Reservoir flow unit multi-parameter division method based on CO2 flooding

Abstract

The invention discloses a reservoir flow unit multi-parameter division method based on CO2 flooding. The method comprises the following steps: S1, determining main influence factors of a flow unit according to oil reservoir storage characteristics of a CO2 drive reservoir; s2, classifying the influence factors determined in the step S1 by adopting an Euclidean distance in a pedigree clustering analysis method, and screening out relatively independent parameters as comprehensive evaluation indexes of flow unit classification; s3, calculating and counting a comprehensive evaluation index of each flow unit in the single well reservoir stratum; and classifying all flow units of the oil reservoir by using a K-Means clustering method, determining a comprehensive evaluation index boundary corresponding to a classification result as a unified standard for dividing the type of the flow unit in the whole CO2 drive oil reservoir area, and dividing the type of the flow unit in each reservoir of the whole CO2 drive oil reservoir by using the unified standard. According to the reservoir flow unit multi-parameter division method, the units with similar lithology and physical properties in the single-layer reservoir body can be divided into the same flow unit by integrating a plurality of influence factors.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field exploration based on CO2 flooding reservoir. 2 A multi-parameter division method for reservoir flow units for flooding. Background Art

[0002] At present, my country uses CO 2 After a long period of development, the flow path of crude oil in the rock pores changes. Since the process and efficiency of oil and gas displacement by gas injection or other injection agents are affected and controlled by factors such as the internal structure of the reservoir, geological interface and rock physical parameters, the efficiency is not high. Long-term gas drive causes changes in the permeability of different areas in the reservoir. Compared with water drive, CO 2 The lower limit of effective thickness of the reservoir changes after flooding. As the basic unit of reservoir development, the flow unit represents a reservoir unit with similar lithology and physical properties within the reservoir. Each flow unit usually represents a specific sedimentary environment and seepage characteristics. Therefore, it is necessary to deepen the study of the flow characteristics of reservoir flow units and understand the CO 2 The heterogeneity of the reservoir after flooding and the change law of fluid flow inside the reservoir are studied in depth. The static characteristics of the reservoir developed by gas flooding are deeply understood. The influence of multiple parameters is fully considered and a reasonable CO 2 The division standard of reservoir flow units is established to form efficient development modes for different types of flow units, providing support for studying the distribution law of remaining oil in the later stage of reservoir development.

[0003] At present, the research methods on flow units at home and abroad can be mainly divided into two types: one is the reservoir parameter analysis method based on mathematical means; the other is the reservoir hierarchy analysis method based on geological research. On the whole, the research method is developing from the most primitive qualitative method to semi-quantitative and quantitative methods. In summary, there is no unified concept at home and abroad in the definition of flow units, and scholars have different understandings of flow units. But in general, the concept of flow unit has developed from the original pure geological concept to a concept that combines geology and reservoir engineering, which has strong practicality; in terms of flow unit research methods, the methods used by researchers are different according to the geological characteristics and data conditions of different regions. These methods have their limitations. At present, there is no systematic research method that can be applied to gas-driven oil field development. Since the process of oil and gas reservoir exploitation will lead to changes in reservoir sensitivity, pore structure, etc., the flow units are also different at different development stages. After CO 2 The lower limit of reservoir physical properties changes after flooding, so it is necessary to further combine the changes in reservoir physical properties to establish a CO 2 A multi-parameter division method for reservoir flow units for flooding. Summary of the invention

[0004] The purpose of the present invention is to provide a CO 2 A multi-parameter division method for reservoir flow units based on flooding to deepen CO 2 Understanding of the flow characteristics of fluids inside the reservoir after the lower limit of the physical properties of the oil field during flooding and development has changed; the method of the present invention can comprehensively consider multiple influencing factors to divide the units with similar lithology and physical properties within a single-layer reservoir into the same flow unit.

[0005] The present invention provides a CO-based 2 The multi-parameter division method of reservoir flow units for flooding includes the following steps: S1. According to CO 2 Displacement reservoir storage characteristics, determine the main influencing factors of flow units; S2, using the Euclidean distance in the pedigree cluster analysis method to classify the influencing factors determined in step S1, and screen out relatively independent parameters as comprehensive evaluation indicators for flow unit classification; S3. Calculate and count the comprehensive evaluation index of each flow unit in the single well reservoir; use the K-Means clustering method to classify all the flow units in the reservoir, and determine the comprehensive evaluation index boundary corresponding to the classification result as the entire CO 2 A unified standard for dividing the flow unit type in the flooding reservoir area, using the unified standard to classify the entire CO 2 The flow unit types in each reservoir of the oil-flooding reservoir are divided.

