Methods for profile control and well selection, methods for predicting the oil production potential of profile control and well selection, and related equipment

Abstract

This invention proposes a profile control and well selection method, a method for predicting the oil production potential of profile control and well selection, and related equipment. The profile control and well selection method includes: calculating the Spearman correlation coefficient between the data of each profile control well and the oil production of a single well group to determine the parameters of the well selection model; then determining the parameter weights for each parameter of the well selection model; next, using the parameter weights, parameter values, and a preset well selection model, obtaining the decision factors for each single well group; finally, sorting the oil production of each single well group according to the ascending order of the decision factors, calculating the cumulative oil production corresponding to each decision factor after sorting, plotting the relationship curve between each decision factor and the cumulative oil production, and determining the priority of profile control and well selection construction. This invention has the advantages of strong adaptability to field needs and easy data acquisition. Furthermore, it can determine the priority of profile control and well selection construction, effectively guiding field practice.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development technology, and particularly relates to a method for profile control and well selection, a method for predicting the oil production potential of profile control and well selection, and related devices. Background Technology

[0002] Heterogeneous reservoirs are prone to forming dominant water channels during waterflooding development, leading to inefficient and ineffective water circulation. Profile control technology involves injecting profile control agents into water wells to block these dominant channels, adjusting the seepage field of the original waterflooded reservoir, thereby expanding the sweep system of subsequent water injection and achieving the effect of stabilizing oil production and reducing water levels. The key to the success of profile control measures lies in well selection. Because the factors affecting the effectiveness of profile control are complex, and the main factors affecting the effectiveness of profile control differ under different reservoir types and development methods, it is difficult to find a universal well selection method.

[0003] Currently, the publicly available well selection methods are mainly divided into two categories: The first category is the single-factor well selection method based on the pressure index. This method calculates the pressure index by monitoring the wellhead pressure drop after the water well is shut in, and then selects wells for profile control. This type of method has poor adaptability to wells that have undergone multiple rounds of profile control. The second category is the multi-factor well selection method. Some well selection methods cannot reflect the oil production effect of profile control measures, some well selection methods have indicators that are not easy to obtain accurately, some well selection methods have complex indicator technologies that are difficult to promote in the field, and some well selection methods are suitable for a certain type of reservoir but have poor adaptability to other reservoirs. Summary of the Invention

[0004] To enrich the methods for profile adjustment and well selection, the method proposed in this invention includes the following steps:

[0005] Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0006] Determine the parameter weights for each parameter in the well selection model;

[0007] By utilizing the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained;

[0008] The oil increase of each single well group is sorted according to the ascending order of decision factors, and the cumulative oil increase corresponding to each decision factor after sorting is calculated. The relationship curve between each decision factor and the cumulative oil increase is plotted, and the priority of profile adjustment and well selection construction is determined according to the trend of the slope of the curve.

[0009] Furthermore, the calculation of the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group, and if the Spearman correlation coefficient meets a preset rule, then the corresponding profile-adjusting well data is used as a parameter of a preset well selection model, including:

[0010] Calculate the Spearman correlation coefficient R between the data of each profile-adjusting well and the oil increase of a single well group;

[0011] If the Spearman correlation coefficient R between profile-adjusting well data and the oil increase of a single well group satisfies the following relationship, then the profile-adjusting well data shall be used as the well selection model parameter:

[0012] R>Rs or R<-Rs,

[0013] Where Rs is the critical value of the Spearman rank correlation coefficient when the confidence level reaches a preset threshold.

[0014] Furthermore, determining the parameter weights for each parameter of the well selection model includes:

[0015] The total correlation coefficient is obtained by summing the absolute values ​​of the Spearman correlation coefficients of all parameters in the well selection model.

[0016] The weights of each parameter in the well selection model are obtained by dividing the Spearman correlation coefficient of each parameter by the total correlation coefficient.

[0017] Furthermore, the decision factors for each single well group are obtained by utilizing the parameter weights, parameter values, and preset well selection model parameters of the well selection model, including:

[0018] If R > Rs, then the corresponding well selection model parameters are assigned values ​​using the ascending half-gradient method; or, if R < -Rs, then the corresponding well selection model parameters are assigned values ​​using the descending half-gradient method.

[0019] The parameter values ​​of each well selection model after assignment are normalized.

