Paleo-flow direction and paleo-slope calculation and optimization method based on preproduct body

Abstract

The invention discloses a paleo-flow direction and paleo-slope calculation and optimization method based on a preproduct body, and belongs to the field of oil and gas field geological exploration, and the method comprises the steps: collecting three-dimensional seismic data and logging data of a research area; identifying a preproduct structure, and generating a preproduct body thickness map; at the thinnest edge of the thickness map, tangent lines are made for all points, and the points are connected to the deposition center in the direction perpendicular to the tangent lines. Calculating paleo-gradient directions and paleo-flow directions corresponding to different connecting lines; flattening the marker bed, calculating apparent thickness values of different positions of the preaccrete at equal intervals, and fitting a preaccrete interpretation horizon trend line; any point on the trend line is selected to calculate the apparent thickness value and the horizontal distance between the point and the intersection point, and decompaction correction is carried out to obtain the real thickness; calculating ancient gradients of the preaccretes in different directions in the same period, and preferably selecting a main gradient and a main flow direction; according to the method, the paleo-flow direction and the paleo-gradient can be rapidly and quantitatively calculated, and an optimization algorithm is further provided to provide a basis for researching migration and evolution characteristics of a preproduct.

Description

Technical Field

[0001] The present invention belongs to the field of petroleum exploration and development, and in particular relates to a method for calculating and optimizing paleoflow direction and paleo-slope based on a foreset body. Background Art

[0002] Determining paleoflow direction and paleo-slope is a core issue in paleogeomorphology and a current research hotspot. Changes in paleoflow direction and paleo-slope determine the primary provenance direction and distribution of sedimentary systems within a sedimentary basin at a given time. They also control the size and distribution of sand bodies, making them crucial for predicting the distribution of favorable reservoirs, optimizing injection and production strategies, and improving oil recovery. Currently, commonly used paleoflow direction analysis methods can be broadly categorized into macroscopic and microscopic approaches. Macroscopic methods primarily include heavy mineral analysis, rock composition analysis, variations in sandstone content, and channel architecture analysis. For example, patent CN117784224 A discloses a paleoflow direction analysis method based on paleochannel architecture and seismic geomorphological analysis. In Petroleum Geophysical Exploration, Vol. 49, No. 6, 2014, Wu Xuguang et al. determined paleoflow direction in the study area based on variations in sandstone content using 3D seismic inversion. In the 2017 issue of the Journal of Sedimentology, Volume 35, Issue 1, Pan Jie et al. analyzed the provenance and paleocurrent direction of Chang 7-8 in the Jinghe Oilfield, combining light and heavy mineral separation tests and debris composition in drilled sandstone cores. Microscopic research methods primarily include field outcrop observation and measurement, oriented arrangement analysis of gravels and elongated fossils, formation dip logging, electrical imaging logging, and magnetic susceptibility anisotropy. For example, patent CN108564610 B discloses a method and apparatus for calculating paleocurrent direction angles in sedimentary bodies. Patent CN117761792 A discloses a method and apparatus for calibrating dip logging data, building models, and determining paleocurrent direction. Patent CN108693562 B discloses a method for determining paleocurrent direction using electrical imaging logging. In the 2024 issue of Earth Science Frontiers, Volume 31, Issue 3, Liu Chiheng et al. conducted field measurements on sedimentary structures and then used Stereonet 11 software to project the raw paleoflow direction data, compiling paleoflow direction rosettes and calculating their dominant orientations. These methods can provide qualitative and semi-quantitative paleoflow direction information, but they also have limitations. Analysis of heavy minerals, rock composition, and sandstone percentages can only determine approximate paleoflow direction. While covering a wide area, they lack high accuracy and require a large amount of test data, making them unsuitable for detailed studies. Formation dip logging and electrical imaging logging are suitable for areas without outcrop data or core samples. They can quantitatively determine paleoflow direction with high accuracy, but are severely limited by the logging data and are expensive, making them unsuitable for large-scale paleoflow direction measurements. Paleoslope is the inclination of paleotopography relative to the horizontal and is a vector unit. Currently, paleoslope calculation methods primarily include structural analysis and sedimentological analysis. For example, patent CN105137482 B discloses a method for calculating the paleoslope of sedimentary bodies. Patent CN106383369 A discloses a method for calculating the paleoslope of slope belts. Patent CN106842293 A discloses a method for restoring balanced profiles based on the paleotopographic characteristics of delta fronts. Patent CN118296094A discloses a method, device, and storage medium for quantitative characterization of paleogeomorphology.In the 2014 issue of Petroleum Geophysical Exploration, Vol. 49, No. 5, Li Huiqiong et al. calculated a maximum paleo-slope of 1.7° during the Yanchang Formation depositional period based on foreset reflections. In the 2019 issue of Vol. 37, No. 4, Zhao Chenfan et al. reconstructed the paleo-slope of the study area using the "compaction simulation to calculate paleo-thickness" method. These methods are primarily qualitative and semi-quantitative, measuring paleo-slope solely by slope magnitude and lacking directional calculations. Furthermore, some quantitative methods are complex, requiring extensive drilling and analysis of test data to first calculate paleo-water depth or paleo-thickness before deriving paleo-slope. This computational process is tedious and costly. More importantly, the migration and evolution of foreset bodies result in distinct variations in paleoflow direction and paleo-slope. The prevailing direction and main slope of foreset bodies directly reflect the dynamic processes of sediment deposition and the evolution of the sedimentary environment. The prevailing direction reveals the primary direction of sediment transport, while the main slope represents the inclination of the sedimentary bedding plane. Existing research has mostly focused on obtaining numerical values ​​for paleoflow direction and paleo-slope, but has failed to effectively select the main flow direction and main slope that reflect sedimentary characteristics. Therefore, how to select the main flow direction and main slope from a multitude of flow direction and slope data has become a key issue in sedimentological research.

