Method for determining demulsification main control factors of high-acid-value naphthenic crude oil

Abstract

The invention provides a method for determining demulsification main control factors of high-acid-value naphthenic crude oil. The method comprises the following steps: acquiring demulsification data of demulsification of high-acid-value naphthenic crude oil with different physical properties in stratums with different properties by using a target demulsifier, and normalizing the demulsification data to the same interval; based on the normalized demulsification data, obtaining a demulsification effect multiple regression model, a demulsification effect ridge regression model, a demulsification effect lasso regression model and a demulsification effect stepwise regression model, and respectively determining weighted evaluation indexes of the models; selecting an optimal model as a demulsification effect model based on the weighted evaluation index, the decision coefficient, the mean square error, the root-mean-square error and the mean absolute error of each model; analyzing the contribution rates of the property parameters of each stratum, the property parameters of the crude oil and the engineering parameters to the demulsification effect parameters of the demulsification effect model, and determining the first N parameters with the maximum contribution rates to the demulsification effect parameters as main control factors in the demulsification process of the high-acid-value naphthenic crude oil by using the target demulsifier.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas development and storage and transportation, and in particular relates to a method for determining main control factors of demulsification of high acid value cycloalkyl crude oil. Background Art

[0002] Crude oil is unprocessed petroleum, a mixture of various liquid hydrocarbons such as alkanes, cycloalkanes, aromatic hydrocarbons and olefins. Crude oil acid number (Total Acid Number, TAN) is an indicator that quantitatively represents the content of acidic oxygen-containing compounds in crude oil. That is, the total amount of free acid contained in organic substances such as oils, polyesters, paraffins, etc. Quantitatively speaking, the number of milligrams of potassium hydroxide required to neutralize the acidic substances in 1g of crude oil sample is expressed in mgKOH / g. TAN less than 0.5mgKOH / g is low acid crude oil, TAN greater than 0.5mgKOH / g is acidic crude oil, and TAN greater than 1.0mgKOH / g is high acid crude oil.

[0003] The most notable feature of crude oil emulsification is the increase in viscosity, which can lead to many problems. For example, it will cause the water-jacket furnace to increase its temperature and consume more fuel; it will cause the pumping units and external pumps to increase their power consumption; it will cause the risk of rod breakage; it will require frequent line sweeping; it will increase pipeline friction, reduce flow rate, increase wellhead back pressure, and increase the risk of pipeline leakage; in refining, it will directly cause frequent fluctuations in the operation of the electric desalter or a rapid increase in current in the refinery's pressure reducing device; in the gathering and transportation process, the large amount of water contained in the emulsified crude oil cannot be removed in time, which will cause pipeline corrosion and other problems.

[0004] The main characteristics of high acid value crude oil are high density, high acid value, high heavy metal content, high colloid content and high viscosity. As the development of oil fields deepens, the heavy components of crude oil will increase, and the acid value will increase. When high acid value crude oil is discharged from the reservoir, it mixes with external fluids and the formation water it contains. When it is sheared at high speed, the natural emulsifiers contained in the crude oil, such as asphaltene, colloid, cyclohexane acid, nitrogen and sulfur organic matter, wax crystals, clay, etc., prompt the mixture to form a stable crude oil emulsion, that is, the crude oil is emulsified. In particular, in recent years, most oil field developments have entered a high water content period. The application of various mining technologies has made crude oil mostly produced in the form of emulsions, and crude oil emulsification has become more and more serious. In the crude oil W / O emulsification system, colloids, asphaltene, etc. gather on the surface of the dispersed phase water droplets to form a stable interface layer, thereby preventing the condensation of water droplets.

[0005] At present, physical and chemical demulsifiers are generally used to solve the problem of crude oil emulsification. For high acid value crude oil, there are problems such as low oil-water separation efficiency in the settling tank, large amount of demulsifier, and overload of the electric dehydrator during the demulsification process. This is mainly because the density of high acid value crude oil is large (0.942-1.031g / cm 3), high viscosity (196.7-5400.8mPa·s), and high colloid content of naphthenic crude oil accounted for an increasing proportion. Naphthenate is the main factor affecting the demulsification and dehydration of high acid value crude oil. In addition, the water content of high acid value crude oil increases, and the crude oil emulsion changes from W / O type to O / W type, which makes its chemical composition and emulsion structure complicated, making it difficult to demulsify and dehydrate by electric field and chemical methods, increasing the difficulty of crude oil demulsification and dehydration. It is enough to see that demulsification is a very complicated problem, which is related to the composition, properties, type and stability factors of crude oil, as well as the molecular structure and properties of demulsifiers. Therefore, the selection of demulsifiers needs to comprehensively consider the dehydration rate, dehydration speed, oil / water interface state, oil content of dewatered water, optimal dosage of demulsifier, and low temperature dehydration performance. At present, in view of this situation with many and complex influencing factors, there is no effective method to effectively determine the main controlling factors of demulsification of high acid value naphthenic crude oil. Summary of the invention

[0006] The object of the present invention is to provide a technical solution capable of determining the main controlling factors in the process of demulsifying high acid value cycloalkyl crude oil by using a certain demulsifier.