[0020] Substituting the parameter weights and values ​​of the well selection model parameters into the following preset well selection model, the decision factors for each single well group are obtained:

[0021]

[0022] Where F is the well selection decision factor, W i Xi represents the parameter weights of each well selection model parameter, and Xi is the normalized value of each well selection model parameter after being assigned a value.

[0023] Furthermore, the oil increase of each single well group is sorted in ascending order according to the decision factors, and the cumulative oil increase corresponding to each decision factor after sorting is calculated, including:

[0024] Sort the decision factors in ascending order;

[0025] The oil production of single well groups corresponding to the decision factors is arranged according to the ranking of the decision factors;

[0026] Calculate the cumulative oil increase for each decision factor corresponding to the current position.

[0027] Furthermore, the profile control and well selection method includes the following steps: plotting the relationship curves between decision factors and oil production of each single well group to predict the oil production potential of profile control measures.

[0028] Based on the same inventive concept, this invention also discloses a profile control and well selection device, including a parameter determination module, a parameter weighting module, a well selection model storage module, and a construction priority determination module, wherein:

[0029] The parameter determination module is used to calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0030] The parameter weighting module is used to determine the parameter weights for each parameter in the well selection model.

[0031] The well selection model storage module is used to store preset well selection models and to obtain the decision factors for each single well group using the parameter weights and parameter values ​​of the well selection model parameters.

[0032] The construction priority determination module is used to sort the oil increase of each single well group according to the ascending order of decision factors, calculate the cumulative oil increase corresponding to each decision factor after sorting, draw the relationship curve between each decision factor and the cumulative oil increase, and determine the priority of profile adjustment and well selection construction based on the trend of curve slope change.

[0033] On the other hand, the present invention also discloses a method for predicting the oil production potential of well profile selection, comprising the following steps:

[0034] Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0035] Determine the parameter weights for each parameter in the well selection model;

[0036] By utilizing the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained; the relationship curves between the decision factors and the oil production of each single well group are plotted to predict the oil production potential of profile control measures.

[0037] Based on the same inventive concept, this invention also discloses a profile control and well selection oil enhancement potential prediction device, which includes a parameter determination module, a parameter weighting module, a well selection model storage module, and an oil enhancement potential prediction module, wherein:

[0038] The parameter determination module is used to calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0039] The parameter weighting module is used to determine the parameter weights for each parameter in the well selection model.

[0040] The well selection model storage module is used to store preset well selection models and to obtain the decision factors for each single well group using the parameter weights and parameter values ​​of the well selection model parameters.

[0041] The oil production potential prediction module is used to predict the oil production potential of profile control measures based on the relationship between decision factors and the oil production of each single well group.

[0042] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0043] This invention discloses at least one method for profile control and well selection, comprising: calculating the Spearman correlation coefficient between the data of each profile control well and the oil increase of a single well group, and determining the parameters of the well selection model; then determining the parameter weights for each parameter of the well selection model; then using the parameter weights, parameter values, and the preset well selection model, obtaining the decision factors for each single well group; finally, sorting the oil increase of each single well group according to the ascending order of the decision factors, calculating the cumulative oil increase corresponding to each decision factor after sorting, plotting the relationship curve between each decision factor and the cumulative oil increase, and determining the priority of profile control and well selection construction.

[0044] This invention establishes a rank-correlation well selection model. By analyzing the Spearman rank correlation between parameters and oil increase, the parameters used in the well selection model are determined. The data type for profile control wells in this invention can be either numerical variables or ordered categorical variables, offering advantages such as strong adaptability to field needs and easy data acquisition. Furthermore, this invention sorts the oil increase of each single well group according to the ascending order of decision factors, calculates the cumulative oil increase corresponding to each decision factor after sorting, and plots the relationship curves between each decision factor and the cumulative oil increase. This determines the priority of profile control well selection operations and can effectively guide field practice. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a well selection and profile adjustment method according to Embodiment 1 of the present invention.

[0047] Figure 2 This is a graph showing the relationship between the decision factors and cumulative oil production of 30 wells in Embodiment 1 of the present invention.

[0048] Figure 3 This is a flowchart illustrating another method for profile control and well selection in Embodiment 2 of the present invention:

[0049] Figure 4 This is a graph showing the relationship between decision factors and increased oil production in Embodiment 2 of the present invention.

