Profile control well selection method, profile control well selection oil increasing potential prediction method and related device

Abstract

The invention provides a profile control well selection method, a profile control well selection oil increasing potential prediction method and a related device, and the profile control well selection method comprises the steps: respectively calculating Spearman correlation coefficients between the data of each profile control well and the oil increasing amount of a single well group, and determining the parameters of a well selection model; determining parameter weights for parameters of the well selection model; obtaining decision factors of each single well group by using the parameter weights and the parameter values of the well selection model parameters and a preset well selection model; and finally, sorting the oil increment of each single well group according to the ascending order of the decision factors, calculating the accumulated oil increment corresponding to each decision factor after sorting, drawing a relation curve between each decision factor and the accumulated oil increment, and determining the priority of profile control well selection construction. The method has the advantages of high field requirement adaptability and easiness in data acquisition. In addition, the priority of profile control well selection construction can be determined, and field practice is effectively guided.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and in particular relates to a profile adjustment and well selection method, a profile adjustment and well selection oil increase potential prediction method and related devices. Background Art

[0002] In the process of water drive development, heterogeneous reservoirs are prone to form water flow dominant channels, resulting in inefficient and ineffective circulation of injected water. Profile control technology is to inject profile control agents from water wells to block dominant channels, adjust the seepage field of the original water drive reservoir, thereby expanding the subsequent injection of water and achieving the effect of stabilizing oil production and reducing water. The key to the success of profile control measures lies in well selection. Due to the complex factors affecting the effect of profile control, the main factors affecting the effect of profile control are different in different types of reservoirs and different development methods, so it is difficult to find a universal well selection method.

[0003] Currently, the well selection methods that have been made public can be divided into two main categories: the first category is a single-factor well selection method based on the pressure index, which monitors the wellhead pressure drop after the water well is shut in, calculates the pressure index, and performs profile adjustment to select wells. This type of method has poor adaptability to multiple rounds of profile adjustment wells; the second category is a multi-factor well selection method, some of which cannot reflect the oil-increasing effect of profile adjustment measures, some of which are difficult to accurately obtain indicators, some of which have complex indicators and are difficult to promote on site, and some of which are targeted at a certain type of oil reservoir and have poor adaptability to other oil reservoirs. Summary of the invention

[0004] In order to enrich the profile adjustment and well selection method, the profile adjustment and well selection method proposed in the present invention comprises the following steps:

[0005] Calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group respectively. If the Spearman correlation coefficient meets the preset rule, use the corresponding profile control well data as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data and test data;

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

[0007] The decision factors of each single well group are obtained by using the parameter weights and parameter values ​​of the well selection model parameters and the preset well selection model;

[0008] The oil increase of each single well group is sorted in ascending order of the 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 drawn, and the priority of profile adjustment and well selection construction is determined according to the slope change trend of the curve.

[0009] Furthermore, the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group is calculated respectively. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, including:

[0010] The Spearman correlation coefficient R between the data of each profile control well and the oil increase of the single well group was calculated respectively;

[0011] If the Spearman correlation coefficient R between the profile control well data and the oil production increase of a single well group satisfies the following relationship, the profile control well data is used as the well selection model parameter:

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

[0013] Among them, Rs is the critical value of the Spearman rank correlation coefficient when the confidence reaches the preset threshold.

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

[0015] The absolute values ​​of the Spearman correlation coefficients of all parameters of the well selection model are summed to obtain the total correlation coefficient;

[0016] The Spearman correlation coefficient of each parameter of the well selection model is divided by the total correlation coefficient to obtain the weight of each parameter of the well selection model.

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

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

[0019] Normalizing the parameter values ​​of each well selection model parameter after assignment;

[0020] Substitute the parameter weights and parameter values ​​of the well selection model parameters into the following preset well selection model to obtain the decision factors of each single well group:

[0021]

[0022] Among them, F is the well selection decision factor, W i is the parameter weight of each well selection model parameter, and Xi is the normalized value after the well selection model parameter is assigned.