[0007] In order to achieve the above object, the present invention provides a method for determining the main controlling factors of demulsification of high acid value cycloalkyl crude oil, the method comprising:

[0008] Obtaining demulsification data of demulsifying high acid value cycloalkyl crude oil with different physical properties in different formations using a target demulsifier; wherein the demulsification data includes formation property parameter data, crude oil property parameter data, engineering parameter data and demulsification effect parameter data;

[0009] Normalizing the demulsification data to the same interval to obtain normalized demulsification data;

[0010] Based on the normalized demulsification data, multiple regression, ridge regression, lasso regression and stepwise regression were used to fit the relationship between demulsification effect parameters and formation property parameters, crude oil property parameters and engineering parameters, and the demulsification effect multiple regression model, demulsification effect ridge regression model, demulsification effect lasso regression model and demulsification effect stepwise regression model were obtained, and the weighted evaluation index of each model was determined respectively; among which, the weighted evaluation index = 0.5×MSE+0.2×MAE+0.2×RMSE+0.1×(1-determination coefficient R 2 ), MSE refers to mean square error, MAE refers to mean absolute error, RMSE refers to root mean square error, R 2 refers to the coefficient of determination;

[0011] Based on the weighted evaluation index of each model, determine the model with the smallest weighted evaluation index; if there is only one model with the smallest weighted evaluation index, then use the model with the smallest weighted evaluation index as the demulsification effect model; if there are two or more models with the smallest weighted evaluation index, then select the best model from the models with the smallest weighted evaluation index as the demulsification effect model based on at least one of the determination coefficient, mean square error, root mean square difference, and mean absolute error;

[0012] The contribution rate of formation property parameters, crude oil property parameters and engineering parameters to the demulsification effect parameters is analyzed for the demulsification effect model, and the top N parameters with the largest contribution rate to the demulsification effect parameters are determined to be the main controlling factors in the demulsification process of high acid value cycloalkyl crude oil using the target demulsifier.

[0013] According to a preferred implementation manner of the above method, the formation property parameters include: clay content and whole rock mineral content; further, the whole rock mineral content includes quartz content, potassium feldspar content, plagioclase content, calcite content, dolomite content, halite content, analcime content, barite content and anatase content.

[0014] According to a preferred implementation of the above method, the crude oil property parameters include: crude oil density, crude oil viscosity, crude oil pour point, crude oil sulfur content, crude oil wax content, crude oil initial distillation point, crude oil final distillation point, crude oil wax precipitation point, crude oil colloid and asphalt content, crude oil acidity, crude oil flash point, crude oil initial water content, and oil layer water content after crude oil sedimentation for 4 hours.

[0015] According to a preferred implementation of the above method, the engineering parameters include the amount of demulsifier added, the dosing cycle, and the temperature when the demulsifier is injected.

[0016] According to a preferred embodiment of the above method, the demulsification effect parameter is selected from the dehydration rate or the water content of the crude oil after demulsification.

[0017] According to a preferred embodiment of the above method, the method further comprises:

[0018] Before normalizing the demulsification data to the same interval, determining whether the demulsification data conforms to a normal distribution, if the demulsification data conforms to a normal distribution, performing subsequent steps before normalizing the demulsification data to the same interval, if the demulsification data does not conform to a normal distribution, reacquiring the demulsification data;

[0019] Further, the method further comprises: determining the extreme value, mean, variance and distribution characteristics of the demulsification data before normalizing the demulsification data to the same interval;

[0020] Wherein, determining whether the demulsification data conforms to the normal distribution can be performed, but not limited to, by visual analysis; the visual analysis can be implemented, but not limited to, by visual charts, and the visual charts include at least one of QQ charts, frequency charts, box plots, and statistical tables; the visual charts can be used to clearly and intuitively check whether the selected variables obey the normal distribution;

[0021] Demulsification data that conform to the normal distribution can effectively avoid the problem of the dependent variable being overly controlled by other variables to be studied during data analysis, thereby ensuring the accuracy and scientificity of data analysis; demulsification data that conform to the normal distribution can also ensure the universality of the sample and reduce errors in human data selection.