[0050] Figure 5 This is a schematic diagram of a process for predicting the oil production potential of well profile selection in Embodiment 3 of the present invention;

[0051] Figure 6 This is a schematic diagram of a profile selection device according to Embodiment 4 of the present invention;

[0052] Figure 7 This is a schematic diagram of a profile-adjusting well selection oil enhancement potential prediction device in Embodiment 5 of the present invention. Detailed Implementation

[0053] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0054] Example 1

[0055] A method for profile control and well selection, combined with Figure 1 As shown, steps S110-S140 are included, specifically:

[0056] Step S110: Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0057] Profile control and well selection methods mainly address three issues: first, how to optimize parameters; second, how to determine parameter weights; and third, how to establish a well selection model. The well selection model should also achieve at least one function: to classify and prioritize wells with profile control potential to guide the construction sequence.

[0058] Based on the above objectives, the first step is to consider the parameter selection of the pre-defined well selection model. Parameter optimization needs to comprehensively consider technical principles, economic benefits, and practical operability. From the perspective of profile control technology principles, parameters that can reflect the development of dominant channels and heterogeneity should be selected; from the perspective of economic benefits, parameters with a high correlation to oil production should be selected; and from the perspective of practicality, parameters that are easy to obtain and have high accuracy should be selected.

[0059] The optimal parameter range can be selected based on the actual conditions of the oilfield. This invention takes into account the practicality and operability of the well selection model, and optimizes the parameters from the following profile control well data. The optimal parameter range can also be expanded according to actual conditions.

[0060] Oil well indicators: sensitivity of oil and water wells, production capacity of well group, water cut, water cut changes in the past year, and differences in oil well fluid volume in the well group.

[0061] Water well indicators: daily water injection rate, injection pressure, pressure rise potential, profile control cycle, porosity, permeability, and cumulative water injection volume.

[0062] Test indicators: oil layer utilization, coefficient of variation of water absorption profile, water absorption intensity difference, thickness of strong water absorption layer, pressure drop before adjustment, PI90, FD, starting pressure, water absorption index, and water drive speed.

[0063] Block indicators: reservoir temperature, crude oil viscosity, and salinity.

[0064] After optimizing the parameters, the next challenge is parameter analysis. The inventors implemented numerous methods, employing grey relational analysis, Peason correlation coefficient, Spearman correlation coefficient, and Kendall correlation coefficient for parameter analysis. They found that grey relational analysis can evaluate the similarity of development trends between factors, but requires numerical data, with a result between 0.5 and 1. Peason correlation coefficient analyzes the linear correlation between two sets of data, but requires the data to be numerical and normally distributed, with a result between -1 and 1, where less than 0 indicates a negative correlation and greater than 0 indicates a positive correlation. Kendall correlation coefficient analyzes the direct correlation between two sets of ordinal or rank variables, suitable for analyzing categorical variables, with a result between -1 and 1. Spearman correlation coefficient analyzes the correlation between the ranks of two sets of variables, with less stringent data requirements, accepting both numerical and ordinal categorical variables, with a result between -1 and 1. Because profile control measures are influenced by many factors and the relationships between these factors are complex and nonlinear, this paper innovatively selects the Spearman correlation coefficient as the analysis parameter based on the adaptability of various analytical methods.

[0065] To make it easier to understand, we will use the GX oilfield as an example to explain this step.

[0066] Step S111: Collect relevant data information of profile control wells (due to the large size of the table data, the relevant data are displayed using labels 1-1 and 1-2 respectively), including oil well data, water well data, test data, single well oil increase, etc., and provide a detailed description of the steps of this invention.

[0067] Table 1-1

[0068]

[0069]

[0070] Table 1-2

[0071]

[0072]

[0073] Step S112: Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group.

[0074] (1) Take a series of profile control well data as x (taking “well group production capacity” as an example) and take “well group oil production” as y.

[0075] (2) The original data x and y are ordered from smallest to largest, and the order of x is X and the order of y is Y. The result of the parameter order calculation is shown in Table 2.

[0076] (3) Calculate the squared difference of the same set of data. The formula is: Taking the first set of data as an example, the difference in rank squares is (6.5-2.5). 2 It equals 16.

[0077] (4) Calculate the rank correlation coefficient using the existing rank correlation formula. Where n is the total number of wells, the rank correlation coefficient R between the production capacity of the well group and the increase in oil production of the well group is R = 1 - 6 × 1165.5 / (30 × (30 × 30 - 1)), which is equal to 0.74.

