Water drive reservoir flow field regulation and control method based on streamline clustering

Abstract

The invention discloses a streamline clustering-based water-drive reservoir flow field regulation and control method, which comprises the following steps of: 1, establishing a block numerical simulation model, and carrying out streamline numerical simulation; 2, connecting a Python program with a streamline numerical simulator, extracting streamline attributes, and obtaining a streamline field; 3, flow field water drive capability characterization indexes are constructed, wherein the indexes comprise the production potential coefficient and the water content; 4, calculating the production potential coefficient and the water content of each streamline, carrying out streamline clustering analysis, and identifying a dominant seepage channel and a low-efficiency water injection region as constraint conditions; and 5, optimizing the injection-production quantity of each well by taking the maximum streamline average oil displacement efficiency as a target function in combination with a clustering result. According to the method, the flow field can be identified and regulated more quickly and accurately, the calculation efficiency is higher, and the method has wide application value in complex water-drive oil reservoirs.

Description

Technical Field

[0001] The invention belongs to the technical field of oilfield water drive development, and in particular relates to a water drive reservoir flow field control method based on streamline clustering. Background Art

[0002] At this stage, traditional development methods are difficult to meet the needs of efficient exploitation, and more refined development measures are urgently needed. Among them, flow field regulation has become an important means to improve the utilization efficiency of injected water. By reasonably adjusting the water injection plan and the liquid production plan, the flow state inside the reservoir can be effectively improved, the ineffective circulation and water injection dead corners can be reduced, and the oil recovery efficiency can be improved. This not only helps to stabilize and increase crude oil production, but also can extend the development life of the oil field, reduce development costs, and achieve efficient utilization of resources. Therefore, it is urgent to strengthen the research and practice of flow field regulation to improve the development efficiency of domestic oil fields.

[0003] In actual reservoir development, in order to improve water injection utilization and crude oil recovery, it is necessary to strengthen the understanding of the underground oil-water four-dimensional seepage field, clarify the distribution of reservoir flow field, and formulate targeted injection and production control strategies. Most of the current technical methods rely on traditional reservoir numerical simulation or tracer method to simulate flow field distribution. The simulation accuracy of these methods still needs to be further improved, and the optimization efficiency is low, making it difficult to achieve efficient optimization of the injection and production system. Summary of the invention

[0004] The purpose of the present invention is to provide a flow field control method for water-driven oil reservoirs based on streamline clustering. The present invention based on streamline simulation can obtain a visualized four-dimensional oil-water seepage field without using a traditional black oil model, and use the streamline clustering method to identify high production potential areas and advantageous seepage areas of the reservoir, which are used as constraints for injection and production control, and use artificial intelligence algorithms to optimize injection and production. Compared with the traditional black oil model, the present invention can significantly improve the calculation efficiency, can be applied to complex reservoir calculation domains, quickly obtain flow field distribution, and carry out injection and production control.

[0005] To achieve the above object, the present invention provides a flow field control method for water drive reservoir based on streamline clustering, comprising the following steps:

[0006] Step 1: Establish a block numerical simulation model and perform streamline numerical simulation.

[0007] Step 2: Use Python program to connect with the streamline numerical simulator, extract streamline properties, and obtain streamline field;

[0008] Step 3: construct the characterization index of flow field water flooding capacity, including production potential coefficient and water content;

[0009] Step 4: Calculate the production potential coefficient and water content of each flow line, perform flow line cluster analysis, identify the dominant seepage channels and inefficient water injection areas as constraints,

[0010] Step 5: Combine the clustering results and optimize the injection and production volume of each well by maximizing the average oil recovery efficiency of the streamlines as the objective function.

[0011] In step 1, the streamline model is established by using the streamline reservoir numerical simulation method, and the streamline simulation is carried out. In step 2, the data that quantitatively characterizes the flow field is extracted by docking with the streamline numerical simulator through the Python program. Compared with the traditional reservoir numerical simulation technology, streamline simulation converts the calculation of three-dimensional grid attributes into one-dimensional streamline attribute calculation. This method has significant computational advantages when processing large-scale data and can reduce the influence of grid direction. In this way, the abstract streamline feature data can be converted into intuitive underground fluid migration laws and residual oil distribution characteristics, so as to more accurately characterize the flow field.

[0012] Furthermore, in step 3, the constructed flow field water flooding capacity characterization index includes production potential coefficient and water content, and the production potential coefficient is defined as follows:

[0013]

[0014]

[0015] In the above formula, F pij represents the production potential coefficient of the jth node on the i-th streamline, SWAT ij represents the water saturation of the streamline node (i, j), S oi Represents the initial oil saturation of the reservoir, S or Represents the residual oil saturation of the reservoir, F pi represents the average oil saturation of the ith streamline, n i Represents the number of nodes of the i-th streamline.

