A method for predicting vertical evolution of compaction diagenetic fractures in a glutenite reservoir

Abstract

The application provides a prediction method for vertical evolution of compaction diagenetic fractures in glutenite reservoirs, which comprises the following steps: obtaining the main rock types and rock compositions of the actual work area of the glutenite reservoir through core observation and thin section identification; preparing artificial rock samples with the same rock composition as the actual work area and in different burial compaction stages through a compaction physical diagenetic simulation experiment device; measuring the fracture parameters of the artificial rock samples in the simulation experiment and the fracture parameters of the actual work area rock samples, respectively calculating the surface fracture ratio of both and the brittleness index of each rock sample in the actual work area, establishing a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic fractures in the glutenite reservoir, and predicting the development of the compaction diagenetic fractures in the glutenite reservoir at different depths in the actual work area. The prediction model established by the application can well predict the development of the compaction diagenetic fractures in the glutenite reservoir, especially the development degree of the compaction diagenetic fractures in the BZ19-6 area, thereby providing a reference for the exploration of oil and gas reservoirs.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas field geological evolution, and particularly relates to a prediction method for vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir. BACKGROUND

[0002] In order to ensure the safety of oilfield development and control economic cost, the geological conditions of the rock stratum need to be deeply understood before or during the oilfield exploitation. In the process of oil and gas exploration and development, compaction diagenetic fractures are important reservoir spaces for oil and gas and have an important influence on the percolation capacity of the reservoir.

[0003] In recent years, compaction diagenetic fractures have been found in oil and gas bearing basins in the east and west of China. For example, in the Wushi Sag of the Tarim Basin, a plurality of groups of compaction diagenetic fractures are developed in the K1g medium-coarse grained sandstone with gravel at a burial depth of more than 6000m, which are the main reservoir spaces for oil and gas, and a high-yield industrial oil and gas layer with a daily oil production of 174 tons and a daily gas production of 2x105m 3 of gas is obtained. For another example, a large number of compaction diagenetic fractures are developed in the carbonate gravel of the fourth member of the Shahejie Formation in the Dongying Sag of the Bohai Bay Basin, which improves the permeability of the reservoir and is laterally reformed into small-scale fracture-caves by organic acid.

[0004] At present, compaction diagenetic fractures are developed in a large amount in sandy conglomerate reservoirs, but the formation and evolution mechanism thereof is not clear. For an oilfield that has not been completely developed or is in the process of being developed, it is very important to quantitatively obtain the vertical distribution of compaction diagenetic fractures in sandy conglomerate reservoirs through prediction. Therefore, at present, there is a lack of a method for quantitatively evaluating the vertical evolution process of compaction diagenetic fractures in sandy conglomerate reservoirs. SUMMARY

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

[0006] The present application provides a prediction method for vertical evolution of compaction diagenetic fractures in a sandy conglomerate reservoir, comprising the following steps,

[0007] S1: judging the basic characteristics of the actual reservoir, obtaining the main rock types and rock compositions of the actual work area sandy conglomerate reservoir through core observation and thin section identification;

[0008] S2: performing a compaction physical diagenetic simulation experiment, preparing artificial rock samples with the same rock compositions as those in the actual work area and at different burial compaction stages through a compaction physical diagenetic simulation experiment device;

[0009] S3: Determine the crack parameters of the simulation experiment, slice the artificial rock sample for microscopic observation and image processing, and obtain the crack length, crack width, crack number, surface porosity, rock size, clastic particle composition and interstitial material content of each artificial rock sample;

[0010] S4: Determine the crack parameters of the actual work area, slice the rock sample of the actual work area for microscopic observation and image processing, and obtain the crack length, crack width, crack number, rock size, clastic particle composition and interstitial material content of the actual work area;

[0011] S5: Establish a prediction model, based on the above parameters, calculate the surface crack rate of each rock sample in the simulation experiment and the actual work area, and the brittleness index of each rock sample in the actual work area, establish a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic cracks in sandy gravel reservoirs, which is used to predict the development of compaction diagenetic cracks in sandy gravel reservoirs at different depths in the actual work area.

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

[0013] S21: According to the clastic component, clay mineral type and content of each type, and the particle size distribution of the clastic particles in the actual work area, configure the artificial rock sample;

[0014] S22: The artificial rock sample is divided into multiple groups according to the rock type, and each group of rock samples is equally divided into n parts, and n simulation environments are set in the diagenetic physical simulation experiment equipment, and the n parts of rock samples of each group are placed in the n simulation environments for 15 days, and the artificial rock sample with a diameter of 3 cm and a height of 7 cm is made.