[0003] After analyzing the above existing technologies, the following problems were found: (1) At present, the calculation of paleocurrent direction is mainly based on qualitative and semi-quantitative methods. Previous researchers have focused on qualitative analysis through heavy mineral and rock composition analysis and the percentage method of sandstone content, or directly used formation dip logging and electrical imaging logging methods for quantitative calculation. These methods require a large amount of analytical test data and logging data, which are costly and not suitable for large-scale paleocurrent direction measurement. (2) The calculation of paleo-slope usually relies on a large amount of drilling and logging data. First, the paleo-water depth or paleo-thickness of the formation is calculated, and then the paleo-slope is deduced from these data. The calculation process is cumbersome and the data requirements are high. In addition, most existing studies only focus on the changes in the size of the slope, but ignore the calculation of the slope direction, and fail to achieve a comprehensive quantitative description of the paleo-slope. (3) Due to the continuous migration and evolution of the foreset body, the paleo-slope and paleo-current direction themselves have variable characteristics. Existing research has largely focused on directly determining paleoflow direction and paleogradient values ​​using large amounts of data. However, there is a lack of research on how to optimize the primary direction and primary slope from this data, making it difficult to reveal the dominant characteristics of sediment transport and sedimentary environmental evolution. Therefore, how to quickly, accurately, and cost-effectively calculate paleoflow direction and paleogradient based on only a small amount of exploration data, and then optimize the primary direction and primary slope that reflect sedimentary characteristics, remains a pressing challenge in current technology. Summary of the Invention