[0022] According to a preferred embodiment of the above method, the method further comprises:

[0023] Based on the normalized demulsification data, two-way factor analysis was performed to determine the relationship between each formation property parameter, each crude oil property parameter, each engineering parameter and the demulsification effect parameter;

[0024] Furthermore, the pairwise factor analysis based on the normalized demulsification data includes:

[0025] Based on the normalized demulsification data, pairwise factor scatter plots between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter are drawn respectively, and then the pairwise factor mathematical relationships between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter are fitted respectively, and then Pearson correlation analysis, Spearman correlation analysis, grey correlation analysis or significance analysis are used to determine the relationship between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter.

[0026] According to a preferred implementation of the above method, the demulsification data are normalized to the same interval using a Min-Max method or a Z-score method.

[0027] According to a preferred implementation of the above method, in the process of normalizing the demulsification data to the same interval, the demulsification data is normalized to [0, 1].

[0028] According to a preferred implementation of the above method, in the process of determining the demulsification effect multivariate regression model, the demulsification effect ridge regression model, the demulsification effect lasso regression model, and the demulsification effect stepwise regression model, the normalized demulsification data is divided into a test set and a training set.

[0029] According to a preferred implementation of the above method, multiple regression, ridge regression, lasso regression and stepwise regression are respectively used to fit the relationship between the demulsification effect parameters and the formation property parameters, the crude oil property parameters and the engineering parameters, and in the process of obtaining the demulsification effect multiple regression model, the demulsification effect ridge regression model, the demulsification effect lasso regression model and the demulsification effect stepwise regression model, the multiple regression, ridge regression, lasso regression and stepwise regression can be performed in a conventional manner;

[0030] In a specific embodiment, the demulsification effect multivariate regression model is determined using the following formula:

[0031] y=Xβ+ε

[0032] Where: y is the response vector, X is the design matrix, β is the parameter vector, and ε is the error vector;

[0033] The least squares estimate of its parameters is:

[0034] β=(X T X) -1 X T y

[0035] Where: y is the response vector, X is the design matrix, and β is the parameter vector;

[0036] The model parameters are determined by minimizing the difference between the model prediction value and the actual observation value, that is, the residual sum of squares. The residual sum of squares is defined as:

[0037] RSS(β)=Σ(y i -x i T β) 2

[0038] Where: y i is the response value corresponding to the i-th matrix element, x i is the i-th matrix element, β is the parameter vector, and RSS(β) is the residual sum of squares of the parameter vector;

[0039] In a specific embodiment, the demulsification effect ridge regression model is determined by:

[0040] Given different design matrices X and response vectors y, determine J(β) = Σ(y i -x i Tβ) 2 +λΣβ j 2 , when J(β)=Σ(y i -x i Tβ) 2 +λΣβ j 2When it is minimum, the demulsification effect ridge regression model can be obtained; where: λ is a non-negative adjustment parameter that determines the strength of regularization; the second term λΣ|β j | is a regularization term, which penalizes the absolute value of the coefficient; when λ = 0, ridge regression degenerates into standard linear regression. When λ increases, the influence of regularization increases and the estimated value of the coefficient shrinks;

[0041] In a specific embodiment, the demulsification effect lasso regression model is determined by:

[0042] Given different design matrices X and response vectors y, determine J(β) = Σ(y i -x i Tβ) 2 +λΣ|β j |, when J(β)=Σ(y i -x i Tβ) 2 +λΣ|β j | is the smallest, the demulsification effect lasso regression model can be obtained; where: λ is a non-negative adjustment parameter that determines the strength of regularization; the second term λΣ|β j | is a regularization term, which penalizes the absolute value of the coefficient;

[0043] In a specific embodiment, the demulsification effect stepwise regression model is determined using a stepwise regression iterative method, which is divided into forward selection, backward elimination or a two-way stepwise process; in forward selection, the model starts with no predictor variables, and then adds predictor variables one by one, each time selecting the predictor variable that provides the greatest improvement; in backward elimination, the model starts with including all predictor variables, and then deletes predictor variables one by one, each time deleting the predictor variable that provides the least improvement to the model; two-way stepwise regression combines forward selection and backward elimination, while considering adding and deleting predictor variables; the decision at each step is based on the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC);

[0044] According to the Akaike Information Criterion, the smaller the AIC value of the model, the better it is. AIC = 2k-2ln(L), where: k is the number of parameters in the model; L is the maximum likelihood of the model;

[0045] According to the Bayesian Information Criterion, the smaller the BIC value of the model, the better it is. BIC = kln(n)-2ln(L), where: k is the number of parameters in the model; L is the maximum likelihood of the model; n is the number of data points;

[0046] AIC takes into account the complexity of the model and the goodness of fit to the data, trying to strike a balance between choosing a model complex enough to fit the data well and choosing a simple model to avoid overfitting. Compared to AIC, BIC tends to choose simpler models. In general, if the main goal of model selection is prediction, you may prefer to use AIC. If the goal is to choose a model that is simple but still explains the data well, you may prefer to use BIC.

