Reservoir prediction method based on sedimentary microfacies tracking

Abstract

The invention provides a reservoir prediction method based on sedimentary microfacies tracking. The reservoir prediction method comprises the following steps: step 1, establishing a sedimentary microfacies model; 2, reservoir prediction is conducted by means of the sedimentary microfacies model established in the step 1; 3, plane sedimentary microfacies distribution prediction is carried out; 4, sedimentary microfacies distribution prediction between well groups is carried out; 5, on the basis of distribution of the single well sedimentary microfacies, longitudinal sedimentary microfacies distribution between the single well and the well group is depicted; 6, reservoir distribution between single wells and well groups is determined; 7, plane representation wells in the well group are selected; and 8, predicting the influence of the sedimentary micro-relative reservoir. According to the method, the sedimentary microfacies model can be accurately established, reservoir distribution can be effectively predicted, and the reservoir prediction accuracy and efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and more specifically, to a reservoir prediction method based on sedimentary microfacies tracking. Background Art

[0002] In the field of oil and gas exploration and development, accurate prediction of the location and distribution of reservoirs is crucial to improving the efficiency of oil and gas extraction and reducing costs. Existing reservoir prediction methods mainly rely on technologies such as geological modeling, seismic interpretation, and logging data analysis. Although these methods can provide relevant information about reservoirs to a certain extent, they still have certain limitations. For example, traditional geological modeling methods often rely on the experience and subjective judgment of geologists, which may lead to increased uncertainty in prediction results. Although seismic interpretation technology can provide macroscopic distribution characteristics of reservoirs, it has limited ability to identify reservoir details at the microscopic level. Although logging data analysis can provide physical properties of downhole rocks, it is challenging in dealing with complex rock-electricity relationships and lithology identification.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the accuracy and efficiency of reservoir prediction in the prior art still need to be improved, especially in complex oil and gas reservoirs, how to accurately identify and predict the vertical and planar distribution of reservoirs, and how to effectively use logging data for quantitative analysis of reservoir characteristics are the main challenges faced by the current technology. In addition, the prior art often lacks sufficient flexibility and adaptability when dealing with rock-electricity relationships, and it is difficult to adapt to the reservoir prediction needs under different geological conditions. Summary of the invention

[0004] The present invention provides a reservoir prediction method based on sedimentary microfacies tracking, comprising: Step 1: Establish sedimentary microfacies model; Step 2: Use the sedimentary microfacies model established in step 1 to predict reservoirs; Step 3: Predict the planar sedimentary microfacies distribution; Step 4: Predict the distribution of sedimentary microfacies between well groups; Step 5: Based on the distribution of sedimentary microfacies of a single well, characterize the vertical distribution of sedimentary microfacies between single wells and well groups; Step 6: Determine the reservoir distribution between individual wells and well groups; Step 7: Selection of plane characterization wells within the well group; Step 8: Prediction of the impact of sedimentary microphases on the reservoir.

[0005] Furthermore, in step 1, the specific method for establishing the sedimentary microfacies model is: Correct the litho-electric depth values of the wells in the study area, and establish a litho-electric depth calculation model based on the formation depth and porosity data.

[0006] Establish a cross plot with lithology thickness as the x-axis and shale content as the y-axis, and conduct cross plot analysis based on this cross plot.

[0007] According to the results of the cross plot analysis, normalize the litho-electric depth values reflecting the reservoir characteristics. The normalization principle is: a (litho-electric value of shale) < b (litho-electric value of sandstone) < c (litho-electric value of oil-bearing shale) < d (litho-electric value of oil-bearing sandstone) < e (litho-electric value of pure sandstone).

[0008] Then, combined with the empirical formula, use the normalized litho-electric depth values to quantitatively identify sand bodies.

[0009] Then, use the cluster analysis method to merge different sand body types to obtain the sedimentary microfacies model of different study intervals.

[0010] Furthermore, the method for correcting the litho-electric depth values is as follows:

[0011] In the formula: is the average shale content of the reservoir; is the shale content of a certain lithology combination in the i-th well; is the average shale content of this well; is the average litho-electric value of pure shale; is the average litho-electric value of pure sandstone; is the average litho-electric value of oil-bearing shale; is the average litho-electric value of oil-bearing sandstone; is the average litho-electric value of the water layer.

[0012] Furthermore, for the formation depth and porosity data, using three density clustering methods based on the K-means algorithm, classify the lithology combinations with shale content less than 35% as pure sandstone, and classify the lithology combinations with shale content greater than 45% as pure shale; Classify the lithology combinations with shale content less than or equal to 35% and greater than or equal to 45% as oil-bearing shale combinations or oil-bearing sandstone combinations. Among them, the calculation formula for the shale content of the lithology combination is:

[0013] In the formula, is the weighted thickness of each layer of this well; is the weighted depth of each layer of this well; is the layer corresponding formation factor (shale content); For the layer The corresponding formation coefficient (mud content); is the formation coefficient length; is the acoustic time difference of each layer in the well; is the number of layers studied.

[0014] Furthermore, the specific steps of merging different sand body types using cluster analysis are: A. Normalize the stratigraphic data of the studied layer; B. Treat the normalized stratigraphic data as n d-dimensional (number of stratigraphic data) vectors in n-dimensional space, determine the average position of n sample points on d coordinate axes in n-dimensional space, and calculate the distance between each sample point and the average position; C. Find the point R farthest from the average position among the n sample points; D. Find the N points with the shortest distance from each point to point R; E. Use point R and these N points to represent all n points; F. Calculate the intra-class variance of N points as the variance of this class; G. Compare the variance of each category, and take out the samples in the category with the smallest variance and group them with R as a new group; H. Calculate the between-class variance matrix of the entire data set outside the new group; I. Continue to loop the calculation of GH for the remaining categories until all sample points are in one group.

[0015] Furthermore, in step 2, the specific method for reservoir prediction using the sedimentary microfacies model established in step 1 is: First, the logging data of the target well is processed to obtain the shale content curve, sand-mud ratio curve and rock-electric value curve corresponding to the studied layer; Then, according to the rock electrical depth model, the shale content curve is depth-corrected to obtain a curve showing the change of shale content with depth. Next, the lithology thickness curve of a single well is drawn by combining the mud content curve with the sand-mud ratio curve interpreted by well logging; Finally, the lithology thickness curve and the rock-electricity value curve are normalized and combined with the rock-electricity depth model to obtain the shale content and sand-mud ratio curve of a single well. Then, the normalized mud content curve and sand-mud ratio curve are intersected and analyzed to obtain the sedimentary facies pattern, which is then combined with the lithology thickness curve to invert and obtain the sedimentary microfacies model.

