Prediction method for vertical evolution of compacted diagenetic fracture of glutenite reservoir

Abstract

The invention provides a method for predicting vertical evolution of compacted diagenetic fractures of glutenite reservoirs. Main rock types and rock components of the glutenite reservoirs in an actual work area are obtained through rock core observation and slice identification; the method comprises the following steps: preparing artificial rock samples which have the same rock components as those of an actual work area and are in different burying and compacting stages by compacting physical diagenesis simulation experiment equipment; measuring fracture parameters of an artificial rock sample in a simulation experiment and fracture parameters of a rock sample in an actual work area, respectively calculating the surface fracture ratios of the artificial rock sample and the rock sample and the brittleness index of each rock sample in the actual work area, and establishing a prediction model for quantitatively evaluating the vertical evolution of the glutenite reservoir compaction diagenesis fracture. The method is used for predicting the development conditions of the compacted diagenetic fractures of glutenite reservoirs with different depths in the actual work area. According to the method, the established prediction model can well predict the development condition of the compacted diagenesis fracture of the glutenite reservoir, especially the development degree of the compacted diagenesis fracture in a BZ19-6 region can be well predicted, and reference is provided for exploration of oil and gas reservoirs.

Description

Technical Field

[0001] The invention belongs to the technical field of geological evolution of oil and gas fields, and in particular relates to a method for predicting the vertical evolution of compaction diagenetic cracks in a sandstone conglomerate reservoir. Background Art

[0002] In order to ensure the safety of oil field development and control economic costs, it is necessary to have a very deep understanding of the geological conditions of the rock formations before or during oil field exploitation. In the process of oil and gas exploration and development, compacted diagenetic fractures are important reservoir spaces for oil and gas, and have a significant impact on the seepage capacity of the reservoir.

[0003] In recent years, compacted diagenetic fractures have been found in petroliferous basins in eastern and western my country. For example, in the Wushi Sag of the Tarim Basin, multiple groups of compacted diagenetic fractures are developed in the Cretaceous gravelly medium-coarse sandstone at a burial depth of more than 6000m, which are the main reservoir space for oil and gas. The daily oil production is 174 tons and the daily gas production is 2×105m 3 Another example is the large number of compacted diagenetic fractures developed in the carbonate gravels of the fourth member of the Shahejie Formation in the Dongying Sag of the Bohai Bay Basin, which increased the permeability of the reservoir and was laterally transformed into small-scale fractures and caves by organic acids.

[0004] At present, compaction diagenetic fractures in conglomerate reservoirs are developed in large numbers, but their formation and evolution mechanism is not clear. For oil fields that have not been fully developed or are waiting to be developed, it is extremely important to quantitatively obtain the vertical distribution of compaction diagenetic fractures in conglomerate reservoirs through prediction. Therefore, at this stage, there is a lack of a method that can quantitatively evaluate the vertical evolution process of compaction diagenetic fractures in conglomerate reservoirs. Summary of the invention

[0005] The problem to be solved by the present invention is to provide a method for predicting the vertical evolution of compaction diagenetic cracks in sandy conglomerate reservoirs. The method can well predict the development of compaction diagenetic cracks in sandy conglomerate reservoirs, especially can well predict the development degree of compaction diagenetic cracks in the BZ19-6 area, and provide a reference for the exploration of oil and gas reservoirs.

