Method, system, medium, and apparatus for determining grain size distribution in a wave-generated sedimentation simulation experiment

Abstract

The present application relates to a wave-deposited simulation experiment sediment grain size ratio determination method, system, medium and equipment, comprising: determining the initial deposition ratio of the target area; adjusting the initial deposition ratio; analyzing the grain size distribution characteristics of the target area; obtaining the target wave height of the target area; determining the experimental wave height of the laboratory; calculating the laboratory matching grain size based on the target wave height and the experimental wave height; using mudstone to simulate particles with a particle size less than silt, and converting particles with a particle size greater than silt to form a coarse-grained experimental sample; based on the weighted average of the target area coarse-fine grain ratio of different sediment facies belt area ratio, combining the coarse-grained sample with the mud ratio, and forming the final laboratory grain size ratio result. The present application breaks through the technical bottleneck that the existing experience ratio cannot consider the prototype grain size characteristics and similarity, significantly improves the accuracy, extrapolation reliability and reservoir engineering application value of wave-deposited physical simulation, and provides more scientific experimental support for oil and gas exploration.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and more specifically, to a method, system, medium, and equipment for determining the grain size distribution of sediments in wave-forming sedimentation simulation experiments. Background Technology

[0002] Sedimentary rocks are the most common type of rock, including chemical sedimentary rocks and clastic sedimentary rocks. Clastic sedimentary rocks are more common, including conglomerate, sandstone, mudstone and other types of different grain sizes. These sedimentary rocks are hard geological bodies formed by sediments being transported by water, wind and biological action and then deposited and solidified by diagenesis in the later stages.

[0003] Sedimentary simulation experiments are a means of predicting sedimentary rock formations. Based on the principle of similarity, this method replicates the original sedimentary conditions of real or past deposits in a laboratory setting. Through experimental reproduction, it explores the sedimentary development process and controlling factors, predicting the development and characteristics of sedimentary bodies. Generally, sedimentary simulation experiments must possess similarity to the original sediments, meaning similarity in geometric scale, sedimentary dynamic processes, and sedimentary kinematics. Since real-world sediments typically have large-scale characteristics, their scale is usually reduced when replicated in the laboratory. However, if the scale deviation occurs during this reduction, the experimental sedimentary process will not resemble the actual sedimentary process, thus rendering the sedimentary simulation experiment incomparable and losing its inherent meaning. For wave-affected sediments, because their dynamic processes are influenced by wave energy, they differ significantly from traditional, well-established water flow processes. Therefore, traditional analogy methods often fail to achieve the desired results. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method, system, medium and equipment for determining the particle size distribution of sediments in wave-forming sedimentation simulation experiments, in view of the problems existing in the prior art.

[0005] The technical solution adopted by this invention to solve its technical problem is: to construct a method for determining the grain size distribution of sediments in wave-forming sedimentation simulation experiments, comprising the following steps: Step S101: Based on the drilling and logging data of the target area, perform statistical analysis and calculations to obtain the initial depositional ratio of coarse-grained to fine-grained sediments in the target area; Step S102: Adjust the initial deposition ratio; Step S103: Perform grain size analysis on the coarse-grained sediments of typical sedimentary segments in the target area after adjusting the sedimentation ratio to obtain the grain size distribution characteristics of the target area. Step S104: Perform elevation analysis on the geology and sedimentary wave-breaking sandbars of the target area to obtain the target wave height of the target area; Step S105: Determine the experimental wave height in the laboratory based on the laboratory's equipment parameters; Step S106: Based on the target wave height and the experimental wave height, and based on the dynamic similarity between wave energy and water flow energy, calculate the quasi-Frode number that characterizes the dynamic relationship when the target area and the laboratory achieve motion similarity, and calculate the laboratory matching particle size according to the quasi-Frode number. Step S107: Use mudstone to simulate particles smaller than silt, and convert particles larger than silt to form coarse-grained experimental samples. Step S108: Based on the weighted average of the coarse and fine grain ratio of the target area according to the area ratio of different sedimentary facies zones, and combined with the ratio of coarse grain samples to mud, the final laboratory particle size distribution result is formed.

[0006] In the method for determining the sediment grain size distribution in wave-forming sedimentation simulation experiments described in this invention, step S101 includes: Based on the logging lithological description data, grain size analysis data and thin section identification data of multiple wells in the target area, the thickness ratio of coarse-grained sediments to fine-grained sediments in the target layer of each well is calculated to obtain the sand-mud ratio of a single well. The target area is divided into sedimentary facies zones according to the sedimentary environment type, and the area ratio of each sedimentary facies zone is calculated. Geological representativeness assessments are conducted on wells located within the same sedimentary facies zone to remove unrepresentative wells affected by local structures or data quality. The initial sedimentary ratio of coarse-grained to fine-grained sediments in the target area was calculated using the dual-weighting method.