[0078] (5) Query the Spearman rank correlation coefficient table (Table 3). When the number of data is 30, the critical value of the rank correlation coefficient with a confidence level of 95% is 0.306.

[0079] Table 2

[0080]

[0081]

[0082] Table 3

[0083]

[0084]

[0085] Step S113: If the Spearman correlation coefficient meets the preset rules, then the corresponding profile control well data is used as the parameter of the preset well selection model.

[0086] When the correlation coefficient R > Rs, it indicates that the parameter is positively correlated with the oil-enhancing effect, and the parameter participates in the model calculation.

[0087] When the correlation coefficient R < -Rs, it indicates that the parameter is negatively correlated with the oil-enhancing effect, and the parameter is involved in the model calculation.

[0088] When the correlation coefficient -Rs < R < Rs, it indicates that the parameter is not strongly correlated with the oil-increasing effect, and the parameter is not included in the model calculation.

[0089] Wherein, Rs is the critical value of the Spearman rank correlation coefficient when the confidence level reaches a preset threshold, and in this embodiment, Rs is 0.306. Table 4 shows the rank correlation coefficient and parameter selection results between the data of each profile control well and the oil production.

[0090] Table 4

[0091]

[0092]

[0093] Table 4 shows that six parameters—well group production capacity, pre-adjustment pressure drop, pre-adjustment water cut change over one year, porosity, sensitivity, and FD—are used as the parameters for the preset well selection model.

[0094] Step S120: Determine the parameter weights for each parameter of the well selection model.

[0095] Specifically, this includes sub-steps S121-S123:

[0096] Step S121: If R > Rs, then the corresponding well selection model parameters are assigned values ​​using the ascending half-gradient method, and the parameter values ​​of each well selection model after assignment are normalized. For example, the well group production capacity, the one-year water cut change during regulation, and the pressure drop before regulation are processed using the ascending half-gradient method. Taking "well group production capacity" as an example, the production capacity of well group X1 is 3.5 t / d. According to the ascending half-gradient processing method in Table 5, (3.5-2) / 18 = 0.083; if the well group production capacity is less than 2 t / d, the value is 0; if the well group production capacity is greater than 20 t / d, the value is 1.

[0097] Alternatively, if R < -Rs, the corresponding well selection model parameters are assigned values ​​using the reduced half-gradient method, and the assigned parameter values ​​are then normalized. For example, porosity, sensitivity, and FD are processed using the reduced half-gradient method. Taking "FD" as an example, the FD of serial number X1 is 0.81. According to the reduced half-gradient processing method in Table 5, (1-0.81) = 0.19; since the result of FD is between 0 and 1, it can be calculated directly.

[0098] Step S122: Calculate the sum of the absolute values ​​of the rank correlation coefficients of all parameters involved in the model calculation to obtain the total correlation coefficient.

[0099] 0.74 + 0.58 + 0.53 + |-0.31| + |-0.37| + |-0.43| = 2.96

[0100] Step S123: Divide the Spearman correlation coefficient of each parameter by the total correlation coefficient to obtain the weights of each parameter in the well selection model.

[0101] For example, the weight of well group production capacity = 0.74 / 2.96 = 0.25

[0102] It is understandable that if parameters for a certain profile control well are missing, the corresponding total correlation coefficient will change. This can overcome the impact of model inaccuracies caused by missing parameters.

[0103] Table 5 illustrates the parameter weights determined for each parameter of the well selection model in the embodiments of the present invention (where Wi represents the weight and i is the parameter number).

[0104] Table 5

[0105]

[0106] Step S130: Using the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained.

[0107] Through extensive research, the inventors constructed a well selection model:

[0108]

[0109] Where F is the well selection decision factor, W i Xi represents the parameter weights of each well selection model parameter, and Xi is the normalized value of each well selection model parameter after being assigned a value.

[0110] Therefore, the parameter values ​​of each well selection model after assignment are normalized; by substituting the parameter weights and values ​​of the well selection model parameters into the following preset well selection model, the decision factors for each single well group can be obtained. The larger the value of the decision factor, the better the benefit of profile adjustment implementation.

[0111] For example, for well J1, the well selection decision factor F1 is:

[0112] F1 = 0.25·X1 + 0.20·X2 + 0.18·X3 + 0.10·X4 + 0.13·X5 + 0.15·X6

[0113] Among them, X1 to X6 are the normalized values ​​of the parameters of each well selection model after assignment.

