A method and device for evaluating the impact of sedimentation control factors based on sedimentation forward modeling

Abstract

The present invention discloses a method and apparatus for evaluating the impact of sedimentary control factors based on sedimentary forward modeling. The method includes determining the corresponding control parameter value range for the sedimentary control factors of a sedimentary system, determining a value selection rule based on the control parameter type, obtaining a series of control parameter values ​​based on the set initial value and the value selection rule, and establishing a control group for the control parameter; based on each control group, utilizing a selected simulation method, obtaining a sedimentary simulation result corresponding to each value in the control group; and quantitatively characterizing the influence trend, sensitivity, and correlation of the control parameter on the sedimentary system characterization parameter based on the sedimentary simulation results. This method establishes a systematic and quantitative technical process for evaluating the impact of sedimentary control factors; it can improve the data mining and processing capabilities based on sedimentary forward modeling, effectively quantitatively evaluate the impact of each control factor on the sedimentary system, and provide quantitative analysis results of influencing factors in sedimentary system distribution prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of sedimentology research in geological exploration and development, and in particular to a sedimentation control factor impact assessment method and device based on sedimentation forward modeling. Background Art

[0002] As sedimentological research progresses toward quantification, process-based, and systematic approaches, forward modeling of sedimentation is gaining increasing attention. It follows the physical laws of sediment erosion, transport, and deposition, simulating the formation of a sedimentary system from scratch. Forward modeling offers numerous advantages. Based on sedimentary mechanisms and theories, it effectively reproduces conceptual sedimentological models; it quantitatively predicts the distribution and scale of sedimentary systems, offering significant potential for data mining; and it offers low cost and time, enabling rapid and multiple simulations, enabling the development of models at geological spatial and temporal scales not possible with flume experiments.

[0003] With the advancement of computer technology, sedimentation forward modeling has experienced rapid growth, but its potential in sedimentological research and oil and gas exploration remains largely untapped. Analysis of the impact of sedimentation-controlling factors based on forward modeling remains primarily qualitative, lacking a systematic, quantitative assessment based on mathematical theory. Summary of the Invention

[0004] In order to enrich the methods for evaluating the impact of sedimentary control factors, the inventors have made the present invention. Through specific implementation methods, a method and device for evaluating the impact of sedimentary control factors based on sedimentary forward modeling are provided. The method and device can improve the data mining and processing capabilities based on sedimentary forward modeling, effectively quantitatively evaluate the impact of various control factors on the sedimentary system, and provide quantitative analysis results of influencing factors in the prediction of sedimentary system distribution.

[0005] In a first aspect, an embodiment of the present invention provides a method for evaluating the impact of sedimentation control factors based on sedimentation forward modeling, comprising:

[0006] Determine the corresponding control parameter value range for the deposition control factor of the deposition system, determine the value selection rule according to the type of the control parameter, obtain a value series of the control parameter according to the set initial value and the value selection rule, and establish a control group of the control parameter, wherein the value series belongs to the value range;

[0007] Based on the control group of each control parameter, the selected simulation method is used to obtain the sedimentation simulation results corresponding to each value in the control group;

[0008] Based on the sedimentation simulation results, the influence trend, sensitivity and correlation of the control parameters on the sedimentation system characterization parameters are quantitatively characterized.

[0009] In a second aspect, an embodiment of the present invention provides a device for evaluating the impact of sedimentation control factors based on sedimentation forward modeling, comprising:

[0010] A control group establishment module is used to determine the corresponding control parameter value range for the deposition control factors of the deposition system, determine the value selection rule according to the type of the control parameter, obtain the value series of the control parameter according to the set initial value and the value selection rule, and establish a control group of the control parameter, wherein the value series belongs to the value range;

[0011] A simulation module is used to obtain a deposition simulation result corresponding to each value in the control group based on the control group of each control parameter using a selected simulation method;

[0012] The sedimentation control factor impact assessment module is used to quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the sedimentation system characterization parameters based on the sedimentation simulation results.

[0013] In a third aspect, an embodiment of the present invention provides a computer storage medium storing computer executable instructions, which, when executed by a processor, implements the above-mentioned deposition control factor impact assessment method based on deposition forward modeling.

[0014] In a fourth aspect, an embodiment of the present disclosure provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned deposition control factor impact assessment method based on deposition forward modeling is implemented.

[0015] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0016] (1) The sedimentary control factor impact assessment method based on sedimentary forward modeling provided in the embodiment of the present invention establishes a systematic and quantitative sedimentary control factor impact assessment technical process, solves the problem of insufficient quantification in the control factor research, helps to deepen the data mining based on sedimentary forward modeling research, effectively quantitatively assesses the impact of various control factors on the sedimentary system, and provides quantitative influencing factor analysis results in the sedimentary system distribution prediction; it can effectively improve the accuracy of sedimentary system distribution prediction, and has the significance of reducing costs and increasing efficiency for basic geological research in conventional oil and gas and unconventional oil and gas exploration and development.

[0017] (2) The method for evaluating the impact of sedimentation control factors based on sedimentation forward modeling provided by the embodiment of the present invention provides an algorithm for a series of control parameter values, thereby providing a data basis for sedimentation forward modeling.

[0018] (3) The sedimentation control factor impact assessment method based on sedimentation forward modeling provided by the embodiment of the present invention introduces quantitative sensitivity analysis into the study of sedimentation response control factors, and proposes a specific calculation method for the sensitivity discrimination coefficient, which effectively quantitatively characterizes the sensitivity of the control parameters and the sedimentation system characterization parameters.