[0006] The present invention provides a method for predicting the vertical evolution of compaction diagenetic cracks in a sandstone reservoir, comprising the following steps:

[0007] S1: Determine the basic characteristics of the actual reservoir, and obtain the main rock types and rock components of the conglomerate reservoir in the actual work area through core observation and thin section identification;

[0008] S2: Conduct compaction physical diagenesis simulation experiments, and prepare artificial rock samples with the same rock composition as the actual work area and at different burial compaction stages through compaction physical diagenesis simulation experimental equipment;

[0009] S3: Determine the fracture parameters of the simulation experiment, observe the artificial rock samples under a slice microscope and perform image processing to obtain the fracture length, fracture width, number of fractures, surface porosity, rock grain size, debris particle composition and filling material content of each artificial rock sample;

[0010] S4: Determine the fracture parameters of the actual work area, observe the rock samples of the actual work area under a slice microscope and process the images to obtain the fracture length, fracture width, number of fractures, rock grain size, debris particle composition, and filling material content of the actual work area;

[0011] S5: Establish a prediction model. Based on the above parameters, calculate the surface fracture rate of each rock sample in the simulation experiment and the actual work area, as well as the brittleness index of each rock sample in the actual work area, and establish a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic cracks in the conglomerate reservoir. This model is used to predict the development of compaction diagenetic cracks in the conglomerate reservoir at different depths in the actual work area.

[0012] Further, the S2 comprises the following steps:

[0013] S21: Configure artificial rock samples according to the debris components, clay mineral types, content of each type and particle size distribution of debris in the actual work area;

[0014] S22: The artificial rock samples are divided into multiple groups according to rock types, and each group of rock samples is divided into n equal parts. n simulation environments are set up in a diagenetic physics simulation experimental device, and the n rock samples in each group are placed in n simulation environments for 15 days to produce artificial rock samples with a diameter of 3 cm and a height of 7 cm.

[0015] Further, the S3 comprises the following steps:

[0016] S31: injecting a blue casting into the artificial rock sample, and uniformly cutting a large thin slice of the casting at a distance of 1.5 cm from the upper part for observation under a microscope;

[0017] S32: Take six adjacent view areas and stitch them together using the C-Tec image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock grain size, debris particle composition and filling material content of the stitched image.

[0018] Further, the S5 comprises the following steps:

[0019] S51: Establish a fitting curve of the surface fracture rate of cracks at different depths obtained from the compaction physical diagenesis simulation experiment and the depth, which is used to characterize the degree of crack development;

[0020] S52: Schematic diagram of compaction diagenetic fracture evolution model obtained by the variation of surface porosity, rock grain size, clastic particle composition and interstitial material content with depth;

[0021] S53: Count and calculate the measured surface fracture rate of the actual work area, and project the measured surface fracture rate onto the fitting curve of surface fracture rate versus depth obtained in the compaction physical diagenetic simulation experiment;

[0022] S54: Establish the fitting curve of the brittleness index of the actual work area at different depths;

[0023] S55: The accuracy of the prediction model is verified by the surface fracture rate and brittleness index of the actual work area. When the prediction model is consistent with the fracture situation in the actual work area, the established prediction model is used to predict the compaction diagenetic fracture development of the sandstone reservoir at other depths in the actual work area.

[0024] Furthermore, the calculation process of the surface seam rate is as follows:

[0025] On the casting thin section, the entire thin section surface area is used as the statistical unit, and the product of the crack length and crack width counted under the microscope is approximately the crack area s. Then, the sum of the areas of each crack is used as the numerator and the statistical area is used as the denominator, and the surface crack rate γ of the crack in this depth section can be calculated.

[0026] Furthermore, the calculation formula of the surface seam rate is as follows:

[0027] s=ld

[0028]

[0029] Where, l- is the crack length;

[0030] d- is the crack width;

[0031] r- is the radius of the sheet.

[0032] Furthermore, the calculation process of the brittleness index is as follows:

[0033] The brittleness index (mineral) B1 is calculated using the method of calculating the rock brittleness index using mineral composition, and the brittleness index (logging) B2 is calculated using the method of calculating the rock brittleness index using conventional logging data. The product of B1 and B2 is regarded as the final brittleness index.