[0016] Furthermore, in step 3, the specific method for predicting the planar sedimentary microphase distribution is: Obtain seismic attribute values ​​at different points from the 3D seismic forward model and perform depth point correction; The corrected seismic attribute values ​​are intersected and analyzed with the mud content, sand-mud ratio and lithology thickness curves interpreted by well logging to obtain the sedimentary facies model, and the planar sedimentary microfacies model is inverted. Combined with the sedimentary microfacies model established in step 1, the sedimentary microfacies types of each logging point are compared and analyzed to obtain the distribution of sedimentary microfacies in each layer on the plane.

[0017] Furthermore, in step 4, the specific method for predicting the distribution of sedimentary microfacies between well groups is: The sedimentary microfacies distribution prediction calculation of a single well is carried out based on the well group, and the sedimentary microfacies distribution of the well group is obtained through the average sedimentary microfacies distribution prediction of the well group; Determine the distribution of sedimentary microfacies between individual wells and well groups; Specifically, in the prediction of single-well sedimentary microfacies distribution, the intersection analysis method is used to analyze the sedimentary microfacies of a single well to obtain the sedimentary microfacies distribution of each layer of a single well. Then, by averaging the sedimentary microfacies predictions of a single well, the average sedimentary facies distribution between well groups is obtained, that is, the sedimentary microfacies between well groups is obtained.

[0018] Furthermore, the method for determining the sedimentary microfacies distribution between a single well and a well group is: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical values ​​of a single well are depth-corrected, and then the corrected rock electrical values ​​are plane-distributed to obtain the sedimentary microfacies distribution map of each layer of the single well, that is, the sedimentary microfacies distribution map of the single well.

[0019] Furthermore, in step 5, based on the distribution of sedimentary microfacies in a single well, the specific method for describing the vertical sedimentary microfacies distribution between a single well and a well group is as follows: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical value of a single well is depth-corrected, and then the corrected rock electrical value is plane-distributed to obtain a sedimentary microfacies distribution map of the single well, i.e., a sedimentary microfacies distribution map of the single well; Then, according to the sedimentary microfacies distribution map of a single well, the vertical sedimentary microfacies distribution between well groups is carried out; specifically, the planar sedimentary microfacies maps of each layer are superimposed to obtain the vertical sedimentary microfacies distribution map between well groups; The logging data of multiple single wells in the well group are combined to obtain the mud content curve, rock electrical value curve and sand-mud ratio curve of the well group; Orthogonal analysis was performed to obtain the cross plots of shale content and rock electrical value, and shale content and sand-mud ratio. The curves of shale content changing with rock electrical value and sand-mud ratio were obtained by fitting the cross plots. The average value is calculated to obtain the sand-mud ratio, mud content and rock electrical value of each layer in the well group; The sedimentary microfacies distribution map is fitted with the sand-mud ratio, mud content curve and rock-electric value curve of each small layer interpreted by well logging to obtain the sedimentary microfacies distribution map of each well, that is, to conduct vertical sedimentary microfacies analysis between single wells; The sedimentary microfacies distribution map of the well group is then obtained by merging.

[0020] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the reservoir prediction method based on sedimentary microfacies tracking described in the present invention can improve the accuracy and reliability of reservoir prediction. By correcting and normalizing the rock electrical depth value, combined with the intersection diagram analysis and cluster analysis method, the method can quantitatively identify sand bodies and effectively merge different sand body types, thereby constructing a more accurate sedimentary microfacies model. This model can better reflect the rock electrical characteristics and lithological characteristics of the reservoir, and provide a more detailed prediction for the vertical and planar distribution of the reservoir.

[0021] In addition, the method of the present invention can also improve the efficiency of reservoir prediction. By using the K-means algorithm and density clustering method to process the formation depth and porosity data, and performing depth correction and intersection analysis on the logging data, the method can quickly extract key reservoir information from complex geological data. This method can not only reduce the reliance on the subjective judgment of geologists, but also shorten the time of reservoir prediction, making oil and gas exploration and development more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic flow chart of a reservoir prediction method based on sedimentary microfacies tracing provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0024] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0025] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0026] Reference below Figure 1 , Figure 1 A schematic diagram of a flow chart of a reservoir prediction method based on sedimentary microfacies tracking provided by an embodiment of the present invention. Figure 1 As shown, a reservoir prediction method 100 based on sedimentary microfacies tracking includes: Step 1: Establish sedimentary microfacies model; Step 2: Use the sedimentary microfacies model established in step 1 to predict reservoirs; Step 3: Predict the planar sedimentary microfacies distribution; Step 4: Predict the distribution of sedimentary microfacies between well groups; Step 5: Based on the distribution of sedimentary microfacies of a single well, characterize the vertical distribution of sedimentary microfacies between single wells and well groups; Step 6: Determine the reservoir distribution between individual wells and well groups; Step 7: Selection of plane characterization wells within the well group; Step 8: Prediction of the impact of sedimentary microphases on the reservoir.

[0027] It should be noted that this method first involves establishing a sedimentary microfacies model, which is a fundamental step in reservoir prediction. The sedimentary microfacies model is a geological model used to describe and simulate the sedimentary characteristics and distribution of subsurface rocks. In this step, by correcting the litho-electric depth values of the wells in the study area and based on the formation depth and porosity data, a litho-electric depth calculation model is established, which is the key to predicting the reservoir distribution.

[0028] Specifically, during the process of establishing the sedimentary microfacies model, it is necessary to correct the litho-electric depth values, which involves the analysis and adjustment of the litho-electric data of the wells to ensure the accuracy of the data. The corrected litho-electric depth values will be combined with the formation depth and porosity data, and through these data, the litho-electric depth values can be calculated, and then a litho-electric depth calculation model can be established. In this process, professional geological software and algorithms may be used to process and analyze the data.

[0029] Preferably, the specific method of establishing the sedimentary microfacies model can be further refined. For example, when correcting the litho-electric depth values, various correction methods can be adopted, such as statistical correction, geostatistical correction, etc., to adapt to different geological conditions and data characteristics.