[0016] Flowline moisture content refers to the ratio of water phase flow rate to total liquid production along the flowline direction. Its calculation formula is:

[0017]

[0018]

[0019] In the above formula, f wij is the moisture content of the jth node on the i-th streamline, FLOWAT ij is the water phase flow rate at the streamline node (i, j), FLOOIL ij is the oil phase flow rate at the streamline node (i, j), f wiis the average moisture content of the ith streamline.

[0020] Furthermore, in step 4, the K-means clustering algorithm is selected to cluster the streamlines, and the streamline characterization parameters are used as clustering data to perform cluster analysis on the streamlines, and the silhouette coefficient method is applied to determine how many categories the streamlines need to be clustered into. The closer the silhouette coefficient is to 1, the better the clustering effect is. The silhouette coefficient calculation formula is as follows:

[0021]

[0022]

[0023] In the above formula, SC i Represents the silhouette coefficient of the i-th data after clustering, b i represents the minimum average distance between the i-th data and the rest of the cluster data, measuring the separation of the clustering results; a i It represents the average distance between the ith data and the data in the same cluster, and measures the cohesion of the clustering results. SC represents the average silhouette coefficient of all data, and measures the quality of the overall clustering results.

[0024] In step 4, the K-means clustering algorithm is combined with the silhouette coefficient algorithm to determine the best four types of streamlines, among which type I streamlines have the highest production potential coefficient and the lowest water content, followed by type II and III streamlines, and type IV streamlines have the lowest production potential coefficient and the highest water content. For the injection and production wells in the areas where the streamlines of types I and II are concentrated, the constraints of increasing injection and raising fluid are adopted, and for the areas where the streamlines of types III and IV are concentrated, the constraints of reducing injection and lowering fluid are adopted.

[0025] Furthermore, in step 5, the objective function streamline average oil recovery efficiency calculation formula is as follows:

[0026]

[0027]

[0028] In the above formula, E Dij is the oil displacement efficiency of the jth node on the i-th streamline, E Di is the average oil displacement efficiency of the ith streamline.

[0029] The method according to claim 1 is characterized in that, in step 5, the optimization variable is the injection and production volume of each well, which is expressed as follows:

[0030]

[0031] In the above formula, to are the liquid volumes of the 1st to the nth injection wells respectively; to They are the liquid volumes of the 1st to mth production wells respectively, where n+m=w.

[0032] The injection and production volume of each well must meet the upper and lower limit constraints of the injection and production volume of each well, and must also meet the constraints on increasing injection and increasing fluid and reducing injection and decreasing fluid in step 4.

[0033]

[0034]

[0035] In the above formula, are the minimum and maximum injection volumes of the ith injection well, m 3 / d; are the minimum and maximum production fluid volumes of the jth production well, m 3 / d.

[0036] Beneficial effects:

[0037] Compared with the traditional black oil numerical simulation, the present invention applies streamline numerical simulation technology, extracts attribute information along the streamline, constructs flow field water drive capacity characterization index, uses cluster analysis method to identify high production potential areas and advantageous seepage channels of the flow field, realizes fluid flow visualization and flow field intensity quantitative characterization, and uses this as a constraint to control the flow field. This method has the computational advantage of large-scale data. At the same time, the fluid flow is a one-dimensional migration along the streamline according to the direction of the pressure gradient, not a three-dimensional movement on the grid, which can more accurately characterize the flow field. By reasonably adjusting the injection and production volume plan, it can effectively improve the flow state inside the reservoir, reduce invalid circulation and water injection dead corners, improve oil recovery efficiency, help stabilize and increase crude oil production, and extend the development life of the oil field and reduce development costs.