[0023] Furthermore, the oil increase of each single well group is sorted in ascending order of 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] Arrange the oil production increase of the single well group corresponding to the decision factors according to the ranking of the decision factors;

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

[0027] Furthermore, the profile control well selection method includes the following steps: drawing a relationship curve between the decision factor and the oil increase of each single well group to predict the oil increase potential of the profile control measures.

[0028] Based on the same inventive idea, the present invention also discloses a profile adjustment and well selection device, including a parameter determination module, a parameter weight module, a well selection model storage module, and a construction priority determination module, wherein:

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

[0030] A parameter weight module is used to determine parameter weights for various parameters of the well selection model;

[0031] The well selection model storage module is used to store the preset well selection model and obtain the decision factor of each single well group by using the parameter weight and parameter value of the well selection model parameters;

[0032] The construction priority determination module is used to sort the oil increase of each single well group in ascending order of the 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 according to the slope change trend of the curve.

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

[0034] Calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group respectively. If the Spearman correlation coefficient meets the preset rule, use the corresponding profile control well data as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data and test data;

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

[0036] The decision factors of each well group are obtained by using the parameter weights, parameter values ​​and preset well selection model of the well selection model parameters; the relationship curve between the decision factors and the oil increase of each well group is drawn to predict the oil increase potential of profile adjustment measures.

[0037] Based on the same inventive idea, the present invention also discloses a profile adjustment well selection oil increase potential prediction device, which includes a parameter determination module, a parameter weight module, a well selection model storage module, and an oil increase potential prediction module, wherein:

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

[0039] A parameter weight module is used to determine parameter weights for various parameters of the well selection model;

[0040] The well selection model storage module is used to store the preset well selection model and obtain the decision factor of each single well group by using the parameter weight and parameter value of the well selection model parameters;

[0041] The oil increase potential prediction module is used to predict the oil increase potential of profile control measures according to the relationship between the decision factor and the oil increase amount 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] The present invention discloses at least one profile adjustment and well selection method, comprising: respectively calculating the Spearman correlation coefficient between each profile adjustment well data and the oil increase of a single well group, and determining the parameters of a well selection model; then determining parameter weights for each parameter of the well selection model; then using the parameter weights and parameter values ​​of the well selection model parameters and a preset well selection model to obtain the decision factors of each single well group; finally, sorting the oil increase of each single well group in ascending order of the decision factors, and calculating the cumulative oil increase corresponding to each decision factor after sorting, drawing a relationship curve between each decision factor and the cumulative oil increase, and determining the priority of profile adjustment and well selection construction.

[0044] The present invention establishes a rank-correlated well selection model, and determines the parameters used in the well selection model through the Spearman rank correlation relationship between the parameters and the oil increase. The type of the profile adjustment well data of the present invention can be a numerical variable or an ordered categorical variable, which has the advantages of strong adaptability to field needs and easy data acquisition. In addition, the present invention sorts the oil increase of each single well group according to the ascending order of the decision factors, calculates the cumulative oil increase corresponding to each decision factor after sorting, draws the relationship curve between each decision factor and the cumulative oil increase, determines the priority of the profile adjustment well selection construction, and can effectively guide field practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 This is a schematic flow chart of a profile adjustment and well selection method in Embodiment 1 of the present invention;

[0047] Figure 2 This is a relationship diagram between the decision factors and the cumulative oil production increase of 30 wells in Example 1 of the present invention;

[0048] Figure 3 The following is a flow chart of another profile adjustment and well selection method in Embodiment 2 of the present invention:

[0049] Figure 4 The relationship diagram between the decision factor and the oil increase amount in the second embodiment of the present invention;

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

[0051] Figure 6 This is a structural schematic diagram of a profile adjustment and well selection device in Embodiment 4 of the present invention;

[0052] Figure 7 This is a schematic structural diagram of a device for predicting the oil production increase potential by profile adjustment, well selection, and oil production increase in the fifth embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0054] Embodiment 1

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

[0056] Step S110, respectively calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data, and test data.

[0057] The profile adjustment and well selection method mainly solves three problems: how to optimize parameters, how to determine parameter weights, and how to establish a well selection model. The well selection model must also achieve at least one function: to be able to grade and sort the profile adjustment potential wells to guide the construction sequence.

