High-precision deposition rate prediction method based on clastic sedimentary rock deposition numerical simulation

Abstract

The invention discloses a high-precision deposition rate prediction method based on clastic sedimentary rock deposition numerical simulation, which comprises the following steps: S1, a preparation stage: collecting geological data of a target area, including basin boundary conditions, flow velocity field conditions, river information, deposition system types, clastic body granularity composition and the like, and providing data support for determining a geological model; s2, constructing a model: determining a geological model and a physical model according to the collected data, and reasonably selecting a geometric scale and a time scale of the model and a prototype when the physical model is determined; according to the method, geological data in multiple aspects are comprehensively considered, the model is accurately constructed, parameters are meticulously calibrated, the prediction precision is greatly improved, reliable data are provided for subsequent research and application, a geometric scale and a time scale are reasonably selected, the model is calibrated, verified and optimized according to actual data, and a continuously improved closed loop is formed. When the model deviates, targeted improvement can be carried out, and reliability and long-term effectiveness are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of sedimentation rate prediction, and particularly to a high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock deposition. Background Art

[0002] In the field of geological research, the prediction of sedimentation rate of clastic sedimentary rocks has always been one of the core topics. From understanding the long geological evolution process of the earth, to exploring mineral resources buried deep underground, to evaluating possible geological disasters, accurate sedimentation rate data plays a key role. For example, in oil exploration, the sedimentation rate can help judge the formation conditions and distribution range of oil reservoirs; in the geological research of coastal zones, it helps predict coastline changes and marine disaster risks.

[0003] However, traditional prediction methods have significant defects. Methods based on simple geological observations and empirical formulas can only roughly estimate the sedimentation rate and are difficult to handle complex and variable sedimentary environments. The actual sedimentation process involves many complex physical processes, including the transportation of particles in water flow, the differential settlement of particles with different particle sizes, the interaction during the accumulation process, and the energy exchange between water flow and sediment. Moreover, there are close coupling relationships between different physical quantities, such as the mutual influence between flow velocity, sediment concentration, and topography. Traditional methods are difficult to comprehensively consider these factors, resulting in a significant reduction in prediction accuracy.

[0004] With the rapid development of computer technology and numerical simulation technology, using numerical simulation to predict sedimentation rate has become the mainstream trend. However, existing numerical simulation methods still have many deficiencies. In the model construction link, the simplification of complex geological conditions and physical processes is excessive, resulting in a large deviation between the model and the actual situation. During the parameter calibration process, the lack of effective data constraints and optimization algorithms makes it difficult for model parameters to accurately reflect the real sedimentation process. In terms of simulation accuracy, it cannot meet the requirements of high-precision sedimentation rate prediction. When facing complex geological structures and dynamically changing sedimentary environments, the reliability of prediction results is low. Therefore, we propose a high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock deposition to solve the above problems. Summary of the Invention

[0005] Based on the technical problems existing in the background art, the present invention proposes a high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock deposition.

[0006] A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock deposition proposed by the present invention includes the following steps:

[0007] S1: Preparation stage: Collect geological data of the target area, including basin boundary conditions, flow velocity field conditions, river information, sedimentary system types, and clastic particle size composition, etc., to provide data support for determining the geological model;

[0008] S2: Model construction: Based on the data collected in S1, determine the geological model and physical model. When determining the physical model, reasonably select the geometric scale and time scale of the model and the prototype to ensure the matching of the flow field and grain size, clarify the movement characteristics of the movable bottom plate and the model experiment level;

[0009] S3: Acquisition of model experiment data: Conduct experiments on the clastic sedimentary rock deposition model, and collect data of various physical processes during the experiment at the same time;

[0010] S4: Establish a numerical model: Establish a three-dimensional numerical model of clastic sedimentary rock deposition according to S3. In the model, fully consider various physical processes during sedimentation, such as particle transportation, settlement, accumulation, etc., and the interaction between different physical quantities;

[0011] S5: Model parameter calibration: Calibrate the parameters of the established numerical model using actual sedimentation data. By comparing the model simulation results with actual sedimentation data, such as sedimentation thickness and sand body distribution in different geological periods, adjust the parameters in the model to make the model more accurately reflect the actual sedimentation process;

[0012] S6: Simulation prediction: Input different sedimentation conditions on the calibrated numerical model to conduct numerical simulation and predict the deposition rate of clastic sedimentary rock at different time periods and different locations;

[0013] S7: Model verification and optimization: Use independent actual sedimentation data to verify the calculated deposition rate and evaluate the accuracy and reliability of the model;

[0014] If there is a large deviation between the model prediction result and the actual data, analyze the reasons and further optimize the model, such as improving the description of the physical process of the model, adjusting the model parameters, etc., until the model can meet the requirements of high-precision deposition rate prediction.

