A method and system for selecting wells for profile control in a large-spacing reservoir

Abstract

The application discloses a method and system for selecting injection wells for profile control in large-well-spacing reservoirs, and belongs to the field of oilfield enhanced oil recovery. An index evaluation system and a comprehensive evaluation mathematical model are established, the evaluation weights of indexes in the index evaluation system are determined, the index values of each well group to be evaluated in the comprehensive evaluation mathematical model are normalized, the index parameter values of each well group to be evaluated are taken as input values, the comprehensive evaluation values of the profile control potential of each well group to be evaluated are obtained, the profile control potential comprehensive evaluation values of each well group to be evaluated are compared with the preset profile control well selection threshold value, and it is determined whether the well group to be evaluated is selectable or not. The potential index of each injection well group in a reservoir for deep profile control at a certain time point is quantitatively calculated and evaluated, the profile control feasibility of the well group is comprehensively evaluated, and the profile control potential of each well group in the same reservoir is further compared horizontally. The application has wide data sources, is convenient to implement, and has reliable evaluation results.

Description

Technical Field

[0001] This invention belongs to the field of enhanced oil recovery in oilfields, and relates to a method and system for well selection and displacement in reservoirs with large well spacing. Background Technology

[0002] Currently, water injection well selection is a widely used technique for controlling water and stabilizing oil production, thereby enhancing oil recovery during the high water-cut phase of water-drive oilfield development. Water injection wells are typically the target wells for this technique. Therefore, scientifically and rationally selecting the best wells for water injection well selection is a prerequisite for the successful implementation of this technology. Regarding well selection for water injection well selection, there are no unified standards or methods both domestically and internationally; the following principles are generally followed:

[0003] (1) The well network is relatively complete, and the connectivity between injection and production wells is good;

[0004] (2) The well group has a high water cut and a rapid increase in water cut;

[0005] (3) The contradictions between planar and longitudinal directions are prominent;

[0006] (4) There is a lot of oil left;

[0007] (5) Take into account factors such as the pressure index of the injection well, the heterogeneity of the water absorption profile, the daily oil production, liquid volume and water cut of the connected wells.

[0008] Existing well selection methods or principles for hydraulic displacement involve relatively broad evaluation indicators without a clear distinction between primary and secondary indicators, and are primarily qualitative. They cannot provide quantitative assessments of specific well groups, nor can they accurately compare the deep hydraulic displacement potential of different well groups within the same reservoir. Furthermore, these evaluation indicators are suitable for hydraulic displacement well selection in conventional reservoirs and cannot reflect the specific characteristics of deep hydraulic displacement well selection in some special reservoirs, such as reservoirs with large well spacing (average well spacing > 300m). Therefore, it is necessary to research and establish quantitative evaluation methods for well selection decisions targeting certain special reservoirs. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art, which is that the well selection method for well displacement cannot form a quantitative evaluation result for a specific well group and cannot accurately compare the deep displacement potential of each well group in the same reservoir, and to provide a well selection method and system for well displacement in reservoirs with large well spacing.

[0010] To achieve the above objectives, the present invention employs the following technical solution:

[0011] A method for selecting wells for oil reservoirs with large well spacing includes the following steps:

[0012] Step 1) Obtain reservoir geological data and test data of the water injection well group, and establish a well selection index evaluation system;

[0013] Step 2) Establish a comprehensive evaluation mathematical model for well selection based on reservoir geology and reservoir test data;

[0014] Step 3) Determine the evaluation weight of each indicator in the well selection index evaluation system, and normalize the values ​​of each indicator of the well group to be evaluated in the comprehensive evaluation mathematical model.

[0015] Step 4) Using the index parameter values ​​of each well group to be evaluated as input values, calculate the comprehensive evaluation value of the driving potential of each well group to be evaluated based on the comprehensive evaluation mathematical model.

[0016] Step 5) Based on the pre-set well selection threshold for adjustment and drive, compare the comprehensive evaluation value of the adjustment and drive potential of each well group to be evaluated with the well selection threshold for adjustment and drive, and determine whether the well group to be evaluated is "selectable" or "not selectable".