[0038] The invention has been successfully applied in Block X of the E Oilfield in Iraq, and the well flow field optimization adjustment has been successfully carried out according to the method. After the method was implemented on site, after 10 years of simulation, the reservoir recovery rate increased from 25.9% to 28.1%, and the comprehensive water content of the reservoir decreased from 78.34% to 77.87%, with a cumulative increase of 150,000 cubic meters of oil, achieving good economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a flow chart of the method for regulating flow field of water drive reservoir based on streamline clustering of the present invention;

[0040] Figure 2 It is a schematic diagram of the streamline field preservation form in a specific embodiment of the present invention;

[0041] Figure 3A flow chart of a K-means clustering algorithm in a specific embodiment of the present invention;

[0042] Figure 4 is the silhouette coefficient score under different streamline cluster numbers in a specific embodiment of the present invention;

[0043] Figure 5 This is the streamline clustering result before flow field regulation in a specific embodiment of the present invention;

[0044] Figure 6 A flow chart of a particle swarm algorithm in a specific embodiment of the present invention;

[0045] Figure 7 The result of optimizing the injection and production volume using the particle swarm algorithm in a specific embodiment of the present invention;

[0046] Figure 8 It is the streamline clustering result after flow field regulation in a specific embodiment of the present invention;

[0047] Fig. 9 A comparison of the changes in the cumulative oil production and water content before and after flow field regulation in a specific embodiment of the present invention; DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the purpose, technical implementation scheme and advantages of the present invention, the present invention is now described in detail in conjunction with the following examples. It should be understood that the specific examples described are only used to explain the present invention, and are intended to provide further description of the present invention, rather than to limit the present invention.

[0049] like Figure 1 As shown, Figure 1 The present invention is a flow chart of the flow field control method of water drive reservoir based on streamline clustering.

[0050] Taking Block X of Iraq's E oil field as an example, the method includes the following steps:

[0051] Step 1: Establish the geological model of Block X of Iraq's E oilfield and conduct streamline simulation.

[0052] Step 2: Connect with the streamline numerical simulator through Python. The resulting streamline consists of multiple nodes, such as Figure 2 As shown, the streamline node attribute information saved in the form of keywords is obtained to obtain the streamline field; the streamline feature extraction data is described in Table 1 below.

[0053] Table 1 Streamline feature extraction data table

[0054] Keywords Streamline features GEOMINDX Streamline node number GEOMETRY 3D coordinates of streamline nodes FLOOIL Oil phase flow rate at streamline nodes FLOWAT Water phase flow at streamline nodes SWAT Water saturation at flowline nodes SGAS Gas saturation at streamline nodes TIME_BEG Streamline flight time

[0055] Step 3: Calculate the water drive capacity characterization parameters of each streamline node, including production potential coefficient and water content. For a certain streamline, take the average value of all streamline nodes as the water drive capacity characterization index of the streamline.

[0056] Step 4: Based on the calculated water drive capacity characterization parameters, the K-means clustering algorithm suitable for flow field characterization is selected to perform streamline clustering analysis. The principle of the K-means clustering algorithm is as follows: Figure 3 The silhouette coefficient algorithm was used to evaluate the clustering effect when the number of streamline clustering clusters increased from 2 to 12. When the number of streamline clustering clusters was 4, the silhouette coefficient value was closest to 1, indicating that the clustering effect was the best, as shown in Figure 2. Figure 4 As shown. Using the K-means clustering algorithm, the streamlines are clustered into 4 clusters. The clustering results are shown in Figure 5 Among them, the type I flow line has the highest production potential coefficient and the lowest water content, while the type IV flow line has the lowest production potential coefficient and the highest water content, as shown in Table 2.

[0057] Table 2 Streamline clustering results

[0058] Streamline category Production potential coefficient Moisture content I 0.791 0.313 II 0.587 0.546 III 0.491 0.729 IV 0.320 0.845

[0059] Step 5. Based on the streamline clustering results, identify the areas with streamline clustering of type I and II in the flow field and the areas with sparse streamlines. The injected water swept volume in this area is small, the streamline control range is small, the production potential is high and the water content is low. The injection and production wells in this area are subject to injection and liquid increase measures to "create streamlines", including I1, I2, P3, and P4 wells, to replenish formation energy and improve the degree of control. Next, identify the areas with streamline clustering of type III and IV in the flow field. This area has low production potential and high water content, has formed a dominant channel for injected water, and has low injection water utilization efficiency. The injection and production wells in this area are subject to injection and liquid reduction measures to "change streamlines", including P1, P2, I3, and I4 wells, to improve the utilization rate of injected water and control the speed of water content rise. Through the particle swarm algorithm, the flow field identification results are used as constraints, and the objective function is to maximize the average streamline oil recovery efficiency to find the optimal injection and production volume. The particle swarm algorithm process is as follows: Figure 6 As shown in the figure, the injection and production volume results of particle swarm optimization are as follows Figure 7 As shown in Figure 2, the flow field distribution after regulation is as follows: Figure 8 As shown in Figure 2, the changes in oil production and water content of the blocks before and after regulation are as follows: Fig. 9 shown.