[0015] Preferably, the preparation stage more specifically includes comprehensively considering basin boundary conditions, such as size, slope, water depth, tectonic movement intensity, waves, base level changes, etc.; flow velocity field conditions, including flow rate, flow velocity, sediment concentration, etc.; the scale and distribution of rivers flowing into lakes or seas; the types of sedimentary systems; the particle size composition of clastic particles, etc. parameters to construct an accurate geological model.

[0016] Preferably, grasp the key factors forming the sedimentary system in nature, ignore the secondary factors, and determine the geometric scale and time scale of the model and the prototype;

[0017] According to the formula determine the length scale, where L H is the length of a certain part of the prototype, and L m is the length of the corresponding part of the model;

[0018] According to the formula determine the flow velocity scale, where V H is the flow velocity of a certain point in the prototype water flow, and V m is the flow velocity of the corresponding point in the model water flow; Achieve a reasonable matching of the flow field and particle size, clarify the movement characteristics of the movable bottom plate and the level of the model experiment, and ensure that the physical model can reflect the main aspects of the clastic sedimentation system.

[0019] Preferably, with the length scale λ L , the area scale and volume scale can be derived from it. Since the area is the square of the length, the area scale is:

[0020]

[0021] Similarly, since the volume is the cube of the length, the volume scale is:

[0022]

[0023] Preferably, let V H be the flow velocity of a certain point in the prototype water flow, and V m be the flow velocity of the corresponding point in the model water flow, then the flow velocity scale is:

[0024]

[0025] where λ t is the time scale. With the flow velocity scale λ v , the acceleration scale can be derived therefrom.

[0026] Preferably, let the gravity acting on the corresponding points of the prototype and model water flows be G g , G m , and the viscous force be R H , R m , then the force scale is:

[0027]

[0028] Since G = ρ·g·V and R = L·ρ··ν, so:

[0029]

[0030] Preferably, the numerical model fluid mass conservation formula is:

[0031]

[0032] where ρ is the fluid density and t is the time, and is the fluid velocity vector.

[0033] Preferably, the particle motion equation:

[0034]

[0035] m p is the particle mass is the particle velocity, F D is the drag force, F g is the gravitational force, F i is the lift force.

[0036] Preferably, the calculation formula for the sedimentation rate of clastic sedimentary rock:

[0037]

[0038] where Δh is the change in sediment thickness and Δt is the time interval.

[0039] Advantages of the preparation of the present invention:

[0040] High-precision prediction: Considering various geological data comprehensively, accurately constructing a model and carefully calibrating parameters, significantly improving the prediction accuracy, and providing reliable data for subsequent research and applications.

[0041] Fully reflecting the physical process: When establishing a numerical model, fully considering physical processes such as particle transportation and sedimentation and the interactions between physical quantities, accurately depicting the sedimentation process with a mathematical model, and the simulation results are closer to reality.

[0042] Flexibly adapting to different scenarios: Reasonably selecting the geometric scale and time scale, being able to flexibly construct a physical model according to different regions and requirements, effectively matching the flow field and particle size, and improving the applicability of the method.

[0043] Continuous optimization and reliability: Calibrating, validating, and optimizing the model with actual data to form a continuously improving closed loop. When there are model deviations, targeted improvements can be made to ensure reliability and long-term effectiveness. Specific implementation manners

[0044] The present invention will be further explained below with specific embodiments.

[0045] In this embodiment, a high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation is proposed, including the following steps:

[0046] S1: Preparation stage: Collect geological data of the target area, including basin boundary conditions, flow velocity field conditions, river information, sedimentary system types, and clastic particle size composition, etc., to provide data support for determining the geological model;

[0047] S2: Model construction: Based on the data collected in S1, determine the geological model and physical model. When determining the physical model, reasonably select the geometric scale and time scale between the model and the prototype to ensure the matching of the flow field and grain size, clarify the movement characteristics of the movable bottom plate and the model experiment level;

[0048] S3: Acquisition of model experiment data: Conduct experiments on the clastic sedimentary rock sedimentation model, and simultaneously collect data on various physical processes during the experiment;

[0049] S4: Establish a numerical model: Establish a three-dimensional clastic sedimentary rock sedimentation numerical model according to S3. In the model, fully consider various physical processes during sedimentation, such as particle transportation, settlement, accumulation, etc., and the interaction between different physical quantities;

