Main control factor evaluation and optimization method for channeling sealing gel

Abstract

The invention relates to the technical field of oil field chemical sealing water / gas channeling, in particular to a channeling sealing gel main control factor evaluation and optimization method, which comprises the following steps: screening factors and levels influencing the stability of gel based on a stability test experiment; forming a multi-target evaluation system including jelly strength, bound water content, polymer molecular weight, jelly network density, dehydration rate and the like, and configuring different weight coefficients to determine target response; a space sample point database is formed, stepwise regression analysis is utilized to establish a response surface function of the gel stability performance adaptive to the oil reservoir environment, and main control factors of the gel stability performance and an action mechanism of the main control factors are determined; and determining stable formation conditions of the gel system in the fixed aging time and the gel stability under the optimal parameters by using a gradient rising method. According to the method, the main control factors influencing the stability of the gel can be determined, the optimal adaptability of the gel and oil reservoir conditions is determined, and water channeling and carbon dioxide gas channeling channels are permanently and effectively blocked.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oilfield chemical water / gas channeling sealing, in particular to a method for evaluating and optimizing main control factors of a gel for sealing channeling. BACKGROUND

[0002] Oil and gas reservoirs in China are mainly developed by injecting fluids (water, CO2, etc.). With the continuous development of oil and gas resources in China, production wells will enter the water channeling / CO2 gas channeling stage. Sealing water / CO2 gas channeling in oil and gas reservoirs has always been a research hotspot and strategic focus in the international energy field. After water drive / CO2 gas drive channeling, the average recovery rate of the first production of oil and gas reservoirs is only between 20%-30%, and a considerable amount of crude oil cannot be produced due to water drive / CO2 drive. Sealing water / CO2 gas channeling by using a plugging agent to plug the water / CO2 gas channeling channel is an effective solution to improve recovery. Gel plugging agents are the most commonly used plugging agents in oilfields. Gel plugging agents have the advantages of controllable gelation, high plugging strength, and long effective period. However, due to the differences in types and concentrations of polymers, crosslinking agents, and additives, various types of gels appear, and the main control factors of the gels in the formation are not clear, which can easily lead to: 1. high-cost, high-performance gels used in mild formation conditions leading to waste; 2. low-performance gels used in harsh formations cannot maintain stable plugging, leading to operation failure; 3. repeated testing of the stability of gels in various formation conditions leads to waste of manpower and financial resources. It is an urgent need for high water-cut oil reservoirs to economically and efficiently control water and increase oil by determining the main control factors of gel stability, determining the best matching relationship between different gels and formation conditions, and determining the best applicable formation conditions for gels and the best gel type for the oil reservoir.

[0003] CN116861145A discloses an oil reservoir gas injection parameter screening and optimization method and system based on response surface method and equipment. The oil reservoir gas injection parameter screening method based on response surface method screens oil reservoir gas injection parameters by response surface analysis method combined with multiple linear regression method. Graphical technology is used to display the functional relationship. By giving an intuitive graph, the optimization region can be intuitively determined for researchers to observe and select the optimal conditions in the experimental design. This method only optimizes the screening of gas injection parameters, and the judgment of the optimization region only relies on intuition without mathematical basis, and does not study the gel sealing of water / CO2 gas channeling.

[0004] CN118098455A discloses a nanofluid core injection parameter optimization method based on response surface method, selects research factors and sets corresponding level values, sets response values, generates displacement experiment scheme based on research factors, factor level values and response values, performs displacement experiment on the displacement experiment scheme, analyzes experimental results to obtain a regression equation, and obtains optimal injection parameters according to the regression equation. The method can optimize the optimal injection parameters of the oil displacement system to guide the application of the oil displacement system in the field, but the method does not consider the influence of nanofluid concentration, type, particle size, temperature, salinity and other factors, and the screening standard only has one item of recovery rate, and no application in water sealing / CO2 gas channeling is carried out.

[0005] Therefore, it is urgent to study a gel main control factor evaluation and optimization method for sealing channeling to meet the requirements of water control and oil increase in high water cut reservoirs. SUMMARY

[0006] The purpose of the present application is to overcome the prior art, and the gel type is varied due to the difference in types and concentrations of polymers, crosslinking agents and additives, and the main control factors of the gel in the formation are not clear. The main control factors affecting the stability of the gel are determined, the optimal matching relationship between different gels and formation conditions is determined, and the optimal gel application formation condition / optimization of the best gel type for the reservoir is determined, which is an urgent need for economic and efficient water control and oil increase in high water cut reservoirs, and a gel main control factor evaluation and optimization method for sealing channeling.

[0007] To achieve the above purpose, the present application provides the following technical scheme: a gel main control factor evaluation and optimization method for sealing channeling, comprising the following steps: S1, based on rheological test or differential scanning calorimetry test experiment, screening factors and levels affecting the stability of the gel; S2, selecting gel strength, irreducible water content, polymer molecular weight, gel network density and dehydration rate as target response evaluation indexes, single index research or configuring weights for response indexes to form a comprehensive response dependent variable considering multiple evaluation standards; S3, based on the designed space sample, carrying out gel stability experiment, obtaining comprehensive response values under multiple test schemes, and jointly constituting the basic data of the response surface model; S4, establishing a gel stability performance response surface function by a stepwise regression fitting method; S5, verifying the effectiveness of the model by variance analysis, determining the significance of the first order term, square term and interaction term of the key factors, and analyzing the interaction between variables by contour line and response surface graph; S6, optimizing parameters by gradient ascent method, and finally determining the optimal gel formula-reservoir environment parameter combination that optimizes the gel stability period after experimental verification.