[0006] In the classification method of the present invention, in step S1, the influencing factors include single factor parameters and combined parameters; The single factor parameters include oil layer depth, porosity, permeability, effective thickness, oil saturation and pore throat radius; The combined parameters include formation coefficient, reservoir coefficient, well flowability index and reservoir quality factor.

[0007] In the division method of the present invention, in step S2, the Euclidean distance coefficient is used to determine the similarity between the influencing factors of the flow unit.

[0008] In the division method of the present invention, the similarity between the influencing factors of the flow unit is obtained according to formula (1): (1) Among them, d (x,y) is the distance between two sample points x and y; i=1,2,3…n is the number of sample points; d (x,y) The smaller the sample x i and i The closer the nature.

[0009] In the classification method of the present invention, in step S2, the "relatively independent parameters" refer to the small similarity between the parameters, so that the correlation between the flow unit evaluation parameters selected as much as possible is minimized.

[0010] In the classification method of the present invention, in step S2, the step of screening the comprehensive evaluation index for flow unit classification according to the similarity of the Euclidean distance coefficient is as follows: Initialize n classes and treat each influencing parameter as a class. If the calculated d (x,y) The smaller the value, the closer the properties of the flow unit influencing parameters are. (x,y) Relatively small parameters are grouped into one category; Determine the distance from the new class to other classes, select the smallest distance, merge the above two classes into a new class, and repeat the calculation of each parameter as above until they are finally merged into one class.

[0011] Preferably, the influence of each type of parameter on the seepage law of the flow unit is used to screen the parameters that can comprehensively reflect the CO 2 The characteristic parameters of the fluid flow law of the flow unit in the flooding reservoir are used as comprehensive evaluation indicators for the classification of flow units.

[0012] In the classification method of the present invention, the comprehensive evaluation index includes formation coefficient and reservoir coefficient; The formation coefficient is used to evaluate the strength of the reservoir flow unit seepage capacity, as shown in formula (2):

[0013] Among them, P 1 is the formation coefficient; k is the reservoir permeability; h is the CO 2 The effective thickness of the reservoir that can be mobilized; The reservoir coefficient is used to evaluate the reservoir flow unit storage capacity, as shown in formula (3):

[0014] Among them, P 2 is the storage coefficient; is the reservoir porosity; h is CO 2 The effective thickness of the reservoir that can be mobilized; s o is the oil saturation of the reservoir.

[0015] Preferably, the steps of dividing the flow units using the K-Means clustering method are as follows: According to the central clustering principle, the flow unit K-means (i.e., flow unit categories) were designed into 2, 3, 4, and 5 categories, and the above four classification results were obtained using the SPSS software K-means clustering algorithm; Analyze the final cluster centers of the four classification results and determine the optimal K-means; The division boundaries of the formation coefficient and reservoir coefficient in the optimal K-means clustering result are taken as the CO 2 Criteria for dividing different types of flow units in reservoirs.

[0016] The steps to determine the optimal K-means are as follows: the final cluster centers between each category must satisfy the uniform interval span, cannot differ too little or too much, and can be defined by the coordinates of each cluster center point.

[0017] The present invention has the following beneficial technical effects: The present invention can divide the reservoir more accurately by integrating multiple influencing factors. 2 In the flooding reservoir, the method of the present invention not only focuses on lithology and physical properties, but also combines reservoir storage characteristics and flow characteristics to ensure the comprehensiveness and rationality of the division. The present invention adopts a combination of pedigree clustering analysis and K-Means clustering to effectively reduce the redundancy and interference between influencing factors, ensure the relative independence and identity between the divided flow units, and improve the classification accuracy. The method of the present invention is not only applicable to CO 2 The reservoir flow unit division of oil-displacement reservoirs is also universal and can be applied to other types of reservoir development and reservoir management. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 In order to analyze the similarity between the influencing parameters of flow units and the evaluation parameters, the pedigree cluster analysis method was used to select relatively independent parameters from numerous parameters.