[0047] According to a preferred implementation of the above method, the optimal model is selected as the demulsification effect model from the models with the smallest weighted evaluation indicators based on at least one of the determination coefficient, mean square error, root mean square deviation, and mean absolute error, including:

[0048] According to a preferred implementation of the above method, the contribution rate is determined by the following formula:

[0049]

[0050] Where: CR i is the contribution rate of the ith parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; a i is the coefficient of the i-th parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; |a j | is the absolute value of the coefficient of the jth parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; It is the sum of the absolute values ​​of the coefficients of the formation property parameters, crude oil property parameters and engineering parameters in the model.

[0051] The technical solution provided by the present invention establishes a mathematical model of the demulsification effect of high acid value crude oil under the comprehensive action of multi-dimensional factors on the basis of a comprehensive analysis of formation property parameters, crude oil property parameters, engineering parameters and demulsification effect parameters, and clarifies the main controlling factors affecting the demulsification effect of crude oil. The technical solution provided by the present invention can more accurately determine the factors affecting the demulsification effect of the target demulsifier when performing high acid value crude oil demulsification, which is of great significance for guiding the use of the target demulsifier. The technical solution provided by the present invention takes into account the influence of multiple factors on the demulsification effect, and allows the search for the best solution in the continuous parameter space, which can more comprehensively consider multi-objective optimization and improve the accuracy of determining the main controlling factors of the crude oil demulsification effect. DETAILED DESCRIPTION

[0052] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below. Here, the exemplary embodiments of the present invention and their description are used to explain the present invention, but are not intended to limit the present invention.

[0053] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps is not limited and can be appropriately adjusted as needed.

[0054] Embodiment 1:

[0055] This embodiment provides a method for determining the main controlling factors of demulsification of high acid value cycloalkyl crude oil, the method comprising:

[0056] 1. Obtain demulsification data of demulsifying high acid value cycloalkane crude oil with different physical properties in different formations using the target demulsifier through literature research data and indoor experimental data; wherein the demulsification data includes formation property parameter data, crude oil property parameter data, engineering parameter data and demulsification effect parameter data;

[0057] Among them, the formation property parameters include: clay content X20, whole rock mineral content; the whole rock mineral content includes quartz content X11, potassium feldspar content X12, plagioclase content X13, calcite content X14, dolomite content X15, halite content X16, analcime content X17, barite content X18, anatase content X19;

[0058] Among them, crude oil property parameters include: crude oil density at 20℃ X1, crude oil viscosity at 50℃ X2, crude oil pour point X3, crude oil sulfur content X4, crude oil wax content X5, crude oil initial distillation point X6, crude oil final distillation point X7, crude oil wax precipitation point X8, crude oil colloid and asphalt content X9, crude oil acidity X10, crude oil flash point X24, crude oil initial water content X25, oil layer water content after crude oil sedimentation for 4 hours X26;

[0059] Among them, the engineering parameters include the amount of demulsifier added X23, the dosing cycle X22, and the temperature when the demulsifier is injected X21;

[0060] Among them, the demulsification effect parameter is the water content Y of the crude oil after demulsification.

[0061] The results are shown in Table 1.

[0062] Table 1

[0063]

[0064]

[0065] 2. Visually analyze the demulsification data obtained through the frequency diagram to determine the extreme value, mean, variance and distribution characteristics of the demulsification data. If the demulsification data conforms to the normal distribution, proceed to the subsequent step 3. If the demulsification data does not conform to the normal distribution, re-acquire the demulsification data.

[0066] The demulsification data in this embodiment conforms to the normal distribution, and the selected samples are universal, scientific and rigorous.

[0067] 3. The demulsification data are normalized to the same interval [0,1] using the Min-Max method to obtain normalized demulsification data.

[0068] Clay content X20, quartz content X11, potassium feldspar content X12, plagioclase content X13, calcite content X14, dolomite content X15, rock salt content X16, analcite content X17, barite content X18, anatase content X19, 20℃ crude oil density X1, 50℃ crude oil viscosity X2, crude oil pour point X3, crude oil sulfur content X4, crude oil wax content X5, crude oil initial distillation point X6, crude oil final distillation point X7, crude oil wax precipitation point X8, crude oil colloid and asphalt The content of blue X9, crude oil acidity X10, crude oil flash point X24, crude oil initial water content X25, oil layer water content after crude oil settling for 4 hours X26, demulsifier dosage X23, dosing cycle X22, demulsifier injection temperature X21, and crude oil water content Y after demulsification are normalized to obtain normalized clay content XX20, normalized quartz content XX11, normalized potassium feldspar content XX12, normalized plagioclase content XX13, and normalized calcite content X X14, normalized dolomite content XX15, normalized halite content XX16, normalized analcime content XX17, normalized barite content XX18, normalized anatase content XX19, normalized crude oil density at 20°C XX1, normalized crude oil viscosity at 50°C XX2, normalized crude oil pour point XX3, normalized crude oil sulfur content XX4, normalized crude oil wax content XX5, normalized crude oil initial distillation point XX6, normalized crude oil Final distillation point XX7, normalized wax precipitation point of crude oil XX8, normalized colloid and asphalt content of crude oil XX9, normalized acidity of crude oil XX10, normalized flash point of crude oil XX24, normalized initial water content of crude oil XX25, normalized water content of oil layer after crude oil sedimentation for 4 hours XX26, normalized demulsifier dosage XX23, normalized dosing cycle XX22, normalized temperature during demulsifier injection XX21, normalized water content of crude oil after demulsification YY.