[0017] Preferably, in step 1), the reservoir geological data includes the reservoir's geographical location, reservoir characteristic parameters, reservoir temperature, reservoir pressure, formation fluid properties, well location data, remaining reserves, and information on the development and distribution of interlayers.

[0018] The test data includes static connectivity analysis data between wells, production dynamic data of water injection wells, production dynamic data of oil production wells, engineering logging and production logging interpretation results tables of water injection wells, water intake profile test data, wellhead pressure drop test data, water injection indicator curve test data, inter-well tracer test data, and production dynamic data of oil production wells.

[0019] Preferably, the well selection index evaluation system includes a first-level evaluation index and a second-level evaluation index;

[0020] The first-level evaluation indicators include three categories: overall characteristics of the water injection well group, heterogeneity of the well group, and injection-production dynamics of the well group.

[0021] The second-level evaluation indicators are further subdivisions of the various indicators in the first-level indicators.

[0022] Preferably, in step 2), the normalization process is as follows:

[0023] Based on the maximum and minimum values ​​of a certain indicator for each well, the corresponding indicator for that well is subjected to dimensionless normalization.

[0024] The normalized calculation formula for the index that is positively correlated with the driving effect is as follows:

[0025] Normalized value = (maximum value - index value) / (maximum value - minimum value) (1)

[0026] The normalized calculation formula for the index that is negatively correlated with the driving effect is as follows:

[0027] Normalized value = 1 - (maximum value - index value) / (maximum value - minimum value) (2)

[0028] After dimensionless normalization, all index values ​​are located in the range [0,1].

[0029] Preferably, step 3) further includes processing for missing data.

[0030] For missing data, a dynamic weighting method is used for processing.

[0031] Preferably, the specific process of step 4) is as follows:

[0032] First, the fuzzy comprehensive evaluation value F of the second-level evaluation index is calculated. j Calculation;

[0033] The values ​​of each factor F of the second-level evaluation index are respectively... ji Its weight value Q ji Multiply and sum them, and the sum is used as the value of the corresponding first-level evaluation index.

[0034] F j =F ji ·Q ji (j=1,2,3,i=1,2,...k) (8)

[0035] k is the number of second-level factors within the first level;

[0036] Secondly, the fuzzy comprehensive evaluation value F of the first-level evaluation index is calculated. Z Calculation of (defined as the drive potential index):

[0037]

[0038] In the formula, n represents the number of evaluation indicators at the first level, and ω j Let be the evaluation weight corresponding to the j-th first-level evaluation indicator.

[0039] Preferably, step 5) specifically involves:

[0040] The threshold F is compared with the newly calculated displacement potential index F of the injection well group within the same reservoir (block). Z Compare and determine whether the water injection well group to be evaluated can be selected as a control well;

[0041] If F≥F Z The well group was determined to be "optional" for evaluation.

[0042] If F≤F Z The evaluation well group was determined to be "unselectable".

[0043] A large-well-spacing reservoir well selection and displacement system includes:

[0044] The data acquisition module is used to acquire reservoir geological data and test data of the water injection well group, and to establish a well selection index evaluation system based on the reservoir geological data and test data of the water injection well group.

[0045] The model building module is used to build a comprehensive evaluation mathematical model for well selection;

[0046] The data processing unit determines the evaluation weight of each indicator and normalizes the values ​​of each indicator of the well group to be evaluated.

[0047] The evaluation module uses the index parameter values ​​of each well group to be evaluated as input values, and calculates the comprehensive evaluation value of the well's driving potential based on the comprehensive evaluation mathematical model. According to the pre-determined driving well selection threshold, it determines whether the well group to be evaluated is "selectable" or "unselectable".

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] This invention discloses a method for selecting wells for deep-layer regulation and displacement in reservoirs with large well spacing. Each indicator needs to be normalized before being included in the calculation. Weights for each indicator are established, and these weights can be determined using subjective methods (expert scoring), objective methods, or a combination of both. This method enables quantitative calculation and evaluation of the potential index for deep-layer regulation and displacement of each injection well group within a reservoir at a specific time point. It allows for a comprehensive evaluation of the feasibility of regulation and displacement for well groups and enables horizontal comparison of the regulation and displacement potential of different well groups within the same reservoir (block). The selected evaluation indicators and their weights reflect the characteristics of deep-layer regulation and displacement in reservoirs with large well spacing, which requires improving inter-layer contradictions near the wellhead and improving intra-layer contradictions further away from the wellhead, primarily focusing on the control of planar deep heterogeneity. The method of this invention has a wide range of data sources, is convenient to implement, and provides reliable evaluation results.

