Method for quantitatively dividing water cut rising mode of oilfield based on improved curve characteristic value

Abstract

The application discloses a kind of based on improved curve eigenvalue quantitative division oilfield water cut rising mode method, comprising the following steps: S1: obtaining the historical production data of multiple production wells, and fitting to obtain the water cut rising mode of each production well;S2: establish improved Logistic growth curve model, and rewrite it as double logarithmic form;S3: with the historical production data of a certain production well, calculate two logarithmic terms, and the calculation result is drawn on double logarithmic coordinate axis and carries out linear regression;S4: obtain fitting eigenvalue;S5: repeat steps S3-S4, the fitting eigenvalue result of all production wells is drawn into scatter diagram;S6: obtain eigenvalue limit;S7: obtain the historical production data of target well, repeat steps S3-S4 to obtain the fitting eigenvalue of target well, in combination with the eigenvalue limit, obtain the water cut rising mode of the target well.The application can quantitatively divide the water cut rising mode of single well in oilfield according to fitting eigenvalue, and has wide application value.

Description

Technical Field

[0001] This invention relates to the field of oilfield water drive development technology, and in particular to a method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues. Background Technology

[0002] Defining the water cut rise pattern in the later stages of oilfield development is crucial for implementing remaining oil potential tapping and adjustment strategies. Traditional water cut rise patterns can be categorized into convex, S-shaped, and concave types, and are generally fitted using type A, B, C, and D water drive curves, Tong's chart, and the Logistic cyclic method. However, these methods suffer from poor fitting results in the early stages of production when fitting water cut rise patterns in certain oilfields, and they cannot effectively and uniformly describe the water cut rise patterns of multiple wells in the target oilfield or quantitatively classify the water cut rise patterns. Summary of the Invention

[0003] To address the aforementioned problems, this invention aims to provide a method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues.

[0004] The technical solution of the present invention is as follows:

[0005] A method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues ​​includes the following steps:

[0006] S1: Obtain historical production data from multiple production wells and fit the data to obtain the water cut rise pattern of each production well;

[0007] S2: Establish an improved Logistic growth curve model and rewrite the improved Logistic growth curve model in double logarithmic form;

[0008] S3: Calculate the two logarithmic terms in the double logarithmic form using the historical production data of a certain production well, and plot the calculation results on the double logarithmic coordinate axis for linear regression;

[0009] S4: Obtain the fitted feature values ​​of the improved Logistic growth curve model based on the results of linear regression;

[0010] S5: Repeat steps S3-S4 to obtain the fitted feature values ​​of all production wells, and plot the fitted feature value results of each production well as a scatter plot.

[0011] S6: Based on the scatter plot and the water cut rise pattern of each well in step S1, obtain the characteristic value boundary for the water cut rise pattern division.

[0012] S7: Obtain historical production data of the target well, repeat steps S3-S4 to obtain the fitting feature value of the target well, and combine the feature value limit to obtain the water cut rise pattern of the target well.

[0013] Preferably, in step S1, the water cut rise mode of each production well is obtained by fitting the type A water drive curve, type B water drive curve, type C water drive curve, type D water drive curve, Tong's chart or Logistic cyclic method.

[0014] Preferably, in step S2, the improved Logistic growth curve model is:

[0015]

[0016] The double logarithmic form is as follows:

[0017]

[0018] In the formula: f w f is the moisture content; f lim Where f is the limiting moisture content; N is the limiting moisture content. p To accumulate oil production, 10 4 m 3 a and b are the fitted feature values.

[0019] Preferably, when the production well is located in an onshore oil field, the limiting water cut is 0.98; when the production well is located in an offshore oil field, the limiting water cut is 1.

[0020] The beneficial effects of this invention are:

[0021] This invention improves the Logistic growth curve, retaining the ease of operation of the original growth curve while achieving a good fitting effect. It overcomes the shortcomings of commonly used water drive curves, Tong's charts, and the original growth curve in terms of poor fitting effect in the early and late stages of water cut rise. At the same time, it can quantitatively classify the water cut rise pattern of single wells in oilfields based on the fitting characteristic values, and has wide application value. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of the linear regression results of the improved Logistic growth curve for a specific embodiment of well X02;

[0024] Figure 2 A schematic diagram of the improved Logistic growth curve fitting results for well X02 in a specific embodiment;

[0025] Figure 3 This is a schematic diagram of the fitting result of the growth curve (original Logistic growth curve) of well X02 before improvement in a specific embodiment;

[0026] Figure 4 This is a feature value chart for a target oil field in a specific embodiment. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0028] This invention provides a method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues, comprising the following steps:

[0029] S1: Obtain historical production data from multiple production wells and fit the data to obtain the water cut rise pattern of each production well.

[0030] In one specific embodiment, the water cut rise pattern of each production well is obtained by fitting type A water drive curves, type B water drive curves, type C water drive curves, type D water drive curves, Tong's chart, or the Logistic cyclic method. It should be noted that this step is mainly to provide support for subsequently defining the characteristic value boundaries of each water cut rise pattern. Besides the water cut rise pattern acquisition method of this embodiment, other methods in the prior art for obtaining water cut rise patterns can also be applied to this invention.

[0031] S2: Establish an improved Logistic growth curve model and rewrite the improved Logistic growth curve model in double logarithmic form.

[0032] In one specific embodiment, the improved Logistic growth curve model is as follows:

[0033]

[0034] The double logarithmic form is as follows:

[0035]

[0036] In the formula: f w f is the moisture content; f limWhere f is the limiting moisture content; N is the limiting moisture content. p To accumulate oil production, 10 4 m 3 a and b are the fitted feature values.

[0037] In one specific embodiment, the limiting water cut is 0.98 when the production well is located in an onshore oil field and 1 when the production well is located in an offshore oil field.

