Deposition control factor influence assessment method and device based on deposition forward modeling

Abstract

The invention discloses a deposition control factor influence assessment method and device based on deposition forward modeling. The method comprises the following steps: determining a corresponding control parameter value range according to deposition control factors of a deposition system, determining a value rule according to the type of the control parameter, obtaining a value series of the control parameter according to a set initial value and the value rule, and establishing a control group of the control parameter; based on each control group, utilizing a selected simulation method to obtain a deposition simulation result corresponding to each value in the control group; and quantitatively characterizing the influence trend, sensitivity and correlation of the control parameters on the characterization parameters of the deposition system according to the deposition simulation result. According to the method, a systematic quantitative deposition control factor influence assessment technical process is established; the method can improve the data mining processing capability based on deposition forward modeling, effectively and quantitatively assesses the influence of each control factor on the deposition system, and provides a quantitative influence factor analysis result in the distribution prediction of the deposition system.

Description

Technical Field

[0001] The 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 sedimentology research develops towards quantification, process and systematization, sedimentation forward modeling has received increasing attention. It follows the physical laws of sediment erosion, transportation and deposition, and simulates the formation process of the sedimentary system from scratch. Sedimentation forward modeling has many advantages. Based on sedimentation mechanism and theory, it can effectively reproduce the sedimentological conceptual model; it can quantitatively predict the distribution and scale of the sedimentary system, and has great potential for data mining; it has low economic and time costs, can quickly realize multiple simulations, and establish a geological space-time scale model that cannot be achieved by flume experiments.

[0003] With the development of computer technology, sedimentary forward modeling has entered a period of rapid development, but its potential in sedimentological research and oil and gas exploration has not yet been fully developed. The analysis of the influence of sedimentary forward modeling on sedimentary control factors is still mainly qualitative, lacking a systematic quantitative evaluation based on mathematical theory. Summary of the invention

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

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

[0006] Determine the corresponding control parameter value range for the deposition control factor 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;

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

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

[0009] In a second aspect, an embodiment of the present invention provides a deposition control factor impact assessment device based on deposition 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 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;

[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 by 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, 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.

[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 solution provided by the embodiment 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 analysis results of influencing factors 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 in 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 deposition control factor impact assessment method based on deposition forward modeling provided in the embodiment of the present invention introduces quantitative sensitivity analysis into the study of deposition 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 deposition system characterization parameters.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose 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 This is a flow chart 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 This is a schematic diagram of length conversion in Embodiment 1 of the present invention;

[0024] Figure 3 It is a schematic diagram of the transformation area in the first embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of slope transformation in Embodiment 1 of the present invention;

[0026] Figure 5 This is a schematic diagram of changing the river curvature in the first embodiment of the present invention;

[0027] Figure 6 A schematic diagram of changing the river depth in the first embodiment of the present invention;

[0028] Figure 7 This is a specific implementation flow chart of the 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] Fig. 9 The influence of the Manning constant on the sedimentation and erosion in the simulation results in the second embodiment of the present invention;

[0031] Fig.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] Fig.11It is a new landform formed by respectively changing the landform parameters on the basis of the original landform of the standard model in the second embodiment of the present invention, wherein a is the original landform, b is the outline superposition map of multiple new landforms formed after changing the shelf slope, c is the outline superposition map of multiple new landforms formed after changing the shelf width, d is the outline superposition map of multiple new landforms formed after changing the slope of the continental slope, e is the canyon axis superposition map of multiple new landforms formed after changing the canyon curvature, f is the canyon 5 profile bottom superposition map of multiple new landforms formed after changing the canyon depth, and g is the outline superposition map of multiple new landforms formed after changing the slope of the abyssal plain;

[0033] Fig.12 A correlation diagram between sedimentation parameters and geomorphic parameters in the simulation results in the second embodiment of the present invention;

[0034] Fig.13 A correlation diagram between waterway distribution and landform parameters in the simulation results of the second embodiment of the present invention;

[0035] Fig.14 The distribution of sensitivity indexes of the delta, canyon and submarine fan sediment volumes to geomorphic parameters in the simulation results of the second embodiment of the present invention;

