is obtained.

[0076] On the basis of the above statistical relationship analysis, a lithologic layer fracture development rate "three-element" prediction model with clear physical meaning can be constructed, that is, the calculation formula of the final fracture development rate data body, which is specifically:

[0077]

[0078] Wherein, FR is the fracture development rate prediction value, i is the rock type, FR i is the fracture development rate prediction value of the i-th rock type; W hi is the weight coefficient of the thickness factor of the i-th rock type, F hi is the fracture development rate predicted by the thickness factor of the i-th rock type; W di is the weight coefficient of the control area fault factor of the i-th rock type, F di is the fracture development rate predicted by the control area fault of the i-th rock type. In the embodiment, i = 1, 2, 3, 4, respectively representing basic lava, acid intrusive rock (i.e. acid lava), volcanic breccia and tuff. The weight coefficient is the correlation coefficient R 2 of the corresponding statistical relationship curve, see Figures 3-6 and Figures 9-12 .

[0079] Step S1: determining a three-dimensional fused seismic attribute body of a target area; the three-dimensional fused seismic attribute body of the target area includes a fused seismic attribute of all points in the target area; the fused seismic attribute is obtained by weighted fusion of a plurality of set seismic attributes.

[0080] Preferably, the method for determining the fused seismic attribute comprises: obtaining several seismic attributes of the sampling points; determining the discrimination sensitivity of each of the seismic attributes to lithology by using the effective number or correspondence analysis method; and taking the weighted sum of the several seismic attributes with the highest discrimination sensitivity as the fused seismic attribute, with the discrimination sensitivity as the weight. In this embodiment, the weighted sum of the top three seismic attributes with the highest discrimination sensitivity is taken as the fused seismic attribute.

[0081] In actual application, the discrimination sensitivity is determined by preliminary screening by experts and quantitative screening by the effective number. The expert screening is to preliminarily select 7 or 8 seismic attributes related to lithology according to previous experience, and then analyze the discrimination effect of each seismic attribute to lithology by using the effective number or correspondence analysis method according to the 7 or 8 seismic attributes, to obtain the discrimination sensitivity.

[0082] Step S2: determining a three-dimensional volcanic rock lithology data volume of the target region according to the three-dimensional fused seismic attribute volume of the target region and the lithology division criterion; the three-dimensional volcanic rock lithology data volume of the target region comprises seismic predicted lithology layers and corresponding lithology layer thicknesses of all points in the target region.

[0083] In actual application, the lithology layer division results of single wells are used, combined with fine synthetic record calibration, to extract seismic attribute values of well trace, and the seismic attributes mainly include wave impedance, wave velocity, energy, waveform and amplitude attribute.

[0084] Firstly, the lithology layers with a thickness greater than one-quarter of the wavelength in multiple wells in the research region are used as sampling points, each sampling point is bounded by the top and bottom of the lithology volume in the vertical direction, and the seismic attributes in the range of the cylindrical body shown in FIG. 2 are extracted with the well point as the center and 5 well traces as the radius in the horizontal direction. Figure 13

[0085] Then, the sensitivity of each well point attribute to lithology is calculated, and the top three seismic attributes with better discrimination effect to lithology are obtained, and the sensitivities of the three seismic attributes are taken as respective weight coefficients to obtain the three-dimensional fused seismic attribute volume for volcanic rock lithology identification by weighted fusion.

[0086] Further, the well point fused attributes of each of the above sampling points are re-counted, and the volcanic rock lithology division criterion, i.e., the data interval of different lithologies, is determined by using the cross-plot and histogram techniques. Specifically, after the well point fused seismic attribute values are obtained, the lithology at the well position is marked to preliminarily judge whether the well point fused seismic attribute values of different lithologies are concentratedly distributed; then, the histogram of the fused seismic attribute values of each type of lithology is made according to the lithology, the distribution interval of the values is counted, and the lithology is interpreted according to the different distribution intervals of different lithologies. ​

[0087] Finally, based on the three-dimensional fused seismic attribute volume and the determined lithology classification criterion, the underground three-dimensional space of volcanic rock lithology classification is completed. Among them: the vertical minimum unit of lithology layering is limited according to the minimum thickness that can be distinguished from the seismic data, generally the thickness of one-quarter wavelength; the horizontal minimum unit of lithology layering contains at least 30 channels, if less than 30 channels, the lithology changes, then it is merged into the adjacent lithology layer to the near well side.