[0019] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0020] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0022] Figure 1 Flowchart of a method for evaluating the impact of sedimentation control factors based on sedimentation forward modeling in Example 1 of the present invention;

[0023] Figure 2 Schematic diagram of length conversion in embodiment 1 of the present invention;

[0024] Figure 3 Schematic diagram of the area transformation in the first embodiment of the present invention;

[0025] Figure 4 Schematic diagram of slope transformation in embodiment 1 of the present invention;

[0026] Figure 5 Schematic diagram of changing the river curvature in the first embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of changing the river depth in Example 1 of the present invention;

[0028] Figure 7 This is a flowchart for a specific implementation of a sedimentation control factor impact assessment method based on sedimentation forward modeling in the second embodiment of the present invention;

[0029] Figure 8 The original landform used in the standard sedimentary forward model in the second embodiment of the present invention;

[0030] Figure 9 The influence of the Manning constant on the sedimentation and erosion in the simulation results in Example 2 of the present invention;

[0031] Figure 10 The influence of the Manning constant on the water flow velocity in the simulation results in the second embodiment of the present invention;

[0032] Figure 11The new landforms are formed by modifying the landform parameters of the original landforms of the standard model in Example 2 of the present invention, where a is the original landform, b is the outline superposition map of multiple new landforms formed after modifying the shelf slope, c is the outline superposition map of multiple new landforms formed after modifying the shelf width, d is the outline superposition map of multiple new landforms formed after modifying the slope of the continental slope, e is the canyon axis superposition map of multiple new landforms formed after modifying the canyon curvature, f is the canyon 5 profile bottom superposition map of multiple new landforms formed after modifying the canyon depth, and g is the outline superposition map of multiple new landforms formed after modifying the slope of the abyssal plain.

[0033] Figure 12 This is a correlation diagram between sedimentary parameters and geomorphic parameters in the simulation results of Example 2 of the present invention;

[0034] Figure 13 This is a correlation diagram between waterway distribution and landform parameters in the simulation results of Example 2 of the present invention;

[0035] Figure 14 The distribution of sensitivity indices of the delta, canyon, and submarine fan sediment volumes to geomorphological parameters in the simulation results of Example 2 of the present invention;

[0036] Figure 15 Comparison of the effects of geomorphic parameters on delta sediment volume in the simulation results of Example 2 of the present invention;

[0037] Figure 16 Comparison of the effects of geomorphic parameters on canyon sediment volume in the simulation results of Example 2 of the present invention;

[0038] Figure 17 Comparison of the effects of geomorphic parameters on submarine fan sediment volume in the simulation results of Example 2 of the present invention;

[0039] Figure 18 The following is a summary of the influence of sedimentation control factors based on sedimentation forward modeling in Example 2 of the present invention;

[0040] Figure 19 Schematic diagram of the structure of a deposition control factor impact assessment device based on deposition forward modeling in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0042] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the present invention pertains. Although the present invention has only described preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0043] The inadequacy of existing technologies in analyzing the impact of sedimentary control factors based on sedimentation forward modeling is mainly reflected in three aspects. First, there is a lack of systematic analysis and theoretical support for the transformation methods of sedimentary control factors input into sedimentation forward modeling. Second, the sensitivity analysis method still mainly adopts the method of qualitative comparison of observation results, and lacks quantitative analysis, especially the failure to introduce sensitivity analysis algorithms based on mathematical theory. Third, sensitivity analysis has not been organically combined with the study of the response of control factors to form a part of the sedimentation forward modeling research module with high technical content and mature methods, and cannot effectively support the study of the response of control factors.

[0044] Currently, Beicip's CougarFlow has made some breakthroughs in this area, enabling sensitivity analysis based on sedimentation forward modeling. This software primarily combines Latin hypercube sampling and response surface modeling methods to define thickness calibration indices and structural calibration indices, thereby evaluating the impact of multiple parameters. The results are displayed using Pareto charts and main effects diagrams. However, this method is limited by its high computational cost and expensive software. Furthermore, it lacks steps to transform complex input parameters such as topography and water level changes. When assessing sensitivity, it fails to consider the possible trend changes and uncertainties in the influence of sedimentation-controlling factors. Therefore, it is more suitable as a pre-processing step for Dionisos-Flow, providing a reference for Dionisos parameter selection. It is not suitable for applying diverse sedimentation forward modeling methods to quantitatively evaluate the impact of sedimentation-controlling factors.

[0045] In order to overcome the problems of insufficient quantitative sensitivity analysis, lack of mathematical methods for parameter variation, and immature control factor research process in the process of using sedimentation forward modeling to study control factors, a method and device for evaluating the impact of sedimentation control factors based on sedimentation forward modeling was proposed.

[0046] Example 1

[0047] The first embodiment of the present invention provides a sedimentation control factor impact assessment method based on sedimentation forward modeling, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0048] Step S11: for the deposition control factors of the deposition system, determine the corresponding control parameter value range, determine the value selection rule according to the type of the control parameter, obtain the control parameter value series according to the set initial value and value selection rule, and establish a control group of the control parameters.

[0049] The obtained value series of the control parameter are all within the value range of the parameter.

[0050] Control groups are designed for quantitative analysis and qualitative analysis. The quantitative analysis aims to identify the quantitative impact of controlling factors on the sedimentary system, requiring quantifiable input parameter changes. While the number of control models is large, the analysis of each model is minimal, focusing on data extraction. Qualitative analysis input parameters can be descriptive, such as depositional process type and provenance direction, or they can be a combination of multiple quantitative parameters, such as hydrodynamically reduced overturning, sediment supply reduction overturning, and lake level rise overturning. Relatively speaking, the number of control models is small, while the analysis of each model is more complex, including qualitative observation and description of its profile planar characteristics and extraction of quantitative parameters.

[0051] The simulation sequence needs to follow the following principles: Principle 1: When debugging parameter values, first take the larger and smaller values, and then experiment with the intermediate values ​​to improve simulation efficiency; Principle 2: First debug the key parameters in the simulation program, and then debug the deposition input parameters to eliminate the influence of the simulation program; Principle 3: First simulate a single process one by one, and then simulate multiple processes, so as to become familiar with the simulation of a single process and eliminate interference from multiple processes.