[0034] Furthermore, the calculation formula of the brittleness index is as follows:

[0035]

[0036] B=B1×B2

[0037] Where W qtz - is the quartz and feldspar content;

[0038] W carb - is the carbonate content;

[0039] W total- is the total mineral content of the rock;

[0040] B- is the final brittleness index;

[0041] B1- is the brittleness index of the mineral composition method;

[0042] B1- is the brittleness index calculated from conventional logging data.

[0043] Furthermore, the conglomerate reservoir is the conglomerate reservoir of the Kongdian Formation in the BZ19-6 condensate gas field.

[0044] Furthermore, the sampling depth of the conglomerate reservoir samples of the Kongdian Formation in the BZ19-6 gas field in the actual work area is 3000m-4500m, and the samples are in the middle stage of vertical compaction.

[0045] The advantages and positive effects of the present invention are:

[0046] The prediction model established by the present invention can well predict the development of compaction diagenetic cracks in sandstone conglomerate reservoirs, especially can well predict the development degree of compaction diagenetic cracks in the BZ19-6 area, providing a reference for the exploration of oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is an overall flow chart of an embodiment of the present invention.

[0048] Figure 2 It is a schematic diagram of the actual position and stratigraphic column of the specific embodiment BZ19-6 of the present invention.

[0049] Figure 3 It is a schematic diagram of the microscopic characteristics of the conglomerate reservoir of the Kongdian Formation BZ19-6 in a simulation experiment according to a specific embodiment of the present invention.

[0050] Figure 4 This is a graph showing how the surface porosity, crack width and number of cracks vary with depth in a compaction simulation experiment in a specific embodiment of the present invention.

[0051] Figure 5 This is a diagram showing the variation of the aperture and number of diagenetic cracks with depth in a compaction physical simulation experiment with different particle sizes.

[0052] Figure 6 This is a diagram showing changes in the aperture and number of diagenetic cracks with depth during compaction of different components in a physical simulation experiment of compaction in a specific embodiment of the present invention.

[0053] Figure 7 It is a schematic diagram of the development types and microscopic characteristics of compaction diagenetic fractures in an actual work area of ​​a specific embodiment of the present invention.

[0054] Figure 8It is a prediction model for the evolution of compaction diagenetic cracks in the diagenetic process of the Kongdian Formation conglomerate in the BZ19-6 area, a specific embodiment of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0057] like Figure 1 As shown, the present invention provides a method for predicting the vertical evolution of compaction diagenetic fractures in sandstone reservoirs, comprising the following steps:

[0058] S1: Determine the basic characteristics of the actual reservoir, and obtain the main rock types and rock components of the conglomerate reservoir in the actual work area through core observation and thin section identification.

[0059] S2: Conduct a compaction physical diagenesis simulation experiment, and prepare artificial rock samples with the same rock composition as the actual work area and at different burial compaction stages through the compaction physical diagenesis simulation experiment equipment. Specifically, S2 includes the following steps:

[0060] S21: Artificial rock samples are prepared according to the debris components, clay mineral types, content of each type and particle size distribution of debris in the actual work area.

[0061] S22: Artificial rock samples are divided into multiple groups according to rock type. Each group of rock samples is divided into n equal parts. n simulation environments are set up in the diagenetic physics simulation experimental equipment. The n rock samples in each group are placed in n simulation environments for 15 days to make artificial rock samples with a diameter of 3 cm and a height of 7 cm.

[0062] S3: Determine the fracture parameters of the simulation experiment, observe the artificial rock samples under a slice microscope and process the images, and obtain the fracture length, fracture width, number of fractures, surface ratio, rock grain size, debris particle composition and filling material content of each artificial rock sample. Specifically, S3 includes the following steps:

[0063] S31: Inject the blue casting into the artificial rock sample, and uniformly cut a large thin slice of the casting at a distance of 1.5 cm from the upper part for observation under a microscope.

[0064] S32: Take six adjacent view areas and stitch them together using the C-Tec image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock grain size, debris particle composition and filling material content of the stitched image.