[0004] The present invention aims to provide a method for calculating and optimizing paleoflow direction and paleogradient based on foreset bodies, addressing the existing limitations of rapid and accurate quantitative calculation of paleoflow direction and paleogradient, particularly the optimization of the primary direction and primary slope that reflect sedimentary characteristics. This method combines 3D seismic data with a limited amount of well logging data. By carefully interpreting multiple foreset bodies within the exploration area and generating maps of their thickness, the method quantitatively calculates paleoflow direction and paleogradient, and optimizes the primary direction and primary slope. Unlike traditional methods that rely on extensive analytical testing and drilling data, this method can rapidly and accurately calculate paleoflow direction and paleogradient based on a limited amount of exploration data, significantly reducing costs and making it particularly suitable for study areas with only 3D seismic and limited well logging data. Furthermore, by precisely calculating the slope direction, the present invention overcomes the existing limitations of only calculating the slope magnitude, achieving a comprehensive quantitative description of the paleogradient. Furthermore, a primary direction and primary slope optimization algorithm is proposed to efficiently select the primary direction and primary slope that align with sedimentological characteristics from a large amount of flow direction and slope data, more accurately reflecting the direction of sediment transport and the evolution of the sedimentary environment.

[0005] The technical solution adopted by the present invention is: A method for calculating and optimizing paleoflow direction and paleo-slope based on a foreset body comprises the following steps: Step 1: Collect 3D seismic data and well logging data in the study area; Step 2: Perform detailed interpretation of the acquired 3D seismic data, use foreset body interface recognition technology to accurately identify the delta progradational structure within the target interval, and generate a foreset body thickness map; Step 3: Draw tangent lines through multiple points on the thinnest edge of the slope bottom of the progradational body thickness map, and connect them to the sedimentary center perpendicular to the tangent lines; calculate the paleoslope direction corresponding to different connecting lines. α w Hegu Flow θ j ; Step 4: Flatten the marker layer of the seismic profile in the vertical tangent direction in step 3, and then calculate the apparent thickness values ​​of different positions of the foreset body at equal intervals from the top to the bottom of the slope along the provenance direction, and fit the trend line of the interpreted horizon of the foreset body; The marker layer refers to a set of widely and stably developed Chang 7 black shales, which show the seismic response characteristics of "one black, two red, one peak and two valleys" on the seismic profile.

[0006] Step 5: Extend the fitted interpretation horizon trend line and compare it with the flattened marker layer ( X Axis) intersects at a certain point, and the apparent thickness value is calculated at any point on the trend line. h i and the horizontal distance from the intersection point L i; Then, the apparent thickness is corrected for compaction based on the logging data to obtain the true thickness. h s ; Step 6: Repeat steps 3 to 5 to calculate the paleoslope of the foreset in different directions during the same period. S i , and optimize the main slope S max ; The corresponding direction is the direction of the main slope; (1) Where: S i is the paleo-slope value in different directions; h s 、 L i are the true thickness and the corresponding horizontal distance, m; (2) The preferred main slope S max It is mainly based on the fact that the maximum slope value corresponds to the steepest inclination of the sediment in that direction, reflecting the area with the most active sediment or the strongest flow.

[0007] Step 7: In the series of paleocurrent directions obtained θ j Among them, the mainstream direction is selected.

[0008] Furthermore, in step 2, the foreset interface identification technology specifically refers to combining the concept of foreset reflection with the characteristics of seismic reflection events to identify and track each period of foreset on the seismic profile. Its basic principles are as follows: 1) Easy to track. Based on the quality of 3D seismic data, the continuity of the black peak phase axis is generally better than that of the red trough, so we choose to track the black peak; 2) Closed interpretation. In any direction, the interpretation of the foreset must be closed, ensuring that each foreset is a single-layer sheet structure in space; 3) Identify the top and bottom interfaces of each foreset. The top interface is a continuous black crest event with strong reflections, often showing onlap or truncation. The bottom interface is a continuous red trough event in contact with the underlying Chang 7 crest event.

[0009] Furthermore, in step 3, due to the anisotropic nature of the terrain, the slope of any point in different directions varies. Therefore, only the slope obtained along the provenance direction can accurately reflect the characteristics of the foreset. The specific steps are as follows: Step 3-1: Determine the location with the greatest stratum thickness as the deposition center; Step 3-2: Draw tangent lines along the thinnest edge of the slope bottom of the foreset thickness map through multiple points; Step 3-3: Draw a perpendicular line to each tangent line in step 3-2 and connect each perpendicular line to the sedimentary center. The azimuth of the perpendicular line is the paleo-slope direction of the foreset body, which represents the sedimentary characteristics of the foreset body.

