Method for predicting emulsification comprehensive index of surfactant flooding system

Abstract

The application provides a method for predicting emulsification comprehensive index of a surfactant oil displacement system, and relates to the technical field of emulsification performance evaluation, which comprises the following steps: obtaining HLB values of a surfactant oil displacement system to be predicted applied to three types of oil reservoirs; inputting the HLB values into a prediction model constructed in advance, and outputting an emulsification comprehensive index prediction value; and screening the emulsification comprehensive index prediction value to obtain a target emulsification comprehensive index of the surfactant oil displacement system to be predicted. The method realizes the prediction of the emulsification comprehensive index of the surfactant oil displacement system, and shortens the time for obtaining the emulsification comprehensive index.

Description

Technical Field

[0001] This invention relates to the field of emulsification performance evaluation technology, and in particular to a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system. Background Technology

[0002] Because surfactants play a significant role in improving oilfield recovery, they emulsify crude oil into oil-in-water emulsions with particle sizes smaller than the pore throat diameter of the rock, which then migrate with the displacement medium. Emulsions with particle sizes larger than the pore throat diameter can block the pore throat, improving reservoir heterogeneity and increasing swept volume. Meanwhile, the China National Petroleum Corporation's enterprise standard Q / SY 1583—2013, "Technical Specification for Surfactants for Binary Composite Flooding," stipulates that the comprehensive emulsification index of surfactants used for oil displacement should be greater than 30%. However, current methods measure the comprehensive emulsification index by sequentially testing the HLB value, emulsifying power, and emulsification stability of the surfactant-assisted oil displacement system. This testing procedure is cumbersome and requires a large amount of data, making the determination of the comprehensive emulsification index of surfactant-assisted oil displacement systems tedious and time-consuming. Summary of the Invention

[0003] This invention provides a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system, which can predict the comprehensive emulsification index based on HLB values, thus shortening the time required to obtain the comprehensive emulsification index.

[0004] In a first aspect, the present invention provides a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system, comprising:

[0005] Obtain the HLB values ​​of the surfactant-driven oil displacement system to be predicted for application in three types of reservoirs;

[0006] The HLB value is input into a pre-built prediction model, and the predicted value of the comprehensive emulsification index is output.

[0007] The predicted values ​​of the emulsification comprehensive index are screened to obtain the target emulsification comprehensive index of the surfactant displacement system to be predicted.

[0008] Preferably, the prediction model is constructed using the following method:

[0009] Obtain the type of the surfactant-driven oil displacement system to be predicted;

[0010] Prepare a standard surfactant flooding system of the aforementioned types; wherein different standard surfactant flooding systems have different HLB values;

[0011] Obtain the emulsification stability, emulsifying power, and comprehensive emulsification index of the standard surfactant-driven oil displacement system;

[0012] The HLB value and emulsifying power of the standard surfactant oil displacement system are fitted to obtain the first fitting parameters;

[0013] The emulsification stability value and emulsification index of the standard surfactant oil displacement system were fitted to obtain the second fitting parameters;

[0014] The prediction model is determined based on the first fitting parameter, the second fitting parameter, the HLB value of the standard surfactant oil displacement system, and the comprehensive emulsification index of the standard surfactant oil displacement system.

[0015] Preferably, the first fitting parameters include quadratic coefficients, linear coefficients, and a constant; the second fitting parameters include cubic coefficients, quadratic coefficients, linear coefficients, and a constant.

[0016] Preferably, the prediction model includes:

[0017]

[0018] Wherein, P1 is the coefficient of the cubic term in the second fitting parameter; P2 is the coefficient of the quadratic term in the second fitting parameter; P3 is the coefficient of the linear term in the second fitting parameter; P4 is a constant in the second fitting parameter; P5 is the coefficient of the quadratic term in the first fitting parameter; P6 is the coefficient of the linear term in the first fitting parameter; P7 is a constant in the first fitting parameter; X is the comprehensive emulsification index of the standard surfactant oil displacement system; and Y is the HLB value of the standard surfactant oil displacement system.