[0065] S4: Determine the fracture parameters of the actual work area, observe the rock samples in the actual work area under a slice microscope and perform image processing to obtain the fracture length, fracture width, number of fractures, rock particle size, debris particle composition, and filling material content in the actual work area.

[0066] S5: Establish a prediction model. Based on the above parameters, calculate the surface fracture rate of each rock sample in the simulation experiment and the actual work area, as well as the brittleness index of each rock sample in the actual work area, and establish a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic cracks in the sandstone conglomerate reservoir to predict the development of compaction diagenetic cracks in the sandstone conglomerate reservoir at different depths in the actual work area. Specifically, S5 includes the following steps:

[0067] S51: Establish a fitting curve of the surface fracture rate of cracks at different depths obtained from the compaction physical diagenesis simulation experiment as a function of depth to characterize the degree of crack development.

[0068] S52: Schematic diagram of the compaction diagenetic fracture evolution model obtained by the changes in surface porosity, rock grain size, debris particle composition and filling material content with depth.

[0069] S53: Count and calculate the measured surface fracture rate of the actual work area, and project it onto the fitting curve of the surface fracture rate versus depth obtained in the compaction physical diagenesis simulation experiment.

[0070] S54: Establish a fitting curve of the brittleness index of the actual work area at different depths.

[0071] S55: The accuracy of the prediction model is verified by the surface fracture rate and brittleness index of the actual work area. When the prediction model is consistent with the fracture situation in the actual work area, the established prediction model is used to predict the compaction diagenetic fracture development of the sandstone reservoir at other depths in the actual work area.

[0072] The calculation process of the surface seam rate is as follows:

[0073] On the casting thin slice, the entire thin slice surface area is used as the statistical unit, and the product of the crack length and crack width counted under the microscope is approximately the crack area s. Then, the sum of the areas of each crack is used as the numerator and the statistical area is used as the denominator to calculate the surface crack rate γ of the crack in the depth section. The calculation formula of the surface crack rate is as follows:

[0074] s=ld

[0075]

[0076] Where, l- is the crack length;

[0077] d- is the crack width;

[0078] r- is the radius of the sheet.

[0079] The calculation process of the brittleness index is as follows: the brittleness index (mineral) B1 is calculated using the method of calculating the rock brittleness index using mineral composition. The calculation formula is as follows:

[0080]

[0081] Where W qtz - is the quartz and feldspar content;

[0082] W carb - is the carbonate content;

[0083] W total - is the total mineral content of the rock;

[0084] The brittleness index (logging) B2 is calculated using conventional logging data. The product of B1 and B2 is regarded as the final brittleness index. The calculation formula is as follows:

[0085] B=B1×B2

[0086] Where, B- is the final brittleness index;

[0087] B1- is the brittleness index of the mineral composition method;

[0088] B2- is the brittleness index calculated from conventional logging data.

[0089] The present invention will be further described below in conjunction with specific implementation cases.

[0090] This specific implementation case takes the Kongdian Formation of the BZ19-6 condensate gas field as the object to predict the vertical evolution of the compaction diagenetic fractures of the sandstone conglomerate reservoir. Among them, the BZ19-6 structural belt is located on the deep structural ridge in the southwest of the Bohai Bay Basin, Bohai Bay Basin, adjacent to the Bonan low uplift in the southeast, the Chengbei low uplift in the west, the Yellow River Mouth Depression in the south, and the main depression of the Bohai Depression in the north. It exists in the form of anticline uplift belt in a large depression. The oil and gas reservoirs in the study area are mainly enriched in the Archean buried hill metamorphic rocks and the thick sandstone conglomerate reservoirs of the Kongdian Formation. The burial depth of the Kongdian Formation sandstone conglomerate reservoir is generally more than 3500m. The main source rock layers are the Shahejie Formation and the Dongying Formation. The regional distribution is stable and the thick East 3rd Section mudstone high-quality caprock, such as Figure 2 shown.