[0038] In the above embodiments, the present invention improves the Logistic growth curve, which can achieve better fitting effect while retaining the ease of operation of the original growth curve; and it can overcome the shortcomings of the commonly used water drive curve, Tong's chart and the original growth curve in the poor fitting effect in the early and late stages of water cut rise.

[0039] S3: Calculate the two logarithmic terms in the form of a double logarithm based on the historical production data of a certain production well, and plot the calculation results on the double logarithmic coordinate axis for linear regression.

[0040] S4: Obtain the fitted feature values ​​of the improved Logistic growth curve model based on the results of linear regression.

[0041] S5: Repeat steps S3-S4 to obtain the fitted feature values ​​of all production wells, and plot the fitted feature value results of each production well as a scatter plot.

[0042] S6: Based on the scatter plot and the water cut rise pattern of each well in step S1, obtain the characteristic value boundary for dividing the water cut rise pattern.

[0043] In this invention, by obtaining the characteristic value limit, the water cut rise pattern of a single well in an oilfield can be quantitatively classified according to the fitted characteristic value of the target well, resulting in clearer results.

[0044] S7: Obtain historical production data of the target well, repeat steps S3-S4 to obtain the fitting feature value of the target well, and combine the feature value limit to obtain the water cut rise pattern of the target well.

[0045] In a specific embodiment, taking a certain second section II oil group in W oilfield as an example, the water cut rise mode of well X02 in the target area is divided using the method of quantitatively dividing the water cut rise mode of oilfield based on the improved curve characteristic value described in this invention.

[0046] In this embodiment, the historical production data of well X02 is shown in Table 1:

[0047] Table 1 Historical Production Data of Well X02

[0048] <![CDATA[Cumulative oil production N p (10 4 m 3 )]]> 6.258 14.63 … 25.31 40.88 65.12

[0049] Based on the improved logistic growth curve model shown in equation (2), the logarithmic term was calculated using historical production data from well X02, and a scatter plot was plotted on the logarithmic coordinate axis for linear regression. Two fitted feature values ​​were obtained based on the slope and intercept of the regression line. The results are as follows: Figure 1 As shown.

[0050] Substituting the fitted feature values ​​into the improved Logistic growth curve model shown in equation (1) yields... Figure 2 The improved Logistic growth curve for well X02 is shown below. Figure 3 Compared to the original Logistic growth curve model, the improved curve fitting effect of this invention is significantly better.

[0051] Similarly, using the double logarithmic form of the improved Logistic growth curve model shown in Equation (2), the data operation of other production wells in the target oilfield was repeated to obtain the fitting characteristic values ​​of all single wells in the target oilfield. Based on the existing method for determining water cut rise patterns, the water cut rise patterns of each single well were roughly divided. The results are shown in Table 2.

[0052] Table 2. Fitting characteristics of single wells and water cut rise patterns

[0053]

[0054]

[0055] By plotting the fitted eigenvalues ​​of each well into a scatter plot and combining it with the water cut rise pattern, the quantitative eigenvalue limits of the water cut rise pattern for the target oilfield can be obtained. The results are shown in Table 3 and... Figure 4 As shown:

[0056] Table 3. Characteristic value limits for water cut increase model of target oilfield.

[0057] b>2.8 S-shaped 2.1<b<2.8 Convex-S type b<2.1 convex

[0058] In this embodiment, the characteristic value of the target well X02 is b = 3.39, and its water cut rise pattern is S-type. This judgment result is the same as the result of the existing water cut rise pattern determination method.

[0059] In summary, this invention, through an improved Logistic growth curve model, retains the operational convenience of the original growth curve while achieving excellent fitting results, overcoming the shortcomings of commonly used water drive curves, Tong's charts, and the original growth curve in terms of poor fitting performance in the early and late stages of water cut rise. By determining the eigenvalue boundaries, it can quantitatively classify the water cut rise pattern of a single well in an oilfield based on the fitting eigenvalues ​​of the target well. Compared with existing technologies, this invention represents a significant advancement.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues, characterized in that, Includes the following steps: S1: Obtain historical production data from multiple production wells and fit the data to obtain the water cut rise pattern of each production well; S2: Establish an improved Logistic growth curve model and rewrite the improved Logistic growth curve model in double logarithmic form; The improved Logistic growth curve model is as follows: （1） The double logarithmic form is as follows: （2） In the formula: Moisture content, in percentages (%) This represents the limiting moisture content, expressed in % . Cumulative oil production, in units of 10. 4 m 3 ; To fit the feature values; S3: Calculate the two logarithmic terms in the double logarithmic form using the historical production data of a certain production well, and plot the calculation results on the double logarithmic coordinate axis for linear regression; S4: Obtain the fitted feature values ​​of the improved Logistic growth curve model based on the results of linear regression; S5: Repeat steps S3-S4 to obtain the fitted feature values ​​of all production wells, and plot the fitted feature value results of each production well as a scatter plot. S6: Based on the scatter plot and the water cut rise pattern of each well in step S1, obtain the characteristic value boundary for the water cut rise pattern division. S7: Obtain historical production data of the target well, repeat steps S3-S4 to obtain the fitting feature value of the target well, and combine the feature value limit to obtain the water cut rise pattern of the target well.

2. The method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues ​​according to claim 1, characterized in that, In step S1, the water cut rise mode of each production well is obtained by fitting the water drive curves of type A, type B, type C, type D, Tong's chart, or Logistic cyclic method.

3. The method for quantitatively classifying oilfield water cut rise patterns based on improved curve eigenvalues ​​according to claim 1, characterized in that, When the production well is located in an onshore oil field, the limiting water cut is 0.98; when the production well is located in an offshore oil field, the limiting water cut is 1.

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