[0019] Preferably, the first fitting parameter is obtained by fitting at least five of the standard surfactant oil displacement systems; the second fitting parameter is obtained by fitting at least twenty of the standard surfactant oil displacement systems.

[0020] Preferably, the surfactant flooding system to be predicted includes a Span 80 and fatty alcohol polyoxyethylene ether sodium sulfate system, a Span 80 and fatty alcohol polyoxyethylene ether system, and a sodium dodecylbenzene sulfonate and fatty alcohol polyoxyethylene ether sodium sulfate system.

[0021] Preferably, when the type is Span80 and sodium fatty alcohol polyoxyethylene ether sulfate system, the HLB value of the standard surfactant oil displacement system is 6 to 11.

[0022] Preferably, when the type is Span80 and fatty alcohol polyoxyethylene ether system, the HLB value of the standard surfactant oil displacement system is 5 to 8.

[0023] Preferably, when the system is sodium dodecylbenzenesulfonate and sodium fatty alcohol polyoxyethylene ether sulfate, the HLB value of the standard surfactant oil displacement system is 11 to 14.

[0024] Preferably, the predicted emulsification index is screened to obtain the target emulsification index of the surfactant-driven oil displacement system to be predicted, including:

[0025] The predicted values ​​of the comprehensive emulsification index are sorted from largest to smallest to obtain the second highest predicted value of the comprehensive emulsification index.

[0026] The second-highest predicted emulsification index is taken as the target emulsification index.

[0027] Secondly, the present invention provides an emulsification index prediction device for a surfactant-driven oil displacement system, comprising:

[0028] The acquisition module is used to acquire the HLB values ​​of the surfactant flooding system to be predicted for application in three types of reservoirs;

[0029] The prediction module is used to input the HLB value into a pre-built prediction model, output the predicted value of the comprehensive emulsification index, and filter the predicted value of the comprehensive emulsification index to obtain the target comprehensive emulsification index of the surfactant displacement system to be predicted.

[0030] Thirdly, embodiments of the present invention also provide a computing device, including a memory and a target processor, wherein the memory stores a computer program, and when the target processor executes the computer program, it implements the method described in any of the first aspects of this specification.

[0031] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any of the first aspects of this specification.

[0032] Fifthly, embodiments of the present invention also provide a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the method described in any of the first aspects of this specification.

[0033] This invention provides a method for predicting the comprehensive emulsification index of surfactant-driven oil displacement systems. This method obtains the HLB value of the surfactant-driven oil displacement system to be predicted and inputs this HLB value into a pre-constructed prediction model, which then outputs a predicted comprehensive emulsification index. Further screening yields the target comprehensive emulsification index. Thus, this method can predict the comprehensive emulsification index solely based on the HLB value, eliminating the need to measure the emulsification stability and emulsifying power of the surfactant-driven oil displacement system, significantly reducing the time required to obtain the comprehensive emulsification index. Attached Figure Description

[0034] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system according to an embodiment of the present invention;

[0036] Figure 2 This is a comparison chart of the predicted value and the actual value provided in Embodiment 1 of the present invention;

[0037] Figure 3 This is another comparison chart of predicted values ​​and actual values ​​provided in Embodiment 1 of the present invention;

[0038] Figure 4 This is a comparison chart of the predicted value and the actual value provided in Embodiment 2 of the present invention;

[0039] Figure 5 This is a comparison chart of the predicted value and the actual value provided in Embodiment 3 of the present invention;

[0040] Figure 6 This is a hardware architecture diagram of a computing device provided in an embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram of the structure of an emulsification comprehensive index prediction device for a surfactant-driven oil displacement system provided in an embodiment of the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0043] like Figure 1 As shown, this embodiment of the invention provides a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system. The method includes:

[0044] Step 100: Obtain the HLB value of the surfactant flooding system to be predicted for application in three types of reservoirs;

[0045] Step 102: Input the HLB value into the pre-built prediction model and output the predicted value of the comprehensive emulsification index;

[0046] Step 104: Screen the predicted values ​​of the comprehensive emulsification index to obtain the target comprehensive emulsification index of the surfactant-driven oil displacement system to be predicted.