[0050] Furthermore, the well selection index system for large-spacing reservoirs includes three main categories of indicators reflecting the overall characteristics of the water injection well group, the heterogeneity of the well group, and the injection-production dynamics of the well group. The quantitative evaluation of well group dynamics, i.e., the calculation of the well group dynamics potential index, is achieved through fuzzy hierarchical comprehensive evaluation. The main category of indicators reflecting the overall reservoir characteristics of the water injection well group includes the well group's average permeability, recovery rate, remaining reserves or remaining recoverable reserves, average well spacing, and the frequency or integrity of interlayer development within the well group. These parameters can be obtained through drilling, logging, reservoir engineering calculations, and expert evaluation.

[0051] Furthermore, the indicators reflecting the heterogeneity of the injection well group include vertical heterogeneity indicators and planar heterogeneity indicators. The vertical heterogeneity indicators are composed of the permeability differences or permeability variation coefficients of each layer calculated by well logging or reservoir engineering; or, they are composed of the relative water absorption variation coefficients of each sub-layer obtained from water absorption profile testing. The planar heterogeneity indicators are composed of the reservoir planar average variation coefficient calculated by well logging or reservoir engineering; or, they are composed of the injection-production well correspondence rate obtained from dynamic analysis or tracer testing.

[0052] Furthermore, the dynamic indicators of well group injection and production include the water absorption capacity indicators of injection wells (consisting of one or more of the following: daily average water injection volume, water injection pressure, pressure index (PI), and water absorption index) and the production indicators of oil wells in the well group (consisting of one or more of the following: average daily production volume and average daily water cut).

[0053] Furthermore, based on the geological and development characteristics of the reservoir and the main factors influencing reservoir regulation and drive, a fuzzy comprehensive well selection method is adopted to establish a comprehensive evaluation mathematical model for well selection. This model defines the overall characteristics of the injection well group, the heterogeneity of the well group, and the injection-production dynamics of the well group as the first-level indicators. The sub-indicators under these three major categories are positioned as the second-level indicators. Fuzzy comprehensive evaluation calculations are performed on the indicators at each level separately, resulting in the final fuzzy comprehensive evaluation quantitative calculation result of the well group's regulation and drive (regulation and drive potential index).

[0054] Furthermore, a method for selecting wells in deep reservoirs with large well spacing allows for the absence of certain evaluation factor values ​​when data for individual well groups is incomplete. To avoid the inability to make comprehensive calculations and evaluations, a "dynamic weighting" method is adopted. Attached Figure Description

[0055] Figure 1 A structural diagram of the hierarchical analysis model for fuzzy comprehensive evaluation;

[0056] Figure 2 This is a screenshot of the calculation program interface. Detailed Implementation

[0057] The present invention will now be described in further detail with reference to the accompanying drawings:

[0058] Example 1

[0059] A method for selecting wells for oil reservoirs with large well spacing includes the following steps:

[0060] S1. Collect various data that can affect the regulation and drive effect of the target block, including reservoir geological data of the block and each water injection well group, development and production dynamic data, and various test data.

[0061] S2. Establish a well selection index system that reflects the characteristics of reservoirs with large well spacing based on actual data.

[0062] S3. Establish a fuzzy comprehensive evaluation hierarchical analysis mathematical model for well selection;

[0063] S4. Determine the evaluation weights for each indicator;

[0064] S5. Normalize the values ​​of various indicators of the well group to be evaluated.

[0065] S6. Perform "dynamic weighting" on indicators where data is missing;

[0066] S7. Manually calculate or compile a calculation program, input the index parameter values ​​of each well group to be evaluated, and calculate the comprehensive evaluation value of the driving potential of each well in a hierarchical manner according to the mathematical model structure.

[0067] S8. Based on the pre-determined well selection threshold, compare the comprehensive evaluation value of each well group with the threshold to determine whether each well is "selectable" or "not selectable".