[0058] Based on the above purpose, we first need to consider the parameter selection of the preset well selection model. Parameter optimization needs to comprehensively consider technical principles, economic benefits and actual operability. From the perspective of profile adjustment technology principles, parameters that can reflect the development of dominant channels and heterogeneity are preferred; from the perspective of economic benefits, parameters with high oil increase correlation are preferred; from the perspective of specific reality, parameters that are easy to obtain and have high accuracy are preferred.

[0059] The preferred range of parameters can be selected according to the actual situation of the oil field. The present invention takes into account the practicality and operability of the well selection model, and selects from the following profile adjustment well data, and can also expand the preferred range of parameters according to the actual situation.

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

[0061] Water well indicators: daily injection volume, injection pressure, pressure rise space, profile adjustment rounds, porosity, permeability, and cumulative water injection volume.

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

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

[0064] After optimizing the parameters, we are faced with the problem of how to perform parameter analysis. The inventors have implemented a large number of methods, respectively, using four methods: grey relational analysis, PeaRson correlation coefficient, Spearman correlation coefficient and Kendall correlation coefficient, to perform parameter analysis. The inventors found that the grey relational method can evaluate the similarity of development trends between factors, but requires the data to be numerical variables, and the evaluation results are between 0.5-1. The PeaRson correlation coefficient analyzes the linear correlation between two sets of data, but requires the data to be numerical variables and to be normally distributed. The evaluation results are between [-1,1]. Less than 0 indicates negative correlation, and greater than 0 indicates positive correlation. The Kendall correlation coefficient can analyze the degree of direct correlation between two sets of ordered variables or rank variables, and is suitable for analyzing categorical variables. The evaluation results are between [-1,1]. The Spearman correlation coefficient can analyze the rank correlation between two sets of variables. It does not require high data and can be a numerical variable or an ordered categorical variable. The evaluation results are between [-1,1]. Since profile control measures are influenced by many factors and the relationships among these factors are complex and nonlinear, this paper innovatively selects the Spearman correlation coefficient analysis parameter based on the adaptability of various analysis methods.

[0065] For ease of understanding, this step is described using the GX oilfield as an example.

[0066] Step S111, collect relevant data information of profile control wells (due to the large table data, the relevant data are displayed with labels 1-1 and 1-2 respectively), including oil well data, water well data, test data, single well oil increase, etc., and give a detailed introduction to the steps of the present invention.

[0067] Table 1-1

[0068]

[0069]

[0070] Table 1-2

[0071]

[0072]

[0073] Step S112, respectively calculating the Spearman correlation coefficient between the data of each profile control well and the oil increase of the single well group.

[0074] (1) Take a column of profile adjustment well data as x (taking "well group production capacity" as an example), and take "well group oil increase" as y.

[0075] (2) The original data x and y are ranked from small to large, the corresponding order of x is X, and the order of y is Y. The calculated results of parameter order are shown in Table 2.

[0076] (3) Calculate the square difference of the same set of data. The calculation formula is: Taking the first set of data as an example, the rank square difference is (6.5-2.5) 2 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 between the production capacity of the well group and the oil increase 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 rule, 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 means that the parameter is positively correlated with the oil-increasing effect, and the parameter participates in the model calculation;

[0087] When the correlation coefficient R<-Rs, it means that the parameter is negatively correlated with the oil-increasing effect, and the parameter participates in the model calculation;

[0088] When the correlation coefficient -Rs<R<Rs, it means that the correlation between the parameter and the oil-increasing effect is not strong, and the parameter does not participate in the model calculation.

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

[0090] Table 4

[0091]

[0092]

[0093] Table 4 shows that the six parameters of well group production capacity, pressure drop before adjustment, water content change in one year before adjustment, porosity, sensitivity, and FD are used as the parameters of the preset well selection model.

[0094] Step S120, determining parameter weights for various parameters of the well selection model.