[0007] In the method for determining the sediment grain size distribution in wave-forming sedimentation simulation experiments described in this invention, step S102 includes: Based on the similarity of sedimentary dynamic parameters, reference rivers with similarity to the target area are selected; The mass percentages of coarse and fine grain components in the modern sediments of the reference river were determined by laser grain size analysis and thin section identification. Calculate the deviation coefficient between the initial sand-mud ratio of the target area and the reference river sand-mud ratio; The initial deposition ratio of the target area is adjusted based on the deviation coefficient.

[0008] In the method for determining the sediment grain size distribution in wave-forming sedimentation simulation experiments described in this invention, step S104 includes: Based on paleowind studies and wind path analysis of the target area, wind speed and wind path are determined. The target wave height of the target area is obtained by calculating based on the wind speed and wind distance.

[0009] In the method for determining the sediment grain size distribution in wave-forming sedimentation simulation experiments described in this invention, step S104 includes: The target wave height of the target area is obtained based on the height of the wave-breaking sandbar in the target area.

[0010] In the method for determining the sediment grain size distribution in the wave-forming sedimentation simulation experiment described in this invention, in step S105, the wave height of the laboratory wave generator is the maximum wave height measured when the rotation speed of the laboratory wave generator is turned up to the maximum, and the laboratory wave generator is adjusted to exceed twice the initial lake level height.

[0011] In the method for determining the sediment grain size distribution in wave-forming sedimentation simulation experiments described in this invention, step S106 includes: The wave energy calculation formula and the water flow energy calculation formula are obtained, and the pseudo-Frode number is characterized by water flow dynamic parameters; The laboratory matching granularity is obtained by calculating based on the pseudo-Frod number, the target wave height, and the experimental wave height.

[0012] This invention also provides a system for determining sediment grain size distribution in wave-forming sedimentation simulation experiments, comprising: The data statistical analysis module is used to perform statistical analysis and calculations based on drilling and logging data of the target area to obtain the initial deposition ratio of coarse-grained sediments to fine-grained sediments in the target area. An adjustment module is used to adjust the initial deposition ratio; The feature analysis and determination module is used to perform grain size analysis on coarse-grained sediments in typical sedimentary segments within the target area after adjusting the sedimentation ratio, and to obtain the grain size distribution characteristics of the target area. The target wave height determination module is used to perform elevation analysis on the geology and sedimentary wave-breaking sandbars of the target area to obtain the target wave height of the target area; The experimental wave height determination module is used to determine the experimental wave height in the laboratory based on the laboratory's equipment parameters. The particle size calculation module is used to calculate the pseudo-Frode number, which characterizes the dynamic relationship when the target area and the laboratory achieve motion similarity, based on the target wave height and the experimental wave height and the dynamic similarity between wave energy and water flow energy, and to calculate the laboratory matching particle size based on the pseudo-Frode number. The sample determination module is used to simulate particles smaller than silt using mudstone, and to convert particles larger than silt into coarse-grained experimental samples. The proportioning module is used to weight the coarse and fine grain ratio of the target area based on the area ratio of different sedimentary facies zones, and combine the coarse sample with the mud ratio to form the final laboratory particle size distribution result.

[0013] The present invention also provides a storage medium storing a computer program adapted for loading by a processor to execute the steps of the method for determining the grain size distribution of sediments in a wave-forming sedimentation simulation experiment as described above.

[0014] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method for determining the grain size distribution of sediments in a wave-forming sedimentation simulation experiment as described above by calling the computer program stored in the memory.

[0015] The method, system, medium, and equipment for determining sediment grain size distribution in wave-forming sedimentary simulation experiments according to the present invention have the following beneficial effects: They include: determining the initial sedimentary ratio of the target area; adjusting the initial sedimentary ratio; analyzing the grain size distribution characteristics of the target area; obtaining the target wave height of the target area; determining the experimental wave height in the laboratory; calculating the laboratory-matched grain size based on the target wave height and the experimental wave height; using mudstone to simulate particles smaller than silt, and converting particles larger than silt to form coarse-grained experimental samples; and weighting the coarse-fine grain ratio of the target area based on the area ratio of different sedimentary facies zones, combined with the ratio of coarse-grained samples to mudstone, to form the final laboratory grain size distribution result. This invention overcomes the technical bottleneck of existing empirical proportioning methods that cannot simultaneously consider the characteristics and similarity of the prototype grain size, significantly improving the accuracy, extrapolation reliability, and reservoir engineering application value of wave-forming sedimentary physical simulation, providing more scientifically based experimental support for oil and gas exploration. Attached Figure Description