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

[0016] S31: Inject blue casting into the artificial rock sample, and uniformly cut a large casting sheet at a distance of 1.5 cm from the upper part for microscopic observation;

[0017] S32: Take six adjacent fields of view and perform image stitching through the Xitu image analysis software to obtain the crack length, crack width, crack number, surface porosity, rock size, clastic particle composition and interstitial material content of the stitched image.

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

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

[0020] S52: Construct a compaction diagenetic crack evolution pattern diagram obtained by the change of surface porosity, rock size, clastic particle composition and interstitial material content with depth;

[0021] S53: Statistics and calculates the actual work area of the measured surface crack rate, which is projected to the surface crack rate with depth fitting curve obtained from the compaction physical diagenetic simulation experiment;

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

[0023] S55: Verify whether the prediction model is accurate by the surface crack rate and brittle index of the actual work area. When the prediction model is consistent with the actual work area of the crack, use the established prediction model to predict the compaction diagenetic fracture development of the sandy gravel reservoir at other depths of the actual work area.

[0024] Further, the calculation process of the surface crack rate is as follows,

[0025] The product of the crack length and crack width under the microscope is approximately the crack area s, and the sum of the areas of each crack is the numerator and the statistical area is the denominator, so the surface crack rate γ of the crack in the depth section can be calculated.

[0026] Further, the calculation formula of the surface crack rate is as follows,

[0027] s=ld

[0028]

[0029] In the formula, l- is the crack length;

[0030] d- is the crack width;

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

[0032] Further, the calculation process of the brittle index is as follows,

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

[0034] Further, the calculation formula of the brittle index is as follows,

[0035]

[0036] B=B1×B2

[0037] In the formula, W qtz - is the quartz and feldspar content;

[0038] W carb - is the carbonate rock 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 glutenite 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 and conglomerate reservoirs, especially the degree of development 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 This is a schematic diagram of the actual location 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 BZ19-6 Kongdian Formation conglomerate reservoir in a simulation experiment in a specific embodiment of the present invention.

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

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

[0052] Figure 6 This is a diagram showing changes in the aperture and number of diagenetic cracks with depth during compaction of different compositions 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 cracks in an actual working area of ​​a specific embodiment of the present invention.

[0054] Figure 8This 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 clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

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

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

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

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

[0060] S21: Prepare 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.

[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 a diagenetic physics simulation experimental device. 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.

[0062] S3: Determine the fracture parameters of the simulation experiment, observe the artificial rock samples under a microscopy microscope and perform image processing to obtain the fracture length, fracture width, number of fractures, surface porosity, rock particle size, debris particle composition and filling material content of each artificial rock sample. Specifically, S3 includes the following steps:

[0063] S31: The blue casting was injected into the artificial rock sample, and large thin slices of the casting were uniformly cut at a distance of 1.5 cm from the upper portion for observation under a microscope.

[0064] S32: Take six adjacent view areas and perform graphic stitching using the West Tu image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock particle size, debris particle composition, and filling material content of the stitched image.

[0065] S4: Determining the crack parameters of the actual work area, slicing the rock samples of the actual work area for microscopic observation and image processing to obtain the crack length, crack width, crack number, rock grain size, clastic grain composition and interstitial material content of the actual work area.

[0066] S5: Establishing a prediction model, based on the above parameters, calculating the surface crack ratio of each rock sample in the simulation experiment and the actual work area, and the brittleness index of each rock sample in the actual work area, establishing a prediction model for quantitatively evaluating the vertical evolution of compaction diagenetic cracks in the sandy gravel reservoir, which is used to predict the development of compaction diagenetic cracks in the sandy gravel reservoir at different depths in the actual work area. Specifically, S5 includes the following steps,

[0067] S51: Establishing a fitting curve of the surface crack ratio of the cracks at different depths obtained from the compaction physical diagenetic simulation experiment, which is used to characterize the crack development degree.

[0068] S52: Constructing a compaction diagenetic crack evolution pattern diagram obtained from the changes of the surface porosity, rock grain size, clastic grain composition and interstitial material content with depth.