[0047] In this invention, by obtaining the HLB value of the surfactant-driven oil displacement system to be predicted and inputting this HLB value into a pre-constructed prediction model, a predicted emulsification index can be output. Further screening yields the target emulsification index. Thus, this method can predict the emulsification index solely based on the HLB value, eliminating the need to measure the emulsification stability and emulsifying power of the surfactant-driven oil displacement system, significantly shortening the time required to obtain the emulsification index.

[0048] It should be noted that Class III oil reservoirs refer to crude oil in Class III oil reservoirs, which are crude oil layers with an effective thickness of less than 1 meter and low permeability. These oil layers are characterized by high temperature, high salinity, and low permeability.

[0049] For step 100, the HLB values ​​of the surfactant flooding system to be predicted for application in the three types of reservoirs are obtained, including the HLB values ​​measured by existing methods, which will not be elaborated on here.

[0050] In step 102, the prediction model is constructed using the following method:

[0051] To determine the types of surfactant-driven oil displacement systems to be predicted;

[0052] Prepare oil displacement systems with various types of standard surfactants; wherein, different standard surfactant oil displacement systems have different HLB values;

[0053] Obtain the emulsification stability, emulsifying power, and comprehensive emulsification index of the standard surfactant-driven oil displacement system;

[0054] The HLB value and emulsifying power of the standard surfactant oil displacement system were fitted to obtain the first fitting parameters;

[0055] The emulsification stability value and emulsification index of the standard surfactant oil displacement system were fitted to obtain the second fitting parameter;

[0056] The prediction model is determined based on the first fitting parameter, the second fitting parameter, the HLB value of the standard surfactant oil displacement system, and the comprehensive emulsification index of the standard surfactant oil displacement system.

[0057] In this invention, after determining the types of surfactants included in the surfactant-driven oil displacement system to be predicted, a series of standard surfactant-driven oil displacement systems of the same type but different HLB values ​​are directly prepared. Then, the emulsification stability, emulsifying power, and comprehensive emulsification index of each standard surfactant-driven oil displacement system are measured using existing testing methods. Then, by fitting the HLB value and emulsifying power, and fitting the emulsification stability value and comprehensive emulsification index, fitting parameters are obtained. Finally, a prediction model is obtained using the fitting parameters, HLB value, and comprehensive emulsification index, so that the model can characterize the correlation between HLB value and comprehensive emulsification index.

[0058] It should be noted that different types of surfactant-based oil displacement systems require different prediction models.

[0059] In a preferred embodiment, the first fitting parameters include quadratic coefficients, linear coefficients, and a constant; the second fitting parameters include cubic coefficients, quadratic coefficients, linear coefficients, and a constant.

[0060] Specifically, a nonlinear fit is performed on the HLB value and emulsifying force to obtain the first fitting parameter; an exponential relationship is performed on the emulsification stability value and the comprehensive emulsification index to obtain the second fitting parameter.

[0061] In a preferred embodiment, the prediction model includes:

[0062]

[0063] Wherein, P1 is the coefficient of the cubic term in the second fitting parameter; P2 is the coefficient of the quadratic term in the second fitting parameter; P3 is the coefficient of the linear term in the second fitting parameter; P4 is the constant in the second fitting parameter; P5 is the coefficient of the quadratic term in the first fitting parameter; P6 is the coefficient of the linear term in the first fitting parameter; P7 is the constant in the first fitting parameter; X is the comprehensive emulsification index of the standard surfactant oil displacement system; Y is the HLB value of the standard surfactant oil displacement system.

[0064] In this invention, the prediction model creatively constructs a correlation between HLB value and emulsification index. By inputting HLB value into the prediction model, the predicted value of the emulsification index of the corresponding surfactant-driven oil displacement system can be output. This method is efficient and simple, and greatly shortens the time to obtain the emulsification index.