[0068] Example 2

[0069] A method for selecting wells for oil reservoir displacement with large well spacing, the implementation plan is as follows:

[0070] (1) Collect geological, development and related test data of reservoirs (blocks) under study or to be evaluated and water injection well groups.

[0071] Specifically, this includes the reservoir's geographical location, reservoir characteristic parameters (porosity, permeability, saturation), reservoir temperature, reservoir pressure, formation fluid (oil, water) physical properties, well location data, remaining reserves, interlayer development and distribution data, inter-well static connectivity analysis data, water injection well production dynamic data, oil production well production dynamic data, water injection well engineering logging and production logging interpretation results tables, water intake profile test data, wellhead pressure drop test data, water injection indicator curve test data, inter-well tracer test data, and oil production well production dynamic data. The above data are the main data required for this invention. The more abundant the data, the more beneficial it is for a comprehensive and objective understanding of the reservoir and the true situation of each well group, and for accurately evaluating the potential for regulation and displacement of specific well groups within the reservoir. The actual data collected is not limited to the above.

[0072] (2) Determine and normalize the evaluation index system for deep well selection in reservoir well groups with large well spacing.

[0073] The evaluation index system is divided into two levels: Level 1 includes three main categories of indicators: overall characteristics of the injection well group, heterogeneity of the well group, and injection-production dynamics of the well group; Level 2 consists of further subdivisions of the above three categories. The overall characteristics of the injection well group include detailed indicators such as the remaining reserves or recoverable reserves of the well group, the average well spacing of the well group, and the frequency or integrity of the development of interlayers within the well group. The heterogeneity indicators of the well group include vertical heterogeneity indicators and planar heterogeneity indicators. Vertical heterogeneity indicators are composed of the permeability or permeability difference or permeability variation coefficient of each layer calculated by well logging or reservoir engineering, or the variation coefficient of relative or absolute water absorption of each sublayer obtained from water absorption profile testing; planar heterogeneity indicators are composed of the average permeability or planar average permeability variation coefficient of the area where each well group is located calculated by well logging or reservoir engineering, or the injection-production correspondence rate obtained from dynamic analysis or tracer testing. The dynamic indicators for well group injection and production include the water absorption capacity indicators of injection wells (consisting of one or more of the following: average daily injection volume, injection pressure, pressure index (PI), and water absorption index) and the production indicators of production wells in the well group (consisting of one or more of the following: average daily production volume and average daily water cut). After summarizing and organizing the actual collected data, specific evaluation indicators (second-level indicators) for deep well selection in large-spacing reservoir well groups are determined based on the richness and reliability of each data point. Compared with conventional reservoir well selection, the evaluation indicator system selected here must include one or more of the following three key indicators: well spacing, interlayer development, and planar heterogeneity.

[0074] Dimensionless normalization was performed on all evaluation index values ​​of the well evaluation team. Based on the maximum and minimum values ​​of a certain index for each well, dimensionless normalization was performed on the corresponding index for that well. The calculation formula for the normalization of the index positively correlated with the drive effect is as follows:

[0075] Normalized value = (maximum value - index value) / (maximum value - minimum value) (1)

[0076] The normalized calculation formula for the index that is negatively correlated with the driving effect is as follows:

[0077] Normalized value = 1 - (maximum value - index value) / (maximum value - minimum value) (2)

[0078] After dimensionless normalization, all index values ​​are located in the range [0,1].

[0079] (3) Determine the weight of each evaluation indicator in the evaluation system.

[0080] The weights of each evaluation indicator (within the range of [0,1]) can be determined by subjective methods (expert scoring), objective methods, or a combination of subjective and objective methods.

[0081] The subjective method for determining weights involves multiple experts scoring each indicator in the first and second levels according to its impact on the reservoir regulation and drive effect at large well spacing, and then averaging the scores from multiple experts to determine the weight of each indicator.

[0082] The objective method for determining weights is suitable for situations with a large number of wells and abundant statistical data for each indicator. The method involves statistically analyzing all indicators for all well groups to be evaluated, and then normalizing the coefficients of variation for each indicator to obtain its objective weight. Suppose there are n evaluation samples, each evaluating one of their m indicators. To facilitate the measurement of the importance of each indicator, firstly, based on the actual values ​​of each factor within the same structural level, and according to their classification criteria, their membership values ​​are obtained, resulting in a membership matrix.