[0036] Fig.15 The comparison of the influence of geomorphic parameters on delta sediment volume in the simulation results of Example 2 of the present invention;

[0037] Fig.16 The comparison of the influence of geomorphic parameters on canyon sediment volume in the simulation results of Example 2 of the present invention;

[0038] Fig.17 The comparison of the influence of geomorphic parameters on the sediment volume of submarine fans in the simulation results of the second embodiment of the present invention; Fig.18 The following is a summary of the influence of deposition control factors based on deposition forward modeling in Example 2 of the present invention;

[0039] Fig.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

[0040] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the 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. On the contrary, 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.

[0041] 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 specified, 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 belongs. Although the present invention only describes 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 methods and / or materials related to the documents. In the event of conflict with any incorporated document, the content of this specification shall prevail.

[0042] The inadequacy of the analysis of the influence of sedimentary control factors on sedimentary forward modeling in the existing technology is mainly reflected in three aspects. First, there is a lack of systematic sorting out and theoretical support for the transformation methods of the input sedimentary control factors in sedimentary forward modeling. Second, the method of sensitivity analysis still mainly adopts the method of qualitative comparison of observation results, and the quantitative analysis is insufficient, especially the failure to introduce the sensitivity analysis algorithm based on mathematical theory. Third, the sensitivity analysis has not been organically combined with the control factor response research to form a part of the sedimentary forward modeling research module with high technical content and mature methods, which cannot well support the control factor response research.

[0043] At present, Beicip's CougarFlow has made some breakthroughs in this regard and can complete sensitivity analysis based on sedimentation forward modeling. This software mainly combines Latin hypercube sampling method and response surface modeling method to define thickness calibration index and structure calibration index, so as to evaluate the influence of multiple parameters. The results are displayed in Pareto chart and main effect chart. However, the shortcomings of this method are high computational cost and expensive software. In addition, there is a lack of steps to transform complex input parameters such as landform and horizontal surface changes. When evaluating sensitivity, it fails to consider the possible trend changes and uncertainties of the influence of sedimentation control factors. Therefore, it is more suitable as a pre-processing step of Dionisos-Flow to provide reference for Dionisos parameter selection. It is not suitable to apply a variety of sedimentation forward modeling methods to quantitatively evaluate the influence of sedimentation control factors.

[0044] In order to overcome the problems of insufficient quantitative sensitivity analysis, lack of mathematical methods for parameter changes, and immature control factor research process in the process of studying control factors using sedimentation forward modeling, a sedimentation control factor impact assessment method and device based on sedimentation forward modeling was proposed.

[0045] Embodiment 1

[0046] Embodiment 1 of the present invention provides a method for evaluating the influence of sedimentation control factors based on sedimentation forward modeling, and the process thereof is as follows: Figure 1 As shown, the following steps are included:

[0047] Step S11: for the deposition control factors of the deposition system, determine the corresponding control parameter value range, determine the value rule according to the type of control parameter, obtain the control parameter value series according to the set initial value and value rule, and establish a control group of control parameters.

[0048] The obtained value series of the control parameter are all within the value range of the parameter.

[0049] In terms of designing the control group, it is divided into a quantitative analysis control group and a qualitative analysis control group. The purpose of the quantitative analysis control group is to discover the quantitative influence of the control factors on the sedimentary system. It requires that the input parameter changes can be quantified. There are many control models, but the analysis work of a single model is small, and the focus is on extracting data. The qualitative analysis input parameters may be descriptive qualitative parameters, such as the type of sedimentary process, source direction, etc., or they may be a combination of multiple quantitative parameters, such as hydrodynamic reduction type retreat, sediment supply reduction type retreat, and lake level rise type retreat. Relatively speaking, there are few control models, and there is more analysis work for a single model, including qualitative observation and description of its profile plane characteristics and extraction of quantitative parameters.

[0050] The simulation sequence needs to follow the following principles: Principle 1: When debugging parameter values, first take larger and smaller values, and then experiment with 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 single process simulation and eliminate interference from multiple processes.

[0051] Specifically, the deposition control factors include at least one of the following factors:

[0052] (1) Geomorphic factors

[0053] 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.