[0060] After the implementation of this method in Block X of the Iraqi E Oilfield, the streamline distribution in the flow field became more uniform, and the number of Class III and IV streamlines was significantly reduced. After 10 years of simulation, the reservoir recovery rate increased from 25.9% to 28.1%, and the comprehensive water content of the reservoir decreased from 78.34% to 77.87%. The cumulative increase in oil production was 150,000 cubic meters, achieving good economic and social benefits.

[0061] The above-described embodiments are preferred implementations of the present invention. Although the present invention has been disclosed through the above-described embodiments, they are not intended to limit the present invention. Any person skilled in the art can make some changes or modifications according to the technical contents disclosed above without departing from the technical solution of the present invention to achieve equivalent implementations. As long as they do not depart from the essence of the technical solution of the present invention, any simple modification, equivalent change or modification made to the above-described embodiments still falls within the protection scope of the technical solution of the present invention.

Claims

1. The present invention provides a flow field control method for water drive reservoirs based on streamline clustering, characterized in that: The steps include: Step 1: Establish a block numerical simulation model and carry out streamline simulation; Step 2: Use Python program to connect with the streamline numerical simulator, extract streamline properties, and obtain the streamline field; Step 3: Construct the characterization index of flow field water flooding capacity, including production potential coefficient and water content; Step 4: Calculate the production potential coefficient and water content of each flow line, perform flow line cluster analysis, and identify the dominant seepage channels and inefficient water injection areas as constraints; Step 5: Combine the clustering results and optimize the injection and production volume of each well by maximizing the average oil recovery efficiency of the streamline as the objective function.

2. The method for controlling flow field in a water-flooding reservoir based on streamline clustering according to claim 1, characterized in that: In step 2, the streamline attribute information saved in the form of keywords is extracted by docking with the streamline numerical simulator through the Python program to obtain the reservoir streamline field distribution.

3. The method for controlling flow field in a water-flooding reservoir based on streamline clustering according to claim 1, characterized in that: In step 3, the water flooding capacity characterization index of the flow field is constructed, including the production potential coefficient and water content. The production potential coefficient is defined as follows: In the above formula, F pij represents the production potential coefficient of the jth node on the i-th streamline, SWAT ij represents the water saturation of the streamline node (i, j), S oi represents the initial oil saturation of the reservoir, S or Represents the residual oil saturation of the reservoir, F pi represents the average oil saturation of the ith streamline, n i represents the number of nodes of the i-th streamline; Flowline moisture content refers to the ratio of water phase flow rate to total liquid production along the flowline direction. The calculation formula is: In the above formula, f wij is the moisture content of the jth node on the i-th streamline, FLOWAT ij is the water phase flow rate at the streamline node (i, j), FLOOIL ij is the oil phase flow rate at the streamline node (i, j), f wi is the average moisture content of the ith streamline.

4. The method according to claim 1, characterized in that: In step 4, the K-means clustering algorithm is selected for streamline clustering, and the streamline characterization parameters are clustered and analyzed. The silhouette coefficient method is used to determine that the streamlines need to be clustered into 4 clusters, among which the type I streamline has the highest production potential coefficient and the lowest water content, followed by the type II and III streamlines, and the type IV streamline has the lowest production potential coefficient and the highest water content. For the injection and production wells in the area where the type I and II streamlines are concentrated, the constraint of increasing injection and raising fluid is adopted, and for the area where the type III and IV streamlines are concentrated, the constraint of reducing injection and lowering fluid is adopted.

5. The method according to claim 1, characterized in that: In step 5, the particle swarm algorithm is used as the optimization algorithm, and the streamline average oil recovery efficiency is used as the objective function. The greater the streamline average oil recovery efficiency, the greater the injection water utilization rate, the higher the cumulative oil production of the oil field, and the better the development effect. The calculation formula of streamline average oil recovery efficiency is as follows: In the above formula, E Dij is the oil displacement efficiency of the jth node on the i-th streamline, E Di is the average oil displacement efficiency of the ith streamline.

6. The method according to claim 1, characterized in that: In step 5, the optimization variable is the injection and production volume of each well, which is expressed as follows: In the above formula, q1 inj To q n inj are the liquid volumes of the 1st to the nth injection wells respectively; q1 pro To q m pro are the liquid volumes of the 1st to the mth production wells, respectively, where n+m=w; The injection and production volume of each well must meet the upper and lower limit constraints of the injection and production volume of each well, and must also meet the constraints on increasing injection and reducing injection and reducing fluid in step 4; In the above formula, are the minimum and maximum injection volumes of the ith injection well, m 3 / d; are the minimum and maximum production fluid volumes of the jth production well, m 3 / d.

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