[0069] S53: Statistically calculating the measured surface crack ratio of the actual work area and projecting it onto the fitting curve of the surface crack ratio with depth obtained from the compaction physical diagenetic simulation experiment.

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

[0071] S55: Verifying whether the prediction model is accurate by the surface crack ratio and the brittleness index of the actual work area, and when the prediction model is consistent with the crack situation of the actual work area, using the established prediction model to predict the development of compaction diagenetic cracks in the sandy gravel reservoir at other depths in the actual work area.

[0072] Wherein, the calculation process of the surface crack ratio is as follows,

[0073] On the cast thin section, taking the entire thin section surface area as the statistical unit, the product of the microscopic statistical crack length and crack width is approximately the crack area s, and the sum of the areas of all cracks is the numerator and the statistical area is the denominator, so the surface crack ratio γ of the cracks in the depth section can be calculated. The calculation formula of the surface crack ratio is as follows,

[0074] s = ld

[0075]

[0076] In the formula, l- is the crack length;

[0077] d- is the crack width;

[0078] r- is the radius of the thin section.

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

[0080]

[0081] In the formula, W qtz is the content of quartz and feldspar;

[0082] W carb is the content of carbonate rock;

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

[0084] The brittleness index (logging) B2 is calculated by using the method for calculating the rock brittleness index by using conventional logging data, and the product of B1 and B2 is regarded as the final brittleness index, and the calculation formula is as follows,

[0085] B = B1 x B2

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

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

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

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

[0090] The present application will be further described below in combination with specific implementation cases. Figure 2 The prediction method of the vertical evolution of the compaction diagenetic fractures of the glutenite reservoir of the Kongdian Formation of the BZ19-6 condensate gas field is used as an object, wherein the BZ19-6 structural belt is located on the deep structural ridge in the southwest of the Bozhong sag in the Bohai Bay Basin, is adjacent to the Bonan low uplift in the southeast, is adjacent to the Chengbei low uplift in the west, is connected with the Yellow River mouth sag in the south, and is the main depression of the Bozhong sag in the north, in the form of an anticline uplift belt in a large sag. The oil and gas reservoirs in the research area are mainly enriched in the Archean buried hill metamorphic rock and the thick glutenite reservoir of the Kongdian Formation, wherein the burial depth of the glutenite reservoir of the Kongdian Formation is generally more than 3500 m, the main source rock layer is the Shahejie Formation and the Dongying Formation, and the regional distribution of the thick mudstone of the third member of the Dongying Formation is stable, and the high-quality cap rock is as shown in

[0091] The prediction method of the vertical evolution of the compaction diagenetic fractures of the glutenite reservoir of the Kongdian Formation of the BZ19-6 condensate gas field is used as an object, wherein the BZ19-6 structural belt is located on the deep structural ridge in the southwest of the Bozhong sag in the Bohai Bay Basin, is adjacent to the Bonan low uplift in the southeast, is adjacent to the Chengbei low uplift in the west, is connected with the Yellow River mouth sag in the south, and is the main depression of the Bozhong sag in the north, in the form of an anticline uplift belt in a large sag. The oil and gas reservoirs in the research area are mainly enriched in the Archean buried hill metamorphic rock and the thick glutenite reservoir of the Kongdian Formation, wherein the burial depth of the glutenite reservoir of the Kongdian Formation is generally more than 3500 m, the main source rock layer is the Shahejie Formation and the Dongying Formation, and the regional distribution of the thick mudstone of the third member of the Dongying Formation is stable, and the high-quality cap rock is as shown in

[0092] S1: Core observation and thin section analysis revealed the primary rock types and component contents of the conglomerate reservoir samples from the Kongdian Formation of the BZ19-6 gas field. The samples used in this example were collected at a depth of approximately 3000-4500 m, and were in the middle stages of vertical compaction. The primary rock types within the reservoir were determined to be 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 Statistics 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 experiments

[0096] Using a diagenetic physics simulation experimental device, artificial rock samples with the same composition as the actual rocks in the work area but at different burial and compaction stages were prepared according to the actual debris composition, 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.

[0097] As a specific scheme, artificial rock samples were divided into two groups, namely granite and gneiss, and each group of rock samples was divided into eight equal parts. In order to simulate the diagenesis conditions at different burial and compaction stages, eight simulation environments were set up in the diagenetic physics simulation experimental equipment. The specific settings of the simulation environments are shown in Table 2.