[0010] Furthermore, in step 4, the specific steps for fitting the foreset body to interpret the horizon trend line are as follows: Step 4-1: Flatten the marker layer of the target layer of the seismic section and measure the total horizontal length of the foreset on the seismic section L 总 ; Step 4-2: L 总 Divide into several parts at intervals of 1000 meters, with the separation points being x i , read each x i The time of the interpretation horizon line of the foreset body corresponding to the point t 1i Corresponding time to the bottom marking layer t 2i , put these parameters into formula (3) to calculate the apparent thickness of the progradation body at different positions on the seismic section h i : (3) Where, h i is the apparent thickness of the progradational body at different positions on the seismic section, m; t 1i Time to interpret horizon lines for the foreset body, ms; t 2i is the time corresponding to the bottom marking layer, ms; v is the stratigraphic velocity, m / s; Step 4-3: Use the least squares method to fit the series of data points obtained in Step 4-1 and Step 4-2 ( x i , h i ), a trend line is fitted, namely the trend line of the foreset body interpretation horizon, and the fitting form is shown in formula (4): (4) Where, x i is the position coordinate, m; h i is the apparent thickness of the foreshortened volume at the corresponding position, m; According to the definition of the least squares method, the sum of the squares of the vertical distances (i.e., errors) of all data points to the trend line is minimized, as shown in formula (5): (5) By taking the derivative of formula (5) with respect to a and b and setting the derivative results to zero, we can obtain the solution that minimizes the sum of squared errors of parameters a and b, as shown in formulas (6) and (7): (6) (7) Compared to the linear derivative method, the least squares method for trend line fitting can optimize the process by balancing the errors between different data points and avoiding the influence of individual outliers on the overall trend line. The calculation process is simple and efficient, especially when dealing with large amounts of data, and can process large amounts of data and produce results in a short time.

[0011] Furthermore, in step 5, the de-compaction correction includes the following steps: Step 5-1: Statistically study the measured porosity of multiple wells in the target layer and fit the Φ-H relationship curves of sandstone and mudstone respectively. The relationship is: (8) Where: Ф For depth H Porosity at is the initial porosity of the surface; C is the compaction coefficient; H is the burial depth of the stratum, m; Step 5-2: Based on the principle of the rock skeleton volume invariance model, the rock skeleton integral equation is constructed, and the original formation thickness is iteratively calculated. Then, the compaction rate T of the single well is calculated based on the ratio of the original formation thickness to the current formation thickness. (9) (10) Where: H and h are the current top surface burial depth and thickness of the stratum, in meters; M and N are the top surface burial depth and thickness of the stratum after compaction recovery, in meters; Z is the depth variable; Step 5-3: Compaction rate T and apparent thickness h i Multiply them together to get the true thickness after de-compaction correction, that is: (11) Where, h s It is the true thickness after de-compaction correction.

[0012] Furthermore, in step seven, the specific operation steps of optimizing the main flow direction are as follows: Step 7-1: Obtain paleocurrents in multiple directions θ j , j=1,2.....m, m represents the number of paleocurrent directions; Step 7-2: Establish a probability density model for paleocurrent direction, as shown in formula (12): (12) Where: θ j It is the ancient flow direction; μ is the mean direction of the distribution (i.e., the mainstream direction); k is the concentration parameter, which indicates that the paleocurrent data is centered around the maximum main flow direction. μ The degree of aggregation (k>0, the larger the more concentrated); I 0( k ) is the zero-order modified Bessel function, which ensures the normalization of the model; Step 7-3: Construct the likelihood function of the sample data points: (13) Step 7-4: Take the logarithm of both sides of formula (13), construct the log-likelihood function, and determine the model parameters μ and k : (14) Step 7-5: Respectively analyze the formula (14) about μ and k Find the partial derivative and set it to zero to get the optimal μ and k The value is then used to calculate the main flow direction, namely: (15) (16) Where: I 1 ( k ) is a first-order modified Bessel function.