[0065] In a preferred embodiment, the first fitting parameter is obtained by fitting at least five standard surfactant oil displacement systems; the second fitting parameter is obtained by fitting at least twenty standard surfactant oil displacement systems.

[0066] In this invention, the first fitting parameter, which includes the quadratic coefficient, is fitted using at least five standard surfactant displacement systems, and the parameter, which includes the cubic coefficient, is fitted using at least twenty standard surfactant displacement systems. This can further improve the fitting degree and thus improve the prediction accuracy of the target emulsification index.

[0067] In a preferred embodiment, the surfactant flooding system to be predicted includes a Span80 and fatty alcohol polyoxyethylene ether sulfate (Span80 / AES) system, a Span80 and fatty alcohol polyoxyethylene ether (Span80 / AEO-3) system, and a sodium dodecylbenzene sulfonate and fatty alcohol polyoxyethylene ether sulfate (SDBS / AES) system.

[0068] Specifically, surfactant types include anionic, nonionic, and anionic-nonionic surfactants. The oilfield-specific surfactant-based enhanced oil recovery systems include anionic-nonionic surfactant systems and nonionic surfactant systems.

[0069] In a preferred embodiment, when the system is Span80 and sodium fatty alcohol polyoxyethylene ether sulfate, the HLB value of the standard surfactant-driven oil displacement system is 6 to 11.

[0070] In a preferred embodiment, when the system is Span80 and fatty alcohol polyoxyethylene ether, the HLB value of the standard surfactant-driven oil displacement system is 5 to 8.

[0071] In a preferred embodiment, when the system is composed of sodium dodecylbenzenesulfonate and sodium fatty alcohol polyoxyethylene ether sulfate, the HLB value of the standard surfactant-driven oil displacement system is 11 to 14.

[0072] For step 104, the predicted values ​​of the comprehensive emulsification index are screened to obtain the target comprehensive emulsification index of the surfactant-driven oil displacement system to be predicted, including:

[0073] The predicted values ​​of the comprehensive emulsification index were sorted from largest to smallest to obtain the second highest predicted value of the comprehensive emulsification index.

[0074] The second-highest predicted emulsification index was used as the target emulsification index.

[0075] In this invention, since the prediction model is a sixth-order function, six X values ​​are obtained after inputting Y (HLB value). Among these six X values, there are negative and positive numbers. And through a large number of experiments, it has been confirmed that the X value in the second position of the root value in descending order is closest to the true value. Therefore, when predicting the surfactant displacement system to be predicted based on the prediction model, the second highest predicted value of the emulsification comprehensive index is selected as the target emulsification comprehensive index to improve the prediction accuracy.

[0076] The present invention will be further described below by way of examples, but the scope of protection of the present invention is not limited to these embodiments.

[0077] In the following embodiments, the emulsifying power and emulsification stability were tested using the test methods disclosed in patent publication CN102200503A.

[0078] Example 1

[0079] 1) For the Span80 / AES system, five standard Span80 / AES systems with different HLB values ​​were prepared. Standard Span80 / AES system 1 was prepared by mixing Span80 and AES in a mass percentage ratio of 76:24, with an HLB value of 6.77; Standard Span80 / AES system 2 was prepared by mixing Span80 and AES in a mass percentage ratio of 68:32, with an HLB value of 7.60; Standard Span80 / AES system 3 was prepared by mixing Span80 and AES in a mass percentage ratio of 56:44, with an HLB value of 8.83; Standard Span80 / AES system 4 was prepared by mixing Span80 and AES in a mass percentage ratio of 50:50, with an HLB value of 9.45; and Standard Span80 / AES system 5 was prepared by mixing Span80 and AES in a mass percentage ratio of 45:56, with an HLB value of 10.07.

[0080] 2) The emulsifying power and emulsifying stability values ​​of each standard Span80 / AES system were obtained by testing using the method in CN102200503A, based on... The formula calculates the comprehensive emulsification index, where X is the comprehensive emulsification index (%), X1 is the emulsification stability value (%), and X2 is the emulsification power (%).