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings: Figure 1 This is a flowchart illustrating the method for determining the sediment particle size distribution in wave-forming sedimentation simulation experiments provided by the present invention. Figure 2 This is a schematic diagram showing the distribution of each research well W1-W4 within the target area provided by the present invention; Figure 3 Taking Well W3 as an example, this paper presents a physical image of coarse-grained sediments in a typical sedimentary layer. Figure 4 This is a statistical diagram of the particle size parameters of wells W3 / W4 in an unmodified estuary; Figure 5 This is a statistical diagram of the particle size parameters of Well W1 in the modified estuary; Figure 6 A statistical diagram of particle size parameters for the strongly modified beach-bar W2 well; Figure 7 A comparative schematic diagram of the grain size distribution characteristics of the target areas of wells W1-W4; Figure 8A schematic diagram for determining the maximum wave height generated by laboratory wave generation; Figure 9 A schematic diagram of the experimental setup for wave-forming sedimentation physics; Figure 10 This is a schematic diagram of the design system for sediment particle size distribution in wave-forming sedimentation simulation experiments provided by the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To address the technical problem of existing technologies that make it difficult to match the sediment analogy required for sedimentary dynamic similarity in laboratory simulations, this invention provides a method for determining sediment grain size distribution in wave-forming sedimentary simulation experiments. In wave-forming sedimentary physics experiments, this method effectively improves the geometric similarity of sediments at scale, the similarity of sedimentary dynamic processes, and the similarity of sedimentary kinematics, as well as the simulation accuracy, extrapolation reliability, and reservoir engineering application value, providing more scientifically based experimental support for oil and gas exploration.

[0019] refer to Figure 1 In a preferred embodiment, the method for determining the sediment grain size distribution in the wave-forming sedimentation simulation experiment includes the following steps: Step S101: Based on the drilling and logging data of the target area, perform statistics and calculations to obtain the initial depositional ratio of coarse-grained sediments to fine-grained sediments in the target area.

[0020] In this embodiment of the invention, based on drilling and logging data of the target area, the proportion of sediments of different grain sizes in the target layer is statistically analyzed, and a weighted average is performed according to the sedimentary facies zone to which different wells belong, to obtain the initial sedimentary ratio of coarse-grained sediments to fine-grained sediments in the target area.

[0021] Preferably, step S101 includes: Based on the logging lithological description data, grain size analysis data, and thin section identification data of multiple wells in the target area, the thickness ratio of coarse-grained sediments to fine-grained sediments in the target interval of each well was calculated to obtain the sand-mud ratio of a single well. The sedimentary facies zones of the target area were divided according to the sedimentary environment type, and the area ratio of each sedimentary facies zone was calculated. The geological representativeness of wells located in the same sedimentary facies zone was assessed, and non-representative wells affected by local structures or data quality were removed. The initial sedimentary ratio of coarse-grained to fine-grained sediments in the target area was calculated using the dual-weighting method.

[0022] In some embodiments, it is assumed that the sand-to-sludge ratio of a single well is The area proportion of each sedimentary facies zone in the target area is: The formula for calculating the initial sedimentary ratio of coarse-grained to fine-grained sediments in the target area using the dual-weighting method is as follows: in, This represents the sand-to-soil ratio used in the root drilling of the k-th well; This indicates the area weight of the facies zone J where the well is located (facies zone area / total area of ​​the target area); This indicates the spatial rooting weight of the well within the facies zone (when wells are uniformly distributed within the facies zone). =1, and when the distribution is uneven, it is allocated according to the proportion of the controlled area.

[0023] Specifically, based on drilling logging (W1, W2, W3, W4, etc.), grain size analysis, and thin section identification data, the proportion of sandstone and mudstone of different grain sizes in different target strata of different wells is statistically analyzed, i.e., W1 (sand: mud), W2 (sand: mud), etc. Then, a weighted average is performed according to the facies zone to which different wells belong, so as to obtain the initial sedimentary ratio of coarse-grained sediments and fine-grained sediments in the target area.

[0024] Step S102: Adjust the initial deposition ratio.

[0025] In this step, the initial sediment ratio of the target area is adjusted by analogy with the fine-to-coarse grain ratio of sediments in similar rivers.