[0088] That is, according to the fused seismic attribute, the three-dimensional volcanic rock lithology data volume of the study area is obtained, including rock type (i.e. seismic predicted lithology layering) and thickness parameter (i.e. lithology layering thickness), the thickness of each lithology layering is the bottom interface depth minus the top interface depth of the lithology layering, which lays the foundation for the next step of obtaining the fracture development rate data volume.

[0089] Step S3: determining the fracture development rate data volume directly converted by the target area according to the three-dimensional volcanic rock lithology data volume of the target area and the second statistical relationship curve; the second statistical relationship curve is a statistical relationship curve of the thickness of a single lithology layering of different rock types and the fracture development rate.

[0090] Step S4: correcting the fracture development rate data volume directly converted by the target area according to the third statistical relationship curve to obtain the fracture development rate data volume predicted by the thickness factor; the third statistical relationship curve is a statistical relationship curve of the directly converted fracture development rate of different rock types and the enhanced weighted fracture development rate.

[0091] In step S0, the third statistical relationship curve is determined according to the drilling identification result, the well point fused seismic attribute and the second statistical relationship curve, specifically including:

[0092] Step S021: determining the seismic predicted lithology data of the sampling point according to the well point fused seismic attribute and the lithology classification criterion; the seismic predicted lithology data of the sampling point includes: the seismic predicted lithology layering of the sampling point and the corresponding lithology layering thickness.

[0093] Step S022: determining the directly converted fracture development rate of the sampling point according to the seismic predicted lithology data of the sampling point and the second statistical relationship curve.

[0094] Step S023: determining the logging interpreted lithology data of the sampling point according to the drilling identification result; the logging interpreted lithology data of the sampling point includes: the logging interpreted lithology layering of the sampling point and the corresponding lithology layering thickness.

[0095] Step S024: determining the logging interpreted fracture development rate of the sampling point according to the logging interpreted lithology data of the sampling point and the second statistical relationship curve.

[0096] Step S025: Taking the seismic prediction lithology layer as the main lithology, using the thickness weighting method of the main lithology enhancement, the well logging interpreted fracture development rate of the sampling point is converted into the enhanced weighted fracture development rate of the sampling point.

[0097] Step S026: Linear fitting is performed with the fracture development rate directly converted from the sampling point as the abscissa and the enhanced weighted fracture development rate of the sampling point as the ordinate to obtain a third statistical relationship curve.

[0098] In the step S025, the well logging interpreted lithology layer is divided into thick lithology layer and thin lithology layer according to the well logging interpreted lithology data; the thick lithology layer is the well logging interpreted lithology layer with a lithology layer thickness greater than one fourth of the wavelength thickness; the thin lithology layer is the well logging interpreted lithology layer with a lithology layer thickness less than or equal to one fourth of the wavelength thickness; when the well logging interpreted lithology layer is the thick lithology layer, the well logging interpreted fracture development rate of the sampling point is directly taken as the enhanced weighted fracture development rate of the sampling point; when the well logging interpreted lithology layer is the thin lithology layer, taking the seismic prediction lithology layer as the main lithology, using the thickness weighting method of the main lithology enhancement, the well logging interpreted fracture development rate corresponding to the non-main lithology is converted into the well logging interpreted fracture development rate corresponding to the main lithology to obtain the enhanced weighted fracture development rate of the sampling point.

[0099] Specifically, the three-dimensional volcanic lithology data obtained in the above steps is directly converted into a fracture development rate data body according to the differences in lithology and the thickness changes in the vertical and horizontal directions by using the mathematical statistical relationship between the lithology layer thickness and the fracture development rate of different rock types as shown in the figure. Figures 9-12 In the conversion process, the scale difference between the well and the seismic data is not considered, so the well-seismic scale conversion error correction is needed for the fracture development rate data body. The general idea of the well-seismic scale conversion error correction is to obtain the correction function of various rock types through fine comparison of well point results, and to perform well-seismic scale conversion error correction by using the correction function.