[0030] Furthermore, various techniques can be used to collect the formation depth and porosity data, such as seismic exploration, well logging, etc., to obtain more accurate data. When establishing the litho-electric depth calculation model, various factors can be considered, such as rock type, geological age, sedimentary environment, etc., to improve the accuracy and applicability of the model.

[0031] In some embodiments, in step 1, the specific method of establishing the sedimentary microfacies model is as follows: Correct the litho-electric depth values of the wells in the study area and establish a litho-electric depth calculation model based on the formation depth and porosity data.

[0032] Establish a crossplot with lithology thickness as the x-axis and shale content as the y-axis, and conduct crossplot analysis based on this crossplot.

[0033] According to the crossplot analysis results, normalize the litho-electric depth values reflecting the reservoir characteristics, and the normalization principle is: a (litho-electric value of mudstone) < b (litho-electric value of sandstone) < c (litho-electric value of oil-bearing mudstone) < d (litho-electric value of oil-bearing sandstone) < e (litho-electric value of pure sandstone).

[0034] Then, combined with the empirical formula, use the normalized litho-electric depth values to quantitatively identify sand bodies.

[0035] Then, use the cluster analysis method to merge different sand body types to obtain the sedimentary microfacies models of different study intervals. It should be noted that this step describes in detail the specific method of establishing a sedimentary microfacies model. This includes correcting the rock electrical depth values ​​of the wells drilled in the study area and establishing a rock electrical depth calculation model based on the formation depth and porosity data. The rock electrical depth value refers to the product of the resistivity of the rock and the depth, and is an important parameter for determining reservoir characteristics. Correcting this value is to eliminate measurement errors and environmental influences to ensure data accuracy.

[0036] Specifically, in the process of correcting the rock electrical depth value, the rock electrical data of each well needs to be analyzed to determine its relationship with the formation depth and porosity. This usually involves collecting the rock electrical data of each well and then using a mathematical model to correct this data. The formation depth refers to the depth of the formation underground, while the porosity refers to the spatial proportion of pores in the rock. These data can be obtained through well logging technology. When establishing a rock electrical depth calculation model, it is necessary to determine the relationship between the mud content and the rock electrical value, which usually involves complex geostatistical analysis.

[0037] Preferably, in the specific operation of correcting the rock electrical depth value, a variety of correction techniques can be used, such as depth correction, temperature correction and pressure correction, to adapt to different geological conditions. When establishing a rock electrical depth calculation model, a machine learning algorithm can be considered to improve the prediction accuracy of the model.

[0038] Furthermore, the establishment of cross plots can be assisted by software tools, which can help geologists understand the relationship between lithology and shale content more intuitively. In the normalization process, different normalization methods can be used, such as minimum-maximum normalization or Z-score normalization, to adapt to different data distribution characteristics.

[0039] In some embodiments, the method for correcting the rock electrical depth value is:

[0040] Where: is the average value of reservoir mud content; is the shale content of a certain lithology combination of the i-th well; is the average value of the mud quality in the well; is the average rock electrical value of pure mudstone; is the average rock electrical value of pure sandstone; is the average rock electrical value of oil-bearing mudstone; is the average rock electrical value of oil-bearing sandstone; It is the average rock electrical value of the water layer.

[0041] It should be noted that this step describes the specific calculation method for the correction of the rock electrical depth value. This method involves calculating the average value of the reservoir shale content and using this average value to correct the rock electrical depth values ​​of different wells. The reservoir shale content refers to the percentage of shale in the reservoir rock, which is an important factor affecting the rock electrical value. Through this correction, the changes in rock electrical values ​​caused by differences in shale content between different wells can be eliminated, thereby improving the accuracy of reservoir prediction.

[0042] Specifically, when correcting the rock electrical depth value, it is necessary to calculate the mud content of each well and find the average value of these mud contents. In this process, it is necessary to determine the average rock electrical values ​​of pure mudstone, pure sandstone, oil-bearing mudstone, oil-bearing sandstone and water layer. These values ​​can be obtained through laboratory analysis or historical data. In the calculation formula, the mud content of each well is multiplied by its corresponding rock electrical value, and then divided by the number of wells to obtain the average value of the reservoir mud content. This average value will be used for subsequent rock electrical depth value correction calculations.

[0043] Preferably, in the operation step of correcting the rock electrical depth value, automated software can be used to process a large amount of data and calculations. Such software can quickly analyze the rock electrical data of each well and calculate the required average value. In addition, different statistical methods can be used to determine the average value of the mud content, such as the weighted average method, which can give different weights according to the depth or geographical location of the well.

[0044] Furthermore, in practical applications, geostatistical methods can also be considered to predict the rock electrical values ​​of wells that do not have direct measurement data, thereby improving the coverage and accuracy of the rock electrical depth value correction for the entire oil field.

[0045] In some embodiments, the formation depth and porosity data use three density clustering methods based on the K-means algorithm to classify the lithology combination with a shale content of less than 35% as pure sandstone, and the lithology combination with a shale content of more than 45% as pure mudstone; The lithology combination with a shale content of less than or equal to 35% and greater than or equal to 45% is classified as an oil-bearing mudstone combination or an oil-bearing sandstone combination. The calculation formula for the shale content of the lithology combination is:

[0046] In the formula, is the weighted thickness of each layer in the well; is the weighted depth of each layer of the well; For the layer The corresponding formation coefficient (mud content); For the layer The corresponding formation coefficient (mud content); is the formation coefficient length; is the acoustic time difference of each layer in the well; is the number of layers studied.

[0047] It should be noted that this step involves using three density clustering methods based on the K-means algorithm to process the stratigraphic depth and porosity data. The K-means algorithm is a commonly used clustering analysis method that is used to group data points into K clusters so that the points within the clusters are as similar as possible and the points between clusters are as different as possible. In this process, lithology combinations with a shale content of less than 35% are classified as pure sandstone, and lithology combinations with a shale content of more than 45% are classified as pure mudstone. This method helps to simplify the geological model and improve the efficiency and accuracy of reservoir prediction.

[0048] Specifically, when using the K-means algorithm for clustering, it is necessary to determine the thresholds of the shale content, namely 35% and 45%, which will be used to distinguish different types of lithology. The calculation of the shale content can be obtained through laboratory analysis or well logging data.