[0083]

[0084] Calculate the mean and variance of the membership degree for each indicator.

[0085]

[0086]

[0087] The coefficient of variation for each indicator is

[0088]

[0089] For V i Normalization yields the weights of each indicator. H = [h1, h2, ..., h n ].

[0090] The method for determining the combined subjective and objective weights is to obtain the combined weights based on the subjective and objective weights calculated using the expert scoring method and the coefficient of variation method. The mathematical expression for calculating the factor weights using the combined subjective and objective method is as follows:

[0091] Q = α·B + β·H (7)

[0092] In the formula, Q represents the set of comprehensive weights for the indicators;

[0093] B and H represent the subjective and objective weight sets of the indicators, respectively.

[0094] α and β are the proportional coefficients of subjective weight and objective weight in the overall weight, respectively, and are usually taken as 0.4 and 0.6.

[0095] (4) Calculate the fuzzy comprehensive evaluation values ​​at each level.

[0096] First, the fuzzy comprehensive evaluation value F of the second-level evaluation index is calculated. j The calculation. The values ​​F of each factor index at level 2 are respectively... jiMultiply it by its weight value Q ji and accumulate, and the accumulated value is used as the value corresponding to the first level

[0097] F j = F ji ·Q ji (j = 1, 2, 3, i = 1, 2,... k) (8)

[0098] k is the number of second-level factors within the first level.

[0099] Secondly, perform the calculation of the fuzzy comprehensive evaluation value F Z (defined as the injection-production adjustment potential index):

[0100]

[0101] In the formula, n represents the number of evaluation indicators at the first level, and ω j is the evaluation weight corresponding to the j-th evaluation indicator at the first level.

[0102] (5) Handling of insufficient or missing data

[0103] If the index value of a certain evaluation factor is lacking in individual well groups, the method of "dynamic weight value" is used for processing. Suppose there are m indicators, and among them, n indicators (n < m) lack data. First, take the weight values of the n indicators lacking data as 0 respectively, then accumulate the theoretical weights of all indicators (not exceeding 1), and divide the theoretical weight value ω i of the i-th indicator by the accumulated value of the theoretical weights of the m - n indicators without lacking data as the weight value for the actual judgment calculation of the i-th indicator, that is, the actual weight value λ i .

[0104]

[0105] (6) Comprehensive evaluation of injection-production adjustment well selection

[0106] According to the injection-production adjustment effect of the injection-production adjustment measure well groups in the reservoir in the past, and combining the data of these well groups as samples, determine the threshold F of the injection-production adjustment potential index of the fuzzy comprehensive evaluation of the reservoir through the range of the injection-production adjustment potential index calculated by the above method. Compare the threshold F with the injection-production adjustment potential index value F Z of the newly calculated injection well groups in the same reservoir (block) to judge whether the to-be-evaluated injection well groups can be selected as injection-production adjustment wells.

[0107] If F ≥ F Z , it is determined that the evaluation well group is "selectable";

[0108] If F ≤ F Z , it is determined that the evaluation well group is "not selectable".

[0109] Example 3

[0110] The Donghe Sandstone Reservoir (HD4) in the Tarim Oilfield has a formation pressure of 51–55.5 MPa, a temperature of 115℃, an average formation water salinity of 244,000 mg / L, an average reservoir permeability of 222 mD, an average porosity of 13.8%, an average well spacing of 916 m, and remaining recoverable reserves of 23.4 million tons. 26 wells have been opened for water injection development, with an average injection pressure of 6.9 MPa. After years of water injection, the water injection efficiency of each well in this reservoir has become increasingly lower, water channeling has become severe, and oil production has declined rapidly. Therefore, it is necessary to select suitable water injection well groups for deep-seated regulation and displacement in this reservoir to control water and stabilize oil production.

[0111] Data were collected on the HD4 Donghe sandstone reservoir and 21 water injection well groups within it, including remaining recoverable reserves, well spacing, interlayer data, water absorption profile test data, inter-well tracer test data, wellhead pressure drop test data, water injection indicator curve test data, and production dynamic data of the injection-production well groups (including daily water injection volume, daily fluid production volume, water injection pressure, water cut, etc.).