[0100] In the well-seismic scale conversion error correction process of the lithology layer fracture development rate of the well point, there are two cases: the lithology layer thickness is greater than one fourth of the wavelength thickness, and the lithology layer thickness is less than (or equal to) one fourth of the wavelength thickness.

[0101] The first case is simple, and the directly converted fracture development rate can be directly applied, but this case is generally less common.

[0102] The second case is more complex and is also common. Specifically, the depth range of each seismic-predicted lithology layer often contains multiple thin lithology layers interpreted from well logs, because the lithology layers interpreted from well logs are usually more detailed, and there can be thin lithology layers of non-main rock types (for example Figure 14 In the upper part, the seismic-predicted lithology is basic lava, which is the main rock type in this depth section, but the lithology profile interpreted from the right side well logs shows three thin non-main rock types R2, R4, and R5 in this depth section, so the lithology layers interpreted from well logs in the depth range of the seismic-predicted lithology layer cannot correspond to it, and thus the fracture development rate directly converted will have a certain degree of error. In order to ensure the scale consistency when applying well-seismic data, the fracture development rate of the non-main lithology layers interpreted from well logs must be enhanced to the fracture development rate of the main rock type with equivalent thickness.

[0103] Therefore, the present application adopts a main lithology enhanced thickness weighting method to enhance and re-weight the fracture development rates of multiple longitudinally continuous thin layers interpreted from well logs to fracture development rates consistent with the thickness of the seismic-predicted lithology body. The specific formula of the main lithology enhanced thickness weighting method is:

[0104]

[0105] When p = 1, it represents that the main lithology is basic lava, pa = 91.91, and pb = -0.267;

[0106] When p = 2, it represents that the main lithology is acidic intrusive rock, pa = 102.95, and pb = -0.267;

[0107] When p = 3, it represents that the main lithology is volcanic breccia, pa = 61.334, and pb = -0.504;

[0108] When p = 4, it represents that the main lithology is tuff, pa = 33.137, and pb = -0.64;

[0109] When q = 1, it represents that the non-main lithology is basic lava, qc = 91.91, and qd = -0.267;

[0110] When q = 2, it represents that the non-main lithology is acidic intrusive rock, qc = 102.95, and qd = -0.267;

[0111] When q = 3, it represents that the non-main lithology is volcanic breccia, qc = 61.334, and qd = -0.504;

[0112] When q = 4, it represents that the non-main lithology is tuff, qc = 33.137, and qd = -0.64.

[0113] wherein, Rpi p is the fracture development rate of the i-th main lithology p in the layer, i = 1, 2, …, n, n is a positive integer; hpi p is the logging interpreted thickness of the i-th main lithology p in the layer, i = 1, 2, …, n, n is a positive integer; pa is the coefficient a of the main lithology p, obtained by pa = (hpi p) / (hpi p + hqi q), pb is the coefficient b of the main lithology p, obtained by pb = (hpi p) / (hpi p + hqi q) ; qc is the coefficient c of the non-main lithology q, obtained by qc = (hqi q) / (hpi p + hqi q), qd is the coefficient d of the non-main lithology q, obtained by qd = (hqi q) / (hpi p + hqi q) ; hqi q is the thickness of the j-th layer of the non-main lithology q in the layer, j = 1, 2, …, m, m is a positive integer; Rqi q is the logging interpreted fracture development rate of the j-th layer of the non-main lithology q in the layer, j = 1, 2, …, m. zpi zpi Figures 9-12 Figures 9-12 Figures 9-12 Figures 9-12 j j

[0114] Figure 14

[0115] Figure 14 Figure 14

[0116] Figure 14 Figures 9-12 ​​​​​​​​​​​​​​​

[0117] The following is a specific calculation process, first introduced to enhance the factor determination process. Figure 14 The upper part, obviously the main lithology should be horizontal line filled with the rock type, that is, to enhance the vertical line filled, blank filled, representing the lithology of layered fracture development rate. This embodiment should be to determine the h2 thickness section, h5 thickness section of volcanic breccia and h4 thickness section of tuff enhancement factor. The theoretical fracture development rate of h2 thickness section, h5 thickness section of volcanic breccia is 61.334x(h2) 0.504 , 61.334x(h5) 0.504 , and the same h2, h5 thickness of the main lithology theoretical development rate is 91.91x(h2) 0.267 , 91.91x(h5) 0.267 , so the enhancement factor of h2 thickness section, h5 thickness section of volcanic breccia is Then the fracture development rate of h2 thickness section, h5 thickness section of volcanic breccia after enhancement is The same method can be obtained h4 thickness section after enhancement of the fracture development rate is