[0081] 3) The above five sets of emulsifying forces (X2) and HLB values ​​(Y) were fitted to obtain the following fitted curves:

[0082] X2 = -5.895Y 2 +95.57Y-344.4

[0083] The first fitting parameters include quadratic coefficient P5 = -5.895, linear coefficient P6 = 95.57, and constant P7 = -344.4; moreover, the stability coefficient (Rsquare) of the fitted curve is 0.989, and the adjusted stability coefficient (Adjrsquare) is 0.988.

[0084] 4) Repeat steps 1) and 2) above to obtain 30 sets of emulsion stability values ​​(X1) and emulsion comprehensive index (X) data, and fit X1 and X to obtain the fitted curve:

[0085] X = 0.0001359X1 3 -0.02139X1 2 +1.74X1+21.73

[0086] The second fitting parameters include cubic coefficient P1 = 0.0001359, quadratic coefficient P2 = -0.02139, linear coefficient P3 = 1.175, and constant P4 = 21.73; moreover, the stability coefficient (Rsquare) of the fitted curve is 0.546, and the adjusted stability coefficient (Adjrsquare) is 0.516.

[0087] 5) Based on the first fitting parameters, the second fitting parameters, the HLB value (Y), and the comprehensive emulsification index (X) mentioned above, the prediction model is obtained:

[0088]

[0089] Inputting HLB values ​​of 6.77, 7.60, 8.83, 9.45, and 10.07 into the prediction model yielded the predicted comprehensive emulsification index. The second highest predicted comprehensive emulsification index (i.e., Figure 3 The predicted value 4) is retained as the target emulsification index, and it is compared with the emulsification index obtained in step 2) (i.e., the true value) to obtain the following result: Figure 2 The chart shows a comparison between predicted and calculated values. (From...) Figure 2 and Figure 3 It can be seen that the predicted value of the second highest emulsification index is very close to the actual value, with an error of less than 10%, so the prediction model is accurate and reliable.

[0090] Example 2

[0091] 1) For the Span80 / AEO-3 system, five standard Span80 / AEO-3 systems with different HLB values ​​were prepared. Standard Span80 / AEO-3 system 1 was prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 78:22, with an HLB value of 5.07; Standard Span80 / AEO-3 system 2 was prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 58:42, with an HLB value of 5.77; Standard Span80 / AEO-3 system 3 was prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 58:42, with an HLB value of 5.77; Standard Span80 / AEO-3 system 4 was prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 58:42, with an HLB value of 5.77; Standard Span80 / AEO-3 system 5 was prepared System 3 of n80 / AEO-3 is prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 50:50, with an HLB value of 6.05; System 4 of standard Span80 / AEO-3 is prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 40:60, with an HLB value of 6.40; System 5 of standard Span80 / AEO-3 is prepared by mixing Span80 and AEO-3 in a mass percentage ratio of 20:80, with an HLB value of 7.10.

[0092] 2) The emulsifying power and emulsifying stability values ​​of each standard Span80 / AEO-3 system were obtained by testing using the method in CN102200503A, based on... The formula calculates the comprehensive emulsification index, where X is the comprehensive emulsification index (%), X1 is the emulsification stability value (%), and X2 is the emulsification power (%).

[0093] 3) The above five sets of emulsifying forces (X2) and HLB values ​​(Y) were fitted to obtain the following fitted curves:

[0094] X2 = -0.7191Y 2 +10.64Y -23.76

[0095] The first fitting parameters include quadratic coefficient P5 = -7191, linear coefficient P6 = 10.64, and constant P7 = -23.76; moreover, the stability coefficient (Rsquare) of the fitted curve is 0.896, and the adjusted stability coefficient (Adjrsquare) is 0.891.