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

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

[0097] Alternatively, if R<-Rs, the corresponding well selection model parameters are assigned using the descending half-gradient method, and the parameter values ​​of the assigned well selection model parameters are normalized. For example, the descending half-gradient method is used to process porosity, sensitivity, and FD. Taking "FD" as an example, the FD of sequence number X1 is 0.81. According to the descending 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 directly calculated.

[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, dividing the Spearman correlation coefficient of each parameter by the total correlation coefficient to obtain the weight of each parameter of the well selection model.

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

[0102] It can be understood that if the parameters of a profile control well are missing, the value of the corresponding total correlation coefficient will change, which can overcome the influence of model inaccuracy caused by missing parameters.

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

[0104] Table 5

[0105]

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

[0107] The inventors have constructed a well selection model through extensive research:

[0108]

[0109] Among them, F is the well selection decision factor, W i is the parameter weight of each well selection model parameter, and Xi is the normalized value after the well selection model parameter is assigned.

[0110] Therefore, the parameter values ​​of each well selection model parameter after assignment are normalized; the parameter weights and parameter values ​​of the well selection model parameters are substituted into the following preset well selection model to obtain the decision factors of each single well group. 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 ​​after the parameters of each well selection model are assigned.

[0114] Step S140, sorting the oil increase of each single well group in ascending order of the decision factors, calculating the cumulative oil increase corresponding to each decision factor after sorting, drawing a relationship curve between each decision factor and the cumulative oil increase, and determining the priority of profile adjustment and well selection construction according to the slope change trend of the curve.

[0115] The method comprises sub-steps S141-S144, wherein:

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

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

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

[0119] Step S143: draw a relationship curve between each decision factor and the cumulative oil increase, and determine the priority of the profile adjustment well selection construction according to the slope change trend of the curve.

[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 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 as shown in

[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 the 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 parameter values ​​of the well selection model parameters and the preset well selection model, the decision factors of each single well group are obtained.

[0131] Step S140, sorting the oil increase of each single well group in ascending order of the decision factors, calculating the cumulative oil increase corresponding to each decision factor after sorting, drawing a relationship curve between each decision factor and the cumulative oil increase, and determining the priority of profile adjustment and well selection construction according to the slope change trend of the curve.

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

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

[0134] Figure 4 The relationship between the decision factor and the oil increase is shown in the figure. Using historical data, the decision factor is calculated, and the relationship between the decision factor and the oil increase is plotted. The relationship curve between the decision factor and the oil increase can be fitted to obtain the oil increase formula. After removing the 4 data with an oil increase of 0, there are 26 groups of data in this figure, and the final fitting relationship is:

[0135] Q=1853.2F-472.56

[0136] Among them: Q is the oil increase amount, and F is the decision factor.

[0137] It is understandable that the order of steps S150 and S140 can be replaced, and the embodiment of the present invention does not limit this. The embodiment of the present invention uses rank correlation analysis to select factors with good oil-increasing effects of measures, uses correlation coefficients as weights, and normalizes the weights to establish a well selection model, conducts graded evaluation of potential wells, and gives the oil-increasing effects of measures based on statistical results.

[0138] Embodiment 3

[0139] like Figure 5 As shown, in some cases, it is necessary to predict the oil-increasing potential of profile adjustment and well selection. Therefore, based on the same inventive idea, the present invention also discloses a method for predicting the oil-increasing potential of profile adjustment and well selection, comprising the following steps:

[0140] Step S210, respectively calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data, and test data.

[0141] The specific operation steps may refer to step S110 of the first embodiment, which will not be described in detail here.

[0142] Step S220, determining parameter weights for various parameters of the well selection model.

[0143] The specific operation steps may refer to step S120 of the first embodiment, which will not be described in detail here.

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

[0145] The specific operation steps may refer to step S130 of the first embodiment, which will not be described in detail here.

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

[0147] The specific operation steps may refer to step S150 of the second embodiment, which will not be described in detail here.

[0148] The present invention establishes a rank-correlated well selection model, and determines the parameters used in the well selection model through the Spearman rank correlation relationship between the parameters and the oil increase. The type of the profile adjustment well data of the present invention can be a numerical variable or an ordered categorical variable, which has the advantages of strong adaptability to field needs and easy data acquisition. In addition, the present invention can fit the relationship curve between the decision factor and the oil increase according to the relationship between the decision factor and the oil increase of each single well group, and obtain the oil increase formula to predict the oil increase effect of the potential well after the profile adjustment is implemented.