[0013] Compared with traditional flow direction analysis methods (such as the mean and maximum methods), this method is not only more accurate but also consistent and asymptotically unbiased over large sample sizes. As the sample size increases, the calculated results become closer to the true values. Furthermore, the paleocurrent probability density model, built on a large amount of paleocurrent data, fully accounts for the periodicity and concentration of water flow directions, making the flow direction analysis more reliable.

[0014] Beneficial effects of the present invention: (1) Compared with previous studies, the calculation of paleoflow direction and paleogradient requires a large amount of seismic data, drilling data, and analytical test data, resulting in a complex calculation process and high cost. The present invention only requires a small amount of 3D seismic data and drilling data. By carefully interpreting multiple sets of foreset bodies in the exploration area, a foreset body thickness map is generated, paleoflow direction and paleogradient are quantitatively calculated, and the main flow direction and main slope are optimized. The calculation process is simple, fast, and low-cost, which improves exploration efficiency and has good promotion and application value. It is particularly suitable for research blocks with only 3D seismic data and a small amount of well logging data.

[0015] (2) Based on the detailed interpretation of the progradational structure of the 3D seismic volume and the generation of a progradational volume thickness map, the present invention not only calculates the magnitude of the paleo-slope but also the slope direction by fitting the interpreted horizon trend lines of the progradational volume. This overcomes the limitation of existing technologies that can only calculate the magnitude of the slope and provides a comprehensive quantitative description of the paleo-slope.

[0016] (3) The algorithm for optimizing the main flow direction and main slope proposed in this paper solves the problems of low efficiency and insufficient accuracy of traditional methods when processing large amounts of paleoslope and paleoflow direction data. Combining mathematical models and computational methods, it is possible to efficiently screen large amounts of flow direction and slope data and accurately identify the main flow direction and main slope that reflect sedimentary characteristics. This helps to better understand the direction of sediment transport and the evolution of the sedimentary environment, providing strong technical support for the exploration and development of oil and gas resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flowchart of the method of the present invention; Figure 2 Schematic diagram of the calculation method of the paleoslope direction and paleocurrent direction of the foreset body of the present invention; Figure 3 Interface diagram of the precursor body (F1) identified by the present invention; Figure 4 This is a schematic diagram of obtaining the trend line of the interpreted horizon of the foreset body of the present invention; Figure 5 This is a schematic diagram of the porosity-depth relationship of sandstone and mudstone fitted in the decompaction correction of the present invention; Figure 6 This is the single well sand and mudstone structure model and compaction rate calculation of the present invention. DETAILED DESCRIPTION

[0018] In this example, the three-dimensional seismic data of the Qingcheng area in the Ordos Basin were analyzed by using the foreset body interface recognition technology to identify the delta prosedition structure, generate a prosedition body thickness map, and calculate the paleoflow direction and paleo-slope direction. The least squares method was then used to fit multiple prosedition body interpretation layer trend lines along the provenance profile, and the decompaction correction technology was used to restore the original sedimentary thickness, calculate the paleo-slope size, and optimize the main slope. Finally, a probability density model for the paleoflow direction was established and the main flow direction was optimized. The present invention can quickly and accurately calculate the paleoflow direction and paleo-slope based on a small amount of exploration data, and optimize the main slope and main flow direction, which greatly reduces the exploration cost. It is particularly suitable for research blocks with only three-dimensional seismic data and a small amount of well logging data. It specifically includes the following steps (see Figure 1 ): Step 1: Collect 3D seismic data and well logging data in the Qingcheng area of ​​the Ordos Basin.

[0019] Step 2: Detailed interpretation of 3D seismic data, using foreset body interface recognition technology, accurately identifies the delta progradational structure within the target interval and generates a foreset body thickness map, including the following steps: 1) According to seismic sequence theory, a set of 20-40 m thick black-gray mud shale is developed at the bottom of Yanchang Formation 7. It is thick and has a stable lateral distribution. It appears as a set of strong amplitude continuous reflections on the seismic profile, serving as a marker layer.