[0096] 4) Repeat steps 1) and 2) above to obtain 40 sets of emulsion stability values ​​(X1) and emulsion comprehensive index (X) data. Fit X1 and X to obtain the fitted curve:

[0097] X = -0.00009X1 3 +0.02083X1 2 -1.353X1+60.5

[0098] The second fitting parameters include cubic coefficient P1 = -0.00009, quadratic coefficient P2 = 0.02083, linear coefficient P3 = -1.353, and constant P4 = 60.5;

[0099] 5) Based on the first fitting parameters, the second fitting parameters, the HLB value (Y), and the comprehensive emulsification index (X) mentioned above, the prediction model is obtained:

[0100]

[0101] Inputting HLB values ​​of 5.07, 5.77, 6.05, 6.40, and 7.10 into the prediction model yields predicted emulsification indices. The second-highest predicted emulsification index (i.e., the predicted value) is retained as the target emulsification index, and compared with the actual emulsification index obtained in step 2). Figure 4 The chart shows a comparison between predicted and calculated values. (From...) Figure 4 It can be seen that the predicted value of the second highest emulsification index is very close to the actual value, with an error of less than 10%, so the prediction model is accurate and reliable.

[0102] Example 3

[0103] 1) For the SDBS / AES system, five standard SDBS / AES systems with different HLB values ​​were prepared. Standard SDBS / AES system 1 was prepared by mixing SDBS and AES at a mass ratio of 90:10, with an HLB value of 11.03; Standard SDBS / AES system 2 was prepared by mixing SDBS and AES at a mass ratio of 78:22, with an HLB value of 11.51; Standard SDBS / AES system 3 was prepared by mixing SDBS and AES at a mass ratio of 50:50, with an HLB value of 12.62; Standard SDBS / AES system 4 was prepared by mixing SDBS and AES at a mass ratio of 44:56, with an HLB value of 12.86; and Standard SDBS / AES system 5 was prepared by mixing SDBS and AES at a mass ratio of 32:68, with an HLB value of 13.33.

[0104] 2) The emulsifying power and emulsifying stability values ​​of each standard SDBS / AES system were obtained by testing using the method in CN102200503A, based on... The formula calculates the comprehensive emulsification index, where X is the comprehensive emulsification index (%), X1 is the emulsification stability value (%), and X2 is the emulsification power (%).

[0105] 3) The above five sets of emulsifying forces (X2) and HLB values ​​(Y) were fitted to obtain the following fitted curves:

[0106] X2 = -0.5672Y 2 -2.887Y + 162.4

[0107] The first fitting parameters include quadratic coefficient P5 = -0.5672, linear coefficient P6 = -2.887, and constant P7 = 162.4; moreover, the stability coefficient (Rsquare) of the fitted curve is 0.863, and the adjusted stability coefficient (Adjrsquare) is 0.857.

[0108] 4) Repeat steps 1) and 2) above to obtain 50 sets of emulsion stability values ​​(X1) and emulsion comprehensive index (X) data. Fit X1 and X to obtain the fitted curve:

[0109] X = 0.0001359X1 3 -0.02139X1 2 +1.74X1+21.73

[0110] The second fitting parameters include cubic coefficient P1 = -0.0003279, quadratic coefficient P2 = 0.06849, linear coefficient P3 = -3.889, and constant P4 = 97.81;

[0111] 5) Based on the first fitting parameters, the second fitting parameters, the HLB value (Y), and the comprehensive emulsification index (X) mentioned above, the prediction model is obtained:

[0112]

[0113] Inputting HLB values ​​of 11.03, 11.51, 12.62, 12.86, and 13.33 into the prediction model yields predicted emulsification indices. The second-highest predicted emulsification index (i.e., the predicted value) is retained as the target emulsification index, and compared with the actual emulsification index obtained in step 2). Figure 5 The chart shows a comparison between predicted and calculated values. (From...) Figure 5 It can be seen that the predicted value of the maximum emulsification index is very close to the actual value, with an error of less than 10%, so the prediction model is accurate and reliable.