[0026] Preferably, step S102 includes: selecting a reference river that is similar to the target area based on the similarity of sedimentary dynamic parameters; determining the mass percentage of coarse and fine-grained components in the modern sediments of the reference river through laser grain size analysis and thin section identification; calculating the deviation coefficient between the initial sand-mud ratio of the target area and the sand-mud ratio of the reference river; and adjusting the initial sedimentary ratio of the target area according to the deviation coefficient.

[0027] It should be noted that, in practice, the selection method for reference rivers similar to the target area is as follows: If the river's sedimentary dynamic parameters meet the set ranges such as catchment area deviation ≤20%, slope deviation ≤15%, and sediment transport deviation ≤25%, then that river is selected as a reference river similar to the target area. Furthermore, components with a grain size ≥0.063mm are typically considered coarse-grained components, and components with a grain size <0.063mm are considered fine-grained components. The specific formula for adjusting the initial sedimentary proportions of the target area based on the deviation coefficient is as follows: Where K is the deviation coefficient; The initial sedimentary ratio of the target area; For reference, the sand-to-silt ratio of rivers.

[0028] When K ≥ 0.3, the initial deposition ratio of the target area is adjusted as follows: .

[0029] Step S103: Perform grain size analysis based on the coarse-grained sediments of typical sedimentary segments in the target area after adjusting the sedimentation ratio to obtain the grain size distribution characteristics of the target area.

[0030] In this step, grain size analysis is performed on the coarse-grained sediments of typical sedimentary segments in the target area after adjusting the sedimentation ratio, to determine the proportion of particles in different grain size segments in the coarse-grained sediments, and to obtain the grain size distribution characteristics of the target area.

[0031] Specifically, grain size analysis is performed on coarse-grained sediments in typical sedimentary sections within the target area to determine the proportion of particles in different grain size ranges within the coarse-grained sediments, thus obtaining the grain size distribution characteristics of the target area. These characteristics represent the proportion of different grain size distributions and the percentage of grain size probability levels. The grain size analysis patterns serve as a reference for subsequent refined laboratory grain size proportioning.

[0032] The calculation can be based on drilling logging (W1, W2, W3, W4, etc.), grain size analysis and thin section identification data to statistically analyze the proportion of sandstone and mudstone of different grain sizes in different target strata of different wells, i.e. W1 (gravel: sand: silt: clay), W2 (gravel: sand: silt: clay), etc., and then a weighted average is performed according to the facies zone to which different wells belong.

[0033] like Figure 3 As shown, Figure 3 This diagram illustrates the sandstone to mudstone ratio in certain sections of a well within a differentially modified shoal transition zone.

[0034] Step S104: Perform elevation analysis on the geology and sedimentary wave-breaking sandbars of the target area to obtain the target wave height of the target area.

[0035] In this step, the basic energy characteristics of the target area are determined by performing elevation analysis on the geology and sedimentary wave-breaking sandbars, thus obtaining wave height parameters to characterize wave energy. There are two methods: the first method involves determining wind speed and wind path based on paleowind studies and wind path analysis of the target area; the target wave height is then calculated based on these parameters. The second method involves obtaining the target wave height based on the height of the wave-breaking sandbars in the target area.

[0036] In the first method, based on paleowind research and wind path analysis of the study area, the wind speed and wind path are determined. The wind speed and wind path are then substituted into the wind-wave relationship to obtain the effective wave height (i.e., the target wave height of the target area) that characterizes wave energy.

[0037] The second method reconstructs paleowave heights by utilizing the quantitative relationship between wave-induced sandbar landform features (such as thickness, slope, and location) and wave height. For example, when waves propagate towards the shore to the wave-breaking zone (where water depth ≈ 1.28 times wave height), their energy decreases sharply, and the sediment they carry accumulates to form wave-breaking sandbars. The geometric parameters of the sandbars are directly controlled by wave height. Compared to the first method, this method can avoid the uncertainties of paleowind models or is suitable for wave height reconstruction in areas without wind records.

[0038] As a preferred embodiment, in the first method described above, the method for determining wind speed and wind path based on paleowind research and wind path analysis of the target area employs one of the following methods: quantitative analysis based on BPT technology, paleowind recovery based on the thickness of wave-breaking sandbars, or paleowind recovery based on the relationship between surf backflow and coastal gravel beach bars, utilizing the thickness of gravel coastal bars to recover paleowind.

[0039] As a specific example, there are three methods for the quantitative restoration of ancient wind force.