[0091] The prediction method for the vertical evolution of compaction diagenetic fractures in the conglomerate reservoir of the Kongdian Formation in the BZ19-6 condensate gas field is as follows:

[0092] S1: Through core observation and thin section identification, the main rock types and component contents of the actual BZ19-6 gas field Kongdian Formation conglomerate reservoir samples were obtained; the actual BZ19-6 gas field Kongdian Formation conglomerate reservoir rock samples used in this embodiment are at a depth of about 3000m-4500m, and the samples are in the middle stage of vertical compaction. It is determined that the main rock types of the reservoir are conglomerate, specifically granite and gneiss. The clastic component content, clay mineral content, and gravel particle size and content of the two conglomerates are shown in Table 1:

[0093] Table 1 Statistical table of clastic component content in the conglomerate of the Kongdian Formation in the BZ19-6 gas field

[0094]

[0095] S2: Conduct compaction physical diagenesis simulation experiment

[0096] Using the diagenetic physical simulation experimental equipment, according to the actual debris components, clay mineral types, content of each type and debris particle size of the Kongdian Formation in the BZ19-6 gas field as described in Table 1, artificial rock samples with the same composition as the rocks in the actual work area but in different burial and compaction stages were prepared.

[0097] As a specific scheme, artificial rock samples are divided into two groups, namely granite and gneiss, and the two groups of rock samples are divided into 8 equal parts; in order to simulate the diagenesis conditions at different burial and compaction stages, 8 simulation environments are set up in the diagenetic physics simulation experimental equipment. When setting the simulation environment, the details are shown in Table 2.

[0098] Table 2 Temperature and pressure of conglomerate simulation experiment corresponding to different burial depths

[0099]

[0100] Eight granite samples and eight gneiss samples were placed in eight simulated environments respectively. After 15 days, artificial rock samples with a diameter of 3 cm and a height of 7 cm were made. The diagenetic physics simulation experimental equipment used in this embodiment is the diagenetic physics simulation experimental equipment used in "Pore Evolution Characteristics and Favorable Reservoir Evaluation of Deep Reservoirs in Dibei Jurassic in Kuqa Depression - Diagenetic Physics Simulation Experimental Study under Burial Mode Constraints".

[0101] As another scheme, different from the above scheme, the two groups of rock samples are divided into 9 equal parts respectively; 8 granite samples and 8 gneiss samples are placed in 8 simulated environments respectively, and 1 granite sample and 1 gneiss sample are kept as spares. The spare samples can be used to make up for the experiment after other experimental samples fail, thereby reducing the probability of direct failure of the experiment.

[0102] S3: Determination of crack parameters for simulation experiments

[0103] The blue casting was injected into the artificial rock sample, and thin sections of the casting were uniformly cut at a distance of 1.5 cm from the upper part for observation under a microscope. Six adjacent fields of view were taken and spliced ​​using the Western Library image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock grain size, debris particle composition and filling material content of the spliced ​​image.

[0104] Among them, the reservoir microscopic images of the simulated depths of 2000m, 4000m and 6000m in the compaction simulation experiment in this embodiment are respectively as follows: Figure 3 As shown in a, b, and c in FIG, the surface porosity, crack width, and crack number obtained by the compaction simulation experiment in this embodiment are shown in FIG. Figure 4 As shown in the figure; the opening and number of compaction diagenetic cracks of rocks of different particle sizes are shown in the figure Figure 5 shown.