[0098] Table 2 Temperature and pressure of conglomerate simulation experiments 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 the "Pore Evolution Characteristics and Favorable Reservoir Evaluation of Deep Jurassic Reservoirs in Dibei, 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; 8 granite samples and 8 gneiss samples are placed in 8 simulation 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 microscopic observation. Six adjacent fields of view were taken and spliced ​​using 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 ​​images.

[0104] The microscopic images of reservoirs at simulated depths of 2000m, 4000m, and 6000m in the compaction simulation experiment in this embodiment are shown in FIG. Figure 3 As shown in a, b, and c, the surface porosity, crack width, and crack number obtained from 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 different rock particle sizes are as follows Figure 5 shown.

[0105] Specifically, the width and number of the cracks in this embodiment are as follows: Figure 4 As shown in the data, in the shallow burial stage of 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; in the 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; in the 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. During the shallow burial-rapid deep burial transition stage at a simulated depth of 3000-4500m, both inherited and non-inherited cracks developed. The average crack width of sample No. 1 increased from 3.69μm to 12.38μm, and the average crack width of sample No. 2 increased from 5.2μm to 12.44μm. When the simulated burial depth was 3000-4000m, the number of cracks in sample No. 1 increased from 40 to 56, and the number of cracks in sample No. 2 increased from 40 to 65. At a simulated burial depth of 4000-4500m, the number of cracks in sample No. 1 decreased to 40, and the number of cracks in sample No. 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 porosity of granite and gneiss tends to decrease with depth. When the simulated depth is 2000m, the surface porosity of No. 1 is 18.7%, and the surface porosity of No. 2 is 20.06%; when the simulated depth is 3000m, the surface porosity of No. 1 is 15.4%, and the surface porosity of No. 2 is 17.21%; when the simulated depth is 3500m, the surface porosity of No. 2 is 18.34%; when the simulated depth is 4000m, the surface porosity of No. 1 is 12.53%, and the surface porosity of No. 2 is 13.44%; when the simulated depth is 6000m, the surface porosity of No. 1 is 11.62%, and the surface porosity of No. 2 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, the artificial rock samples were classified according to gravel grade and sand grade to obtain their crack aperture and crack number; the results of the debris particle composition of the artificial rock samples are shown in the figure. Figure 6 As shown, the crack opening and number of its main components, feldspar and quartz, were obtained.

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

[0109] The rock samples from the actual work area were observed under a microtome and image processed to obtain the crack length, crack width, number of cracks, rock particle size, debris particle composition, and filling material content of 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 in Figure 3, f, g, h, and i are the microscopic images of fracture development at BZ19-6-B 3855.75 m, BZ19-6-A 3890 m, BZ19-6-A 3780 m, and BZ19-6-A 3571 m, respectively. The fracture development status is consistent with the fracture development status between the simulated depth of 3000-4500 m in S3.

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

[0112] In this embodiment, the fragments fractured in the actual working area are mainly feldspar, quartz and rigid rock fragments, which is consistent with the fragment composition results of the simulation experiment in S3.

[0113] Therefore, the crack development type, rock size grade and clastic particle composition of the actual work area are consistent with the simulation experiment, thereby proving the effectiveness of the simulation experiment in forming cracks.

[0114] S5: Establishing a prediction model

[0115] Based on the above parameters, the surface crack rate of each rock sample in the simulation experiment and the actual work area, and the brittleness index of each rock sample in the actual work area are calculated, thereby establishing a 3-section characteristic prediction model of the crack evolution of the deep sand-gravel reservoir in the BZ19-6 structural belt of the Bozhong Sag, which is used to predict the development of the compaction diagenetic cracks in the sand-gravel reservoir at different depths in the actual work area.

[0116] The calculation process of the surface crack rate of the cracks is as follows:

[0117] On the cast thin section, the product of the crack length and the crack width under the microscope is approximated as the crack area s, and the sum of the areas of the cracks is taken as the numerator and the statistical area is taken as the denominator, so that the surface crack rate γ of the cracks in the depth section can be calculated, and the calculation formula is as follows:

[0118] s = ld

[0119]

[0120] In the formula, l- is the crack length;

[0121] d- is the crack width;

[0122] r- is the radius of the thin section.