[0040] The first method employs the Beach Particle Technique (BPT). This method is based on solitary wave theory, drawing upon the theory and formulas for the critical velocity of unidirectional flow transporting sedimentary particles to obtain the critical wave conditions for transporting gravel particles in a gravel beach. The approach is as follows: ① Analyze the size distribution characteristics of gravel in a gravelly beach near the lake shoreline, and determine the critical shear force required to transport a specific gravel based on these characteristics; ② Convert the critical shear force into the critical velocity of the wave transporting this particle; ③ Convert the critical velocity into the breaking wave height; ④ Convert the breaking wave height into the corresponding effective wave height in the deep water area; ⑤ Calculate the wind pressure coefficient using the wind-wave relationship based on parameters such as the effective wave height and wind path in the deep water area; ⑥ Obtain the wind speed from the wind pressure coefficient.

[0041] Because the size distribution of sediments near lake shorelines is influenced by various conditions and is often subject to erosion, it cannot accurately reflect the actual conditions at the time. Furthermore, shoreline sediments are largely controlled by provenance. Therefore, this method is generally suitable for outcrop studies, but not for situations where only drilling data is available without corresponding high-quality outcrops.

[0042] The second method involves reconstructing paleoaeodynamic forces using the thickness of the wave-breaking sandbar. The height of the wave-breaking sandbar in the target area is statistically analyzed using core samples and then calculated using the formula below.

[0043] In the formula: Indicates the original thickness (m) of the wave-breaking sandbar; The depth of the water at the top of the wave-breaking sandbar is indicated in meters (m). Indicates the water depth (m) of the groove on the shore side of the wave-breaking sandbar; Indicates the base slope of the sandbar; Indicates the slope of the sandbar towards the shore; Indicates the length of the wind zone (m); Indicates the significant wave height (m) in deep water. U This indicates the wind speed (m / s) at a point 10 m above the water surface. It represents ancient style and strength.

[0044] The third method is based on the relationship between surf backflow and coastal gravel bars, using the thickness of the gravel coastal bars to reconstruct paleowind forces. The thickness of the gravel coastal bars approximately records the extreme height of the surf backflow, and is reconstructed using the following formula: In the formula: Indicates the thickness of gravelly coastal sandbars; Indicates the height of the storm-induced backwater level; Indicates the height of the wave rise; Indicates the rise of waves; K This represents the overall friction coefficient, which can be taken as 3.6 × 10⁻⁶ here. -6 ; d Indicates the average water depth of a body of water; γ Indicates the angle between the wind direction and the normal perpendicular to the shoreline; F It represents the ancient style of Cheng.

[0045] Step S105: Determine the experimental wave height in the laboratory based on the laboratory's equipment parameters.

[0046] Preferably, in step S105, the wave height of the laboratory wave generator is the maximum wave height measured when the rotation speed of the laboratory wave generator is turned up to the maximum, and the laboratory wave generator is adjusted to be more than twice the initial lake level height.

[0047] Step S106: Based on the target wave height and the experimental wave height, and based on the dynamic similarity between wave energy and water flow energy, calculate the quasi-Frod number that characterizes the dynamic relationship when the target area and the laboratory achieve motion similarity, and calculate the laboratory matching granularity based on the quasi-Frod number.

[0048] Preferably, step S106 includes: obtaining wave energy calculation formulas and water flow energy calculation formulas, and using hydrodynamic parameters to characterize the quasi-Frod number; and calculating based on the quasi-Frod number, target wave height, and experimental wave height to obtain the laboratory matching granularity.

[0049] Specifically, the formula for calculating wave energy is: The formula for calculating water flow energy is: In the formula: m Indicates the river's flow rate; v Indicates flow rate; Indicates density; Indicates acceleration; Represents wave height; Indicates wave energy; It represents the energy of water flow.

[0050] Based on the correlation between wave energy and the square of wave height, and between current energy and current density, and considering that these energies are in the same dimension, the Frod number, a hydrodynamic parameter, is used to express the similarity of motion. The specific expression is as follows: In the formula: Represents the Frode number; Indicates acceleration; Indicates wind speed; Indicates wavelength.

[0051] The Frod number, a dynamic parameter used for water flow, is applied to waves to form a quasi-Frod number, which can be expressed by the formula: In the formula: Denotes quasi-Frode numbers; Represents wave height; Indicates acceleration; Indicates wavelength.

[0052] Based on the above analysis, it can be concluded that when the target region and the laboratory achieve motion similarity, they should have the same or similar quasi-Frode numbers. Therefore, the corresponding granularity within the laboratory can be obtained from the granularity of the target region. Thus, due to = ,but: This allows us to obtain the corresponding particle size for simulated experiments in the laboratory.