[0114] Step S140: Sort the oil increase of each single well group according to the ascending order of decision factors, calculate the cumulative oil increase corresponding to each decision factor after sorting, draw the relationship curve between each decision factor and the cumulative oil increase, and determine the priority of profile adjustment and well selection construction based on the slope change trend of the curve.

[0115] Includes sub-steps S141-S144, where:

[0116] Step S141: Sort the decision factors in ascending order.

[0117] Step S142: Arrange the oil production of the single well group corresponding to the decision factor according to the ranking of the decision factor.

[0118] Step S143: Calculate the cumulative oil increase for each decision factor corresponding to the current position.

[0119] Step S143: Plot the relationship curves between each decision factor and the cumulative increase in oil production. Based on the trend of the slope of the curves, determine the priority of profile control and well selection.

[0120] Figure 2 Figure 2 shows the relationship diagram between the decision factors of 30 wells and the cumulative oil increment in the embodiments of the present invention. Using historical data, the decision factors are calculated, sorted from small to large, and the relationship diagram between the decision factors and the cumulative oil increment is plotted. Those with the same slope change trend in the diagram are taken as one priority level, so that the relationship between the decision factors and the priority levels can be established. Among them, the larger the decision factor, the higher the priority for implementing the profile control wells.

[0121] In Figure 2 when the decision factor F < 0.4, the change in oil increment shows a certain trend, and the oil increment effect is relatively low. The profile control priority is low, and it is not recommended to implement, with low economic benefits.

[0122] When 0.4 < F < 0.7, the change in oil increment is relatively fast. The profile control priority is medium, and construction can be carried out opportunely.

[0123] When F > 0.7, the oil increment is relatively high. The profile control priority is high, and construction should be carried out preferentially.

[0124] The present invention establishes a rank correlation well selection model. By the Spearman rank correlation relationship between parameters and oil increment, the parameters used in the well selection model are determined. The data type of the profile control well data in the present invention can be numerical variables or ordered categorical variables, with the advantages of strong adaptability to on-site needs and easy data acquisition. In addition, the present invention sorts the oil increments of each single well group in ascending order of the decision factor, calculates the cumulative oil increment corresponding to each decision factor after sorting, plots the relationship curve between each decision factor and the cumulative oil increment, and determines the priority of profile control well selection construction, which can effectively guide on-site practice.

[0125] Embodiment 2

[0126] Compared with Embodiment 1, the profile control well selection method disclosed in the embodiments of the present invention can not only classify and rank the profile control potential wells to guide the construction sequence, but also predict the oil increment effect after implementing profile control on the potential wells.

[0127] Combined with Figure 3 shown in, this profile control well selection method includes:

[0128] Step S110, calculate the Spearman correlation coefficient between each profile control well data and the oil increment of the single well group respectively. If the Spearman correlation coefficient meets the preset rules, the corresponding profile control well data is used as the parameter of the preset well selection model, where the profile control well data is determined from existing oil well data, water well data, and test data.

[0129] Step S120, determine the parameter weights for each parameter of the well selection model.

[0130] Step S130: Using the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained.

[0131] Step S140: Sort the oil increase of each single well group according to the ascending order of decision factors, calculate the cumulative oil increase corresponding to each decision factor after sorting, draw the relationship curve between each decision factor and the cumulative oil increase, and determine the priority of profile adjustment and well selection construction based on the slope change trend of the curve.

[0132] Step S150: Based on the relationship between decision factors and the oil production of each single well group, predict the oil production potential of profile control measures.

[0133] Specifically, using the decision factors of each single well group obtained in step S130, the data with an increase in oil production of 0 are removed, and the remaining decision factors are fitted with the oil production data of each single well group to obtain the relationship between the decision factors and the oil production of each single well group. The relationship is then used to predict the oil production potential of profile control measures.

[0134] Figure 4 The graph illustrates the relationship between decision factors and fuel increase. Using historical data, decision factors are calculated, and the graph showing the relationship between decision factors and fuel increase is plotted. A curve showing the relationship between decision factors and fuel increase can be fitted, leading to the fuel increase formula. After removing four data points with a fuel increase of 0, the graph contains 26 data sets. The final fitted relationship is as follows:

[0135] Q = 1853.2F - 472.56

[0136] Where Q represents the increase in oil production, and F represents the decision factor.