[0114] It should be noted that, Figures 2 to 5 For each HLB value, the surfactant-driven oil displacement system was tested multiple times to obtain the true values ​​of multiple emulsification comprehensive indices. Figure 3 Only the predicted values ​​that are positive are shown in the table.

[0115] like Figure 6 , Figure 7 As shown, this invention provides a device for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system. The device can be implemented via software, hardware, or a combination of both. From a hardware perspective, as... Figure 6The diagram shown is a hardware architecture diagram of a computing device for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system according to an embodiment of the present invention. (Except for...) Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, the computing device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 7 As shown, a device in a logical sense is formed by the CPU of its computing device reading the corresponding computer program from the non-volatile memory into the main memory for execution. This embodiment provides a device for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system. The device includes:

[0116] The acquisition module 700 is used to acquire the HLB value of the surfactant flooding system to be predicted for application in three types of oil reservoirs;

[0117] The prediction module 702 is used to input HLB values ​​into a pre-built prediction model and output the predicted value of the comprehensive emulsification index; and to filter the predicted value of the comprehensive emulsification index to obtain the target comprehensive emulsification index of the surfactant displacement system to be predicted.

[0118] In some specific implementations, the acquisition module 700 can be used to perform the above step 100, and the prediction module 702 can be used to perform the above steps 102 and 104.

[0119] In one embodiment of the present invention, a construction module is further included, which is configured to perform the following operations:

[0120] To determine the types of surfactant-driven oil displacement systems to be predicted;

[0121] Obtain the emulsification stability, emulsifying power, and comprehensive emulsification index of standard surfactant displacement systems of the above types; wherein, the HLB values ​​of different standard surfactant displacement systems are different;

[0122] The HLB value and emulsifying power of the standard surfactant oil displacement system were fitted to obtain the first fitting parameters;

[0123] The emulsification stability value and emulsification index of the standard surfactant oil displacement system were fitted to obtain the second fitting parameter;

[0124] The prediction model is determined based on the first fitting parameter, the second fitting parameter, the HLB value of the standard surfactant oil displacement system, and the comprehensive emulsification index of the standard surfactant oil displacement system.

[0125] In one embodiment of the present invention, the prediction model includes:

[0126]

[0127] Wherein, P1 is the coefficient of the cubic term in the second fitting parameter; P2 is the coefficient of the quadratic term in the second fitting parameter; P3 is the coefficient of the linear term in the second fitting parameter; P4 is the constant in the second fitting parameter; P5 is the coefficient of the quadratic term in the first fitting parameter; P6 is the coefficient of the linear term in the first fitting parameter; P7 is the constant in the first fitting parameter; X is the comprehensive emulsification index of the standard surfactant oil displacement system; Y is the HLB value of the standard surfactant oil displacement system.

[0128] In one embodiment of the present invention, the prediction module 702 is further configured to perform the following operations:

[0129] The predicted values ​​of the comprehensive emulsification index are sorted from largest to smallest to obtain the largest predicted value of the comprehensive emulsification index;

[0130] The maximum predicted emulsification index is used as the target emulsification index.

[0131] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on an emulsification index prediction device for a surfactant-driven oil displacement system. In other embodiments of the present invention, an emulsification index prediction device for a surfactant-driven oil displacement system may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0132] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0133] This invention also provides a computing device, including a memory and a target processor. The memory stores a computer program, and when the target processor executes the computer program, it implements a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system according to any embodiment of this invention.

[0134] This invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program causes the processor to perform a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system according to any embodiment of this invention.

[0135] Embodiments of this application also provide a computer program product, which includes a computer program. A processor of a computer device reads the computer program from a computer-readable storage medium and executes the computer program, causing the computer device to perform a method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system as described in any of the above embodiments.

[0136] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0137] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0138] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0139] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0140] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the function of any of the above embodiments.