[0053] In the formula: Indicates the height of the wave generated in the laboratory; Indicates the laboratory wavelength; Indicates the target wave height in the target area; Indicates the wavelength of the target region.

[0054] This invention introduces a wave-forming depositional dynamics similarity criterion based on the "quasi-Flood number": unifying wave energy, wave height, wavelength, and grain size into dimensionless parameters to achieve quantitative dynamic matching between the prototype and the experiment, which is the core innovation that distinguishes it from traditional methods.

[0055] Step S107: Use mudstone to simulate particles smaller than silt, and convert particles larger than silt to form coarse-grained experimental samples.

[0056] Since the dynamic characteristics of fine sediment particles are similar after reaching the muddy size, in step S107, no particle size conversion is performed for particles smaller than silt, and ordinary mudstone is used. However, for particles larger than silt, the corresponding particle size conversion is performed to form a coarse particle size ratio and obtain the coarse part experimental sample (i.e. coarse experimental sample).

[0057] Step S108: Based on the weighted average of the coarse and fine grain ratio of the target area according to the area ratio of different sedimentary facies zones, and combined with the ratio of coarse grain samples to mud, the final laboratory particle size distribution result is formed.

[0058] In this step, based on the distribution ratio of different phase zones in the laboratory, a weighted average is performed according to the area ratio and the ratio of coarse and fine particles in the target area of ​​different phase zones to obtain the relative ratio of coarse and fine particles in the laboratory. The coarse particles obtained in step S107 are then mixed with the clay according to this ratio to form the final laboratory particle size distribution.

[0059] To more intuitively illustrate the practical application of the method for determining sediment grain size distribution in the above-mentioned wave-forming sedimentation simulation experiment, the following section combines... Figures 2-9 The main steps are demonstrated.

[0060] like Figure 2 As shown, Figure 2 A schematic diagram showing the distribution of each research well W1-W4 within the target area is presented; Figure 3 Taking Well W3 as an example, a physical image of coarse-grained sediments in a typical sedimentary layer is shown; Figure 4 , Figure 5 and Figure 6 The diagrams show the statistical parameters of particle size from wells W3 / W4 in an unmodified estuary, well W1 in a modified estuary, and well W2 in a heavily modified beach-bar. Figure 7 The grain size distribution characteristics of the target area of ​​wells W1-W4 are presented.

[0061] As shown in Table 1 below, based on the wave sandbar identification method, the average wave height in the ancient uplift area is 1.87m, and the average wind speed is about 13.58m / s, which is about level 6 wind.

[0062] Table 1. Wave heights in ancient uplift areas determined by the wave sandbar identification method. By analyzing the equipment in the laboratory, the maximum wave height generated by the laboratory wave generator was determined as follows: Figure 8 As shown. A schematic diagram of the final wave-forming depositional physics experiment is shown below. Figure 9 As shown in the figure, ① water pump, ② faucet, ③ water tank, ④ water pipe, ⑤ video recording equipment, ⑥ drone, ⑦ camera, ⑧ scanner, ⑨ scale bar, ⑩ wave generator, ⑪ source area, ⑫ experimental area, ⑬ water storage tank.

[0063] The method for designing sediment grain size distribution in wave-forming sedimentary simulation experiments provided in this embodiment involves statistically analyzing different grain size ratios through drilling and logging, and obtaining an initial coarse-to-fine ratio through facies zone weighting. After correction by analogy with similar rivers, the distribution characteristics of grain size in typical stratigraphic sections are obtained through grain size analysis. Combining sandbar elevation and equipment capabilities, the prototype wave height energy number and the maximum experimental wave height are determined. Using dynamic similarity, the experimental matching grain size is calculated based on the pseudo-Frod number, and mudstone is used to replace silt and below, converting coarse grains to form the sample. Finally, the laboratory grain size distribution is output by weighting coarse and fine grains and mud content according to facies zone area. The advantages of this method are: full-process constraint by geological data, ensuring the experimental exponentiality of the prototype based on dynamic similarity, balancing accuracy and operability with mudstone substitution, and preserving heterogeneity through facies zone weighting, significantly improving the realism, reliability, and engineering applicability of wave-forming sedimentary physical simulation.

[0064] like Figure 10 As shown, the present invention also provides a system for determining the grain size distribution of sediments in wave-forming sedimentation simulation experiments, comprising: The data statistical analysis module 100 is used to perform statistics and calculations based on the drilling and logging data of the target area to obtain the initial deposition ratio of coarse-grained sediments to fine-grained sediments in the target area.

[0065] Adjustment module 200 is used to adjust the initial deposition ratio.