[0137] It is understood that the order of steps S150 and S140 can be substituted, and this embodiment of the invention does not impose any restrictions on this. This embodiment of the invention utilizes rank correlation analysis to select factors that have a good oil-increasing effect with the measures, uses correlation coefficients as weights, and normalizes the weights to establish a well selection model, classifies and evaluates potential wells, and gives the oil-increasing effect of the measures based on statistical results.

[0138] Example 3

[0139] like Figure 5 As shown, in some situations, it is necessary to predict the oil production potential of profile control and well selection. Therefore, based on the same inventive concept, this invention also discloses a method for predicting the oil production potential of profile control and well selection, including the following steps:

[0140] Step S210: Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0141] For specific operating steps, please refer to step S110 of Embodiment 1, which will not be repeated here.

[0142] Step S220: Determine the parameter weights for each parameter of the well selection model.

[0143] For specific operating steps, please refer to step S120 of Embodiment 1, which will not be repeated here.

[0144] Step S230: Using the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained.

[0145] For specific operating steps, please refer to step S130 in Embodiment 1, which will not be repeated here.

[0146] Step S240: Based on the relationship between decision factors and the oil production of each single well group, predict the oil production potential of profile control measures.

[0147] For specific operating steps, please refer to step S150 of Embodiment 2, which will not be repeated here.

[0148] This invention establishes a rank-correlation well selection model. By analyzing the Spearman rank correlation between parameters and oil production increase, the parameters used in the well selection model are determined. The data type for profile control wells in this invention can be either numerical variables or ordered categorical variables, offering advantages such as strong adaptability to field needs and easy data acquisition. Furthermore, based on the relationship between decision factors and the oil production increase of each single well group, this invention can fit a curve showing the relationship between decision factors and oil production increase, deriving an oil production increase formula to predict the oil production increase effect after profile control of potential wells.

[0149] Example 4

[0150] like Figure 6 As shown, corresponding to Embodiments 1 and 2, this embodiment of the invention also discloses a profile adjustment and well selection device, including a parameter determination module 10, a parameter weighting module 20, a well selection model storage module 30, and a construction priority determination module 40, wherein:

[0151] The parameter determination module 10 is used to calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0152] The parameter weighting module 20 is used to determine the parameter weights for each parameter of the well selection model.

[0153] The well selection model storage module 30 is used to store the preset well selection model and to obtain the decision factors of each single well group by using the parameter weights and parameter values ​​of the well selection model parameters.

[0154] The construction priority determination module 40 is used to sort the oil increase of each single well group according to the ascending order of decision factors, calculate the cumulative oil increase corresponding to each decision factor after sorting, draw the relationship curve between each decision factor and the cumulative oil increase, and determine the priority of profile adjustment and well selection construction based on the slope change trend of the curve.

[0155] In other embodiments, the profile control and well selection device further includes an oil production potential prediction module 50, which is used to predict the oil production potential of profile control measures based on the relationship between decision factors and the oil production of each single well group.

[0156] The specific working method of the profile adjustment and well selection device is described in Examples 1 and 2, and will not be repeated here.

[0157] Example 5

[0158] like Figure 7 As shown, corresponding to Embodiment 3, this embodiment of the invention also discloses a profile control and well selection oil enhancement potential prediction device, including a parameter determination module 10', a parameter weighting module 20', a well selection model storage module 30', and an oil enhancement potential prediction module 40', wherein:

[0159] The parameter determination module 10' is used to calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data.

[0160] The parameter weighting module 20' is used to determine the parameter weights for each parameter of the well selection model;

[0161] The well selection model storage module 30' is used to store the preset well selection model and to obtain the decision factors of each single well group by using the parameter weights and parameter values ​​of the well selection model parameters;

[0162] The oil production potential prediction module 40' is used to predict the oil production potential of profile control measures based on the relationship between decision factors and the oil production of each single well group.

[0163] The specific working method of the well oil enhancement potential prediction device is described in Example 3, and will not be repeated here.

[0164] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0165] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” just as “including,” is interpreted as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.”