[0054] (2) Factors of water level change

[0055] It includes the 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.

[0056] (3) Clastic sediment input factors

[0057] Including components of sediment input, sediment supply rate, water supply rate, water flow rate, etc.

[0058] (4) Carbonate rock growth factors

[0059] (5) Sedimentation process factors

[0060] These include temperature, salinity, carbonate growth rate, etc. When the deposition process includes waves, wind, rainfall, etc., the various control parameters also include parameters of the corresponding deposition process.

[0061] In some embodiments, determining the corresponding control parameter value range includes, according to set range conditions, determining the corresponding control parameter value range through at least one of literature research, basic research in the study area and semi-empirical estimation method.

[0062] Among them, literature research includes researching the literature of the study area, establishing sedimentary types, sediment grain size distribution, paleohydrological conditions, paleogeomorphology, tectonic history, sea-lake level changes, etc.; researching the parameter selection of the selected modeling method in each application example; researching the hydrological conditions, sediment grain size distribution and other parameters in similar sedimentary environments. Among them, basic research in the study area includes basic research on the paleogeography and sedimentary system of the study area, collecting field outcrops, rock samples, core logging, earthquakes, paleontology, geochemistry and other data in the study area and conducting preliminary analysis. Among them, semi-empirical calculations are based on semi-empirical formulas and quantitative methods summarized by predecessors to calculate unknown quantities using known quantities, such as the BQART method, which uses source area parameters to calculate sediment flux, and the fulcrum method, which uses transport channel parameters to calculate sediment flux.

[0063] Specifically, the scope conditions are set, including at least one of the following conditions:

[0064] It is within the parameters of nature and consistent with geological understanding;

[0065] Matching model accuracy and modeling scale;

[0066] Meet the requirements of the simulation program;

[0067] If there is actual data, it is consistent with the actual data;

[0068] If it is a numerical experiment, it meets the experimental purpose.

[0069] 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.

[0070] 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.

[0071] The optimization of simulation methods and corresponding model parameters includes: determining the sedimentation forward simulation method according to the attributes 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 selecting the optimal value.

[0072] Further, determining the sedimentation forward simulation method according to the attributes of the research target includes determining the sedimentation forward simulation method according to at least one of the following attributes of the research target:

[0073] (1) Research scale

[0074] The research scale is used to characterize whether the research target belongs to the basin, reservoir or event level.

[0075] 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.

[0076] (2) Research focus

[0077] The research focuses on the sedimentation mechanism or the distribution law of the sedimentary system.

[0078] 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.

[0079] (3) Requirements for actual data fidelity

[0080] In terms of fidelity to actual data, the grid-based simulation method controlled by geostatistics is suitable for research that requires strict consistency with actual data, while the process-based simulation method controlled by sedimentary process principles is suitable for research that does 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.

[0081] (4) Whether the source area is included

[0082] In terms of whether the source area is included, the complete source-sink system type includes the source area, which can simulate the erosion process of the source area and generate sediment flux controlled by the landform, structure, climate, etc. of the source area. The sediment forward modeling that only simulates the sink area needs to set the sediment flux, but cannot simulate the generation process of the sediment source in the sink area.

[0083] (5) Number of deposition processes

[0084] 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.

[0085] After the simulation method is determined through the above analysis, the impact analysis of the model parameters corresponding to the simulation method is carried out. In the simulation method, attention should be paid 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, the geometric constant in the geometric law method, etc. It is necessary to clarify the key method parameters in the simulation method and establish its reasonable value range. Then, through preliminary multiple simulation analysis and comparison, the role of the simulation method parameters is clarified, and finally the simulation method parameter values ​​are determined. The same values ​​are used in all subsequent simulations, so as to exclude the influence of the simulation method parameters in the subsequent sedimentation control factor research.

[0086] The selected simulation method is used to perform simulation based on 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.