[0020] 2) Identify the F1 interface of the foreset. The top interface is a continuous strong reflection seismic reflection feature (black crest), and truncation can be identified below the interface. The bottom interface is the red phase axis and the lower supercontact of the underlying long 7 crest phase axis (see Figure 3 ).

[0021] 3) Close the fine interpretation and generate the thickness map of the foreset body F1. The interpretation accuracy is 40*40, and the foreset body F1 appears as a single-layer sheet structure in space (see Figure 2 ).

[0022] Step 3: Draw tangent lines at each point on the thinnest edge of the slope bottom of the foreset body (F1) thickness map, and connect them to the sediment center perpendicular to the tangent line. Calculate the paleo-slope direction α corresponding to different connecting lines. w Hegu Flow θ j ; Taking the J-J' direction as an example, the specific steps are: 1) Determine the depocenter, that is, the location with the greatest stratum thickness is determined as the depocenter (the dark red part in the thickness map); 2) Draw a tangent line (Z-Z') at the thinnest point on the edge of the thickness map, and draw a section J-J' perpendicular to the tangent line toward the sedimentary center. The direction of this line is the paleo-slope direction of the foreset body, representing the sedimentary characteristics of the foreset body (see Figure 2 ); 3) On the plane map, the azimuth (α1) of the connecting line is calculated to be NE48.62°, which is the direction of the paleoslope of the foreset F1 (see Figure 2 ), the calculation results of paleo-slope in other directions are shown in Table 2.

[0023] Step 4: Flatten the marker layer of the seismic profile in step 3, and then calculate the apparent thickness of the foreset body at different positions at equal intervals from the top to the bottom of the slope along the provenance direction, and fit the trend line of the interpreted horizon of the foreset body. Taking a profile as an example, the specific steps are as follows: 1) Flatten the marker layer 7 on the seismic profile and measure the total horizontal length of the foreset L 总 =33000m.

[0024] 2) L 总 Divide into 33 parts every 1000 meters, with the division points as follows: x i . Read each x i The time of the interpretation horizon line of the foreset body corresponding to the point t 1i Corresponding time to the bottom marking layer t 2i , these parameters are brought into formula (3), where the formation velocity v The apparent thickness of the progradation body at different positions on the seismic section is calculated to be 3800m / s. h i , as shown in Table 1.

[0025] Table 1. Apparent thickness distribution data of different parts of the foreset body F1 (section J-J') ; 3) Substitute the above data points into formulas (4) to (7) to calculate the trend line equation parameters a = -0.0086, b = 273.75. This is the trend line equation for the pre-sedimentary interpretation horizon, see formula (17). Fit the trend line, see Figure 4 .

[0026] (17) Step 5: Extend the fitted interpretation horizon trend line to the flattened marker layer ( X Axis) intersects at a point (31831,0), and the apparent thickness value is calculated by taking a point on the trend line. h i =190.15m horizontal distance from the intersection point L i = 20831m. Then, the apparent thickness is corrected for compaction by combining the well logging data to obtain the true thickness.h s The specific steps are: 1) Statistics were collected for the measured porosity and corresponding depth values ​​of the sandstone and mudstone sections of Chang 3 to Chang 7 in 6 wells in the Qingcheng work area, and the porosity of the sandstone and mudstone was fitted according to the exponential function distribution. Ф~H Burial depth relationship curve (see Figure 5 ).

[0027] Fitting the porosity of sand and mudstone Ф and burial depth H The relationship equation is as follows: sandstone: (18) Mudstone: (19) 2) Based on the principle of the “rock skeleton volume invariance” model, the rock skeleton integral equation (Formula (9)) is constructed, and the original formation thickness of the single well X1 is iteratively calculated. Then, based on the ratio of the original formation thickness to the current formation thickness (Formula (10)), the compaction rate T of the single well X1 is calculated to be 1.24 (see Figure 6 ).