[0112] Based on the collected data, a fuzzy hierarchical comprehensive evaluation structure for reservoir well selection and displacement in large-well-spacing reservoirs was optimized. The first level of evaluation consists of three main indicators: overall well group characteristics, reservoir heterogeneity, and injection-production dynamics. The second level of evaluation includes three subdivided indicators for overall well group characteristics: remaining recoverable reserves, average well spacing, and interlayer frequency. The second level of evaluation also includes indicators for reservoir heterogeneity: the coefficient of variation of water absorption profile (water absorption fraction) and tracer injection-production-receiving efficiency, representing vertical and planar heterogeneity. Finally, the second level of evaluation includes daily water injection volume, water injection pressure, and average water cut of the well group.

[0113] The structure of the hierarchical analysis model for fuzzy comprehensive evaluation of reservoir well selection with large well spacing is as follows: Figure 1 As shown.

[0114] The evaluation index values ​​of the well groups to be evaluated were subjected to dimensionless normalization. First, the maximum and minimum values ​​of each index involved in the evaluation were determined based on statistical data, as shown in Table 1.

[0115] Table 1 shows the maximum and minimum values ​​of each indicator.

[0116]

[0117]

[0118] The original values ​​of the indicators used for fuzzy comprehensive evaluation of the 21 water injection well groups are shown in Table 2.

[0119] Table 2 Original values ​​of indicators for each well group

[0120]

[0121] The blank spaces in Table 2 indicate that the corresponding data for the relevant indicators are missing.

[0122] The evaluation weights for each indicator were determined. The weights for each indicator were determined using an expert scoring method, as shown in Table 3 below.

[0123] Table 3 Weighting of Each Indicator

[0124]

[0125] According to equations (1) to (10), the normalization of index values, fuzzy comprehensive evaluation, and calculation of the driving potential index of each well group are performed using the developed calculation software. The software interface is as follows: Figure 2 As shown, by simply inputting the original values ​​shown in Tables 1 and 2, and the weights of each index shown in Table 3, the batch calculation of the driving potential index of each well group can be achieved (as shown in Table 4).

[0126] Table 4. Calculation Results of Fuzzy Comprehensive Evaluation for Well Selection in Optimization and Driving

[0127]

[0128] Based on the results of well selection and measures for early-stage dynamite-drive in the HD4 Donghe sandstone reservoir, the potential index threshold for well selection is set at 0.5. That is, wells with a dynamite-drive potential index ≥ 0.5 are considered "selectable" for dynamite-drive, while those with a dynamite-drive potential index ≥ 0.5 are considered "unselectable".

[0129] The calculation results show that among the 21 well groups, 9 have a displacement potential index value ≥ 0.5, while the rest are less than 0.5. The calculation results indicate that at the time point corresponding to the data used for evaluation, 9 well groups in the HD4 Donghe sandstone reservoir can be selected as wells for displacement control measures (Table 4).

[0130] Example 3

[0131] A large-well-spacing reservoir well selection and displacement system includes:

[0132] The data acquisition module is used to acquire reservoir geological data and test data of the water injection well group, and to establish a well selection index evaluation system based on the reservoir geological data and test data of the water injection well group.

[0133] The model building module is used to build a comprehensive evaluation mathematical model for well selection;

[0134] The data processing unit determines the evaluation weight of each indicator and normalizes the values ​​of each indicator of the well group to be evaluated.

[0135] The evaluation module uses the index parameter values ​​of each well group to be evaluated as input values, and calculates the comprehensive evaluation value of the well's driving potential based on the comprehensive evaluation mathematical model. According to the pre-determined driving well selection threshold, it determines whether the well group to be evaluated is "selectable" or "unselectable".

[0136] In summary, in practical applications, evaluation indicators can be appropriately increased or decreased based on the availability of reservoir and well group data, and incomplete data for individual well groups is also permissible. By using data from different periods, the potential for well group adjustment and drive can be calculated at different times, enabling optimized well selection decisions.