[0087] When running sedimentation simulation, attention should be paid to simulation duration and storage of simulation results. The simulation duration is convenient. If the simulation duration is long, it is necessary to reduce the simulation duration and improve efficiency by reducing the bottom shape resolution and time resolution in the initial simulation or a large number of control experiments. After the parameter values ​​are determined or when the standard model is established, a higher bottom shape resolution and time resolution are used to achieve the best balance between simulation effect and simulation efficiency. The simulation storage results require a lot of storage space. Therefore, in order to save storage space, the output result storage frequency can be reduced in the initial simulation or a large number of control experiments, and the resolution can be reduced at the same time. After the parameter values ​​are determined or when the standard model is established, the output result storage frequency and resolution can be increased to improve the efficiency of storage space utilization. At the same time, attention should be paid to the classification mark of the storage results. When debugging parameters, important parameter values ​​should be marked on the storage name.

[0088] The analysis results are divided into qualitative observation and quantitative analysis. The qualitative observation mainly includes observing the planar distribution characteristics of sedimentary parameters, vertical profile characteristics, single-point vertical characteristics of key locations, evolutionary characteristics, and comparison of different models. The quantitative observation requires extracting various sedimentary parameters, calculating the consistency rate with the actual situation, and quantitatively comparing different models.

[0089] 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.

[0090] 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 times, such as the amount of sedimentation and erosion in the corresponding period of each system domain, the amount of sedimentation and erosion in each region of the source-channel-sink, and the hydrological parameters, such as the average velocity, average water flow, etc. When there are other research objectives, it is also necessary to count the relevant parameters, such as the area of ​​sedimentation and erosion in a certain area, the thickness of sedimentation and erosion, and the possible curvature change of the river.

[0091] thick (i，j) =Z final(i，j) -Z 0(i，j)

[0092]

[0093] Erosion area = ab·numero

[0094] numero is thick i.j <0 grid number

[0095]

[0096] Deposition area = ab·numdepo

[0097] numdepo is thick i.j >0 grid number

[0098] Sedimentary discontinuity area = ab·numhiatus

[0099] numhiatus is thick i.j = 0 grid number

[0100] The erosion area, erosion volume, deposition area and deposition volume are calculated by the above-mentioned grid statistics method.

[0101] Step S13: quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the characterization parameters of the deposition system according to the deposition simulation results.

[0102] The sensitivity coefficient is calculated by local sensitivity analysis, and then the influence of various control factors on the sedimentary system and hydrodynamics is compared in combination with relevant maps. Finally, the impact is quantitatively evaluated from three dimensions: change trend, sensitivity, and correlation.

[0103] Firstly, the quantitative fitting relationship between the controlling factors and the sedimentation parameters and hydrodynamic related parameters is established, and the fitting curve type and changing trend are clarified.

[0104] The modified Morris screening method was then used to calculate parameter sensitivity, and the sensitivity discrimination coefficient was calculated as follows:

[0105]

[0106] 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 t-th model run, Y t+1 is the output value of the sedimentary system characterization parameter of the model running for the t+1th time; Y0 is the output value of the sedimentary system characterization parameter of the model running for the initial time, 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.

[0107] When the fitting relationship shows an obvious turning point, the sensitivity discriminant coefficient can be calculated for the positive correlation part or the negative correlation part respectively. When the number of samples is large, the outliers in the i-th run can be eliminated, and then the average value can be calculated. When multiple control factors are tested, the sensitivity of each control factor to a certain feature of the sedimentary system can be compared using a spider diagram.

[0108] 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.

[0109] 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 calculation 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 control factors are clarified, and their correlation coefficients (i.e., the r in the fitting results) are compared. 2 ).

[0110] 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.

[0111] 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.

[0112] The method for evaluating the impact of sedimentation control factors based on sedimentation forward modeling provided in the first embodiment of the present invention provides an algorithm for a series of control parameter values, thereby providing a data basis for sedimentation forward modeling.

[0113] 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, proposes a specific calculation method for the sensitivity discrimination coefficient, and effectively quantitatively characterizes the sensitivity of control parameters and deposition system characterization parameters.