[0052] Specifically, the deposition control factors include at least one of the following factors:

[0053] (1) Geomorphic factors

[0054] It includes initial landforms, sedimentation rate, structural changes, etc., among which the landform characteristics can be characterized by multiple landform parameters, such as slope, area, elevation, river length, width, depth, etc.

[0055] (2) Level change factors

[0056] This includes changes in shoreline position and water depth over time, which can be specifically expressed by parameters such as frequency, trend, and amplitude of the changes.

[0057] (3) Clastic sediment input factors

[0058] Including the components of sediment input, sediment supply rate, water supply rate, water flow rate, etc.

[0059] (4) Carbonate rock growth factors

[0060] (5) Sedimentation process factors

[0061] These include temperature, salinity, carbonate growth rate, etc. When the sedimentation process includes waves, wind, rainfall, etc., the various control parameters also include parameters corresponding to the sedimentation process.

[0062] In some embodiments, determining the corresponding control parameter value range includes determining the corresponding control parameter value range according to the set range conditions through at least one method of literature research, basic research in the study area, and semi-empirical estimation method.

[0063] Literature research includes research on the study area to identify sedimentary types, sediment grain size distribution, paleohydrologic conditions, paleogeomorphology, tectonic history, and sea-lake level changes; research on parameter selection for the selected modeling method in various application cases; and research on parameters such as hydrological conditions and sediment grain size distribution in similar sedimentary environments. Furthermore, basic research on the study area includes conducting basic research on the paleogeography and sedimentary systems of the study area, collecting and conducting preliminary analysis of field data such as outcrops, rock samples, core logging, seismic, paleontological, and geochemical data. Furthermore, semi-empirical calculations involve using known quantities to calculate unknown quantities based on semi-empirical formulas and quantitative methods developed by previous researchers. For example, the BQART method uses source area parameters to calculate sediment flux, while the fulcrum method uses transport channel parameters to calculate sediment flux.

[0064] Specifically, the scope conditions are set, including at least one of the following conditions:

[0065] It is within the parameters of nature and consistent with geological understanding;

[0066] Matching model accuracy and modeling scale;

[0067] meet the requirements of the simulation program;

[0068] If there is actual data, it is consistent with the actual data;

[0069] If it is a numerical experiment, it meets the experimental purpose.

[0070] Through the above process, the control parameters of the deposition system and their value ranges are determined. The establishment of a specific control parameter value series will be introduced in detail later.

[0071] Step S12: Based on the control group of each control parameter, using the selected simulation method, obtain the deposition simulation result corresponding to each value in the control group.

[0072] The optimization of simulation methods and corresponding model parameters includes determining the sedimentation forward simulation method according to the properties of the research target; setting multiple values ​​of the model parameters corresponding to the simulation method, comparing the simulation results corresponding to each value, and screening the optimal value.

[0073] Furthermore, determining the sedimentation forward modeling method according to the attributes of the research target includes determining the sedimentation forward modeling method according to at least one of the following attributes of the research target:

[0074] (1) Research scale

[0075] The research scale is used to characterize whether the research target belongs to the basin, reservoir or event level.

[0076] The event level corresponds to short-term events at the time level of 0.1 to 10 years, the reservoir level corresponds to medium-term deposition at the time level of 1 to 10 ka, and the basin level corresponds to long-term events at the time level of >100 ka.

[0077] (2) Research focus

[0078] The research focuses on the sedimentation mechanism or the distribution law of sedimentary system.

[0079] The hydrodynamic method is suitable for studying the sedimentation mechanism, while the diffusion method and the geometric law method are suitable for studying the distribution law of the sedimentary system.

[0080] (3) Requirements for actual data fidelity

[0081] In terms of fidelity to actual data, the grid-based simulation method controlled by geostatistics is suitable for studies that require strict consistency with actual data, while the process-based simulation method controlled by sedimentary process principles is suitable for studies that do not require strict consistency with actual data. Object-based and rule-based simulation methods are in between. From grid-based, object-based, rule-based to process-based, the degree of fidelity to data decreases, while the degree of compliance with sedimentary process principles increases.

[0082] (4) Whether the source area is included

[0083] Regarding whether the source area is included, the complete source-sink system model includes the source area, which can simulate the erosion process in the source area and generate sediment fluxes controlled by the source area's landforms, structure, climate, etc. Simulating only the sink area requires setting the sediment flux, but cannot simulate the generation process of the sink area's sediment source.

[0084] (5) Number of deposition processes

[0085] In terms of the number of included sedimentation processes, multi-module simulation can simulate multiple sedimentation processes, such as simulating wave action and river action at the same time, which is suitable for the study of complex sedimentation systems, while single-module simulation only includes a single sedimentation process and is suitable for the study of simple sedimentation systems.

[0086] After determining the simulation method through the above analysis, the impact analysis of the model parameters corresponding to the simulation method is carried out. In the simulation method, it is necessary to pay attention to the parameters corresponding to the determined simulation method, such as the diffusion coefficient in the diffusion equation method, the Manning constant in the hydrodynamic method, and the geometric constant in the geometric law method. It is necessary to clarify the key method parameters in the simulation method and establish their reasonable value range. Then, through preliminary multiple simulation analysis and comparison, the role of the simulation method parameters is clarified. Finally, the simulation method parameter values ​​are determined and the same values ​​are used in all subsequent simulations to eliminate the influence of the simulation method parameters in subsequent sedimentation control factor research.

[0087] The selected simulation method is used to simulate the optimal values ​​of the corresponding model parameters. For each control parameter, the simulation results corresponding to each value are obtained when other conditions are the same.