[0050] S5: Model parameter calibration: Calibrate the parameters of the established numerical model using actual sedimentation data. By comparing the model simulation results with actual sedimentation data, such as sedimentation thickness and sand body distribution in different geological periods, adjust the parameters in the model to make the model more accurately reflect the actual sedimentation process;

[0051] S6: Simulation prediction: Input different sedimentation conditions on the calibrated numerical model, conduct numerical simulations, and predict the sedimentation rates of clastic sedimentary rocks at different time periods and different locations;

[0052] S7: Model verification and optimization: Use independent actual sedimentation data to verify the calculated sedimentation rate, and evaluate the accuracy and reliability of the model;

[0053] If there is a large deviation between the model prediction results and the actual data, analyze the reasons and further optimize the model, such as improving the description of the physical process of the model, adjusting the model parameters, etc., until the model can meet the requirements of high-precision sedimentation rate prediction.

[0054] In this embodiment, the preparation stage more specifically includes comprehensively considering basin boundary conditions, such as size, slope, water depth, tectonic movement intensity, waves, base level changes, etc.; flow velocity field conditions, including flow rate, flow velocity, sediment concentration, etc.; the scale and distribution of rivers flowing into lakes or seas; the type of sedimentary system; parameters such as the particle size composition of clastic particles, etc., so as to construct an accurate geological model, grasp the key factors for the formation of sedimentary systems in nature, ignore secondary factors, and determine the geometric scale and time scale between the model and the prototype;

[0055] According to the formula Determine the length scale, where LH is the length of a certain part of the prototype, L m is the length of the corresponding part of the model;

[0056] According to the formula the velocity scale is determined, where V H is the velocity of a certain point of the prototype water flow, V m is the velocity of the corresponding point of the model water flow; Achieve a reasonable match between the flow field and the particle size, clarify the movement characteristics of the movable bottom plate and the level of the model experiment, and ensure that the physical model can reflect the main aspects of the clastic sedimentary system.

[0057] With the length scale λ L , the area scale and volume scale can be derived from it. Since the area is the square of the length, the area scale is:

[0058]

[0059] Similarly, since the volume is the cube of the length, the volume scale is:

[0060]

[0061] Let V H be the velocity of a certain point of the prototype water flow, V m be the velocity of the corresponding point of the model water flow, then the velocity scale is:

[0062]

[0063] where λ t is the time scale. With the velocity scale λ v , the acceleration scale can be derived therefrom.

[0064] Preferably, let the gravity acting on each corresponding point of the prototype and model water flows be G g , G m , and the viscous force be R H , R m , then the force scale is:

[0065]

[0066] Since G = ρ·g·V, R = L·ρ··ν, so:

[0067]

[0068] Preferably, the numerical model fluid mass conservation formula is:

[0069]

[0070] where ρ is the fluid density, t is the time, is the fluid velocity vector.

[0071] In this embodiment, the particle motion equation:

[0072]

[0073] m p is the particle mass is the particle velocity, F D is the drag force, F g is the gravitational force, F i is the lift force.

[0074] Preferably, the calculation formula for the sedimentation rate of clastic sedimentary rocks:

[0075]

[0076] where Δh is the change in sediment thickness and Δt is the time interval.

[0077] The present invention comprehensively considers various geological data such as basin boundary conditions, flow velocity field conditions, river information, sedimentary system types, and the particle size composition of clastic bodies. Through precise model construction and meticulous parameter calibration, the prediction accuracy of the sedimentation rate of clastic sedimentary rocks is significantly improved. Compared with traditional methods, it can more accurately reflect the changes in sedimentation rates at different time periods and different locations, providing a more reliable data basis for subsequent research and applications. When establishing a numerical model, various physical processes such as particle transportation, settlement, and accumulation during the sedimentation process, as well as the interactions between different physical quantities, are fully considered. By using mathematical models such as the fluid mass conservation formula and the particle motion equation, the sedimentation process is accurately depicted, making the simulation results closer to the actual situation.

[0078] By reasonably selecting the geometric scale and time scale of the model and the prototype, a physical model can be flexibly constructed according to different target regions and research requirements. Whether it is a simple sedimentary environment or a complex geological structure area, the matching of the flow field and particle size can be effectively achieved, ensuring that the model can reflect the main characteristics of the clastic sedimentary system, improving the applicability of the method. Using actual sedimentation data to calibrate, verify, and optimize the model forms a continuously improved closed-loop system. When there is a deviation between the model prediction results and the actual data, the physical process description of the model can be improved or the model parameters can be adjusted until the high-precision sedimentation rate prediction requirements are met, ensuring the reliability and long-term effectiveness of the model.