Claims

1. A profile control and well selection method, characterized in that, Includes the following steps: Calculate the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group. If the Spearman correlation coefficient meets the preset rules, the corresponding profile-adjusting well data is used as the parameter of the preset well selection model. The profile-adjusting well data is determined from existing oil well data, water well data, and test data. Determine the parameter weights for each parameter in the well selection model; By utilizing the parameter weights and values ​​of the well selection model parameters and the preset well selection model, the decision factors for each single well group are obtained; The oil increase of each single well group is sorted according to the ascending order of decision factors, and the cumulative oil increase corresponding to each decision factor after sorting is calculated. The relationship curve between each decision factor and the cumulative oil increase is plotted, and the priority of profile adjustment and well selection construction is determined according to the trend of the slope of the curve.

2. The method of claim 1, wherein, The profile adjustment and well selection method also includes the following steps: Based on the relationship between decision factors and the oil production of each single well group, the oil production potential of profile control measures is predicted.

3. The method of claim 2, wherein, Based on the relationship between decision factors and the oil production increase of each single well group, the oil production increase potential of profile control measures is predicted, including: After removing data with zero oil increase, the remaining decision factors are fitted with the oil increase data of each single well group to obtain the relationship between the decision factors and the oil increase of each single well group. The relationship is then used to predict the oil increase potential of profile control measures.

4. The method of claim 1 or 2, wherein, The calculation of the Spearman correlation coefficient between the data of each profile-adjusting well and the oil increase of a single well group, and if the Spearman correlation coefficient meets a preset rule, then the corresponding profile-adjusting well data is used as a parameter of a preset well selection model, including: Calculate the Spearman correlation coefficient R between the data of each profile-adjusting well and the oil increase of a single well group; If the Spearman correlation coefficient R between profile-adjusting well data and the oil increase of a single well group satisfies the following relationship, then the profile-adjusting well data shall be used as the well selection model parameter: R>Rs or R<-Rs, Where Rs is the critical value of the Spearman rank correlation coefficient when the confidence level reaches a preset threshold.

5. The method of claim 1 or 2, wherein, The determination of parameter weights for each parameter in the well selection model includes: The total correlation coefficient is obtained by summing the absolute values ​​of the Spearman correlation coefficients of all parameters in the well selection model. The weights of each parameter in the well selection model are obtained by dividing the Spearman correlation coefficient of each parameter by the total correlation coefficient.

6. The method of claim 1 or 2, wherein, The decision factors for each single well group are obtained by utilizing the parameter weights and values ​​of the well selection model parameters and the preset well selection model, including: If R > Rs, then the corresponding well selection model parameters are assigned values ​​using the ascending half-gradient method; or, if R < -Rs, then the corresponding well selection model parameters are assigned values ​​using the descending half-gradient method. The parameter values ​​of each well selection model after assignment are normalized. Substituting the parameter weights and values ​​of the well selection model parameters into the following preset well selection model, the decision factors for each single well group are obtained: ； Where F is the well selection decision factor, Wi is the parameter weight of each well selection model parameter, and Xi is the normalized value of each well selection model parameter after assignment.

7. The method of claim 1 or 2, wherein, The oil increase of each single well group is sorted in ascending order according to the decision factors, and the cumulative oil increase corresponding to each decision factor after sorting is calculated, including: Sort the decision factors in ascending order; The oil increment of the single well group corresponding to the decision factor is arranged according to the order of the decision factor; The cumulative oil increment of each decision factor corresponding to the current position is calculated respectively.

8. A profile control and well selection device, characterized in that The method comprises a parameter determination module, a parameter weight module, a selected well model storage module and a construction priority determination module, wherein: The parameter determination module is configured to calculate the Spearman correlation coefficient between each profile control well data and the oil increment of the single well group respectively, and if the Spearman correlation coefficient meets a preset rule, the corresponding profile control well data is taken as the parameter of the preset selected well model, wherein the profile control well data is determined from the existing oil well data, water well data and test data; The parameter weight module is configured to determine the parameter weight of each parameter of the selected well model; The selected well model storage module is configured to store the preset selected well model, and obtain the decision factor of each single well group by using the parameter weight and parameter value of the selected well model parameter; The construction priority determination module is configured to arrange the oil increment of each single well group according to the ascending order of the decision factor, calculate the cumulative oil increment corresponding to each decision factor after the arrangement, draw the relationship curve of each decision factor and the cumulative oil increment, and determine the priority of the profile control selected well construction according to the slope change trend of the curve.

9. The profile control and well selection device of claim 8, wherein, The method further comprises an oil increment potential prediction module, which is configured to predict the oil increment potential of the profile control measure according to the relationship between the decision factor and the oil increment of each single well group.

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