[0088] When running a sedimentation simulation, attention should be paid to simulation duration and the storage of simulation results. For example, if simulation duration is long, it is necessary to reduce simulation time and improve efficiency during initial simulations or a large number of control experiments by reducing the bottom shape resolution and time resolution. Later, after parameter values ​​are finalized or when a standard model is established, higher bottom shape resolution and time resolution can be used to achieve the optimal balance between simulation effect and efficiency. Simulation results require a large amount of storage space. To save storage space, the output result storage frequency can be reduced and the resolution can be lowered during initial simulations or a large number of control experiments. Later, after parameter values ​​are finalized or when a standard model is established, the output result storage frequency and resolution can be increased to improve storage space utilization efficiency. It is also important to pay attention to the classification tags of stored results. When debugging parameters, indicate important parameter values ​​in the storage name.

[0089] In terms of analysis results, it is divided into qualitative observation and quantitative analysis. Qualitative observation mainly includes observing the planar distribution characteristics of sedimentary parameters, vertical profile characteristics, single-point vertical characteristics of key locations, evolution characteristics, and comparison of different models; in terms of quantitative observation, it is necessary to extract various sedimentary parameters, calculate the consistency rate with the actual situation, and quantitatively compare different models.

[0090] The sedimentation simulation results include sedimentation amount, erosion amount, sedimentation area, erosion area, sedimentation thickness, erosion thickness and curvature change, as well as the average water flow.

[0091] The total amount of sedimentation and total erosion in the statistical calculation results, as well as the amount of sedimentation and erosion in different regions and time periods, such as the amount of sedimentation and erosion in the corresponding period of each system domain, the amount of sedimentation and erosion corresponding to each region in the source-channel-sink, and the hydrological parameters such as average velocity, average water flow, etc. When there are other research objectives, relevant parameters also need to be statistically analyzed, such as the area of ​​sedimentation and erosion in a certain area, the thickness of sedimentation and erosion, and possible changes in the curvature of the river.

[0092] thick (i，j) =Z final(i，j) -Z 0(i，j)

[0093]

[0094] Erosion area = ab·numero

[0095] numero is thick i.j <0 grid number

[0096]

[0097] Deposition area = ab·numdepo

[0098] numdepo is thick i.j >0 grid number

[0099] Sedimentary hiatus area = ab·numhiatus

[0100] numhiatus for thick i.j = 0 grid number

[0101] The erosion area, erosion volume, deposition area and deposition volume are calculated using the above grid statistics method.

[0102] Step S13: quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the deposition system characterization parameters based on the deposition simulation results.

[0103] The sensitivity coefficient was calculated by local sensitivity analysis, and then the influence of various control factors on the sedimentary system and hydrodynamics was compared in combination with relevant maps. Finally, the impact was quantitatively evaluated from three dimensions: change trend, sensitivity, and correlation.

[0104] First, the quantitative fitting relationship between the controlling factors and sedimentation parameters and hydrodynamic-related parameters is established, and the fitting curve type and change trend are clarified.

[0105] The modified Morris screening method was then used to calculate the parameter sensitivity, and the sensitivity discrimination coefficient was calculated as follows:

[0106]

[0107] In formula (1), SN is the sensitivity discriminant coefficient, t is the number of model runs, Q is the total number of model runs, and Y t is the output value of the sedimentary system characterization parameter of the model run t, Y t+1 is the output value of the sedimentary system characterization parameter of the model t+1th operation; Y0 is the output value of the sedimentary system characterization parameter of the initial operation of the model, P t P is the percentage change of the control parameter value relative to the initial value when the model is run for the tth time, t+1 The percentage change of the control parameter value relative to the initial value when the model is run for the t+1th time.

[0108] When the fitted relationship shows a clear turning point, the sensitivity discriminant coefficient can be calculated for the positive or negative correlation part separately. When the number of samples is large, outliers from the i runs can be removed and the average value calculated. When testing multiple control factors, spider plots can be used to compare the sensitivity of each control factor to a specific characteristic of the sedimentary system.

[0109] Other sensitivity coefficient calculation methods can also be used, such as the perturbation analysis method in local sensitivity analysis and the Sobal method in global sensitivity analysis.

[0110] Then, a supplementary analysis is conducted to convert the coordinates of the sedimentation parameters and the control factors, calculate the variation range of the sedimentation parameters and the variation range of the control factors, perform linear regression, and compare the slopes of the curves of each factor. If the slope is positive, it is a positive correlation, and if the slope is negative, it is a negative correlation. A high slope indicates high sensitivity, and a low slope indicates low sensitivity. On the one hand, the sensitivity is clarified by comparing the calculated results of the sensitivity coefficient with the slope results, and the high, medium and low categories of sensitivity are determined. On the other hand, the correlation coefficients between the hydrological parameters and the sedimentation parameters and the various control factors are clarified, and their correlation coefficients (i.e., the r in the fitting results) are compared. 2 ).

[0111] Through the analysis of the above three steps, three parameters, namely, change trend, sensitivity and correlation, are obtained to describe the response of a certain characteristic of the sedimentary system to the controlling factors.

[0112] The method for evaluating the impact of sedimentary control factors based on sedimentary forward modeling provided in Example 1 of the present invention establishes a systematic and quantitative technical process for evaluating the impact of sedimentary control factors, solves the problem of insufficient quantification in the study of control factors, helps to deepen data mining based on sedimentary forward modeling research, effectively quantitatively evaluates the impact of various control factors on the sedimentary system, and provides quantitative analysis results of influencing factors in the prediction of sedimentary system distribution; it can effectively improve the accuracy of sedimentary system distribution prediction, and has the significance of reducing costs and increasing efficiency for basic geological research in conventional oil and gas and unconventional oil and gas exploration and development.

[0113] The first embodiment of the present invention provides a sedimentation control factor impact assessment method based on sedimentation forward modeling, provides an algorithm for a series of control parameter values, and provides a data basis for sedimentation forward modeling.

[0114] The first embodiment of the present invention provides a deposition control factor impact assessment method based on deposition forward modeling, which introduces quantitative sensitivity analysis into the study of deposition response control factors and proposes a specific calculation method for the sensitivity discrimination coefficient, effectively quantitatively characterizing the sensitivity of control parameters and deposition system characterization parameters.