[0149] Embodiment 4

[0150] like Figure 6 As shown, corresponding to the first and second embodiments, the embodiment of the present invention further discloses a profile adjustment and well selection device, including a parameter determination module 10, a parameter weight 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 respectively calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data, and test data;

[0152] A parameter weight module 20 is used to determine 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 obtain the decision factor of each single well group by using the parameter weight and parameter value 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 in ascending order of the 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 according to the slope change trend of the curve.

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

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

[0157] Embodiment 5

[0158] like Figure 7 As shown, corresponding to the third embodiment, the embodiment of the present invention further discloses a profile adjustment well selection oil increase potential prediction device, comprising a parameter determination module 10', a parameter weight module 20', a well selection model storage module 30', and an oil increase potential prediction module 40', wherein:

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

[0160] A parameter weight module 20', used to determine 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 obtain the decision factor of each single well group by using the parameter weight and parameter value of the well selection model parameters;

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

[0163] The specific working method of the profile adjustment and well selection oil production potential prediction device is referred to the third embodiment and will not be described in detail here.

[0164] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are clearly stated in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0165] The above description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is encompassed in a manner similar to the term "including", as explained by "including," used as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A profile adjustment and well selection method, characterized in that: The following steps are involved: Calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group respectively. If the Spearman correlation coefficient meets the preset rule, use the corresponding profile control well data as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data and test data; Determine parameter weights for each parameter of the well selection model; The decision factors of each single well group are obtained by using the parameter weights and parameter values ​​of the well selection model parameters and the preset well selection model; The oil increase of each single well group is sorted in ascending order of the 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 drawn, and the priority of profile adjustment and well selection construction is determined according to the slope change trend of the curve.

2. The profile adjustment and well selection method according to claim 1, characterized in that: The profile adjustment and well selection method further comprises the following steps: 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.

3. The profile adjustment and well selection method according to claim 2, characterized in that: According to 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: The data with oil increase of 0 are removed, and 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, and the relationship is used to predict the oil increase potential of profile control measures.

4. The profile adjustment and well selection method as described in any one of claims 1 or 2, characterized in that: The Spearman correlation coefficient between each profile control well data and the oil increase of the single well group is calculated respectively. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, including: The Spearman correlation coefficient R between the data of each profile control well and the oil increase of the single well group was calculated respectively; If the Spearman correlation coefficient R between the profile control well data and the oil production increase of a single well group satisfies the following relationship, the profile control well data is used as the well selection model parameter: R>Rs or R<-Rs, Among them, Rs is the critical value of the Spearman rank correlation coefficient when the confidence reaches the preset threshold.

5. The profile adjustment and well selection method as described in any one of claims 1 or 2, characterized in that: Determining parameter weights for each parameter of the well selection model includes: The absolute values ​​of the Spearman correlation coefficients of all parameters of the well selection model are summed to obtain the total correlation coefficient; The Spearman correlation coefficient of each parameter of the well selection model is divided by the total correlation coefficient to obtain the weight of each parameter of the well selection model.

6. The profile adjustment and well selection method as described in any one of claims 1 or 2, characterized in that: The decision factors of each single well group are obtained by using the parameter weights, parameter values ​​and the preset well selection model of the well selection model parameters, including: If R>Rs, the corresponding well selection model parameters are assigned using the ascending half-gradient method, or, if R<-Rs, the corresponding well selection model parameters are assigned using the descending half-gradient method; Normalizing the parameter values ​​of each well selection model parameter after assignment; Substitute the parameter weights and parameter values ​​of the well selection model parameters into the following preset well selection model to obtain the decision factors of each single well group: Among them, F is the well selection decision factor, W i is the parameter weight of each well selection model parameter, and Xi is the normalized value after the well selection model parameter is assigned.