[0049] More specifically, in actual operation, it is necessary to collect the mud content data of each lithology combination and input it into the K-means algorithm. The algorithm will classify the lithology combination into different categories based on this data, namely pure sandstone, pure mudstone, oil-bearing mudstone combination or oil-bearing sandstone combination. These categories will be used for subsequent reservoir prediction and analysis.

[0050] Preferably, in the process of implementing the K-means algorithm, the parameters of the algorithm can be adjusted to optimize the clustering results. For example, the number of clusters K can be adjusted, or different distance measurement methods (such as Euclidean distance, Manhattan distance, etc.) can be used to measure the similarity between data points.

[0051] Furthermore, other clustering algorithms, such as DBSCAN or hierarchical clustering, can be considered to adapt to different geological conditions and data characteristics. In actual operation, the clustering results can also be verified and adjusted in combination with the expertise and experience of geologists to ensure the accuracy and geological significance of the clustering.

[0052] In some embodiments, the specific steps of merging different sand body types using cluster analysis are: A. Normalize the stratigraphic data of the studied layer; B. Treat the normalized stratigraphic data as n d-dimensional (number of stratigraphic data) vectors in n-dimensional space, determine the average position of n sample points on d coordinate axes in n-dimensional space, and calculate the distance between each sample point and the average position; C. Find the point R farthest from the average position among the n sample points; D. Find the N points with the shortest distance from each point to point R; E. Use point R and these N points to represent all n points; F. Calculate the intra-class variance of N points as the variance of this class; G. Compare the variance of each category, and take out the samples in the category with the smallest variance and group them with R as a new group; H. Calculate the between-class variance matrix of the entire data set outside the new group; I. Continue to loop the calculation of GH for the remaining categories until all sample points are in one group.

[0053] It should be noted that this step describes in detail the specific operation process of merging different sand body types using cluster analysis. Cluster analysis is a statistical method used to group similar data points into the same category. In this context, sand body types refer to different rock groups that are distinguished based on the physical properties of the rocks, such as rock electrical depth values. Merging these sand body types helps to build a more accurate sedimentary microfacies model.

[0054] Specifically, the operation steps of the cluster analysis method include normalizing the stratigraphic data of the study layer, that is, converting the data to a uniform scale for comparison and analysis. The normalized stratigraphic data are regarded as vectors in a multidimensional space, each vector represents a sample point, and has multiple dimensions (the number of stratigraphic data). Next, the average position of these sample points in the multidimensional space is determined, and the distance between each sample point and the average position is calculated. In this process, it is necessary to select a suitable distance measurement method, such as Euclidean distance or Manhattan distance, and set a suitable normalization method, such as minimum-maximum normalization or Z-score normalization.

[0055] Preferably, the operation steps of the cluster analysis method can be further refined or alternative solutions can be provided. For example, when determining the average position of the sample points, a weighted average method can be used, in which the weight of each sample point is based on its importance or representativeness in the data set. When calculating the distance between the sample point and the average position, different distance measurement methods can be considered to adapt to different data distribution characteristics.

[0056] Furthermore, different clustering algorithms, such as hierarchical clustering or density clustering, can be selected to improve the accuracy and efficiency of clustering. After clustering is completed, indicators such as silhouette coefficient can be used to evaluate the quality of clustering, and clustering parameters can be adjusted or different clustering methods can be selected as needed.

[0057] In some embodiments, in step 2, the specific method for reservoir prediction using the sedimentary microfacies model established in step 1 is: First, the logging data of the target well is processed to obtain the shale content curve, sand-mud ratio curve and rock-electric value curve corresponding to the studied layer; Then, according to the rock electrical depth model, the shale content curve is depth-corrected to obtain a curve showing the change of shale content with depth. Next, the lithology thickness curve of a single well is drawn by combining the mud content curve with the sand-mud ratio curve interpreted by well logging; Finally, the lithology thickness curve and the rock-electricity value curve are normalized and combined with the rock-electricity depth model to obtain the shale content and sand-mud ratio curve of a single well. Then, the normalized mud content curve and sand-mud ratio curve are intersected and analyzed to obtain the sedimentary facies pattern, which is then combined with the lithology thickness curve to invert and obtain the sedimentary microfacies model.

[0058] It should be noted that this step involves a specific method for reservoir prediction using an established sedimentary microfacies model. Reservoir prediction refers to predicting the location and characteristics of underground reservoirs through geological and geophysical data. In this process, the well logging data of the target well must first be processed to obtain the mud content curve, sand-mud ratio curve, and rock-electric value curve corresponding to the study layer. Well logging data refers to the physical and chemical properties of downhole rocks obtained through well logging technology, which is crucial for understanding reservoir characteristics.

[0059] Specifically, processing logging data includes cleaning, correcting and interpreting the raw data to ensure the accuracy and usability of the data. The shale content curve shows the proportion of shale in the rock at different depths, the sand-mud ratio curve shows the ratio of sand and mud, and the rock electrical value curve reflects the resistivity characteristics of the rock. Depth correction is a key step, which involves adjusting the shale content curve according to the rock electrical depth model to reflect the change of shale content with depth. This process may require the use of specific software and algorithms, as well as the expertise of geologists to interpret the data.

[0060] Preferably, the operational steps of reservoir prediction can be further refined or alternatives can be provided. For example, when processing well logging data, an automated data processing process can be used to improve efficiency and reduce human errors. In the depth correction process, a variety of correction models can be used, such as statistical correction models or machine learning models, to adapt to different geological conditions.

[0061] Furthermore, the accuracy of reservoir prediction can be improved by combining 3D seismic data with well logging data through seismic inversion technology. When drawing the lithology thickness curve of a single well, a variety of geological interpretation methods, such as lithology identification and sequence stratigraphy methods, can be used to improve the accuracy of the lithology thickness curve. Through these refined steps and alternatives, the accuracy and reliability of reservoir prediction can be further improved.

[0062] In some embodiments, in step 3, the specific method for predicting the planar deposition microphase distribution is: Obtain seismic attribute values ​​at different points from the 3D seismic forward model and perform depth point correction; The corrected seismic attribute values ​​are intersected and analyzed with the mud content, sand-mud ratio and lithology thickness curves interpreted by well logging to obtain the sedimentary facies model, and the planar sedimentary microfacies model is inverted. Combined with the sedimentary microfacies model established in step 1, the sedimentary microfacies types of each logging point are compared and analyzed to obtain the distribution of sedimentary microfacies in each layer on the plane.