[0114] In some embodiments, the influence of each control parameter can also be reflected in the form of a control parameter influence table. The depth of color is used to indicate the strength of the correlation, the darker the darker, the stronger the correlation, and the lighter the light, the weaker the correlation; the shape is used to indicate the influence trend, a rectangle indicates no influence, a right-opening triangle indicates an increase as the control factor increases, a left-opening triangle indicates a decrease as the control factor decreases, a diamond indicates an increase first and then a decrease, and a combination of two triangles indicates a decrease first and then an increase; the sensitivity is shown by the width of the shape, the higher the sensitivity, the wider the shape.

[0115] The determination of the control parameter value series includes the following cases according to its type:

[0116] (1) The control parameter is length

[0117] According to the initial value of the set length, 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 a series of length values:

[0118] y' j =y j , 1≤j <n (2)

[0119] y' j =y n +(jn)b', n≤j <m (3)

[0120] y' j =y n+(mn)b'+(jm)b, m≤j≤r y (4)

[0121] In formulas (2)-(4), y' j Represents 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 the transformation, y n represents the length coordinate value of the nth grid node in the y direction before the transformation, b' represents the length step after the transformation, b represents the length step before the transformation, r y Indicates the maximum grid node number in the y direction, n and m respectively indicate the starting grid node number and the ending grid node number in the y direction of the length region to be transformed.

[0122] (2) The control parameter is area

[0123] According to the set initial length and width values, see Figure 3 As shown, the length coordinate value and width coordinate value of the grid node in the grid model are transformed according to the following equations (5) and (6) to obtain the area value series:

[0124] x' i =x1+(i-1)a', 1≤i≤r x (5)

[0125] y' j =y1+(j-1)b', 1≤j≤r y (6)

[0126] 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.

[0127] (3) The control parameter is slope

[0128] 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 a series of slope values:

[0129] z' k =z k , z k≤z begin (7)

[0130] z' k =cz k , z begin <z k <z end (8)

[0131] z' k =z k +(c-1)z end , z i ≥z end (9)

[0132] In formulas (7)-(9), z' k Represents 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.

[0133] (4) The control parameter is the river curvature and the river extension direction is consistent with the y direction

[0134] According to the set initial length and width values, see Figure 5 As shown, 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 curvature:

[0135]

[0136] 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 jth grid node in the y direction before the transformation, y qs1 and 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.

[0137] (5) The control parameter is the river depth

[0138] According to the set initial length and width values, see Figure 6 As shown in the figure, 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:

[0139]

[0140]

[0141] In formulas (11) and (12), y' j Represents 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 the 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 respectively 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, and a is the transformation coefficient.

[0142] (6) The control parameters are the control parameters corresponding to the water level change factor or the clastic sediment input factor.

[0143] According to the variation range of the control parameters corresponding to the geomorphic factors and the set initial values, an equal interval method is adopted to obtain a series of control parameter values, or a selected sampling method is adopted to obtain a series of control parameter values.

[0144] Embodiment 2

[0145] 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:

[0146] Step 1: The research object is the passive continental margin delta-canyon channel-submarine fan sedimentary system, which is a basin-scale research object, including river turbidity currents, landslides and other sedimentary processes. The purpose of the study is to explore the control of landforms on the sedimentary system, including 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 areas: shelf, slope canyon, and submarine fan ( Figure 8 ).

[0147] Step 2: When the simulation software simulates turbidity currents and rivers, the Manning constant is an important parameter. Therefore, a control experiment for the Manning constant was designed. The values ​​of the Manning constant were 0.004, 0.1, 1.6, and 2.2, respectively. 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 ( Fig. 9 and Fig.10), and finally chose the Manning constant as 0.1, assuming that its sedimentation erosion and water flow distribution met the research requirements and had a reasonable distribution.

[0148] Table 1 The values ​​of Manning constant and the calculation results of the corresponding model sedimentation and erosion parameters

[0149]

[0150] The third step: designing the experimental plan, combining the geomorphological characteristics of the shelf and the purpose of the study, and finally establishing the parameters of the quantitative experimental control group as six quantitative parameters: shelf slope, shelf length, canyon curvature, canyon depth, canyon curvature, canyon slope, and deep sea surface slope.