[0028] 3) Compare the compaction rate T with the corresponding apparent thickness h i Multiply to get the true thickness after de-compaction correction h s ,Right now: (20) Where: T is the compaction rate; h s is the true thickness; h i is the measured apparent thickness; Step 6: Calculate the paleo-slope of profile J-J' according to formula (21) S 1 is 0.63°, direction α 1 is NE48.62°. The paleo-slope calculation results of seismic profiles in other directions are shown in Table 2. According to formula (22), the maximum paleo-slope is 1.08°, and the slope direction is NE42.70°.

[0029] (twenty one) (twenty two).

[0030] Table 2 Calculation results of sedimentary paleo-slope and paleo-flow direction in the Qingcheng area of ​​the Ordos Basin ; Step 7: Based on a series of calculated paleocurrent directions θj The results are shown in Table 2. The main flow direction is optimized. The specific steps are: 1) Obtaining paleocurrents in multiple directions ( θ j ): θ 1=21.57°, θ 2=43.64°, ..., θ 16 =50.72° 2) Establish a probability density model for paleocurrent direction, see formula (12).

[0031] 3) Construct the log-likelihood function and determine the model parameters μ and k : (twenty three) 4) For the above formula about μ and k Find the partial derivative and get the optimal μ and k The value is then used to calculate the main flow direction, specifically: (twenty four) Obtain μ is 47.15°, then the main flow direction is 47.15°. K Find the partial derivative and set it to zero, that is: (25) Calculate and look up the table K is 3.25. K The larger the value, the more concentrated the current direction distribution is, and the selected paleocurrent direction is the mainstream direction of the foreset body.

Claims

1. A method for calculating and optimizing paleoflow direction and paleo-slope based on a foreset body, characterized in that: The following steps are involved: Step 1: Collect 3D seismic data and well logging data in the study area; Step 2: Perform detailed interpretation of the acquired 3D seismic data, use foreset body interface recognition technology to accurately identify the delta progradational structure within the target interval, and generate a foreset body thickness map; Step 3: Draw tangent lines through multiple points on the thinnest edge of the slope bottom of the progradational body thickness map, and connect them to the sedimentary center perpendicular to the tangent lines; calculate the paleoslope direction corresponding to different connecting lines. α w Hegu Flow θ j ; Step 4: Flatten the marker layer of the seismic profile in the vertical tangent direction in step 3, and then calculate the apparent thickness values ​​of different positions of the foreset body at equal intervals from the top to the bottom of the slope along the provenance direction, and fit the trend line of the interpreted horizon of the foreset body; Step 5: Extend the fitted interpretation horizon trend line and intersect it with the flattened marker layer at a certain point. Calculate the apparent thickness value at any point on the trend line. h i and the horizontal distance from the intersection point L i ; Then, the apparent thickness is corrected for compaction based on the logging data to obtain the true thickness. h s ; Step 6: Repeat steps 3 to 5 to calculate the paleoslope of the foreset in different directions during the same period. S i , and optimize the main slope S max , the corresponding direction is the direction of the main slope; （1） Where: S i is the paleo-slope value in different directions; h s 、 L i are the true thickness and the corresponding horizontal distance, m; （2） Step 7: In the series of paleocurrent directions obtained θ j Among them, the mainstream direction is selected.

2. The method for calculating and optimizing paleoflow direction and paleo-slope based on a progradational body according to claim 1, characterized in that: In step 2, the foreset body interface identification technology specifically refers to combining the concept of foreset reflection with the characteristics of seismic reflection event to identify and track the foreset bodies of each period on the seismic profile.

3. The method for calculating and optimizing paleoflow direction and paleo-slope based on a progradation body according to claim 1, characterized in that: In step three, the specific steps are as follows: Step 3-1: Determine the location with the greatest stratum thickness as the deposition center; Step 3-2: Draw tangent lines along the thinnest edge of the slope bottom of the foreset thickness map through multiple points; Step 3-3: Draw a perpendicular line to each tangent line in step 3-2 and connect each perpendicular line to the sedimentary center. The azimuth of the perpendicular line is the paleo-slope direction of the foreset body, which represents the sedimentary characteristics of the foreset body.