[0115] In some embodiments, the influence of each control parameter can be represented by a control parameter influence table. The strength of the correlation is represented by color depth, with darker colors representing stronger correlations and lighter colors representing weaker correlations. The influence trend is represented by shape, with a rectangle representing no influence, a right-facing triangle representing an increase as the control factor increases, a left-facing triangle representing a decrease as the control factor decreases, a diamond representing an increase followed by a decrease, and a combination of two triangles representing a decrease followed by an increase. The sensitivity is represented by the width of the shape, with the higher the sensitivity, the wider the shape.

[0116] The determination of the control parameter value series includes the following cases according to its type:

[0117] (1) The control parameter is length

[0118] According to the initial value of the length set, see Figure 2 As shown, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following formulas (2)-(4) to obtain the length value series:

[0119] y' j =y j , 1≤j <n (2)

[0120] y' j =y n +(jn)b', n≤j <m (3)

[0121] y' j =y n+(mn)b'+(jm)b, m≤j≤r y (4)

[0122] In formulas (2)-(4), y' j Indicates the length coordinate value of the jth grid node in the y direction after transformation, y j Indicates the length coordinate value of the j-th grid node in the y direction before transformation, y n represents the length coordinate value of the nth grid node in the y direction before transformation, b' represents the length step after transformation, b represents the length step before transformation, r y Indicates the maximum grid node number in the y direction, n and m respectively indicate the starting grid node number and ending grid node number in the y direction of the length region to be transformed.

[0123] (2) The control parameter is area

[0124] According to the initial length and width values ​​set, see Figure 3 As shown in the figure, the length coordinate value and width coordinate value of the grid node in the grid model are transformed according to the following formulas (5) and (6) to obtain the value series of the area:

[0125] x' i =x1+(i-1)a', 1≤i≤r x (5)

[0126] y' j =y1+(j-1)b', 1≤j≤r y (6)

[0127] In formulas (5) and (6), x' i represents the width coordinate value of the i-th grid node in the x-direction after the transformation, x1 represents the width coordinate value of the first grid node in the x-direction before the transformation, and y' j represents the length coordinate value of the jth grid node in the y direction after the transformation, y1 represents the length coordinate value of the first grid node in the y direction before the transformation, a' represents the width step after the transformation, b' represents the length step after the transformation, r y Indicates the maximum grid node number in the y direction, r x Indicates the maximum grid node number in the x direction.

[0128] (3) The control parameter is slope

[0129] According to the set initial value of elevation, see Figure 4 As shown, the elevation of the grid nodes in the grid model is transformed according to the following equations (7)-(9) to obtain the slope value series:

[0130] z' k =z k , z k≤z begin (7)

[0131] z' k =cz k , z begin <z k <z end (8)

[0132] z' k =z k +(c-1)z end , z i ≥z end (9)

[0133] In formulas (7)-(9), z' k Indicates the elevation coordinate value of the kth grid node in the z direction after transformation, z k Indicates the elevation coordinate value of the kth grid node in the z direction before transformation, z begin and z end They represent the minimum and maximum elevation values ​​in the z direction of the slope area to be transformed, and c is the elevation transformation coefficient.

[0134] (4) The control parameter is the river curvature and the river extension direction is consistent with the y direction

[0135] According to the initial length and width values ​​set, see Figure 5 As shown in Figure 1, the width coordinate values ​​of the grid nodes in the grid model are transformed according to the following formula (10) to obtain the value series of the river channel curvature:

[0136]

[0137] In formula (10), x' i Indicates the width coordinate value of the i-th grid node in the x direction after transformation, x i Indicates the width coordinate value of the i-th grid node in the x direction before transformation, y j Indicates the length coordinate value of the j-th grid node in the y direction before transformation, y qs1 and y qs1+1 They represent the length coordinate values ​​of the grid nodes corresponding to the s1th peak and s1+1th peak of the river curvature respectively, and a is the transformation coefficient.

[0138] (5) The control parameter is the river depth

[0139] According to the initial length and width values ​​set, see Figure 6 As shown in Figure 2, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (11) and (12) to obtain the value series of the river channel depth:

[0140]

[0141]

[0142] In formulas (11) and (12), y' j Indicates the length coordinate value of the jth grid node in the y direction after transformation, y j Indicates the length coordinate value of the jth grid node in the y direction before transformation, x i Indicates the width coordinate value of the i-th grid node in the x direction before transformation, x S2 、x S3 and x S4 They represent the coordinate values ​​of the tangent point width of the highest point on the left bank of the river section along the x direction, the coordinate values ​​of the tangent point width of the lowest point at the bottom, and the coordinate values ​​of the tangent point width of the highest point on the right bank, respectively. a is the transformation coefficient.

[0143] (6) The control parameters are the control parameters corresponding to the level change factors or the debris sediment input factors.

[0144] According to the variation range of the control parameters corresponding to the geomorphic factors and the set initial values, a series of control parameter values ​​is obtained by adopting an equal interval method, or a selected sampling method is used to obtain a series of control parameter values.

[0145] Example 2

[0146] The second embodiment of the present invention provides a method for evaluating the impact of sedimentation control factors based on sedimentation forward modeling. Figure 7 As shown, further, the detailed execution process of each step is as follows:

[0147] Step 1: The research object is the passive continental margin delta-canyon channel-submarine fan sedimentary system, which is a basin-scale research object and includes multiple sedimentary processes such as river turbidity and landslide. The research purpose is to explore the control of landforms on the sedimentary system, including the two aspects of the genetic mechanism and the distribution of sedimentary laws. It is an idealized virtual experiment with low requirements for data fidelity and does not include the source area. Therefore, LECODE was finally established as the sedimentary forward modeling software. The original landforms in the reference model are divided into three regions: shelf, slope canyon, and submarine fan ( Figure 8 ).