[0151] Step 4: The hydrological parameters in the input parameters are derived from literature research and basic research on the study area. The literature research established the range of variation of sediment input parameters and geomorphic parameters. In particular, the geomorphic parameters involved in the shelf slope, shelf length, canyon curvature, canyon depth, canyon curvature, canyon slope, and deep sea level slope were all given a reasonable range of variation based on the literature research and combined with the original landforms of the research target.

[0152] Step 5: Use the method in step 5 of the invention to analyze the original landforms ( Fig.11 a) to change the 6 landform parameters.

[0153] Changing the shelf slope mainly uses the formula mentioned above to change the slope of the area above -200 meters, forming 7 bottom shapes with the same characteristics except for the different shelf slopes ( Fig.11 b). Measure the slopes at points AB in line 1. See Table 2 for the slope changes.

[0154] Changing the shelf length mainly uses the formula mentioned above, changing the length of the area from x = 140000 to x = 200000. After recalculating the position of each grid node, using Surfer difference, 5 bottom shapes are finally generated. Among the 5 bottom shapes, the length from the canyon mouth to x = 200000 has changed, but the geomorphic features of the shelf are basically maintained ( Fig.11 c). Measure the distance from the edge to point E on line 7. The distance change is shown in Table 2.

[0155] The slope gradient was changed mainly 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. Thus, 10 models with different slope gradients were formed ( Fig.11 d). Measure the slopes at points BD in line 1. See Table 2 for the slope changes.

[0156] The formula mentioned above is mainly used to change the canyon curvature. Taking x=120000 as an example, when the curvature increases, the grid point corresponding to x=120000 moves downward by a certain distance, and when the curvature decreases, it moves upward by a certain distance. This distance is given by the following algorithm. When the canyon covered by x=80000 to 140000 moves up or down, the surfer interpolation is performed again, and the canyon curvature changes, forming 9 bottom shapes with different canyon curvatures, slightly changed slope landform features, but the landform features in the canyon are preserved ( Fig.11 e) The canyon axis curvature between EF is measured, and its changes are shown in Table 2.

[0157] When changing the canyon depth, for each section parallel to the Y axis from x=80000 to 140000 covered by the canyon, the above formula is used to change the bottom shape in the canyon part, and then 9 bottom shapes with different canyon depths are generated ( Fig.11 f). Measure the canyon depth at line 3. The depth variation is shown in Table 2.

[0158] The slope of the submarine plain was changed mainly by using the formula mentioned above, which changed the slope of the area below -3800, thus forming 11 models with different slopes of the deep-sea plain ( Fig.11 g). Measure the slope change at line 8. The slope change is shown in Table 2.

[0159] Table 2 Changes of geomorphic parameters

[0160]

[0161] Step 6: Use different bottom shapes, keep other input parameters the same, run the model, and then calculate the sedimentary erosion volume and area of ​​the delta area, canyon area, and submarine fan area from the simulation results. The delta area is the area with an elevation greater than -200 meters, while the canyon is the area with an elevation below -200 meters and greater than -3800 meters in the initial landform, and the abyssal plain is the area with an elevation below -3800 meters. The calculation formula is as described above.

[0162] Combined with the distribution pattern of waterways in the study area, the number of waterways in each area was counted, and according to the geomorphological characteristics of the study area, they were divided into three types: flowing through the canyon, flowing through the continental slope and then merging into the canyon, and flowing through the continental slope directly into the deep sea plain.

[0163] 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 ( Fig.12 ), and the correlation between geomorphic parameters and waterway distribution types ( Fig.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 ( Fig.14 ). Finally, the increase ratio and delta of each geomorphic parameter ( Fig.15 ),canyon( Fig.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 ( Fig.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 influence of geomorphic parameters was evaluated from three dimensions: change trend, sensitivity and correlation (Table 3).

[0164] Table 3 Sensitivity and correlation of geomorphic parameters to sediment volume

[0165]

[0166] 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 shapes to indicate the impact trend. Rectangles indicate that it is basically unchanged. Right-opening triangles indicate that it increases as the control factor increases. Left-opening 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 ( Fig.18 ).