[0066] The feature analysis and determination module 300 is used to perform grain size analysis based on the coarse-grained sediments of typical sedimentary segments in the target area after adjusting the sedimentation ratio, so as to obtain the grain size distribution characteristics of the target area.

[0067] The target wave height determination module 400 is used to perform elevation analysis on the geological and sedimentary wave-breaking sandbars in the target area to obtain the target wave height of the target area.

[0068] The experimental wave height determination module 500 is used to determine the experimental wave height in the laboratory based on the laboratory's equipment parameters.

[0069] The particle size calculation module 600 is used to calculate the quasi-Frode number, which represents the dynamic relationship between the target area and the laboratory when they achieve motion similarity, based on the target wave height and the experimental wave height, and based on the dynamic similarity between wave energy and water flow energy. The matching particle size of the laboratory is then calculated based on the quasi-Frode number.

[0070] The sample determination module 700 is used to simulate particles smaller than silt using mudstone, and to convert particles larger than silt into coarse-grained experimental samples.

[0071] The proportioning module 800 is used to weight the coarse and fine grain ratio of the target area based on the area ratio of different sedimentary facies zones, and combine the coarse sample with the mud ratio to form the final laboratory particle size distribution result.

[0072] Specifically, the specific coordination and operation process between the modules in the wave-forming sedimentation simulation experiment sediment grain size distribution determination system can be referred to the above-mentioned wave-forming sedimentation simulation experiment sediment grain size distribution determination method, and will not be repeated here.

[0073] Compared with traditional solutions, the present invention has the following advantages: (1) The entire process is coupled with the "geology-experiment" dual scale. The process progresses step by step from drilling, sequence, grain size, energy to equipment capacity, which not only preserves the authenticity of regional geology, but also achieves laboratory dynamic similarity, avoiding the distortion caused by simple empirical ratios.

[0074] (2) By introducing the “quasi-Frod number” as a unified similarity criterion, wave energy, particle size, water depth and wave height are converted into dimensionless numbers, which can quantitatively assess the motion similarity between the experiment and the prototype and significantly reduce the experimental extrapolation error.

[0075] (3) The approach of “mudstone substitution + particle size conversion” solves the problem that it is difficult to reproduce the original proportion of extremely fine particles below silt, and avoids the experimental operation difficulties caused by using a large amount of real mud, thus taking into account both accuracy and operability.

[0076] (4) By using the area weighted average of the facies zone, the planar heterogeneity is incorporated into the final ratio, so that the experimental results can directly serve the prediction of reservoirs in different facies zones and enhance the guiding value of the experiment.

[0077] This invention focuses on the three-dimensional synergy of "geological authenticity, dynamic similarity, and experimental operability." Through drilling constraints, a unified quasi-Frod number criterion, mudstone substitution, and facies weighting, it overcomes the technical bottleneck that existing empirical ratios cannot simultaneously take into account the prototype grain size characteristics and similarity. It significantly improves the accuracy, extrapolation reliability, and reservoir engineering application value of wave-forming sedimentary physical simulation, providing more scientifically based experimental support for oil and gas exploration.

[0078] Furthermore, an electronic device of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the method for determining the sediment grain size distribution in a wave-forming sedimentation simulation experiment as described above. Specifically, according to embodiments of the present invention, the processes described above with reference to the flowchart can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such embodiments, when the computer program is downloaded, installed, and executed by an electronic device, it performs the functions defined in the methods of the embodiments of the present invention. The electronic device in the present invention can be a terminal such as a laptop, desktop computer, tablet computer, or smartphone, or it can be a server.

[0079] Furthermore, one type of storage medium of the present invention stores a computer program thereon, which, when executed by a processor, implements the method for determining sediment particle size distribution in wave-forming sedimentation simulation experiments as described above. Specifically, it should be noted that the storage medium described above in the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0080] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0081] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0082] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0084] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They do not limit the scope of protection of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should fall within the scope of the claims of the present invention.