[0063] It should be noted that this step involves a specific method for predicting the planar sedimentary microfacies distribution. Planar sedimentary microfacies distribution prediction refers to predicting the distribution of reservoirs on a plane by analyzing and interpreting seismic data and well logging data in geology. This step is crucial for understanding the lateral changes of reservoirs and optimizing drilling strategies.

[0064] Specifically, when predicting the planar sedimentary microfacies distribution, it is first necessary to obtain the seismic attribute values ​​at different points from the three-dimensional seismic forward model and perform depth point correction. The three-dimensional seismic forward model is a three-dimensional model that simulates the underground structure, which can help us understand the physical properties of underground rocks. Depth point correction refers to adjusting the seismic attribute values ​​to reflect changes in actual geological depth. Next, the corrected seismic attribute values ​​are intersected with the mud content, sand-mud ratio and lithology thickness curves interpreted by well logging to obtain the sedimentary facies pattern, and invert to obtain a planar developed sedimentary microfacies model. Intersection analysis is a geological analysis technique that identifies geological features by comparing the relationship between different parameters.

[0065] Preferably, the operational steps of the planar sedimentary microfacies distribution prediction can be further refined or alternatives can be provided. For example, when obtaining seismic attribute values, a variety of seismic data processing techniques, such as amplitude change, frequency change or waveform feature analysis, can be used to improve the quality and resolution of the data. In the depth point correction process, geostatistical methods, such as Kriging or trend surface analysis, can be used to more accurately reflect the changes in geological depth. In the intersection analysis, a variety of logging data, such as resistivity, acoustic time difference or density logging data, can be combined to obtain a more comprehensive sedimentary facies model.

[0066] Furthermore, machine learning methods, such as random forests or neural networks, can be used to improve the prediction accuracy of sedimentary microfacies models. Through these refined steps and alternatives, the accuracy and reliability of planar sedimentary microfacies distribution predictions can be further improved.

[0067] In some embodiments, in step 4, the specific method for predicting the distribution of sedimentary microfacies between well groups is: The sedimentary microfacies distribution prediction calculation of a single well is carried out based on the well group, and the sedimentary microfacies distribution of the well group is obtained through the average sedimentary microfacies distribution prediction of the well group; Determine the distribution of sedimentary microfacies between individual wells and well groups; Specifically, in the prediction of single-well sedimentary microfacies distribution, the intersection analysis method is used to analyze the sedimentary microfacies of a single well to obtain the sedimentary microfacies distribution of each layer of a single well. Then, by averaging the sedimentary microfacies predictions of a single well, the average sedimentary facies distribution between well groups is obtained, that is, the sedimentary microfacies between well groups is obtained.

[0068] It should be noted that this step describes the specific method of predicting the distribution of sedimentary microfacies between well groups. Prediction of sedimentary microfacies distribution between well groups refers to predicting the distribution of sedimentary microfacies between multiple wells, which is of great significance for a comprehensive understanding of reservoir characteristics and optimization of oilfield development. In oilfield development, a well group refers to a group of wells in a certain area that is divided into a group for the convenience of management and research.

[0069] Specifically, when predicting the distribution of sedimentary microfacies between well groups, the single-well sedimentary microfacies distribution prediction calculation is first performed based on the well group. This means that the sedimentary microfacies of all wells in each well group need to be analyzed, and then the sedimentary microfacies distribution of the well group is obtained through the average sedimentary microfacies distribution prediction of the well group. In this process, the sedimentary microfacies distribution between single wells and well groups needs to be determined, which usually involves the summary and analysis of the single-well sedimentary microfacies distribution prediction results.

[0070] More specifically, the intersection analysis method can be used to analyze the sedimentary microfacies of a single well to obtain the sedimentary microfacies distribution of each layer of a single well, and then by averaging the sedimentary microfacies predictions of a single well, the average sedimentary facies distribution between well groups can be obtained, that is, the sedimentary microfacies between well groups can be obtained.

[0071] Preferably, the operation steps of the prediction of sedimentary microfacies distribution between well groups can be further refined or alternative solutions can be provided. For example, in the prediction of sedimentary microfacies distribution in a single well, a variety of geostatistical methods can be used to improve the accuracy of the prediction, such as Gaussian process regression or Bayesian method. When aggregating well group data, a weighted average method can be used, where the weight can be determined based on the production, geographical location or other geological parameters of the well.

[0072] Furthermore, the accuracy of the prediction of sedimentary microfacies distribution between well groups can be improved by combining 3D seismic data with downhole geophysical data through seismic inversion technology. When analyzing the distribution of sedimentary microfacies, machine learning techniques such as support vector machines or deep learning networks can be used to identify and predict the changes in sedimentary microfacies. Through these refined steps and alternatives, the accuracy and reliability of the prediction of sedimentary microfacies distribution between well groups can be further improved.

[0073] In some embodiments, the method for determining the sedimentary microfacies distribution between a single well and a well group is: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical values ​​of a single well are depth-corrected, and then the corrected rock electrical values ​​are plane-distributed to obtain the sedimentary microfacies distribution map of each layer of the single well, that is, the sedimentary microfacies distribution map of the single well.

[0074] It should be noted that this step involves specific methods for determining the distribution of sedimentary microfacies between individual wells and well groups. Sedimentary microfacies distribution refers to the spatial distribution of rock types and characteristics formed in different sedimentary environments in geology. This step is crucial for understanding the geological structure of the reservoir and optimizing oil and gas exploration strategies.

[0075] Specifically, when determining the sedimentary microfacies distribution between a single well and a well group, firstly, the logging data statistics are performed on a well group basis to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each small layer in the well group. Well logging data statistics refers to the collection and analysis of physical and chemical property data of downhole rocks, which are very important for understanding reservoir characteristics and sedimentary environment. Next, the logging data is fitted with the sedimentary microfacies of the target layer to obtain a curve of the change of mud content with sedimentary microfacies. Fitting refers to the use of mathematical models to describe the relationship between data in order to predict the value of unknown data points. Then, the mud content in the rock electrical depth model is intersected with the rock electrical value to obtain a curve of the relationship between the mud content and the rock electrical value, that is, a curve of the change of mud content with sedimentary microfacies. Intersection analysis is a geological analysis technique that identifies geological features by comparing the relationship between different parameters.