[0137] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for selecting wells for profile control in a large spacing reservoir, characterized in that, Includes the following steps: Step 1) Obtain reservoir geological data and test data of the water injection well group, and establish a well selection index evaluation system; Step 2) Establish a comprehensive evaluation mathematical model for well selection based on reservoir geology and reservoir test data; Step 3) Determine the evaluation weight of each indicator in the well selection index evaluation system, and normalize the values ​​of each indicator of the well group to be evaluated in the comprehensive evaluation mathematical model; Step 4) Using the index parameter values ​​of each well group to be evaluated as input values, calculate the comprehensive evaluation value of the driving potential of each well group to be evaluated based on the comprehensive evaluation mathematical model. Step 5) Based on the pre-set well selection threshold for adjustment and drive, compare the comprehensive evaluation value of the adjustment and drive potential of each well group to be evaluated with the well selection threshold for adjustment and drive, and determine whether the well group to be evaluated is "selectable" or "not selectable". In step 1), the reservoir geological data includes the reservoir's geographical location, reservoir characteristic parameters, reservoir temperature, reservoir pressure, formation fluid properties, well location data, remaining reserves, and information on the development and distribution of interlayers. The test data includes static connectivity analysis data between wells, production dynamic data of water injection wells, interpretation results of engineering logging and production logging of water injection wells, water intake profile test data, wellhead pressure drop test data, water injection indicator curve test data, inter-well tracer test data, and production dynamic data of oil production wells. The well selection index evaluation system includes first-level evaluation indicators and second-level evaluation indicators; The first-level evaluation indicators include three categories: overall characteristics of the water injection well group, heterogeneity of the well group, and injection-production dynamics of the well group. The second-level evaluation indicators are further subdivisions of the various indicators in the first-level indicators; The specific process of step 4) is as follows: First, the fuzzy comprehensive evaluation value of the second level evaluation index is calculated . The values ​​of each factor in the second-level evaluation index are respectively... Its weight value Multiply and sum them, and the sum is used as the value of the corresponding first-level evaluation index. （ ， ）（8） k is the number of second-level factors within the first level; Secondly, the fuzzy comprehensive evaluation values ​​of the first-level evaluation indicators are calculated. Calculation: （9） In the formula, n represents the number of evaluation indicators at the first level. Let be the evaluation weight corresponding to the j-th first-level evaluation indicator.

2. The method according to claim 1, wherein, In step 3), the specific process of normalization is as follows: Based on the maximum and minimum values ​​of a certain indicator for each well, the corresponding indicator for that well is subjected to dimensionless normalization. The normalized calculation formula for the index that is positively correlated with the driving effect is as follows: Normalized value = (maximum value - index value) / (maximum value - minimum value) (1) The normalized calculation formula for the index that is negatively correlated with the driving effect is as follows: Normalized value = 1 - (maximum value - index value) / (maximum value - minimum value) (2) After dimensionless normalization, all index values ​​are located in the range [0,1].

3. The method for selecting wells for reservoir displacement in large well spacing according to claim 1, characterized in that, Step 3) also includes processing for missing data. For missing data, a dynamic weighting method is used for processing.

4. The method for selecting wells for reservoir displacement in large well spacing according to claim 1, characterized in that, Step 5) is specifically as follows: Use threshold The newly calculated water injection well group within the same reservoir has a displacement potential index value. Compare and determine whether the water injection well group to be evaluated can be selected as a control well; If , the well group is determined to be "selectable"; If , the evaluation well group is determined as "unselectable".

5. A large spacing reservoir profile control selective well system, characterized in that, The method for selecting wells for large-spacing reservoirs as described in claim 1 includes: The data acquisition module is used to acquire reservoir geological data and test data of the water injection well group, and to establish a well selection index evaluation system based on the reservoir geological data and test data of the water injection well group. The model building module is used to build a comprehensive evaluation mathematical model for well selection; The data processing unit determines the evaluation weight of each indicator and normalizes the values ​​of each indicator of the well group to be evaluated. The evaluation module uses the index parameter values ​​of each well group to be evaluated as input values, calculates the comprehensive evaluation value of the well's driving potential based on the comprehensive evaluation mathematical model, and compares and determines whether the well group to be evaluated is "selectable" or "unselectable" according to the pre-determined driving well selection threshold.

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