[0148] Step 2: When the simulation software simulates turbidity currents and rivers, the Manning constant is an important parameter. Therefore, a control experiment was designed for the Manning constant. The Manning constant values ​​were 0.004, 0.1, 1.6, and 2.2. Then, the sedimentation and erosion parameters (Table 1) and velocity distribution in the four models were calculated. The influence of the Manning constant on the sedimentation parameters and hydrological parameters was analyzed to clarify its role and establish the simulation parameter selection ( Figure 9 and Figure 10), and finally chose the Manning constant of 0.1, assuming that its sedimentation erosion and water flow distribution meet the research requirements and are reasonably distributed.

[0149] Table 1 Manning constant values ​​and corresponding model sedimentation and erosion parameter calculation results

[0150]

[0151] The third step is to design the experimental plan. Based on the characteristics of the shelf landform and the research purpose, the parameters of the quantitative experimental control group are finally established as six quantitative parameters: shelf slope, shelf length, canyon curvature, canyon depth, canyon curvature, canyon slope, and deep sea level slope.

[0152] Step 4: The hydrological parameters used as input parameters were derived from literature research and basic research on the study area. This literature research established the ranges of variation for the sediment input parameters and geomorphological parameters. In particular, the geomorphological parameters involved, such as shelf slope, shelf length, canyon curvature, canyon depth, canyon curvature, canyon slope, and deep-sea surface slope, were all determined based on literature research and, combined with the original landforms of the study target, to give reasonable ranges.

[0153] Step 5: Use the method in step 5 of the invention to analyze the original landforms ( Figure 11 a) Change the six landform parameters.

[0154] Changing the shelf slope mainly uses the formula mentioned above to change the slope of the area above -200 meters, forming 7 bedforms with the same characteristics except for the shelf slope ( Figure 11 b) Measure the slopes at points A and B on line 1. See Table 2 for slope changes.

[0155] Changing the shelf length mainly uses the formula mentioned above. The length of the area between x = 140,000 and x = 200,000 is changed. After recalculating the position of each grid node, the Surfer difference is applied to finally generate 5 bottom shapes. Among the 5 bottom shapes, the length from the canyon mouth to x = 200,000 varies, but the geomorphological characteristics of the shelf are basically maintained ( Figure 11 c) Measure the distance from the edge to point E on line 7. The distance change is shown in Table 2.

[0156] The slope gradient was changed by using the formula mentioned above. The slope gradient between -3800 and -200 was changed. In addition, the elevation of the area below -3800 was reduced accordingly. This resulted in 10 models with different slope gradients ( Figure 11 d). Measure the slopes at points B and D on line 1. See Table 2 for the slope changes.

[0157] The formula mentioned above is mainly used to change the canyon curvature. Taking x = 120,000 as an example, when the curvature is increased, the grid point corresponding to x = 120,000 is translated downward by a certain distance, and when the curvature is reduced, it is translated upward by a certain distance. This distance is given by the following algorithm. After the canyon covered by x = 80,000 to 140,000 is moved up or down, surfer interpolation is performed again, thereby changing the canyon curvature, forming 9 bedforms with different canyon curvatures, slightly changed landform features of the continental slope, but the landform features within the canyon are preserved ( Figure 11 e) The canyon axis curvature between E and F was measured, and its changes are shown in Table 2.

[0158] When changing the canyon depth, for each section parallel to the Y axis from x=80000 to 140000, the above formula is applied to change the bottom shape in the canyon part, and then 9 bottom shapes with different canyon depths are generated ( Figure 11 f) Measure the canyon depth at point 3 along the line. The depth variation is shown in Table 2.

[0159] The slope of the submarine plain was changed mainly by using the formula mentioned above. The slope of the area below -3800 elevation was changed, thus forming 11 models with different abyssal plain slopes ( Figure 11 g). Measure the slope change at line 8. See Table 2 for the slope change.

[0160] Table 2 Changes of geomorphic parameters

[0161]

[0162] Step 6: Run the model using different bedforms, keeping all other input parameters constant. From the simulation results, calculate the sedimentary erosion volume and area for deltas, canyons, and submarine fans. Deltas are defined as areas with elevations greater than -200 meters, canyons are defined as areas with elevations between -200 meters and -3800 meters in the initial landform, and abyssal plains are defined as areas with elevations below -3800 meters. The calculation formulas are as described above.

[0163] Combined with the distribution pattern of waterways in the study area, the number of waterways in each area was counted. According to the geomorphological characteristics of the study area, they were divided into three types: flowing through canyons, flowing through continental slopes and then merging into canyons, and flowing through continental slopes directly into deep-sea plains.

[0164] Step 7: Calculate the sensitivity and correlation between geomorphic parameters and sedimentary parameters and hydrological parameters. First, clarify the correlation between geomorphic parameters and sedimentary volume ( Figure 12 ), and the correlation between geomorphic parameters and waterway distribution types ( Figure 13), and it was determined whether the change trend was obviously parabolic or linear. Then, the sensitivity coefficient of each geomorphic parameter was calculated using the local sensitivity parameter analysis method and the Morris method, and the distribution of the sensitivity coefficient was represented by a spider diagram ( Figure 14 ). Finally, the increase ratio and delta of each geomorphic parameter ( Figure 15 ),canyon( Figure 16 )(The change multiple of the control parameter on the horizontal axis, the change multiple of the sedimentary parameter on the vertical axis), submarine fan ( Figure 17 ) The increase ratio of the sediment volume was used to perform a linear fit, and its slope was used to illustrate the sensitivity. At the same time, its R 2 , the impact of geomorphic parameters was evaluated from three dimensions: change trend, sensitivity and correlation (Table 3).