[0069] 4. Based on the normalized demulsification data, the pairwise factor scatter plots between each formation property parameter, each crude oil property parameter, each engineering parameter and the demulsification effect parameter are drawn respectively, and then the pairwise mathematical relationships between each formation property parameter, each crude oil property parameter, each engineering parameter and the demulsification effect parameter are fitted respectively, and then the Pearson correlation analysis is used to determine the relationship between each formation property parameter, each crude oil property parameter, each engineering parameter and the demulsification effect parameter.

[0070] The single factor fitting relationship of X1 is: YY=-33.429632+38.382090XX1

[0071] The single factor fitting relationship of X2 is: YY=0.871526+0.004006XX2

[0072] The single factor fitting relationship of X3 is: YY=0.273977 -0.22560XX3

[0073] The single factor fitting relationship of X4 is: YY=6.769214 -4.933839XX4

[0074] The single factor fitting relationship of X5 is: YY=3.538332-2.66681XX5

[0075] The single factor fitting relationship of X6 is: YY=-2.113277+3.8628XX6

[0076] The single factor fitting relationship of X7 is: YY=-1.285159+0.40455XX7

[0077] The single factor fitting relationship of X8 is: YY=4.152554 -6.2065XX8

[0078] The single factor fitting relationship of X9 is: YY=0.233866+0.089764XX9

[0079] The single factor fitting relationship of X10 is: YY=-0.146426+0.587107XX10

[0080] The single factor fitting relationship of X11 is: YY=0.733333+0.000000XX11

[0081] The single factor fitting relationship of X12 is: YY=0.733333+0.000000XX12

[0082] The single factor fitting relationship of X13 is: YY=0.733333+0.000000XX13

[0083] The single factor fitting relationship of X14 is: YY=17.1000-16.250000XX14

[0084] The single factor fitting relationship of X15 is: YY=0.733333+0.000000XX15

[0085] The single factor fitting relationship of X16 is: YY=-15.81100+16.250000XX16

[0086] The single factor fitting relationship of X17 is: YY=0.733333+0.000000XX17

[0087] The single factor fitting relationship of X18 is: YY=0.733333+0.000000XX18

[0088] The single factor fitting relationship of X19 is: YY=0.733333+0.000000XX19

[0089] The single factor fitting relationship of X20 is: YY=0.733333+0.000000XX20

[0090] The single factor fitting relationship of X21 is: YY=0.733333-0.000000XX21

[0091] The single factor fitting relationship of X22 is: YY=0.733333 -0.000000XX22

[0092] The single factor fitting relationship of X23 is: YY=0.733333-0.000000XX23

[0093] The single factor fitting relationship of X24 is: YY=0.733333+0.000000XX24

[0094] The single factor fitting relationship of X25 is: YY=0.733333-0.000000XX25

[0095] The single factor fitting relationship of X26 is: YY=0.733333+0.000000XX26

[0096] 5. The normalized demulsification data is divided into a test set and a training set. In this embodiment, the normalized data of groups 1, 2, and 3 in Table 1 are the test set, and the normalized data of groups 4, 5, and 6 are the training set. Based on the test set and training set data, multiple regression, ridge regression, lasso regression, and stepwise regression are used to fit the relationship between the demulsification effect parameters and the formation property parameters, the crude oil property parameters, and the engineering parameters, respectively, to obtain the demulsification effect multiple regression model, the demulsification effect ridge regression model, the demulsification effect lasso regression model, and the demulsification effect stepwise regression model, and the weighted evaluation index of each model is determined respectively; wherein, the weighted evaluation index = 0.5 × MSE + 0.2 × MAE + 0.2 × RMSE + 0.1 × (1-determination coefficient R 2 ), MSE refers to mean square error, MAE refers to mean absolute error, RMSE refers to root mean square error, R 2 refers to the coefficient of determination;

[0097] Among them, the multivariate regression model of demulsification effect is determined by the following formula:

[0098] y=Xβ+ε

[0099] Where: y is the response vector, X is the design matrix, β is the parameter vector, and ε is the error vector;