[0118] After the fracture development rate of lithology layer is enhanced, the fracture development rate R1 of the equivalent thickness of the main rock type (H1 thickness section in this embodiment) is further obtained by thickness weighting, and the specific calculation formula is as follows:

[0119]

[0120] At this point, the enhanced and weighted logging interpretation fracture development rate R1 is obtained, which can be used to correct the direct conversion fracture development rate data body mentioned above. Next is to calculate the correction coefficient of various rock types, and the specific method process is as follows:

[0121] The seismic lithology layer of the core well in the preferred research area is the sample point (i.e. sampling point), and the direct conversion fracture development rate of the lithology layer and the enhanced and weighted logging interpretation development rate of the lithology layer (when the lithology layer thickness is greater than one fourth of the wavelength thickness, it does not need to be enhanced and weighted) are extracted. The two-dimensional crossplot is made with the direct conversion fracture development rate as the horizontal coordinate and the enhanced and weighted logging interpretation development rate as the vertical coordinate, and the linear relationship between the direct conversion fracture development rate and the enhanced and weighted logging interpretation development rate of different rock types of lithology layer is fitted, as shown in Figure 15 , Figure 16 , Figure 17 and Figure 18 , so that the correlation coefficient R 2The correlation coefficient should be no less than 0.79, and the statistical relationship with the highest correlation coefficient should be selected. This typically includes linear, exponential, power, and quadratic relationships. In this study area, the two exhibit a good linear relationship.

[0122] Then according to Figures 15-18 The mathematical relationship shown is used to correct the well-to-seismic scale conversion error of the directly converted fracture development rate data volume, resulting in a unified well-to-seismic scale fracture development rate data volume, i.e., F. hi The data body, next we need to determine F di Data body.

[0123] Step S5: Based on the three-dimensional volcanic rock lithology data volume and the first statistical relationship curve of the target area, determine the fracture development rate data volume predicted by the controlled area fault; the first statistical relationship curve is the statistical relationship curve between the vertical distance from the controlled area fault and the fracture development rate of different rock types; the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on the drilling identification results of the sampling points and the well point fused seismic attributes, specifically determined by the above step S0.

[0124] Specifically, Figures 3-6 The mathematical statistical relationship between the vertical distance to the controlled fracture zone and the fracture development rate, shown above, is applied to well-free locations based on the three-dimensional volcanic rock lithology data obtained in the above steps. To reduce the computational load and improve efficiency, the three-dimensional distribution of the volcanic rock lithology in the study area was first gridded. The size of the voxel during gridding was determined to be 500m*500m*20m (length, width, height (thickness)). That is to say, although... Figures 3-6 Only the relationship between the actual drilling location (distance from the controlled fracture zone) and the development rate of lithological stratification fractures in the well was statistically analyzed. However, from a probabilistic point of view, since the two have such a good relationship, they should also have such a relationship in locations without wells. This is the significance of using statistical relationships for prediction.

[0125] For specific promotion purposes Figures 3-6 In the mathematical formula, the computer performs calculations using volumetric elements. The calculation process first determines the rock type, then obtains the distance from the controlled fracture zone based on coordinates. These two parameters are then used to calculate the fracture development rate for each volumetric element. Finally, the calculation results are smoothed to obtain the final F. di Data body.

[0126] Step S6: Perform a weighted calculation on the crack development rate data volume predicted by the thickness factor and the crack development rate data volume predicted by the controlled zone fracture to obtain the final crack development rate data volume; the final crack development rate data volume includes the predicted crack development rate values ​​for all points in the target area. That is: in F hi Fdi On the basis of the data volume obtained, the lithology layering fracture development rate "ternary" prediction model is used for weighted calculation of the data volume to obtain the final fracture development rate data volume.