[0165] Table 3 Sensitivity and correlation of geomorphic parameters to sediment volume

[0166]

[0167] Step 8: Use the control factor impact summary table to reflect the impact of each control factor. Use color depth to indicate correlation. Dark colors represent strong correlation and light colors represent weak correlation. Use shape to indicate the impact trend. Rectangles indicate that it is basically unchanged. Right-facing triangles indicate that it increases as the control factor increases. Left-facing triangles indicate that it decreases as the control factor decreases. Diamonds indicate that it increases first and then decreases. The combination of two triangles indicates that it decreases first and then increases. The sensitivity is shown by the width of the shape. The higher the sensitivity, the wider the shape ( Figure 18 ).

[0168] The technical problem to be solved by the present invention is to overcome the problems of insufficient quantitative research on sensitivity analysis, lack of mathematical methods for parameter changes, and immature control factor research processes in the process of using sedimentary forward modeling to study control factors. A quantitative assessment method for the influence of sedimentary control factors based on sedimentary forward modeling that meets the characteristics of sedimentary forward modeling and the needs of sedimentological research is proposed. It covers a variety of technical details such as input parameter transformation based on geomorphological parameters, sedimentary erosion calculation, and sensitivity calculation. It sorts out the complete process from the selection of sedimentary forward modeling methods, establishing the value range, to the comprehensive assessment of control factors, and establishes a comprehensive assessment method for the influence of control factors based on comprehensive sensitivity analysis, correlation analysis, and change trends. The present invention can further improve the research process of sedimentary forward modeling research, solve the defects and shortcomings of the existing technology such as low degree of quantification and insufficient support for mathematical analysis, improve the data mining and processing capabilities based on sedimentary forward modeling, effectively quantitatively assess the influence of various control factors on the sedimentary system, and provide quantitative analysis results of influencing factors in the prediction of sedimentary system distribution. The technology of the present invention has broad application value in reservoir and caprock distribution prediction, analysis of main controlling factors of deposition, and quantitative deposition research. It can effectively improve the accuracy of sedimentary system distribution prediction, and has the significance of reducing costs and increasing efficiency for basic geological research in conventional oil and gas and unconventional oil and gas exploration and development. It also has the prospect of further developing related software for widespread promotion and application.

[0169] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a deposition control factor impact assessment device based on deposition forward modeling, the structure of the device is as follows: Figure 19 Shown, including:

[0170] A control group establishment module 191 is used to determine the corresponding control parameter value range for the deposition control factors of the deposition system, determine the value selection rule according to the type of the control parameter, obtain the value series of the control parameter according to the set initial value and the value selection rule, and establish a control group of the control parameter, wherein the value series belongs to the value range;

[0171] A simulation module 192 is configured to obtain a deposition simulation result corresponding to each value in the control group using a selected simulation method based on the control group of each control parameter;

[0172] The sedimentation control factor impact assessment module 193 is used to quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the sedimentation system characterization parameters based on the sedimentation simulation results.

[0173] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0174] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned deposition control factor impact assessment method based on deposition forward modeling is implemented.

[0175] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned deposition control factor impact assessment method based on deposition forward modeling is implemented.

[0176] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0177] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0178] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0179] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0180] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0181] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0182] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A sedimentation control factor impact assessment method based on sedimentation forward modeling, characterized in that: include: Determine the corresponding control parameter value range for the deposition control factor of the deposition system, determine the value selection rule according to the type of the control parameter, obtain a value series of the control parameter according to the set initial value and the value selection rule, and establish a control group of the control parameter, wherein the value series belongs to the value range; Based on the control group of each control parameter, the selected simulation method is used to obtain the sedimentation simulation results corresponding to each value in the control group; Quantitatively characterize the influence trend, sensitivity and correlation of control parameters on sedimentary system characterization parameters based on sedimentation simulation results; The determining of the value rule according to the type of the control parameter, and obtaining the value series of the control parameter according to the set initial value and the value rule, include: If the control parameter is length, based on the set initial value of length, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (1)-(3) to obtain the length value series: and' j =and j ,1≤j <n (1) y' j =y n +(j-n)b',n≤j <m (2) y' j =y n +(m-n)b'+(j-m)b,m≤j≤r y (3) In formulas (1)-(3), y' j Indicates the length coordinate value of the jth grid node in the y direction after transformation, y j Indicates the length coordinate value of the j-th grid node in the y direction before transformation, y n represents the length coordinate value of the nth grid node in the y direction before transformation, b' represents the length step after transformation, b represents the length step before transformation, r y Indicates the maximum grid node number in the y direction, n and m respectively indicate the starting grid node number and ending grid node number in the y direction of the length region to be transformed; If the control parameter is area, based on the set initial length and width values, the length coordinate values ​​and width coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (4) and (5) to obtain the value series of the area: x' i =x1+(i-1)a',1≤i≤r x (4) yes j =y1+(j-1)b',1≤j≤r y (5) In formulas (4) and (5), x' i represents the width coordinate value of the i-th grid node in the x-direction after the transformation, x1 represents the width coordinate value of the first grid node in the x-direction before the transformation, y1 represents the length coordinate value of the first grid node in the y-direction before the transformation, a' represents the width step after the transformation, r x Indicates the maximum grid node number in the x direction; If the control parameter is slope, according to the set initial value of elevation, the elevation of the grid nodes in the grid model is transformed according to the following equations (6)-(8) to obtain the slope value series: With' k =with k ,With k ≤of begin (6) With' k =cz k ,With begin <z k <z end (7) With' k =with k +(c-1)z end ,With i ≥of end (8) In formulas (6)-(8), z' k Indicates the elevation coordinate value of the kth grid node in the z direction after transformation, z k Indicates the elevation coordinate value of the kth grid node in the z direction before transformation, z begin and z end They represent the minimum and maximum elevation values ​​in the z direction of the slope area to be transformed, and c is the elevation transformation coefficient; If the control parameter is the river channel curvature, and the river channel extension direction is consistent with the y direction, according to the set initial values ​​of length and width, the width coordinate values ​​of the grid nodes in the grid model are transformed according to the following formula (9) to obtain the value series of the river channel curvature: ,and qs1 ≤y j <y qs1+1 (9) In formula (9), x i Indicates the width coordinate value of the i-th grid node in the x direction before transformation, y qs1 and y qs1+1 They represent the length coordinate values ​​of the grid nodes corresponding to the s1th peak and s1+1th peak of the river curvature respectively; If the control parameter is the channel depth, based on the set initial length and width values, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (10) and (11) to obtain the value series of the channel depth: ,x S2 ≤x i <x S3 (10) ,x S3 ≤x i ≤x S4 (11) In formulas (10) and (11), x S2 、x S3 and x S4 They represent the coordinate values ​​of the tangent point width of the highest point on the left bank of the river section along the x direction, the coordinate values ​​of the tangent point width of the lowest point at the bottom, and the coordinate values ​​of the tangent point width of the highest point on the right bank.