[0100] The least squares estimate of its parameters is:

[0101] β=(X T X) -1 X T y

[0102] Where: y is the response vector, X is the design matrix, and β is the parameter vector;

[0103] The model parameters are determined by minimizing the difference between the model prediction value and the actual observation value, that is, the residual sum of squares. The residual sum of squares is defined as:

[0104] RSS(β)=Σ(y i -x i T β) 2

[0105] Where: y i is the response value corresponding to the i-th matrix element, x i is the i-th matrix element, RSS(β) is the residual sum of squares of the parameter vector;

[0106] Among them, the demulsification effect ridge regression model is determined by the following method:

[0107] Given different design matrices X and response vectors y, determine J(β) = Σ(y i -x iT β) 2 +λΣβ j 2 , when J(β)=Σ(y i -x i T β) 2 +λΣβ j 2 When it is minimum, the demulsification effect ridge regression model can be obtained; where: λ is a non-negative adjustment parameter that determines the strength of regularization; the second term λΣ|β j | is a regularization term, which penalizes the absolute value of the coefficient; when λ = 0, ridge regression degenerates into standard linear regression. When λ increases, the influence of regularization increases and the estimated value of the coefficient shrinks;

[0108] Among them, the demulsification effect lasso regression model is determined by the following method:

[0109] Given different design matrices X and response vectors y, determine J(β) = Σ(y i -x i Tβ) 2 +λΣ|β j |, when J(β)=Σ(y i -x i Tβ) 2 +λΣ|β j | is the smallest, the demulsification effect lasso regression model can be obtained; where: λ is a non-negative adjustment parameter that determines the strength of regularization; the second term λΣ|β j | is a regularization term, which penalizes the absolute value of the coefficient;

[0110] Among them, the demulsification effect stepwise regression model is determined by the stepwise regression iteration method, which is divided into forward selection, backward elimination or bidirectional stepwise process; in forward selection, the model starts with no predictor variables, and then adds predictors one by one, each time selecting the predictor variable that provides the greatest improvement; in backward elimination, the model starts with including all predictors, and then removes predictors one by one, each time removing the predictor variable that provides the least improvement to the model; bidirectional stepwise regression combines forward selection and backward elimination, considering both adding and removing predictors; the decision at each step is based on the Akaike Information Criterion (AIC);

[0111] According to the Akaike Information Criterion, the smaller the AIC value of the model, the better it is, AIC = 2k-2ln(L), where: k is the number of parameters in the model; L is the maximum likelihood of the model.

[0112] In this embodiment, the multivariate regression model of demulsification effect is:

[0113] YY=0.0151744734XX2-0.2156646151XX6+0.3739089082XX7+0.4641529961XX8-0.1368554593XX9-0.2746455669XX16

[0114] Among the 26 parameters, only the coefficients of XX2, XX6, XX7, XX8, XX9, and XX16 are not zero, indicating that the effective parameters of this method are the above parameters.

[0115] The mean square error (MSE) value of the multivariate regression model is 0.0474, the root mean square error (RMSE) value is 0.0382, the mean absolute error (MAE) value is 0.0211, the determination coefficient value is 0.0178, and the weighted evaluation index is 1.337756e-01.

[0116] In this embodiment, the demulsification effect ridge regression model is:

[0117] YY=0.7499725398-0.0000256675XX1+0.0055725857XX2+0.0001873785XX3-0.0000237000XX 4-0.0000376257XX5-0.0021261409XX6-0.0026686883XX7-0.0005629154XX8-0.0008027071 XX9-0.0001653029XX10-0.0000685273XX14-0.0026774931XX16-0.0021040198XX21-0.0000 121037XX22-0.0068934094XX23-0.0006286024XX24-0.0008883881XX25-0.0004608081XX26

[0118] The mean square error (MSE) value of the ridge regression model is 1.2022e-28, the root mean square error (RMSE) value is 1.6180e-15, the mean absolute error (MAE) value is 1.2130e-15, the determination coefficient value is 0.0309, and the weighted evaluation index is 9.691358e-02.

[0119] In this embodiment, the demulsification effect lasso regression model is: YY=0.7499725398-0.0000229077XX1+0.0050346262XX2+0.0001514526XX3-0.0000219202XX4-0.0000376149XX5-0.0019821761XX6-0.0024304759XX7-0.0005484089XX8-0.0 007513521XX9-0.0001510126XX10-0.0000626418XX14-0.0024788497XX16-0.0019570564XX21-0 .0000098460XX22-0.0063549588XX23-0.0005815853XX24-0.0008270538XX25-0.0004260289XX26

[0120] The mean square error (MSE) value of the lasso regression model is 0.0458, the root mean square error (RMSE) value is 0.0375, the mean absolute error (MAE) value is 0.0221, the determination coefficient value is 0.0183, and the weighted evaluation index is 1.330155e-01.