4. The method for calculating and optimizing paleoflow direction and paleo-slope based on a progradation body according to claim 1, characterized in that: In step 4, the specific steps for fitting the trend line of the foreset body interpretation horizon are as follows: Step 4-1: Flatten the marker layer of the target layer of the seismic section and measure the total horizontal length of the foreset on the seismic section L 总 ; Step 4-2: L 总 Divide into several parts at intervals of 1000 meters, with the separation points being x i , read each x i The time of the interpretation horizon line of the foreset body corresponding to the point t 1i Corresponding time to the bottom marking layer t 2i , put these parameters into formula (3) to calculate the apparent thickness of the progradation body at different positions on the seismic section h i : （3） Where, h i is the apparent thickness of the progradational body at different positions on the seismic section, m; t 1i Time to interpret horizon lines for the foreset body, ms; t 2i is the time corresponding to the bottom marking layer, ms; v is the stratigraphic velocity, m / s; Step 4-3: Use the least squares method to fit the series of data points obtained in Step 4-1 and Step 4-2 ( x i , h i ), a trend line is fitted, namely the trend line of the foreset body interpretation horizon, and the fitting form is shown in formula (4): （4） Where, x i is the position coordinate, m; h i is the apparent thickness of the foreshortened volume at the corresponding position, m; According to the definition of the least squares method, the sum of the squares of the vertical distances of all data points to the trend line is minimized, as shown in formula (5): （5） By taking the derivative of formula (5) with respect to a and b and setting the derivative results to zero, we can obtain the solution that minimizes the sum of squared errors of parameters a and b, as shown in formulas (6) and (7): （6） （7）。 5. The method for calculating and optimizing paleoflow direction and paleo-slope based on a progradation body according to claim 1, characterized in that: In step 5, the de-compaction correction includes the following steps: Step 5-1: Statistically study the measured porosity of multiple wells in the target layer and fit the Φ-H relationship curves of sandstone and mudstone respectively. The relationship is: （8） Where: Ф For depth H Porosity at is the initial porosity of the surface; C is the compaction coefficient; H is the burial depth of the stratum, m; Step 5-2: Based on the principle of the rock skeleton volume invariance model, the rock skeleton integral equation is constructed, and the original formation thickness is iteratively calculated. Then, the compaction rate T of the single well is calculated based on the ratio of the original formation thickness to the current formation thickness. （9） （10） Where: H and h are the current top surface burial depth and thickness of the stratum, in meters; M and N are the top surface burial depth and thickness of the stratum after compaction recovery, in meters; Z is the depth variable; Step 5-3: Compaction rate T and apparent thickness h i Multiply them together to get the true thickness after de-compaction correction, that is: （11） Where, h s It is the true thickness after de-compaction correction.

6. The method for calculating and optimizing paleoflow direction and paleo-slope based on a progradation body according to claim 1, characterized in that: In step seven, the specific operation steps of optimizing the main flow direction are as follows: Step 7-1: Obtain paleocurrents in multiple directions θ j , j=1,2.....m, m represents the number of paleocurrent directions; Step 7-2: Establish a probability density model for paleocurrent direction, as shown in formula (12): （12） Where: θ j It is the ancient flow direction; μ is the mean direction of the distribution; k is the concentration parameter, which indicates that the paleocurrent data is centered around the maximum main flow direction. μ the degree of aggregation; I 0( k ) is the zero-order modified Bessel function, which ensures the normalization of the model; Step 7-3: Construct the likelihood function of the sample data points: （13） Step 7-4: Take the logarithm of both sides of formula (13), construct the log-likelihood function, and determine the model parameters μ and k : （14） Step 7-5: Respectively analyze the formula (14) about μ and k Find the partial derivative and set it to zero to get the optimal μ and k The value is then used to calculate the main flow direction, namely: （15） （16） Where: I 1 ( k ) is a first-order modified Bessel function.

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