[0076] Preferably, the operation steps for determining the distribution of sedimentary microfacies between single wells and well groups can be further refined or alternative solutions can be provided. For example, when performing logging data statistics, an automated data acquisition system can be used to improve the accuracy and completeness of the data. In the fitting process, a variety of mathematical models, such as linear regression, nonlinear regression or machine learning models, can be used to adapt to different data characteristics and geological conditions. In the intersection analysis, a variety of logging data, such as resistivity, acoustic time difference or density logging data, can be combined to obtain a more comprehensive sedimentary microfacies change curve.

[0077] Furthermore, geostatistical methods, such as Kriging or trend surface analysis, can be used to improve the prediction accuracy of the variation curve of mud content with sedimentary microfacies. Through these refined steps and alternatives, the accuracy and reliability of the prediction of sedimentary microfacies distribution between single wells and well groups can be further improved.

[0078] In some embodiments, in step 5, based on the distribution of sedimentary microfacies in a single well, the specific method for describing the vertical sedimentary microfacies distribution of a single well and between well groups is: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical value of a single well is depth-corrected, and then the corrected rock electrical value is plane-distributed to obtain a sedimentary microfacies distribution map of the single well, i.e., a sedimentary microfacies distribution map of the single well; Then, according to the sedimentary microfacies distribution map of a single well, the vertical sedimentary microfacies distribution between well groups is carried out; specifically, the planar sedimentary microfacies maps of each layer are superimposed to obtain the vertical sedimentary microfacies distribution map between well groups; The logging data of multiple single wells in the well group are combined to obtain the mud content curve, rock electrical value curve and sand-mud ratio curve of the well group; Orthogonal analysis was performed to obtain the cross plots of shale content and rock electrical value, and shale content and sand-mud ratio. The curves of shale content changing with rock electrical value and sand-mud ratio were obtained by fitting the cross plots. The average value is calculated to obtain the sand-mud ratio, mud content and rock electrical value of each layer in the well group; The sedimentary microfacies distribution map is fitted with the sand-mud ratio, mud content curve and rock-electric value curve of each small layer interpreted by well logging to obtain the sedimentary microfacies distribution map of each well, that is, to conduct vertical sedimentary microfacies analysis between single wells; The sedimentary microfacies distribution map of the well group is then obtained by merging.

[0079] It should be noted that this step describes the specific method of describing the vertical distribution of sedimentary microfacies in a single well and between well groups based on the distribution of sedimentary microfacies in a single well. Vertical sedimentary microfacies distribution refers to the distribution of rock types and characteristics formed in different sedimentary environments in the vertical direction. This step is crucial for a comprehensive understanding of the geological structure of the reservoir and the optimization of oil and gas exploration strategies.

[0080] Specifically, when describing the vertical sedimentary microfacies distribution between single wells and well groups, the logging data is first statistically analyzed based on the well group to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each small layer in the well group. These parameters are key indicators for describing the physical properties of reservoir rocks. The sand-mud ratio reflects the ratio of sandstone to mudstone, the mud content refers to the percentage of mud in the rock, the rock electrical value reflects the resistivity characteristics of the rock, and the lithology thickness refers to the thickness of a specific lithology layer. Next, the logging data is fitted with the sedimentary microfacies of the target layer to obtain a curve showing the change of mud content with sedimentary microfacies. This step involves using a mathematical model to describe the relationship between logging data and sedimentary microfacies in order to predict the value of unknown data points. Then, the mud content in the rock electrical depth model is intersected with the rock electrical value to obtain a curve showing the change of mud content with rock electrical value, that is, a curve showing the change of mud content with sedimentary microfacies.

[0081] Preferably, the operation steps for characterizing the vertical sedimentary microfacies distribution between single wells and well groups can be further refined or alternative solutions can be provided. For example, when performing logging data statistics, an automated data acquisition system can be used to improve the accuracy and completeness of the data. In the fitting process, a variety of mathematical models, such as linear regression, nonlinear regression or machine learning models, can be used to adapt to different data characteristics and geological conditions. In the intersection analysis, a variety of logging data, such as resistivity, acoustic time difference or density logging data, can be combined to obtain a more comprehensive sedimentary microfacies change curve.

[0082] Furthermore, geostatistical methods, such as Kriging or trend surface analysis, can be used to improve the prediction accuracy of the variation curve of mud content with sedimentary microfacies. Through these refined steps and alternatives, the accuracy and reliability of the prediction of the vertical sedimentary microfacies distribution of single wells and well groups can be further improved.

[0083] In some embodiments, in step 6, the specific method for determining the reservoir distribution between single wells and well groups is: The reservoir distribution of a single well is calculated based on the sedimentary microfacies of the single well to obtain the reservoir distribution of the single well; Then, the single well logging data of the well group is used to fit the shale content change curve, and the cross-plot of the sand-shale ratio and shale content of the well group is obtained; The sand-mud ratio and mud content of the well group are used to obtain the mud content change curve and the curve of mud content changing with the sand-mud ratio; The change of mud content in each well group is obtained according to the mud content curve of the well group, and then the vertical sand-mud ratio curve of the well group is obtained to obtain the reservoir distribution of the target layer.

[0084] It should be noted that this step involves specific methods for determining reservoir distribution between individual wells and well groups. Reservoir distribution refers to the distribution of oil and gas reservoirs in underground space, which is crucial for oil and gas exploration and development. Determining reservoir distribution can help optimize drilling locations and improve oil and gas recovery.

[0085] Specifically, when determining the reservoir distribution, the reservoir distribution is first calculated based on the sedimentary microfacies of a single well to obtain the reservoir distribution of a single well. This involves analyzing the geological data of a single well, such as rock electrical value, mud content, and sand-mud ratio, to determine the location and characteristics of the reservoir. Next, the mud content change curve is fitted using the well logging data of a single well in the well group to obtain the sand-mud ratio and mud content cross plot of the well group. The mud content change curve reflects the change of mud content with depth, while the sand-mud ratio and mud content cross plot shows the relationship between the sand-mud ratio and mud content. The fitting and analysis of these data helps to identify the boundaries and characteristics of the reservoir. Then, the sand-mud ratio and mud content of the well group are used to obtain the mud content change curve and the curve of mud content changing with sand-mud ratio. The mud content change of each well group is obtained according to the mud content curve of the well group, and then the vertical sand-mud ratio curve of the well group is obtained to obtain the reservoir distribution of the target layer.