[0121] In this embodiment, the demulsification effect stepwise regression method calculation result is:

[0122] YY=-89.805477+0.203777XX7-0.148123XX8+0.129656XX9-9.892106XX10+92.455576XX16

[0123] Through calculation, the contribution coefficient of the final distillation point X7 is 0.001982; the contribution coefficient of the wax precipitation point X8 is 0.001440; the contribution coefficient of the crude oil colloid and asphalt content X9 is 0.001261; the contribution coefficient of the acidity X10 is 0.096199; and the contribution coefficient of the rock salt content X16 is 0.899118.

[0124] The evaluation parameter variance MSE value of the stepwise regression model is 4.0384e-13, the root mean square error RMSE value is 4.0384e-13, the mean absolute error MAE value is 3.6790e-13, the determination coefficient value is 0.0309, and the weighted evaluation index is 0.09691358.

[0125] 6. Based on the weighted evaluation index of each model, determine the model with the smallest weighted evaluation index; if there is only one model with the smallest weighted evaluation index, then the model with the smallest weighted evaluation index is used as the demulsification effect model; if there are two or more models with the smallest weighted evaluation index, then according to the determination coefficient, mean square error, root mean square difference, and mean absolute error, select the best model from these models with the smallest weighted evaluation index as the demulsification effect model.

[0126] The smaller the weighted evaluation index, the better the model fit. According to the above results, the weighted evaluation index of the ridge regression model and the stepwise regression model is equal and the smallest; the determination coefficient R of the ridge regression model with the smallest weighted evaluation index and the stepwise regression model is compared. 2 , the coefficient of determination R of the ridge regression model with the smallest weighted evaluation index and the stepwise regression model 2 equal; therefore, it is necessary to compare other evaluation indicators of the ridge regression model and the stepwise regression model. Analysis shows that the mean square error, root mean square error, and mean absolute error of the ridge regression model are all smaller than the corresponding indicators of the stepwise regression model, which indicates that the ridge regression model is the best model. Therefore, the ridge regression model is selected as the demulsification effect model.

[0127] 7. The contribution rate of formation property parameters, crude oil property parameters and engineering parameters to the demulsification effect parameters is analyzed in the demulsification effect model, and the top 6 parameters with the largest contribution rate to the demulsification effect parameters are determined to be the main controlling factors in the demulsification process of high acid value cycloalkyl crude oil using the target demulsifier.

[0128] The contribution rate is determined by the following formula:

[0129]

[0130] Where: CR i is the contribution rate of the ith parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; a i is the coefficient of the i-th parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; |a j | is the absolute value of the coefficient of the jth parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; It is the sum of the absolute values ​​of the coefficients of the formation property parameters, crude oil property parameters and engineering parameters in the model.

[0131] In the demulsification effect model, the contribution coefficient of crude oil density at 20℃ is 0.0010; the contribution coefficient of crude oil viscosity at 50℃ is 0.2151; the contribution coefficient of crude oil pour point is 0.0072; the contribution coefficient of crude oil sulfur content is 0.0009; the contribution coefficient of crude oil wax content is 0.0015; the contribution coefficient of crude oil initial distillation point is 0.0821; the contribution coefficient of crude oil final distillation point is 0.1030; the contribution coefficient of crude oil wax precipitation point is 0.0217; the contribution coefficient of colloid + asphalt content is 0.031 0; the contribution coefficient of crude oil acidity is 0.0064; the contribution coefficient of calcite content is 0.0026; the contribution coefficient of rock salt content is 0.1034; the contribution coefficient of demulsifier injection temperature is 0.0812; the contribution coefficient of dosing cycle is 0.0005; the contribution coefficient of demulsifier dosage is 0.2661; the contribution coefficient of crude oil flash point is 0.0243; the contribution coefficient of crude oil initial water content is 0.0343; the contribution coefficient of oil water content after 4h sedimentation time is 0.0178.

[0132] The main controlling factors are arranged in order from largest to smallest as follows:

[0133] The contribution coefficient of demulsifier dosage is 0.2661; the contribution coefficient of crude oil viscosity at 50℃ is 0.2151; the contribution coefficient of rock salt content is 0.1034; the contribution coefficient of crude oil final distillation point is 0.1030; the contribution coefficient of crude oil initial distillation point is 0.0821; the contribution coefficient of demulsifier injection temperature is 0.0812.

[0134] Analysis of the above results shows that the amount of demulsifier added and the temperature when the demulsifier is injected are the most important controllable engineering factors, and other factors are the most important uncontrollable factors; among them, the temperature when the demulsifier is injected has a certain influence on the viscosity. Therefore, for a given treatment object, its own properties remain unchanged within a certain range, and the demulsification problem can be optimally solved by controlling the amount of demulsifier added and the temperature when the demulsifier is injected.