[0105] Specifically, in this embodiment, the width and number of the cracks are as follows: Figure 4 As shown in the data, in the shallow burial stage with a simulated burial depth of 2000-3000m, the compaction diagenetic fractures are mainly inherited fractures. The number of cracks in sample No. 1 increases from 28 to 40, and the number of cracks in sample No. 2 increases from 26 to 40; at a simulated burial depth of 2000-2500m, the average width of the compaction diagenetic fractures in sample No. 1 increases from 7.25μm to 12.63μm, and the average width of the compaction diagenetic fractures in sample No. 2 increases from 4.03μm to 9.53μm; at a simulated burial depth of 2500-3000m, the average width of the cracks in sample No. 1 decreases from 13.36μm to 3.69μm, and the average width of the cracks in sample No. 2 decreases from 9.53μm to 5.2μm. In the transition stage from shallow burial to rapid deep burial at a simulated depth of 3000-4500m, both inherited and non-inherited fractures developed. The average width of cracks in sample 1 increased from 3.69μm to 12.38μm, and the average width of cracks in sample 2 increased from 5.2μm to 12.44μm. When the simulated burial depth was 3000-4000m, the number of cracks in sample 1 increased from 40 to 56, and the number of cracks in sample 2 increased from 40 to 65. At a simulated burial depth of 4000-4500m, the number of cracks in sample 1 decreased to 40, and the number of cracks in sample 2 decreased to 46. In the simulated early stage of deep burial at a depth of 4500-6000m, compaction diagenetic fractures were mainly non-inherited fractures. The crack widths of the two samples were comparable, with the average value decreasing from 12.40 μm to 6.05 μm. The average number of cracks in sample 1 increased from 40 to 52, and the average number of cracks in sample 2 increased from 46 to 60.

[0106] In this embodiment, the surface rates of granite and gneiss tend to decrease with depth. When the simulated depth is 2000m, the surface rate of No. 1 surface is 18.7%, and the surface rate of No. 2 surface is 20.06%; when the simulated depth is 3000m, the surface rate of No. 1 surface is 15.4%, and the surface rate of No. 2 surface is 17.21%; when the simulated depth is 3500m, the surface rate of No. 2 surface is 18.34%; when the simulated depth is 4000m, the surface rate of No. 1 surface is 12.53%, and the surface rate of No. 2 surface is 13.44%; when the simulated depth is 6000m, the surface rate of No. 1 surface is 11.62%, and the surface rate of No. 2 surface is 12.4%.

[0107] In this embodiment, the results of the rock particle size of the artificial rock sample are as follows: Figure 5 As shown in the figure, artificial rock samples are classified according to gravel grade and sand grade to obtain their fracture aperture and fracture quantity; the results of the debris particle composition of artificial rock samples are shown in the figure. Figure 6 As shown, the crack opening and number of cracks of its main components, feldspar and quartz, were obtained.

[0108] S4: Determine the crack parameters of the actual work area

[0109] The rock samples in the actual work area were observed under a slice microscope and image processed to obtain the crack length, crack width, number of cracks, rock particle size, debris particle composition, and filling material content in the actual work area.

[0110] Specifically, in this embodiment, the compaction diagenetic cracks in the actual work area mainly develop in the range of 3000-4500m. The crack development types obtained by taking samples from different mines in the same work area are as follows: Figure 7 As shown, f, g, h and i are the microscopic images of fracture development at BZ19-6-B 3855.75m, BZ19-6-A 3890m, BZ19-6-A 3780m and BZ19-6-A 3571m, respectively. The fracture development status is consistent with the fracture development status between the simulated depth of 3000-4500m in S3.

[0111] In this embodiment, in the conglomerate reservoir of the Kongdian Formation in the BZ19-6 gas field, the average aperture of the compacted diagenetic fractures in the gravel-grade particles is 14 μm, and the average number of fractures is 14; the average aperture of the compacted diagenetic fractures in the sand-grade particles is 8.62 μm, and the average number of fractures is 45, which is consistent with the simulation experiment results in S3. Both results show that compared with the sand-grade particles, the gravel-grade particles have larger fracture apertures and fewer fractures.

[0112] In this embodiment, the fragments of the actual work area that were fractured were mainly feldspar, quartz and rigid rock fragments, which was consistent with the fragments composition results of the simulation experiment in S3.