Claims

1. A method for determining the grain size distribution of sediments in a wave-forming sedimentation simulation experiment, characterized in that, Includes the following steps: Step S101: Based on the drilling and logging data of the target area, perform statistical analysis and calculations to obtain the initial depositional ratio of coarse-grained to fine-grained sediments in the target area; Step S102: Adjust the initial deposition ratio; Step S103: Perform grain size analysis on the coarse-grained sediments of typical sedimentary segments in the target area after adjusting the sedimentation ratio to obtain the grain size distribution characteristics of the target area. Step S104: Perform elevation analysis on the geology and sedimentary wave-breaking sandbars of the target area to obtain the target wave height of the target area; Step S105: Determine the experimental wave height in the laboratory based on the laboratory's equipment parameters; Step S106: Based on the target wave height and the experimental wave height, and based on the dynamic similarity between wave energy and water flow energy, calculate the quasi-Frode number that characterizes the dynamic relationship when the target area and the laboratory achieve motion similarity, and calculate the laboratory matching particle size according to the quasi-Frode number. Step S107: Use mudstone to simulate particles smaller than silt, and convert particles larger than silt to form coarse-grained experimental samples. Step S108: Based on the weighted average of the coarse and fine grain ratio of the target area according to the area ratio of different sedimentary facies zones, and combined with the ratio of coarse grain samples to mud, the final laboratory particle size distribution result is formed.

2. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to claim 1, characterized in that, Step S101 includes: Based on the logging lithological description data, grain size analysis data and thin section identification data of multiple wells in the target area, the thickness ratio of coarse-grained sediments to fine-grained sediments in the target layer of each well is calculated to obtain the sand-mud ratio of a single well. The target area is divided into sedimentary facies zones according to the sedimentary environment type, and the area ratio of each sedimentary facies zone is calculated. Geological representativeness assessments are conducted on wells located within the same sedimentary facies zone to remove unrepresentative wells affected by local structures or data quality. The initial sedimentary ratio of coarse-grained to fine-grained sediments in the target area was calculated using the dual-weighting method.

3. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to claim 1, characterized in that, Step S102 includes: Based on the similarity of sedimentary dynamic parameters, reference rivers with similarity to the target area are selected; The mass percentages of coarse and fine grain components in the modern sediments of the reference river were determined by laser grain size analysis and thin section identification. Calculate the deviation coefficient between the initial sand-mud ratio of the target area and the reference river sand-mud ratio; The initial deposition ratio of the target area is adjusted based on the deviation coefficient.

4. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to claim 1, characterized in that, Step S104 includes: Based on paleowind studies and wind path analysis of the target area, wind speed and wind path are determined. The target wave height of the target area is obtained by calculating based on the wind speed and wind distance.

5. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to claim 1, characterized in that, Step S104 includes: The target wave height of the target area is obtained based on the height of the wave-breaking sandbar in the target area.

6. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to claim 1, characterized in that, In step S105, the wave height of the laboratory wave generator is the maximum wave height measured when the rotation speed of the laboratory wave generator is turned up to the maximum, and the laboratory wave generator is adjusted to exceed twice the initial lake level height.

7. The method for determining sediment grain size distribution in wave-forming sedimentation simulation experiments according to any one of claims 1-6, characterized in that, Step S106 includes: The wave energy calculation formula and the water flow energy calculation formula are obtained, and the pseudo-Frode number is characterized by water flow dynamic parameters; The laboratory matching granularity is obtained by calculating based on the pseudo-Frod number, the target wave height, and the experimental wave height.

8. A system for determining sediment grain size distribution in wave-forming sedimentation simulation experiments, characterized in that, include: The data statistical analysis module is used to perform statistical analysis and calculations based on drilling and logging data of the target area to obtain the initial deposition ratio of coarse-grained sediments to fine-grained sediments in the target area. An adjustment module is used to adjust the initial deposition ratio; The feature analysis and determination module is used to perform grain size analysis on coarse-grained sediments in typical sedimentary segments within the target area after adjusting the sedimentation ratio, and to obtain the grain size distribution characteristics of the target area. The target wave height determination module is used to perform elevation analysis on the geology and sedimentary wave-breaking sandbars of the target area to obtain the target wave height of the target area; The experimental wave height determination module is used to determine the experimental wave height in the laboratory based on the laboratory's equipment parameters. The particle size calculation module is used to calculate the pseudo-Frode number, which characterizes the dynamic relationship when the target area and the laboratory achieve motion similarity, based on the target wave height and the experimental wave height and the dynamic similarity between wave energy and water flow energy, and to calculate the laboratory matching particle size based on the pseudo-Frode number. The sample determination module is used to simulate particles smaller than silt using mudstone, and to convert particles larger than silt into coarse-grained experimental samples. The proportioning module is used to weight the coarse and fine grain ratio of the target area based on the area ratio of different sedimentary facies zones, and combine the coarse sample with the mud ratio to form the final laboratory particle size distribution result.

9. A storage medium, characterized in that, The storage medium stores a computer program adapted for loading by a processor to perform the steps of the method for determining the grain size distribution of sediments in a wave-forming sedimentation simulation experiment as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method for determining the grain size distribution of sediments in a wave-forming sedimentation simulation experiment as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

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