[0127] Embodiment Two

[0128] In order to perform the method corresponding to the above-mentioned embodiment one, to realize the corresponding functions and technical effects, the following provides a system for predicting the fracture development degree of volcanic rock, comprising:

[0129] A three-dimensional fused seismic attribute volume determination module is configured to determine a three-dimensional fused seismic attribute volume of a target region; the three-dimensional fused seismic attribute volume of the target region comprises fused seismic attributes of all point positions of the target region; the fused seismic attributes are obtained by weighted fusion of a plurality of set seismic attributes.

[0130] A three-dimensional volcanic rock lithology data volume determination module is configured to determine a three-dimensional volcanic rock lithology data volume of the target region according to the three-dimensional fused seismic attribute volume of the target region and a lithology classification criterion; the three-dimensional volcanic rock lithology data volume of the target region comprises well-logging interpreted lithology layers and corresponding lithology layer thicknesses of all point positions of the target region.

[0131] A directly converted fracture development rate data volume determination module is configured to determine a directly converted fracture development rate data volume of the target region according to the three-dimensional volcanic rock lithology data volume of the target region and a second statistical relationship curve; the second statistical relationship curve is a statistical relationship curve of single lithology layer thicknesses and fracture development rates of different rock types.

[0132] A well-seismic scale conversion error correction module is configured to perform well-seismic scale conversion error correction on the directly converted fracture development rate data volume of the target region according to a third statistical relationship curve to obtain a fracture development rate data volume predicted by thickness factors; the third statistical relationship curve is a statistical relationship curve of directly converted fracture development rates and enhanced weighted fracture development rates of different rock types.

[0133] A control area fault fracture development rate data volume determination module is configured to determine a fracture development rate data volume predicted by control area faults according to the three-dimensional volcanic rock lithology data volume of the target region and a first statistical relationship curve; the first statistical relationship curve is a statistical relationship curve of vertical distances from control area faults and fracture development rates of different rock types; the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on drilling identification results of sampling points and well point fused seismic attributes.

[0134] The crack development rate data body calculation module is configured to perform weighted calculation on the crack development rate data body predicted by the thickness factor and the crack development rate data body predicted by the control area fault to obtain a final crack development rate data body.

[0135] Embodiment three

[0136] The embodiment of the present application also provides an electronic device, including a memory and a processor, the memory is used for storing a computer program, and the processor is used for running the computer program to make the electronic device execute the method for predicting the crack development degree of the volcanic rock in the embodiment one. The electronic device can be a server.

[0137] In addition, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method for predicting the crack development degree of the volcanic rock in the embodiment one.

[0138] To sum up, the method, system, device and medium for predicting the crack development degree of the volcanic rock can be used to predict the crack of the volcanic rock reservoir in combination with the conventional logging, the imaging logging and the three-dimensional seismic data, overcome the defect that the seismic data can only predict the crack development zone, and effectively predict the crack development rate of the volcanic rock, thereby providing support for the quantitative evaluation of the crack development degree.

[0139] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts of each embodiment can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0140] The principles and implementation manners of the present application are described by using specific examples in the present application, and the above embodiment description is only used to help understand the method of the present application and its core idea; meanwhile, for the general technical personnel in the art, according to the idea of the present application, the specific implementation manner and application range will be changed. In conclusion, the content of the specification should not be understood as the limitation of the present application.