[0113] Therefore, the fracture development type, rock grain size and debris particle composition in the actual work area are consistent with the simulation experiment, which proves the effectiveness of fracture formation in the simulation experiment.

[0114] S5: Building a predictive model

[0115] Based on the above parameters, the surface fracture rate of each rock sample in the simulation experiment and the actual work area, as well as the brittleness index of each rock sample in the actual work area were calculated respectively, so as to establish a three-stage characteristic prediction model of fracture evolution of deep conglomerate reservoirs in the BZ19-6 structural belt in the Bozhong Sag, which is used to predict the development of compaction diagenetic fractures in conglomerate reservoirs at different depths in the actual work area.

[0116] Among them, the calculation process of the crack surface rate is:

[0117] On the casting thin slice, the entire thin slice surface area is used as the statistical unit, and the product of the crack length and crack width counted under the microscope is approximately the crack area s. Then, the sum of the areas of each crack is used as the numerator and the statistical area is used as the denominator to calculate the surface crack rate γ of the crack in the depth section. The calculation formula is as follows:

[0118] s=ld

[0119]

[0120] Where, l- is the crack length;

[0121] d- is the crack width;

[0122] r- is the radius of the sheet.

[0123] In addition, the specific calculation process of the final brittleness index of the reservoir using two methods, mineral composition and conventional logging data, is as follows:

[0124] The brittleness index (mineral) B1 is calculated using the method of calculating the rock brittleness index based on mineral composition:

[0125]

[0126] Where W qtz - is the quartz and feldspar content;

[0127] W carb - is the carbonate content;

[0128] W total - is the total mineral content of the rock.

[0129] The brittleness index (logging) B2 is calculated using conventional logging data, and the product of B1 and B2 is regarded as the final brittleness index, that is,

[0130] B=B1×B2

[0131] The prediction model established is as follows: Figure 8 As shown in the figure, a fitting curve of the surface fracture rate of cracks at different depths obtained by the compaction physical diagenesis simulation experiment is established to characterize the degree of crack development; at the same time, a schematic diagram of the compaction diagenesis crack evolution pattern obtained by the changes in surface fracture rate, rock grain size, debris particle composition and filling material content with depth is constructed. On this basis, the measured surface fracture rate of each mine in the actual work area near 3000m-4500m is counted and calculated, and the points are projected onto the fitting curve of the surface fracture rate with depth obtained in the compaction physical diagenesis simulation experiment, and the fitting curve of the brittleness index of the actual work area at different depths is established. The accuracy of the prediction model is verified by the surface fracture rate and brittleness index of the actual work area. When the prediction model is consistent with the crack situation in the actual work area, the established prediction model is used to predict the development of compaction diagenesis cracks in the sandstone conglomerate reservoir at other depths in the actual work area.

[0132] In summary, the prediction model established by the present invention can well predict the development of compaction diagenetic fractures in sandstone conglomerate reservoirs, especially can well predict the development degree of compaction diagenetic fractures in the BZ19-6 area, providing a reference for the exploration of oil and gas reservoirs.

[0133] It should be noted that all crack openings in the present invention refer to the physical width of the crack. The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment, it is not used to limit the present invention. Any technical personnel in the field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandstone reservoir, characterized by: The following steps are included: S1: Determine the basic characteristics of the actual reservoir, and obtain the main rock types and rock components of the conglomerate reservoir in the actual work area through core observation and thin section identification; S2: Conduct compaction physical diagenesis simulation experiments, and prepare artificial rock samples with the same rock composition as the actual work area and at different burial compaction stages through compaction physical diagenesis simulation experimental equipment; S3: Determine the fracture parameters of the simulation experiment, observe the artificial rock samples under a slice microscope and perform image processing to obtain the fracture length, fracture width, number of fractures, surface porosity, rock grain size, debris particle composition and filling material content of each artificial rock sample; S4: Determine the fracture parameters of the actual work area, observe the rock samples of the actual work area under a slice microscope and process the images to obtain the fracture length, fracture width, number of fractures, rock grain size, debris particle composition, and filling material content of the actual work area; S5: Establish a prediction model. Based on the above parameters, calculate the surface fracture rate of each rock sample in the simulation experiment and the actual work area, as well as the brittleness index of each rock sample in the actual work area, and establish a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic cracks in the conglomerate reservoir. This model is used to predict the development of compaction diagenetic cracks in the conglomerate reservoir at different depths in the actual work area.