[0086] Preferably, the operation steps for determining the reservoir distribution between single wells and well groups can be further refined or alternative solutions can be provided. For example, when calculating the reservoir distribution, geostatistical methods, such as Kriging or trend surface analysis, can be used to improve the prediction accuracy of the shale content change curve. When fitting the shale content change curve, a variety of mathematical models, such as polynomial regression, exponential regression or machine learning models, can be used to adapt to different data characteristics and geological conditions.

[0087] Furthermore, the accuracy of reservoir distribution prediction can be improved by combining 3D seismic data with logging data and using seismic inversion technology. When analyzing the curve of shale content changing with sand-mud ratio, multivariate statistical analysis methods, such as principal component analysis or factor analysis, can be used to identify key factors affecting reservoir distribution. Through these refined steps and alternatives, the accuracy and reliability of reservoir distribution prediction for single wells and well groups can be further improved.

[0088] In some embodiments, in step 7, the specific method for selecting the plane characterization wells in the well group is: Analyze the single-well plane sedimentary microfacies of all wells in the well group to obtain the plane distribution map of each small sedimentary microfacies; Comparative statistics are used to obtain the dominant sedimentary microfacies types in each layer, so as to select the representative wells representing each layer in the well group; Finally, based on the known planar distribution of the characterizing well, the planar distribution of sedimentary microfacies inside and outside the well is predicted.

[0089] It should be noted that this step involves the selection method of plane characterization wells within the well group. Plane characterization wells refer to wells within the well group that can represent the geological characteristics of the entire well group. Their selection is crucial for understanding and predicting the distribution of sedimentary microfacies within the well group. Selecting these wells can help geologists more effectively analyze and predict the geological structure and oil and gas distribution within the well group.

[0090] Specifically, in the process of selecting plane characterization wells within the well group, the single well plane sedimentary microfacies of all wells in the well group are first analyzed to obtain the plane distribution map of each small sedimentary microfacies. The plane distribution map of sedimentary microfacies is a map that shows the distribution of sedimentary environment and rock types in a specific geological period. The dominant sedimentary microfacies types in each layer are obtained by comparative statistics, so as to select the characterization wells representing each layer in the well group. This step involves a comprehensive analysis of the geological data of each well to determine which wells best represent the geological characteristics of the entire well group. Finally, based on the known plane distribution of the characterization wells, the plane distribution of sedimentary microfacies inside and outside the well is predicted.

[0091] Preferably, the operation steps of selecting plane characterization wells within a well group can be further refined or alternative solutions can be provided. For example, when analyzing the plane sedimentary microfacies of a single well, geostatistical methods such as Kriging or trend surface analysis can be used to improve the prediction accuracy of the plane distribution map of sedimentary microfacies. When determining the dominant sedimentary microfacies type, cluster analysis or pattern recognition technology can be used to automatically identify and classify different sedimentary microfacies.

[0092] Furthermore, the accuracy of characterization well selection can be improved by combining 3D seismic data with logging data and using seismic inversion technology. When predicting the planar distribution of sedimentary microfacies inside and outside the well, machine learning techniques such as random forests or neural networks can be used to improve the accuracy and reliability of predictions. Through these refined steps and alternatives, the accuracy of planar characterization well selection within the well group and the efficiency of geological analysis can be further improved.

[0093] The above-mentioned embodiments of the present invention have the following beneficial effects: the reservoir prediction method based on sedimentary microfacies tracking of the present invention can provide a systematic and standardized process to predict reservoir distribution, from establishing a sedimentary microfacies model to predicting the sedimentary microfacies distribution between well groups, and each step is aimed at improving the accuracy and efficiency of the prediction. Through this method, consistency and comparability can be ensured between different well groups and horizons. At the same time, by characterizing the vertical sedimentary microfacies distribution of a single well and between well groups, the location and range of the reservoir can be more accurately determined, thereby optimizing oil and gas exploration and development strategies.

[0094] In addition, the present invention can improve the scientificity and objectivity of reservoir prediction by comprehensively applying a variety of technical means, such as rock electrical depth value correction, normalization processing, cluster analysis, etc. This method can reduce the reliance on the subjective judgment of geologists and reduce prediction errors. At the same time, through in-depth analysis of key parameters such as mud content, rock electrical value and sand-mud ratio, the geological characteristics of the reservoir can be more comprehensively understood, providing solid data support for the efficient development of oil and gas fields.

[0095] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0096] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A reservoir prediction method based on sedimentary microfacies tracking, characterized by: It includes the following steps: Step 1: Establish sedimentary microfacies model; Step 2: Use the sedimentary microfacies model established in step 1 to predict reservoirs; Step 3: Predict the planar sedimentary microfacies distribution; Step 4: Predict the distribution of sedimentary microfacies between well groups; Step 5: Based on the distribution of sedimentary microfacies of a single well, characterize the vertical distribution of sedimentary microfacies between single wells and well groups; Step 6: Determine the reservoir distribution between individual wells and well groups; Step 7: Selection of plane characterization wells within the well group; Step 8: Prediction of the impact of sedimentary microphases on the reservoir.

2. The reservoir prediction method based on sedimentary microfacies tracing according to claim 1, characterized in that: In step 1, the specific method for establishing the sedimentary microfacies model is: The rock electrical depth values ​​of the wells drilled in the study area were corrected, and a rock electrical depth calculation model was established based on the formation depth and porosity data. A cross plot is established with the lithology thickness as the x-axis and the mud content as the y-axis, and cross analysis is performed based on the cross plot. According to the intersection analysis results, the rock electrical depth values ​​reflecting the reservoir characteristics are normalized, and the normalized rock electrical depth values ​​are used to quantitatively identify sand bodies. Cluster analysis was used to merge different sand body types and obtain sedimentary microfacies models for different study intervals.

3. The reservoir prediction method based on sedimentary microfacies tracing according to claim 2 is characterized in that: The method for correcting the rock electrical depth value is: Where: is the average value of reservoir mud content; is the shale content of a certain lithology combination of the i-th well; is the average value of the mud quality in the well; is the average rock electrical value of pure mudstone; is the average rock electrical value of pure sandstone; is the average rock electrical value of oil-bearing mudstone; is the average rock electrical value of oil-bearing sandstone; It is the average rock electrical value of the water layer.