[0141] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system, characterized in that, include: Obtain the HLB values ​​of the surfactant-driven oil displacement system to be predicted for application in three types of reservoirs; Class III oil reservoirs refer to crude oil in Class III oil reservoirs, which are crude oil in oil layers with an effective thickness of less than 1m and low permeability. The HLB value is input into a pre-built prediction model, and the predicted value of the comprehensive emulsification index is output. The predicted values ​​of the emulsification comprehensive index are screened to obtain the target emulsification comprehensive index of the surfactant displacement system to be predicted. The prediction model is constructed using the following method: Obtain the type of the surfactant-driven oil displacement system to be predicted; Prepare a standard surfactant flooding system of the aforementioned types; wherein different standard surfactant flooding systems have different HLB values; Obtain the emulsification stability, emulsifying power, and comprehensive emulsification index of the standard surfactant-driven oil displacement system; The HLB value and emulsifying power of the standard surfactant oil displacement system are fitted to obtain the first fitting parameters; The emulsification stability value and emulsification index of the standard surfactant oil displacement system are fitted to obtain a second fitting parameter; the first fitting parameter includes quadratic coefficients, primary coefficients, and a constant; the second fitting parameter includes cubic coefficients, quadratic coefficients, primary coefficients, and a constant. The prediction model is determined based on the first fitting parameters, the second fitting parameters, the HLB value of the standard surfactant oil displacement system, and the comprehensive emulsification index of the standard surfactant oil displacement system; the prediction model includes: in, P 1 represents the coefficient of the cubic term in the second fitting parameters; P 2 represents the coefficient of the quadratic term in the second fitting parameters; P 3 represents the coefficient of the first-order term in the second fitting parameters; P 4 is a constant in the second fitting parameters; P 5 represents the coefficient of the quadratic term in the first fitting parameters; P 6 represents the coefficient of the first-order term in the first fitting parameters; P 7 is a constant in the first fitting parameters; X The emulsification index of the standard surfactant-driven oil displacement system is given. Y The HLB value of the standard surfactant-driven oil displacement system is given.

2. The method according to claim 1, characterized in that, The first fitting parameter is obtained by fitting at least five of the standard surfactant oil displacement systems; the second fitting parameter is obtained by fitting at least twenty of the standard surfactant oil displacement systems.

3. The method according to claim 1, characterized in that, The predicted surfactant-driven oil displacement systems include Span80 and fatty alcohol polyoxyethylene ether sodium sulfate system, Span80 and fatty alcohol polyoxyethylene ether system, and sodium dodecylbenzene sulfonate and fatty alcohol polyoxyethylene ether sodium sulfate system.

4. The method according to claim 3, characterized in that, When the type is Span80 and sodium fatty alcohol polyoxyethylene ether sulfate system, the HLB value of the standard surfactant oil displacement system is 6~11.

5. The method according to claim 3, characterized in that, When the type is Span80 and fatty alcohol polyoxyethylene ether system, the HLB value of the standard surfactant oil displacement system is 5~8.

6. The method according to claim 3, characterized in that, When the system is composed of sodium dodecylbenzenesulfonate and sodium fatty alcohol polyoxyethylene ether sulfate, the HLB value of the standard surfactant oil displacement system is 11-14.

7. The method according to any one of claims 1 to 6, characterized in that, The predicted emulsification index values ​​are screened to obtain the target emulsification index of the surfactant-driven oil displacement system to be predicted, including: The predicted values ​​of the comprehensive emulsification index are sorted from largest to smallest to obtain the second highest predicted value of the comprehensive emulsification index. The second-highest predicted emulsification index is taken as the target emulsification index.

8. A device for predicting the comprehensive emulsification index of a surfactant-driven oil displacement system, characterized in that, For implementing the method as described in any one of claims 1 to 7, comprising: The acquisition module is used to acquire the HLB values ​​of the surfactant flooding system to be predicted for application in three types of reservoirs; The prediction module is used to input the HLB value into a pre-built prediction model, output the predicted value of the comprehensive emulsification index, and filter the predicted value of the comprehensive emulsification index to obtain the target comprehensive emulsification index of the surfactant displacement system to be predicted.

9. A computing device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program, implements the method as described in any one of claims 1-7.

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