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

[0124] The brittleness index (mineral) B1 calculated by the method of calculating the brittleness index of rocks using mineral composition is as follows:

[0125]

[0126] In the formula, W qtz - is the content of quartz and feldspar;

[0127] W carb - is the content of carbonate rocks;

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

[0129] The brittleness index (logging) B2 calculated by the method of calculating the brittleness index of rocks using conventional logging data is as follows:

[0130] B = B1 x B2

[0131] The prediction model is specifically as shown in the following table: Figure 8 The fitting curve of the face fracture ratio of the fractures at different depths obtained by the compaction physical diagenetic simulation experiment is used to characterize the fracture development degree; at the same time, a compaction diagenetic fracture evolution pattern diagram obtained by the changes of the face porosity, rock grain size, detrital particle component and interstitial material content with depth is constructed. On this basis, the measured face fracture ratio of each mine in the actual work area near 3000m-4500m is counted and calculated, which is projected onto the fitting curve of the face fracture ratio with depth obtained by the compaction physical diagenetic simulation experiment, and the fitting curve of the brittleness index of the actual work area at different depths is established, and the prediction model is verified whether it is accurate by the face fracture ratio and the brittleness index of the actual work area, when the prediction model is consistent with the fracture condition of the actual work area, the prediction model is used to predict the compaction diagenetic fracture development of the sandy conglomerate reservoir at other depths in the actual work area.

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

[0133] It should be noted that all the fracture openings in the present application refer to the physical width of the fracture. The above description is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, but as long as it does not deviate from the technical solution of the present application, any modification, equivalent change and modification of the above embodiments according to the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

1. A method for predicting the vertical evolution of compaction diagenetic fractures in a sandy conglomerate 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 compositions of the sandstone and conglomerate reservoirs in the actual work area through core observation and thin section identification; S2: Conduct compaction physical diagenetic simulation experiments, using compaction physical diagenetic simulation experimental equipment to prepare artificial rock samples with the same rock composition as the actual work area and at different burial compaction stages; S3: measuring the fracture parameters of the simulation experiment, observing the artificial rock samples under a microtome and performing image processing to obtain the fracture length, fracture width, number of fractures, porosity, rock particle size, debris particle composition, and interstitial material content of each artificial rock sample; S4: Determine the fracture parameters of the actual work area, observe the rock samples in the actual work area under a microtome 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; 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 sandy conglomerate reservoir. The prediction model is used to predict the development of compaction diagenetic cracks in the sandy conglomerate reservoir at different depths in the actual work area. The S5 includes the following steps: S51: Establish a fitting curve of the surface fracture rate versus depth of cracks at different depths obtained from the compaction physical diagenetic simulation experiment to characterize the degree of crack development; S52: Schematic diagram of the evolution pattern of compaction diagenetic fractures obtained from the changes in surface porosity, rock grain size, clastic particle composition and interstitial material content with depth; S53: Count and calculate the measured surface fracture rate in 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 fitting curves of the actual work area brittleness index at different depths. S55: Verify the accuracy of the prediction model by measuring 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, use the established prediction model to predict the development of compaction diagenetic fractures in the sandstone and conglomerate reservoirs at other depths in the actual work area. The calculation process of the surface seam rate is as follows: 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 segment. The calculation formula of the surface crack rate is as follows: s=ld Where, l is the crack length; d—is the crack width; r—is the radius of the sheet; 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 conventional well logging data method. The product of B1 and B2 is regarded as the final brittleness index. The calculation formula of the brittleness index is as follows: B=B1×B2 Where W qtz — is the quartz and feldspar content; W carb — is the carbonate rock 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—Brittleness index calculated from conventional logging data.

2. The method for predicting the vertical evolution of compaction diagenetic cracks in a sandy conglomerate reservoir according to claim 1, characterized in that: Said S2 comprises the following steps, S21: Prepare 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 type, 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. The n rock samples in each group are placed in the n simulation environments for 15 days to produce artificial rock samples with a diameter of 3 cm and a height of 7 cm.

3. The method for predicting the vertical evolution of compaction diagenetic cracks in a sandy conglomerate reservoir according to claim 1 or 2, characterized in that: Said 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 perform graphic stitching using the West Tu image analysis software to obtain the crack length, crack width, number of cracks, surface porosity, rock particle size, debris particle composition, and filling material content of the stitched image.

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

5. The method for predicting the vertical evolution of compaction diagenetic cracks in a sandy conglomerate reservoir according to claim 4, characterized in that: The sampling depth of the glutenite 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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