4. The reservoir prediction method based on sedimentary microfacies tracing according to claim 2 is characterized in that: The formation depth and porosity data were clustered using three density clustering methods based on the K-means algorithm, and the lithology combinations with a shale content of less than 35% were classified as pure sandstone, and the lithology combinations with a shale content of more than 45% were classified as pure mudstone; The lithology combination with a shale content of less than or equal to 35% and greater than or equal to 45% is classified as an oil-bearing mudstone combination or an oil-bearing sandstone combination. The calculation formula for the shale content of the lithology combination is: In the formula, is the weighted thickness of each layer in the well; is the weighted depth of each layer of the well; For the layer The corresponding formation coefficient; For the layer The corresponding formation coefficient; is the formation coefficient length; is the acoustic time difference of each layer in the well; is the number of layers studied.

5. The reservoir prediction method based on sedimentary microfacies tracing according to claim 2, characterized in that: The specific steps of merging different sand body types using cluster analysis are as follows: A. Normalize the stratigraphic data of the studied layer; B. Treat the normalized stratigraphic data as n d-dimensional (number of stratigraphic data) vectors in n-dimensional space, determine the average position of n sample points on d coordinate axes in n-dimensional space, and calculate the distance between each sample point and the average position; C. Find the point R farthest from the average position among the n sample points; D. Find the N points with the shortest distance from each point to point R; E. Use point R and these N points to represent all n points; F. Calculate the intra-class variance of N points as the variance of this class; G. Compare the variance of each category, and take out the samples in the category with the smallest variance and group them with R as a new group; H. Calculate the between-class variance matrix of the entire data set outside the new group; I. Continue to loop the calculation of GH for the remaining categories until all sample points are in one group.

6. The reservoir prediction method based on sedimentary microfacies tracing according to claim 1, characterized in that: In step 2, the specific method for reservoir prediction using the sedimentary microfacies model established in step 1 is: First, the logging data of the target well is processed to obtain the shale content curve, sand-mud ratio curve and rock-electric value curve corresponding to the studied layer; Then, according to the rock electrical depth model, the shale content curve is depth-corrected to obtain a curve showing the change of shale content with depth. Next, the lithology thickness curve of a single well is drawn by combining the mud content curve with the sand-mud ratio curve interpreted by well logging; Finally, the lithology thickness curve and the rock-electricity value curve are normalized and combined with the rock-electricity depth model to obtain the shale content and sand-mud ratio curve of a single well. Then, the normalized mud content curve and sand-mud ratio curve are intersected and analyzed to obtain the sedimentary facies pattern, which is then combined with the lithology thickness curve to invert and obtain the sedimentary microfacies model.

7. The reservoir prediction method for sedimentary microfacies tracking according to claim 1, characterized in that: In step 3, the specific method for predicting the planar sedimentary microphase distribution is: Obtain seismic attribute values ​​at different points from the 3D seismic forward model and perform depth point correction; The corrected seismic attribute values ​​are intersected and analyzed with the mud content, sand-mud ratio and lithology thickness curves interpreted by well logging to obtain the sedimentary facies model, and the planar sedimentary microfacies model is inverted. Combined with the sedimentary microfacies model established in step 1, the sedimentary microfacies types of each logging point are compared and analyzed to obtain the distribution of sedimentary microfacies in each layer on the plane.

8. The reservoir prediction method based on sedimentary microfacies tracing according to claim 1, characterized in that: In step 4, the specific method for predicting the distribution of sedimentary microfacies between well groups is: The sedimentary microfacies distribution prediction calculation of a single well is carried out based on the well group, and the sedimentary microfacies distribution of the well group is obtained through the average sedimentary microfacies distribution prediction of the well group; Determine the distribution of sedimentary microfacies between individual wells and well groups; Specifically, in the prediction of single-well sedimentary microfacies distribution, the intersection analysis method is used to analyze the sedimentary microfacies of a single well to obtain the sedimentary microfacies distribution of each layer of a single well. Then, by averaging the sedimentary microfacies predictions of a single well, the average sedimentary facies distribution between well groups is obtained, that is, the sedimentary microfacies between well groups is obtained.

9. The reservoir prediction method based on sedimentary microfacies tracing according to claim 8, characterized in that: The method for determining the sedimentary microfacies distribution between a single well and a well group is: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical values ​​of a single well are depth-corrected, and then the corrected rock electrical values ​​are plane-distributed to obtain the sedimentary microfacies distribution map of each layer of the single well, that is, the sedimentary microfacies distribution map of the single well.

10. The reservoir prediction method based on sedimentary microfacies tracing according to claim 1, characterized in that: In step 5, based on the distribution of single well sedimentary microfacies, the specific method for describing the vertical distribution of single well and between well groups of sedimentary microfacies is as follows: Logging data statistics are carried out in units of well groups to obtain the average sand-mud ratio, mud content, rock electrical value and lithology thickness of each layer in the well group; Fit the logging data with the sedimentary microfacies of the target layer to obtain the curve of the change of mud content with sedimentary microfacies; The mud content in the rock-electric depth model is cross-analyzed with the rock-electric value to obtain the relationship curve between the mud content and the rock-electric value, that is, the curve between the mud content and the sedimentary microfacies. The two fitted curves are intersected and analyzed to obtain the curve of rock electrical value and sedimentary microfacies change; The rock electrical value of a single well is depth-corrected, and then the corrected rock electrical value is plane-distributed to obtain a sedimentary microfacies distribution map of the single well, i.e., a sedimentary microfacies distribution map of the single well; Then, according to the sedimentary microfacies distribution map of a single well, the vertical sedimentary microfacies distribution between well groups is carried out; specifically, the planar sedimentary microfacies maps of each layer are superimposed to obtain the vertical sedimentary microfacies distribution map between well groups; The logging data of multiple single wells in the well group are combined to obtain the mud content curve, rock electrical value curve and sand-mud ratio curve of the well group; Orthogonal analysis was performed to obtain the cross plots of shale content and rock electrical value, and shale content and sand-mud ratio. The curves of shale content changing with rock electrical value and sand-mud ratio were obtained by fitting the cross plots. The average value is calculated to obtain the sand-mud ratio, mud content and rock electrical value of each layer in the well group; The sedimentary microfacies distribution map is fitted with the sand-mud ratio, mud content curve and rock-electric value curve of each small layer interpreted by well logging to obtain the sedimentary microfacies distribution map of each well, that is, to conduct vertical sedimentary microfacies analysis between single wells; The sedimentary microfacies distribution map of the well group is then obtained by merging.

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