[0019] Figure 2 This is a distribution range diagram between porosity and permeability in different wells and different layers in the reservoir.

[0020] Figure 3 It is the distribution of the intervals between cluster centers with different K values.

[0021] Figure 4 For CO 2 K-means clustering distribution results of all flow units in the flooding reservoir. DETAILED DESCRIPTION

[0022] The technical solutions in the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0023] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0024] The present invention provides a CO-based 2 The multi-parameter division method of reservoir flow units for flooding comprises the following steps: Step S1, combined with the actual data analysis of the reservoir site, screen out the influencing and evaluation parameters related to the flow unit. The single factor parameters include: oil layer depth, porosity, permeability, effective thickness, oil saturation, pore throat radius (R35); the combined parameters include: formation coefficient, reservoir coefficient, interwell flow capacity index (IFCI), reservoir quality factor (RQI).

[0025] Considering the serious heterogeneity in the flow unit of the reservoir, if the flow unit is to be accurately divided, it is necessary to first clarify the seepage law of the flow unit. A flow unit is a reservoir unit with similar lithology and physical properties in a reservoir. Each flow unit usually represents a specific sedimentary environment and seepage characteristics. The present invention applies the principle of spectral cluster analysis to select relatively independent and representative evaluation parameters from a large number of influencing parameters, and then quantitatively characterizes the CO 2 The seepage and storage capacity of the reservoir flow unit.

[0026] Step S1 uses the following formula to calculate the similarity of the minimum distance between each influencing parameter of the flow unit: (Formula 1) In formula 1, d (x,y) is the Euclidean distance between two sample points x and y; i=1,2,3…n is the number of sample points; d (x,y) The smaller the sample x i and i The closer the nature.

[0027] The Euclidean distance between the influencing parameters of the flow unit calculated according to Formula 1, such as the clustering spectrum Figure 1 As shown in Figure 2, the influencing parameters of the flow units are divided into three types by the similarity of the minimum distance: ① Category I includes interwell flow capacity index, formation coefficient, and effective thickness; ② Class II includes reservoir quality factor (RQI), pore throat radius (R35), permeability, and reservoir coefficient; ③Class III includes porosity, oil saturation, and depth.

[0028] In the actual analysis case, according to the single factor porosity and permeability intersection chart Figure 2As shown in the figure, it is found that reservoir heterogeneity is often quite serious. The data of high permeability layer and low permeability layer show a maximum difference of nearly 1000 times, resulting in a large difference in permeability between reservoirs with basically the same porosity. The method of classifying flow units using a single parameter such as porosity or permeability is obviously not applicable. Comprehensive consideration of combined parameters can effectively reduce the error caused by a single factor being too large or too small. Therefore, the present invention adopts the method of selecting CO from the above combined parameters. 2 Evaluation parameters of reservoir flow unit.

[0029] The formulas for calculating each combined parameter are as follows: Formula 2 In formula 2, P 1 is the formation coefficient, which reflects the seepage capacity of the reservoir; k is the reservoir permeability; h is the CO 2 The effective thickness of the reservoir that can be mobilized; Formula 3 In formula 3, P 2 is the reservoir coefficient, which reflects the storage capacity of the reservoir; is the reservoir porosity; h is CO 2 The effective thickness of the reservoir that can be mobilized; s o is the oil saturation of the reservoir.

[0030] Formula 4 In formula 4, IFCI is the interwell flow capacity index, which reflects the flow capacity of reservoir fluid; K 1 is the permeability of low permeability reservoir; K 2 is the permeability of high permeability reservoir; h 1 is the effective thickness of the low permeability layer; h 2 is the effective thickness of the high permeability layer.

[0031] Formula 5 In formula 5, RQI is the reservoir quality factor, which reflects the quality of reservoir seepage; k is the reservoir permeability; is the reservoir porosity.

[0032] According to Formula 2, Formula 3, Formula 4 and Formula 5, the four combined parameters are compared and analyzed. It can be seen that the three evaluation parameters, formation coefficient, reservoir quality factor and interwell flowability index, can reflect the strength of reservoir flowability. The reservoir coefficient comprehensively considers the changes in oil saturation and porosity. 2 After flooding, the lower limit of the effective thickness of the reservoir changes. Combined with the oil saturation of the reservoir, the present invention finally selects two comprehensive evaluation indicators, formation coefficient and reservoir coefficient, and combines CO 2The flow units are divided according to the lower limit of the effective thickness after flooding.