[0079] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation, characterized in that It includes the following steps: S1: Preparation stage: Collect geological data of the target area, including basin boundary conditions, flow velocity field conditions, river information, sedimentary system types, and clastic particle size composition, etc., to provide data support for determining the geological model; S2: Model construction: Determine the geological model and physical model according to the data collected in S1. When determining the physical model, reasonably select the geometric scale and time scale of the model and the prototype to ensure the matching of the flow field and grain size, clarify the movement characteristics of the movable bottom plate and the model experiment level; S3: Acquisition of model experiment data: Conduct experiments on the clastic sedimentary rock sedimentation model, and at the same time collect data of various physical processes during the experiment; S4: Establish a numerical model: Establish a three-dimensional clastic sedimentary rock sedimentation numerical model according to S3. In the model, fully consider various physical processes during sedimentation, such as particle transportation, settlement, accumulation, etc., and the interaction between different physical quantities; S5: Model parameter calibration: Calibrate the parameters of the established numerical model using actual sedimentation data. By comparing the model simulation results with actual sedimentation data, such as sedimentation thickness and sand body distribution in different geological periods, adjust the parameters in the model to make the model more accurately reflect the actual sedimentation process; S6: Simulation prediction: Input different sedimentation conditions on the calibrated numerical model to conduct numerical simulations and predict the sedimentation rates of clastic sedimentary rocks at different time periods and different locations; S7: Model verification and optimization: Use independent actual sedimentation data to verify the calculated sedimentation rate, and evaluate the accuracy and reliability of the model; If there is a large deviation between the model prediction result and the actual data, analyze the reasons and further optimize the model, such as improving the description of the physical process of the model, adjusting the model parameters, etc., until the model can meet the requirements of high-precision sedimentation rate prediction.

2. A high-precision sedimentation rate prediction method based on sediment numerical simulation of clastic sedimentary rocks according to claim 1, characterized in that More specifically, the preparation stage includes comprehensively considering basin boundary conditions, such as size, slope, water depth, tectonic movement intensity, waves, base level changes, etc.; Flow velocity field conditions, including flow rate, flow velocity, sediment concentration, etc.; the scale and distribution of rivers flowing into lakes or seas; the types of sedimentary systems; parameters such as the grain size composition of clastic particles, so as to construct an accurate geological model.

3. A high-precision sedimentation rate prediction method based on numerical simulation of detrital sedimentary rock sedimentation according to claim 1, characterized in that Grasp the key factors forming the sedimentary system in nature, ignore the secondary factors, and determine the geometric scale and time scale of the model and the prototype; According to the formula to determine the length scale, where L H is the length of a certain part of the prototype, and L m is the length of the corresponding part of the model; According to the formula to determine the flow velocity scale, where V H is the flow velocity of a certain point in the prototype water flow, and V m is the flow velocity of the corresponding point in the model water flow; to achieve a reasonable matching of the flow field and grain size, clarify the movement characteristics of the movable bottom plate and the levels of the model experiment, and ensure that the physical model can reflect the main aspects of the clastic sedimentation system.

4. A high-precision sedimentation rate prediction method based on numerical simulation of detrital sedimentary rock sedimentation according to claim 2, characterized in that, With the length scale ratio λ L , the area scale ratio and volume scale ratio can be derived from it. Since area is the square of length, the area scale ratio is as follows: Similarly, because volume is the cube of length, the volume scale is:

5. A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation according to claim 4, characterized in that, The said V H is the flow velocity at a certain point of the prototype water flow, and V m is the flow velocity at the corresponding point of the model water flow. Then the velocity scale is as follows: where λ t is the time scale. With the flow velocity scale λ v , the acceleration scale can be derived therefrom.

6. A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation according to claim 5, characterized in that: Let the gravity acting on the corresponding points of the prototype and the model water flow be G g and G m and the viscous force be R H and R m Then the scale of force is as follows: Because G = ρ·g·V, R = L·ρ··ν, so:

7. A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation according to claim 1, characterized in that: The fluid mass conservation formula of the numerical model is: where ρ is the fluid density and t is the time, is the fluid velocity vector.

8. A high-precision sedimentation rate prediction method based on numerical simulation of clastic sedimentary rock sedimentation according to claim 1, characterized in that Particle motion equation: m p is the particle mass is the particle velocity, F D is the drag force, F g is the gravitational force, F i is the lift force.

9. A high-precision sedimentation rate prediction method based on numerical simulation of detrital sedimentary rock sedimentation according to claim 8, characterized in that Clastic sedimentary rock sedimentation rate calculation formula: Where Δh is the change in sedimentation thickness and Δt is the time interval.

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