2. The method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1, characterized in that: The S2 comprises the following steps, S21: Configure artificial rock samples according to the debris components, clay mineral types, content of each type and particle size distribution of debris in the actual work area; S22: The artificial rock samples are divided into multiple groups according to rock types, and each group of rock samples is divided into n equal parts. n simulation environments are set up in a diagenetic physics simulation experimental device, and the n rock samples in each group are placed in n simulation environments for 15 days to produce artificial rock samples with a diameter of 3 cm and a height of 7 cm.

3. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: The S3 comprises the following steps, S31: injecting a blue casting into the artificial rock sample, and uniformly cutting a large thin slice of the casting at a distance of 1.5 cm from the upper part for observation under a microscope; S32: Take six adjacent view areas and stitch them together using the C-Tec image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock grain size, debris particle composition and filling material content of the stitched image.

4. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: The S5 comprises the following steps: S51: Establish a fitting curve of the surface fracture rate of cracks at different depths obtained from the compaction physical diagenesis simulation experiment and the depth, which is used to characterize the degree of crack development; S52: Schematic diagram of compaction diagenetic fracture evolution model obtained by the variation of surface porosity, rock grain size, clastic particle composition and interstitial material content with depth; S53: Count and calculate the measured surface fracture rate of the actual work area, and project the measured surface fracture rate onto the fitting curve of surface fracture rate versus depth obtained in the compaction physical diagenetic simulation experiment; S54: Establish the fitting curve of the brittleness index of the actual work area at different depths; S55: The accuracy of the prediction model is verified by the surface fracture rate and brittleness index of the actual work area. When the prediction model is consistent with the fracture situation in the actual work area, the established prediction model is used to predict the compaction diagenetic fracture development of the sandstone reservoir at other depths in the actual work area.

5. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: The calculation process of the surface seam rate is as follows: On the casting thin section, the entire thin section surface area is used as the statistical unit, and the product of the crack length and crack width counted under the microscope is approximately the crack area s. Then, the sum of the areas of each crack is used as the numerator and the statistical area is used as the denominator, and the surface crack rate γ of the crack in this depth section can be calculated.

6. The method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 5, characterized in that: The calculation formula of the surface seam rate is as follows: s=ld Where, 1- is the crack length; d- is the crack width; r- is the radius of the sheet.

7. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: The calculation process of the brittleness index is as follows: The brittleness index (mineral) B1 is calculated using the method of calculating the rock brittleness index using mineral composition, and the brittleness index (logging) B2 is calculated using the method of calculating the rock brittleness index using conventional logging data. The product of B1 and B2 is regarded as the final brittleness index.

8. The method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 7, characterized in that: The calculation formula of the brittleness index is as follows, R=B1×B2 Where W qtz - is the quartz and feldspar content; W carb - is the carbonate content; W total - is the total mineral content of the rock; B- is the final brittleness index; B1- is the brittleness index of the mineral composition method; B2- is the brittleness index calculated from conventional logging data.

9. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: The conglomerate reservoir is the conglomerate reservoir of the Kongdian Formation in the BZ19-6 condensate gas field.

10. The method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir according to claim 9, characterized in that: The sampling depth of the conglomerate reservoir samples of the Kongdian Formation in the BZ19-6 gas field in the actual work area is 3000m-4500m, and the samples are in the middle stage of vertical compaction.

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