[0167] The technical problem to be solved by the present invention is to overcome the problems of insufficient quantitative research on sensitivity analysis in the process of using sedimentary forward modeling to study control factors, lack of mathematical methods for parameter changes, and immature research process of control factors, and propose a quantitative evaluation method of 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, covering a variety of technical details such as input parameter transformation based on geomorphic parameters, sedimentary erosion calculation, and sensitivity calculation, and combing the complete process from the selection of sedimentary forward modeling methods, establishing the value range, to the comprehensive evaluation of control factors, and establishing a comprehensive sensitivity analysis, correlation analysis, and comprehensive evaluation method of the influence of control factors on the change trend. The present invention can further improve the research process of sedimentary forward modeling research, solve the defects and deficiencies of low quantification and insufficient mathematical analysis support in the prior art, improve the data mining processing capability based on sedimentary forward modeling, effectively quantitatively evaluate 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, sedimentation main controlling factor analysis, and quantitative sedimentation research. It can effectively improve the accuracy of sedimentary system distribution prediction, and has cost-reducing and efficiency-increasing significance 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 wide promotion and application.

[0168] 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: Fig.19 As shown, including:

[0169] A control group establishment module 191 is used to determine the corresponding control parameter value range for the deposition control factor 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;

[0170] A simulation module 192 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 by using a selected simulation method;

[0171] The deposition control factor impact assessment module 193 is used to quantitatively characterize the influence trend, sensitivity and correlation of the control parameters on the deposition system characterization parameters based on the deposition simulation results.

[0172] Regarding the device 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.

[0173] 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.

[0174] 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.

[0175] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate 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 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 mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0176] It should be understood that the specific order or hierarchy of steps in the disclosed process 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 protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0177] In the above 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 an embodiment of the claimed subject matter requires more features than those set forth in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, 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.

[0178] 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 can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0179] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a 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 can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0180] For software implementation, the techniques described in this application can be implemented with 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 outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0181] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A method for evaluating the impact of sedimentation control factors 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 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; Based on the control group of each control parameter, the selected simulation method is used to obtain the deposition simulation result corresponding to each value in the control group; Based on the deposition simulation results, the influence trend, sensitivity and correlation of the control parameters on the deposition system characterization parameters are quantitatively characterized.

2. The method according to claim 1, characterized in that 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 the 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 Represents 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 the transformation, y n represents the length coordinate value of the nth grid node in the y direction before the transformation, b' represents the length step after the transformation, b represents the length step before the transformation, r y Indicates the maximum grid node number in the y direction, n and m respectively indicate the starting grid node number and the ending grid node number in the y direction of the length region to be transformed.

3. The method according to claim 1, characterized in that 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 area, according to the set initial length value and initial width value, the length coordinate value and width coordinate value of the grid node 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, 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.

4. The method according to claim 1, characterized in that: 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 the slope, according to the set initial value of the 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 =from k ,With k ≤of begin (6) With' k =cz k ,With begin <z k <z end (7) With' k =from k +(c-1)z end ,With i ≥from end (8) In formulas (6)-(8), z' k Represents 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.

5. The method according to claim 1, characterized in that 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 the river curvature, and the extension direction of the river is consistent with the y direction, according to the set initial length value and initial width value, the width coordinate value of the grid node in the grid model is transformed according to the following formula (9) to obtain the value series of the river curvature: In formula (9), 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 jth grid node in the y direction before the transformation, y qs1 and 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.

6. The method according to claim 1, characterized in that 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 the channel depth, according to 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: In formulas (10) and (11), y' j Represents 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 the 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 respectively 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, and a is the transformation coefficient.

7. 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): 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 t-th model run, Y t+1 is the output value of the sedimentary system characterization parameter of the model running for the t+1th time; Y0 is the output value of the sedimentary system characterization parameter of the model running for the initial time, 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.

8. The method according to any one of claims 1 to 7, characterized in that: The deposition simulation results include deposition amount, erosion amount, deposition area, erosion area, deposition thickness, erosion thickness and curvature change, and also include average water flow.

9. A device for evaluating the impact of sedimentation control factors based on sedimentation forward modeling, characterized in that: The device comprises: 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 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; 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 by 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.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the deposition control factor impact assessment method based on deposition forward modeling according to any one of claims 1 to 8 is implemented.

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