[0033] Step S2, when cluster analysis method is usually used to divide flow units, the cluster analysis method is generally first applied to classify known samples, and then the stepwise analysis method is applied to discriminate and classify unknown samples, and then the discriminant function characterizing different flow units is obtained, thereby completing the automatic classification of flow units within the reservoir. In practical application, this method is mostly faced with the problem of a small number of known samples, and the results of cluster analysis plus discriminant analysis are often not ideal. Therefore, the present invention adopts the method of directly performing statistics on the data within the entire range of the reservoir in the study block and completing cluster analysis, so that the type of flow unit can be divided in one step.

[0034] According to Formula 2 and Formula 3, the formation coefficient P of each well and reservoir in the study block is calculated and sorted out. 1 and the storage coefficient P 2 size.

[0035] Step S3, cluster analysis method uses mature IBM SPSS Statistics data editor to sort out the formation coefficient P 1 and the storage coefficient P 2 Data processing. First, import the sorted data into the SPSS database and select the P 1 and P 2 Data, open the Analysis-Classification-K-means Clustering in the SPSS interface, set the K value of K-means clustering (that is, the flow units are divided into several categories), and rely on the data processing module to automatically obtain the classification results and the cluster intersection diagram of the formation coefficient and the reservoir coefficient.

[0036] Step S4: According to the K-means cluster classification result, the span distribution of each classification formation coefficient interval and reservoir coefficient interval should be guaranteed to be uniform and representative, so as to select a K value that can reflect the distribution of flow units in the entire study block.

[0037] Step S5: According to the final K-means clustering classification results, the boundary values ​​of the formation coefficient and reservoir coefficient corresponding to each type of flow unit can be obtained, and finally the CO 2 The division standards of various flow units in various reservoirs are as follows.

[0038] Embodiment 1, This example takes a gas drive block in Jilin Oilfield as an example. 2 The various flow units of the oil reservoir are divided into Figure 1-4 To explain: Based on the influence and evaluation parameters related to the flow unit, the single factor parameters and combined parameter values ​​of each well and each layer in the block are counted and calculated; According to the principle of pedigree clustering method: its main idea is to regard the data of all parameters as points in space, which can be connected by edges. The edge weight value between two points that are far away is lower, while the edge weight value between two points that are close is higher. By cutting the graph composed of all data points, the sum of the edge weights between different subgraphs after cutting is as low as possible, and the sum of the edge weights within the subgraph is as high as possible, so as to achieve the purpose of clustering. Formula 1 is used to calculate the Euclidean distance of each influencing and evaluation parameter related to the flow unit in the block, and the cluster pedigree diagram of the flow unit influencing parameters is obtained, such as Figure 1 shown.

[0039] according to Figure 1 Based on the similarity of the minimum distance in the flow unit, the 10 influencing and evaluation parameters of the flow unit are divided into three categories: Category I includes interwell flow capacity index, formation coefficient, and effective thickness; Category II includes reservoir quality factor (RQI), pore throat radius (R35), permeability, and storage coefficient; Category III includes porosity, oil saturation, and depth. A sensitivity analysis was carried out on the single factor influencing parameters porosity and permeability, and a porosity and permeability intersection diagram was drawn, as shown in the figure. Figure 2 As shown: In the single factor chart, the maximum difference between high permeability and low permeability is nearly 1000 times, so the selection of combined evaluation parameters can effectively reduce the numerical result error; Using formation coefficient P 1 and the storage coefficient P 2 The flow units were divided and the K-means clustering method was used to cluster the CO 2 Cluster analysis was performed on the flow units of the block reservoirs, and the flow units were divided into 2, 3, 4, and 5 categories (i.e., K value = 2, 3, 4, and 5); The final cluster center point data table with different K values ​​is statistically shown in Table 1: Table 1 Final cluster center points with different K values

[0040] According to the distribution results of the four final cluster centers, Figure 3 As shown in the figure, when K value = 2, the classification result is too general and cannot accurately characterize the seepage characteristics of the reservoir; when K value = 3, the span of the reservoir coefficient interval between the cluster centers is too large, which makes the storage capacity difference between the various flow units too large; when K value = 5, the formation coefficient intervals between the cluster centers are close to overlapping, which cannot reflect the difference in seepage between the various flow units; when K value = 4, the span intervals between the cluster centers are uniform, which can better reflect the size of the seepage capacity and storage capacity between the flow units of the reservoir. Therefore, the flow units are finally divided into four categories according to the cluster center results. The classification results are shown in the figure. Figure 4As shown in Table 2, the division standard of flow units in this block was established based on the classification results of formation coefficient and reservoir coefficient.