Claims

1. A method for predicting the degree of fracture development in volcanic rocks, characterized in that, include: Determine the three-dimensional fused seismic attribute volume of the target area; the three-dimensional fused seismic attribute volume of the target area includes the fused seismic attributes of all points in the target area; The fused seismic attributes are obtained by weighted fusion of several set seismic attributes; Based on the three-dimensional fused seismic attribute volume and lithological classification criteria of the target area, the three-dimensional volcanic rock lithological data volume of the target area is determined; the three-dimensional volcanic rock lithological data volume of the target area includes the seismic predicted lithological layers and the corresponding lithological layer thicknesses of all points in the target area; Based on the three-dimensional volcanic rock lithology data volume and the second statistical relationship curve of the target area, the fracture development rate data volume of the target area is determined directly; the second statistical relationship curve is the statistical relationship curve between the thickness of a single lithological layer and the fracture development rate of different rock types. Based on the third statistical relationship curve, the fracture development rate data volume directly converted from the target area is corrected for well-seismic scale conversion error to obtain the fracture development rate data volume predicted by the thickness factor; the third statistical relationship curve is the statistical relationship curve between the fracture development rate directly converted from different rock types and the enhanced weighted fracture development rate. Based on the three-dimensional volcanic rock lithology data volume and the first statistical relationship curve of the target area, the fracture development rate data volume predicted by the controlled area fault is determined; the first statistical relationship curve is the statistical relationship curve between the vertical distance from the controlled area fault and the fracture development rate of different rock types; the first statistical relationship curve, the second statistical relationship curve and the third statistical relationship curve are determined based on the drilling identification results of the sampling points and the well point fused seismic attributes; The crack development rate data volume predicted by the thickness factor and the crack development rate data volume predicted by the controlled zone fracture are weighted and calculated to obtain the final crack development rate data volume; the final crack development rate data volume includes the predicted crack development rate values ​​of all points in the target area.

2. The method for predicting the degree of fracture development in volcanic rocks according to claim 1, characterized in that, Also includes: Obtain drilling identification results and wellpoint fusion seismic attributes from sampling points; The drilling identification results are the results of core sampling and thin section analysis. Based on the drilling identification results, the first statistical relationship curve and the second statistical relationship curve are determined; Based on the drilling identification results, the well point fusion seismic attributes, and the second statistical relationship curve, a third statistical relationship curve is determined.

3. The method for predicting the degree of fracture development in volcanic rocks according to claim 2, characterized in that, Based on the drilling identification results, the wellpoint fusion seismic attributes, and the second statistical relationship curve, a third statistical relationship curve is determined, specifically including: Based on the well point fusion seismic attributes and lithological classification criteria, the seismic predicted lithological data of the sampling points are determined; the seismic predicted lithological data of the sampling points includes: the seismic predicted lithological stratification of the sampling points and the corresponding lithological stratification thickness; Based on the seismic prediction lithology data of the sampling points and the second statistical relationship curve, the fracture development rate directly converted from the sampling points is determined; Based on the drilling identification results, the well logging interpretation lithological data of the sampling points are determined; the well logging interpretation lithological data of the sampling points include: the well logging interpretation lithological stratification of the sampling points and the corresponding lithological stratification thickness; Based on the well logging interpretation lithological data of the sampling points and the second statistical relationship curve, the well logging interpretation fracture development rate of the sampling points is determined; Using the earthquake-predicted lithological stratification as the main lithology, a thickness-weighted method enhanced by the main lithology is adopted to convert the well-logging interpretation fracture development rate of the sampling points into the enhanced weighted fracture development rate of the sampling points; A third statistical relationship curve is obtained by linearly fitting the crack development rate directly converted from the sampling points on the x-axis and the crack development rate enhanced and weighted by the sampling points on the y-axis.

4. The method for predicting the degree of fracture development in volcanic rocks according to claim 2, characterized in that, Based on the drilling evaluation results, the first statistical relationship curve and the second statistical relationship curve are determined, specifically including: The rock type of the sampling point was determined based on the drilling identification results. The drilling identification results are repositioned to depth, and logging data at each depth point is calculated; the logging data includes: area average results of natural gamma curve values, sonic curve values, neutron curve values, density curve values, and deep resistivity curve values; Based on the rock type at the sampling points and the logging data at each depth, the thickness of a single lithological layer is determined, and the thickness of each fracture segment in a single lithological layer is determined using microresistivity scanning imaging logging images. Based on the thickness of the individual lithological layer and the thickness of each fracture segment within the individual lithological layer, the well logging interpretation development rate parameter of the individual lithological layer is determined; Based on the rock type of the sampling point, the thickness of the individual lithological layer, the well logging interpretation development rate parameter of the individual lithological layer, and the vertical distance of the individual lithological layer from the fault in the control area, a first statistical relationship curve and a second statistical relationship curve are determined.