2. The method according to claim 1, characterized in that The sensitivity of the control parameters to the deposition system characterization parameters is quantitatively characterized based on the deposition simulation results, including: According to the deposition simulation results, the sensitivity of the control parameters to the deposition system characterization parameters is quantitatively characterized by the following formula (12): ， （12） In formula (12), SN is the sensitivity discriminant coefficient, t is the number of model runs, Q is the total number of model runs, and Y t is the output value of the sedimentary system characterization parameter of the model run t, Y t+1 is the output value of the sedimentary system characterization parameter of the model t+1th operation; Y0 is the output value of the sedimentary system characterization parameter of the initial operation of the model, P t P is the percentage change of the control parameter value relative to the initial value when the model is run for the tth time, t+1 The percentage change of the control parameter value relative to the initial value when the model is run for the t+1th time.

3. The method according to any one of claims 1 to 2, characterized in that: The deposition simulation results include deposition amount, erosion amount, deposition area, erosion area, deposition thickness, erosion thickness and curvature change, as well as average water flow.

4. A sedimentation control factor impact assessment device based on sedimentation forward modeling, characterized in that: The device comprises: The control group establishment module is used to determine the corresponding control parameter value range for the deposition control factors of the deposition system, determine the value rule according to the type of the control parameter, obtain the value series of the control parameter according to the set initial value and the value rule, and establish a control group of the control parameter, wherein the value series belongs to the value range; the value rule is determined according to the type of the control parameter, and the value series of the control parameter is obtained according to the set initial value and the value rule, and is used to: If the control parameter is length, based on the set initial value of length, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (1)-(3) to obtain the length value series: If the control parameter is length, based on the set initial value of length, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (1)-(3) to obtain the length value series: and' j =and j ,1≤j <n (1) y' j =y n +(j-n)b',n≤j <m (2) y' j =y n +(m-n)b'+(j-m)b,m≤j≤r y (3) In formulas (1)-(3), y' j Indicates the length coordinate value of the jth grid node in the y direction after transformation, y j Indicates the length coordinate value of the j-th grid node in the y direction before transformation, y n represents the length coordinate value of the nth grid node in the y direction before transformation, b' represents the length step after transformation, b represents the length step before transformation, r y Indicates the maximum grid node number in the y direction, n and m respectively indicate the starting grid node number and ending grid node number in the y direction of the length region to be transformed; If the control parameter is area, based on the set initial length and width values, the length coordinate values ​​and width coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (4) and (5) to obtain the value series of the area: x' i =x1+(i-1)a',1≤i≤r x (4) yes j =y1+(j-1)b',1≤j≤r y (5) In formulas (4) and (5), x' i represents the width coordinate value of the i-th grid node in the x-direction after the transformation, x1 represents the width coordinate value of the first grid node in the x-direction before the transformation, y1 represents the length coordinate value of the first grid node in the y-direction before the transformation, a' represents the width step after the transformation, r x Indicates the maximum grid node number in the x direction; If the control parameter is slope, according to the set initial value of elevation, the elevation of the grid nodes in the grid model is transformed according to the following equations (6)-(8) to obtain the slope value series: With' k =with k ,With k ≤of begin (6) With' k =cz k ,With begin <z k <z end (7) With' k =with k +(c-1)z end ,With i ≥of end (8) In formulas (6)-(8), z' k Indicates the elevation coordinate value of the kth grid node in the z direction after transformation, z k Indicates the elevation coordinate value of the kth grid node in the z direction before transformation, z begin and z end They represent the minimum and maximum elevation values ​​in the z direction of the slope area to be transformed, and c is the elevation transformation coefficient; If the control parameter is the river channel curvature, and the river channel extension direction is consistent with the y direction, according to the set initial values ​​of length and width, the width coordinate values ​​of the grid nodes in the grid model are transformed according to the following formula (9) to obtain the value series of the river channel curvature: ,and qs1 ≤y j <y qs1+1 (9) In formula (9), x i Indicates the width coordinate value of the i-th grid node in the x direction before transformation, y qs1 and y qs1+1 They represent the length coordinate values ​​of the grid nodes corresponding to the s1th peak and s1+1th peak of the river curvature respectively; If the control parameter is the channel depth, based on the set initial length and width values, the length coordinate values ​​of the grid nodes in the grid model are transformed according to the following equations (10) and (11) to obtain the value series of the channel depth: ,x S2 ≤x i <x S3 (10) ,x S3 ≤x i ≤x S4 (11) In formulas (10) and (11), x S2 、x S3 and x S4 They represent the coordinate values ​​of the tangent point width at the highest point on the left bank, the tangent point width at the lowest point on the bottom, and the tangent point width at the highest point on the right bank of the river section along the x direction respectively; A simulation module is used to obtain a deposition simulation result corresponding to each value in the control group based on the control group of each control parameter using a selected simulation method; The sedimentation control factor impact assessment module is used to quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the sedimentation system characterization parameters based on the sedimentation simulation results.

5. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed by a processor, implement the deposition control factor impact assessment method based on deposition forward modeling according to any one of claims 1 to 3.

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