[0135] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for determining the main controlling factors of demulsification of high acid value naphthenic crude oil, the method comprising: Obtaining demulsification data of demulsifying high acid value cycloalkyl crude oil with different physical properties in different formations using a target demulsifier; wherein the demulsification data includes formation property parameter data, crude oil property parameter data, engineering parameter data and demulsification effect parameter data; Normalizing the demulsification data to the same interval to obtain normalized demulsification data; Based on the normalized demulsification data, multiple regression, ridge regression, lasso regression and stepwise regression were used to fit the relationship between demulsification effect parameters and formation property parameters, crude oil property parameters and engineering parameters, and the demulsification effect multiple regression model, demulsification effect ridge regression model, demulsification effect lasso regression model and demulsification effect stepwise regression model were obtained, and the weighted evaluation index of each model was determined respectively; among which, the weighted evaluation index = 0.5×MSE+0.2×MAE+0.2×RMSE+0.1×(1-determination coefficient R 2 ), MSE refers to mean square error, MAE refers to mean absolute error, RMSE refers to root mean square error, R 2 refers to the coefficient of determination; Based on the weighted evaluation index of each model, determine the model with the smallest weighted evaluation index; if there is only one model with the smallest weighted evaluation index, then use the model with the smallest weighted evaluation index as the demulsification effect model; if there are two or more models with the smallest weighted evaluation index, then select the best model from the models with the smallest weighted evaluation index as the demulsification effect model based on at least one of the determination coefficient, mean square error, root mean square difference, and mean absolute error; The contribution rate of formation property parameters, crude oil property parameters and engineering parameters to the demulsification effect parameters is analyzed for the demulsification effect model, and the top N parameters with the largest contribution rate to the demulsification effect parameters are determined to be the main controlling factors in the demulsification process of high acid value cycloalkyl crude oil using the target demulsifier.

2. The method according to claim 1, wherein: Formation property parameters include: clay content, whole-rock mineral content; Preferably, the whole rock mineral content includes quartz content, potassium feldspar content, plagioclase content, calcite content, dolomite content, halite content, analcime content, barite content, and anatase content.

3. The method according to claim 1, wherein: Crude oil property parameters include: crude oil density, crude oil viscosity, crude oil pour point, crude oil sulfur content, crude oil wax content, crude oil initial distillation point, crude oil final distillation point, crude oil wax precipitation point, crude oil colloid and asphalt content, crude oil acidity, crude oil flash point, crude oil initial water content, and oil layer water content after crude oil sedimentation for 4 hours.

4. The method according to claim 1, wherein: Engineering parameters include demulsifier dosage, dosing cycle, and demulsifier injection temperature.

5. The method according to claim 1, wherein: The demulsification effect parameter is the dehydration rate or the water content of the crude oil after demulsification.

6. The method according to claim 1, wherein: The method further includes: Before normalizing the demulsification data to the same interval, determine whether the demulsification data conforms to the normal distribution. If the demulsification data conforms to the normal distribution, perform the subsequent steps before normalizing the demulsification data to the same interval. If the demulsification data does not conform to the normal distribution, reacquire the demulsification data.

7. The method according to claim 1, wherein: The method further includes: Based on the normalized demulsification data, two-way factor analysis was performed to determine the relationship between each formation property parameter, each crude oil property parameter, each engineering parameter and the demulsification effect parameter; Preferably, performing pairwise factor analysis based on normalized demulsification data includes: Based on the normalized demulsification data, pairwise factor scatter plots between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter are drawn respectively, and then the pairwise factor mathematical relationships between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter are fitted respectively, and then Pearson correlation analysis, Spearman correlation analysis, grey correlation analysis or significance analysis are used to determine the relationship between each formation property parameter, each crude oil property parameter, each engineering parameter and demulsification effect parameter.

8. The method according to claim 1, wherein: Normalizing the demulsification data to the same interval using the Min-Max method or the Z-score method; and / or In the process of normalizing the demulsification data to the same interval, the demulsification data are normalized to [0, 1].

9. The method according to claim 1, wherein: According to at least one of the determination coefficient, mean square error, root mean square deviation, and mean absolute error, the optimal model is selected from the models with the smallest weighted evaluation indicators as the demulsification effect model, including:

10. The method according to claim 1, wherein: The contribution rate is determined by the following formula: Where: CR i is the contribution rate of the ith parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; a i is the coefficient of the ith parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; |a j | is the absolute value of the coefficient of the jth parameter among the formation property parameters, crude oil property parameters and engineering parameters in the model; It is the sum of the absolute values ​​of the coefficients of the formation property parameters, crude oil property parameters and engineering parameters in the model.

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