5. The method for predicting the degree of fracture development in volcanic rocks according to claim 3, characterized in that, Using the earthquake-predicted lithological stratification as the main lithology, and employing a thickness-weighted method enhanced by the main lithology, the well-logging interpretation fracture development rate of the sampling points is converted into a fracture development rate enhanced by the sampling points. Specifically, this includes: Based on the well logging interpretation lithological data, the well logging interpretation lithological stratification is divided into thick lithological stratification and thin lithological stratification; the thick lithological stratification is the well logging interpretation lithological stratification with a lithological stratification thickness greater than one-quarter wavelength thickness; the thin lithological stratification is the well logging interpretation lithological stratification with a lithological stratification thickness less than or equal to one-quarter wavelength thickness; When the well logging interpretation lithological stratification is a thick lithological stratification, the well logging interpretation fracture development rate of the sampling point is directly used as the enhanced weighted fracture development rate of the sampling point; When the well logging interpretation lithological stratification is a thin lithological stratification, the seismic prediction lithological stratification is taken as the main lithology. The thickness weighting method enhanced by the main lithology is adopted to convert the well logging interpretation fracture development rate corresponding to the non-main lithology into the well logging interpretation fracture development rate corresponding to the main lithology, so as to obtain the fracture development rate after enhanced weighting of sampling points.

6. The method for predicting the degree of fracture development in volcanic rocks according to claim 1, characterized in that, The method for determining the fused seismic attributes includes: Obtain several seismic attributes of the sampling points; The sensitivity of each seismic attribute to lithology was determined using significant number analysis or correspondence analysis. Using the distinguishing sensitivity as a weight, the weighted sum of several seismic attributes with the highest distinguishing sensitivity is taken as the fused seismic attribute.

7. The method for predicting the degree of fracture development in volcanic rocks according to claim 4, characterized in that, The drilling identification results are then repositioned to depth, and logging data at each depth point is calculated, specifically including: The drilling identification results were repositioned to depth to obtain logging curves for each depth point; The N-point area method is used to calculate the logging data at each depth point based on the logging curve.

8. A system for predicting the degree of fracture development in volcanic rocks, characterized in that, include: A three-dimensional fused seismic attribute volume determination module is used to determine the three-dimensional fused seismic attribute volume of a target area; the three-dimensional fused seismic attribute volume of the target area includes the fused seismic attributes of all points in the target area; the fused seismic attributes are obtained by weighted fusion of several set seismic attributes; The three-dimensional volcanic rock lithology data volume determination module is used to determine the three-dimensional volcanic rock lithology data volume of the target area based on the three-dimensional fused seismic attribute volume and lithology classification criteria of the target area; the three-dimensional volcanic rock lithology data volume of the target area includes the seismic predicted lithological layers and the corresponding lithological layer thicknesses of all points in the target area; The direct conversion fracture development rate data volume determination module is used to determine the directly converted fracture development rate data volume of the target area based on the three-dimensional volcanic rock lithology data volume and the second statistical relationship curve of the target area; the second statistical relationship curve is the statistical relationship curve between the thickness of a single lithological layer and the fracture development rate of different rock types. The well-seismic scale conversion error correction module is used to correct the well-seismic scale conversion error of the fracture development rate data volume directly converted from the target area according to the third statistical relationship curve, so as to obtain the fracture development rate data volume predicted by the thickness factor; the third statistical relationship curve is the statistical relationship curve between the fracture development rate directly converted from different rock types and the enhanced weighted fracture development rate. The controlled-area fracture development rate data volume determination module is used to determine the fracture development rate data volume predicted by the controlled-area fracture based on the three-dimensional volcanic rock lithology data volume of the target area and the first statistical relationship curve; the first statistical relationship curve is the statistical relationship curve between the vertical distance from the controlled-area fracture and the fracture development rate for different rock types; the first statistical relationship curve, the second statistical relationship curve, and the third statistical relationship curve are determined based on the drilling identification results of the sampling points and the well point fused seismic attributes; The crack development rate data volume calculation module is used to perform weighted calculation on the crack development rate data volume predicted by the thickness factor and the crack development rate data volume predicted by the controlled zone fracture to obtain the final crack development rate data volume; the final crack development rate data volume includes the predicted crack development rate values ​​of all points in the target area.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory being used to store a computer program, and the processor running the computer program to cause the electronic device to perform the method for predicting the degree of development of volcanic rock fissures as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method for predicting the degree of development of volcanic rock fissures as described in any one of claims 1 to 7.

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