7. The profile adjustment and well selection method as described in any one of claims 1 or 2, characterized in that: Sort the oil increase of each single well group in ascending order of the decision factors, and calculate the cumulative oil increase corresponding to each decision factor after sorting, including: Sort the decision factors in ascending order; Arrange the oil production increase of the single well group corresponding to the decision factors according to the ranking of the decision factors; Calculate the cumulative oil increase corresponding to the current position of each decision factor.

8. A method for predicting the oil production potential of profile adjustment and well selection, characterized in that: The following steps are involved: Calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group respectively. If the Spearman correlation coefficient meets the preset rule, use the corresponding profile control well data as the parameter of the preset well selection model, wherein the profile control well data is determined from the existing oil well data, water well data and test data; Determine parameter weights for each parameter of the well selection model; The decision factors of each single well group are obtained by using the parameter weights and parameter values ​​of the well selection model parameters and the preset well selection model; 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.

9. The method for predicting the oil production increase potential by profile adjustment and well selection as described in claim 8 is characterized in that: The Spearman correlation coefficient between each profile control well data and the oil increase of the single well group is calculated respectively. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection model, including: The Spearman correlation coefficient R between the data of each profile control well and the oil increase of the single well group was calculated respectively; If the Spearman correlation coefficient R between the profile control well data and the oil production increase of a single well group satisfies the following relationship, the profile control well data is used as the well selection model parameter: R>Rs or R<-Rs, Among them, Rs is the critical value of the Spearman rank correlation coefficient when the confidence reaches the preset threshold.

10. The method for predicting the oil production increase potential by profile adjustment and well selection as described in claim 8 is characterized in that: Determining parameter weights for each parameter of the well selection model includes: The absolute values ​​of the Spearman correlation coefficients of all parameters of the well selection model are summed to obtain the total correlation coefficient; The Spearman correlation coefficient of each parameter of the well selection model is divided by the total correlation coefficient to obtain the weight of each parameter of the well selection model.

11. The method for predicting the oil production potential of profile adjustment and well selection as described in claim 8 is characterized in that: The decision factors of each single well group are obtained by using the parameter weights, parameter values ​​and the preset well selection model of the well selection model parameters, including: If R>Rs, the corresponding well selection model parameters are assigned using the ascending half-gradient method, or, if R<Rs, the corresponding well selection model parameters are assigned using the descending half-gradient method; Normalizing the parameter values ​​of each well selection model parameter after assignment; Substitute the parameter weights and parameter values ​​of the well selection model parameters into the following preset well selection model to obtain the decision factors of each single well group: Among them, F is the well selection decision factor, W i is the parameter weight of each well selection model parameter, and Xi is the normalized value after the well selection model parameter is assigned.

12. A profile adjustment and well selection device, characterized in that: It includes parameter determination module, parameter weight module, well selection model storage module and construction priority determination module, among which: The parameter determination module is used to respectively calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection 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 used to determine parameter weights for various parameters of the well selection model; The well selection model storage module is used to store the preset well selection model and obtain the decision factor of each single well group by using the parameter weight and parameter value of the well selection model parameters; The construction priority determination module is used to sort the oil increase of each single well group according to the ascending order of the 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 according to the slope change trend of the curve.

13. A profile adjustment and well selection device, characterized in that: It also includes an oil increase potential prediction module, which is used to predict the oil increase potential of profile adjustment measures based on the relationship between the decision factors and the oil increase amount of each single well group.

14. A profile adjustment and well selection oil production potential prediction device, characterized in that: It includes parameter determination module, parameter weight module, well selection model storage module and oil increase potential prediction module, among which: The parameter determination module is used to respectively calculate the Spearman correlation coefficient between each profile control well data and the oil increase of the single well group. If the Spearman correlation coefficient meets the preset rule, the corresponding profile control well data is used as the parameter of the preset well selection 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 used to determine parameter weights for various parameters of the well selection model; The well selection model storage module is used to store the preset well selection model and obtain the decision factor of each single well group by using the parameter weight and parameter value of the well selection model parameters; The oil increase potential prediction module is used to predict the oil increase potential of profile control measures according to the relationship between the decision factor and the oil increase amount of each single well group.

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