[0041] Table 2 Division criteria of flow units in a gas drive block in Jilin Oilfield

[0042] In this embodiment, the test area is divided into four types of flow units according to the unified standard shown in Table 2, among which the seepage capacity and storage capacity of the first type of flow unit are the best, the seepage capacity and storage capacity of the second and third types of flow units are second, and the seepage capacity and storage capacity of the fourth type of flow unit are the worst.

[0043] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-parameter division method of reservoir flow units based on CO2 flooding, comprising the following steps: S1. Determine the main influencing factors of the flow unit based on the oil storage characteristics of the CO2 flooding reservoir; S2, using the Euclidean distance in the pedigree cluster analysis method to classify the influencing factors determined in step S1, and screen out relatively independent parameters as comprehensive evaluation indicators for flow unit classification; S3. Calculate and count the comprehensive evaluation index of each flow unit in a single well reservoir; use the K-Means clustering method to classify all the flow units in the reservoir, and determine the boundaries of the comprehensive evaluation index corresponding to the classification results as the unified standard for dividing the flow unit type in the entire CO2 drive reservoir area, and use the unified standard to divide the flow unit types in each reservoir of the entire CO2 drive reservoir.

2. The division method according to claim 1, characterized in that: In step S1, the influencing factors include single factor parameters and combined parameters; The single factor parameters include oil layer depth, porosity, permeability, effective thickness, oil saturation and pore throat radius; The combined parameters include formation coefficient, reservoir coefficient, well flowability index and reservoir quality factor.

3. The division method according to claim 1 or 2, characterized in that: In step S2, the Euclidean distance coefficient is used to determine the similarity between the influencing factors of the flow unit.

4. The division method according to claim 3, characterized in that: According to formula (1), the similarity between the influencing factors of the flow unit is obtained: （1） Among them, d (x,y) is the distance between two sample points x and y; i=1,2,3…n is the number of sample points; d (x,y) The smaller the sample x i and i The closer the nature.

5. The division method according to claim 4, characterized in that: In step S2, the steps of screening the comprehensive evaluation index for flow unit classification according to the similarity of the Euclidean distance coefficient are as follows: Initialize n classes and treat each influencing parameter as a class. If the calculated d (x,y) The smaller the value, the closer the properties of the flow unit influencing parameters are. (x,y) Relatively small parameters are grouped into one category; Determine the distance from the new class to other classes, select the smallest distance, merge the above two classes into a new class, and repeat the calculation of each parameter as above until they are finally merged into one class.

6. The division method according to claim 5, characterized in that: Through the influence of each type of parameters on the seepage law of the flow unit, according to the similarity of the minimum distance coefficient, the characteristic parameters that can comprehensively reflect the fluid flow law of the flow unit in the CO2 flooding reservoir are screened as the comprehensive evaluation index for the classification of the flow unit.

7. The division method according to claim 6, characterized in that: The comprehensive evaluation indexes include formation coefficient and reservoir coefficient; The formation coefficient is used to evaluate the strength of the reservoir flow unit seepage capacity, as shown in formula (2): Among them, P1 is the formation coefficient; k is the reservoir permeability; h is the effective thickness of the CO2 flooding reservoir; The reservoir coefficient is used to evaluate the reservoir flow unit storage capacity, as shown in formula (3): Among them, P2 is the reservoir coefficient; is the reservoir porosity; h is the effective thickness of the CO2 flooding reservoir; s o is the oil saturation of the reservoir.

8. The division method according to claim 7, characterized in that: The steps to divide the flow units using the K-Means clustering method are as follows: According to the central clustering principle, the K-means of the flow units were designed into 2, 3, 4 and 5 categories, and the above four classification results were obtained using the K-means clustering algorithm of SPSS software; Analyze the final cluster centers of the four classification results and determine the optimal K-means; The division boundaries corresponding to the formation coefficient and reservoir coefficient in the optimal K-means clustering results are used as the division criteria for different types of flow units in CO2 reservoirs.

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