[0008] Preferably, in the step S2, the comprehensive response dependent variable is: ; wherein y k is a target response evaluation index including gel strength, bound water content, polymer molecular weight, gel network density, dehydration rate, δ k is the weight of each response index, and the weight of each index is determined according to the focus of actual application requirements.

[0009] Preferably, in the step S3, the central composite design or Box-Benhnken design gel stability test scheme is used to carry out stability test experiment to obtain response values, and the influence factor level and the response result jointly constitute the basic data of the response surface model.

[0010] Preferably, in the step S4, the most significant variable is added by forward attempt and the insignificant variable is checked and removed by backward, and when no new variable can be added and no variable can be removed, the response surface function is determined: ; wherein Y is a comprehensive response target representing gel stability, β0 is a constant term, x is a key influence factor, β i is a first-order term coefficient, β ij is a coefficient of two-factor interaction term, and β ii is a factor square term coefficient.

[0011] Preferably, in the step S5, the p value of the response surface model is less than 0.05, which proves that the model has statistical significance, the determination coefficient and the adjusted determination coefficient of the model are higher than 90%, which proves that the model has high accuracy, reliability and prediction ability, the p values of the first-order term, the square term and the interaction term are less than 0.05, which indicates that the influence of the key term on the gel stability is significant, the determination coefficient: ; the adjusted determination coefficient: ; wherein, is an experimental value, is a response model calculation value, is an average value of experimental results. p is the number of variables, n is the sample size.

[0012] Preferably, in the step S6, the specific steps of using gradient ascent method to optimize parameters include: S61, initializing parameters, setting physical constraints and optimization parameters; S62, calculating the gradient of the response surface model; Partial derivatives of the response surface model to parameters: ; Gradient vector: ; S63, update the parameter value, ; Wherein, alpha is a learning rate; S64, judge whether the algorithm iteration reaches convergence, if the convergence condition is met, the gel formula-reservoir environment parameter optimal combination is obtained, and the optimal gel stability performance is obtained by substituting the response surface model, otherwise, return to step S62.

[0013] Compared with the prior art, the beneficial effects of the present application are: (1) The present application can quickly predict the stability of the gel under complex reservoir conditions, and the complex conditions can include temperature, salinity, divalent salt ion concentration, aging time, shear rate, shear time, formation water dilution ratio and other environmental conditions, and also include polymer concentration, crosslinking agent concentration and other strengthening agent or functional additive concentration and other internal influencing factors of the gel.

[0014] (2) The present application is not limited to macro stability performance, and can use gel strength, bound water content, polymer molecular weight, gel network density, dehydration rate and the like as indexes to establish a regression equation and evaluate stability alone or in combination.

[0015] (3) The present application can determine the main control factors of internal and external factors affecting the stability of the gel, avoid the interference of other factors, and also obtain the influence of the interaction of two factors after superposition.

[0016] (4) The present application can quickly obtain a gel formula with stable performance meeting the requirements according to the established gel stability regression equation and the field conditions. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flow chart of the main control factor evaluation and optimization method for channeling sealing gel according to the present application, using gel strength / water phase state test results as evaluation criteria; Figure 2 is the fitting effect of the gel strength regression equation under the action of multiple factors by using the response surface method; Figure 3 is the single-factor influence effect of the gel strength regression equation; Figure 4 is the interaction influence effect of the gel strength regression equation; Figure 5 is the fitting effect of the bound water proportion regression equation under the action of multiple factors by using the response surface method; Figure 6 is the single factor effect of the regression equation of the bound water ratio; Figure 7 is the interaction effect of the regression equation of the bound water ratio. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application.

[0019] The application provides a method for evaluating and optimizing a main factor of a gel for channeling, comprising the following steps: S1, screening factors and levels affecting the stability of the gel based on rheological test or differential scanning calorimetry test experiment; S2, selecting gel strength, bound water content, polymer molecular weight, gel network density and dehydration rate as target response evaluation indexes, or configuring weights for the response indexes to form a comprehensive response dependent variable considering multiple evaluation standards, and the comprehensive response dependent variable is: ; wherein y k is the target response evaluation index including the gel strength, the bound water content, the polymer molecular weight, the gel network density and the dehydration rate, δ k is the weight of each response index, and the weight of each index is determined according to the actual application requirement.

[0020] S3, based on the designed space points, carrying out a gel stability experiment to obtain comprehensive response values under multiple test schemes, which together constitute the basic data of the response surface model, combining the key factors and levels, adopting a central composite design or a Box-Benhnken design gel stability test scheme to carry out a stability test experiment to obtain response values, and the influence factor levels and the response results together constitute the basic data of the response surface model; S4, establishing a gel stability performance response surface function through a stepwise regression fitting method, through bidirectional stepwise regression, trying to add the most significant variable forwardly, checking and removing the insignificant variable backwardly, and when no new variable can be added and no variable can be removed, the response surface function is determined: ; wherein Y is a comprehensive response target representing the gel stability, β0 is a constant term, x is a key influence factor, β i is a first-order term coefficient, β ij is a coefficient of a two-factor interaction term, and β ii is a square term coefficient of a factor.

[0021] S5, the effectiveness of the model is verified by variance analysis, the significance of the key factors, the square term and the interaction term is determined, the interaction between variables is analyzed by contour map and response surface map, the p value of the response surface model is less than 0.05, which proves that the model has statistical significance, the determination coefficient and the adjusted determination coefficient of the model are higher than 90%, which proves that the model has high accuracy, reliability and prediction ability, the p value of the linear term, the square term and the interaction term is less than 0.05, which indicates that the influence of the key term on the stability of the gel is significant, Determination coefficient: ; Adjusted determination coefficient: ; Among them, is the experimental value, is the calculated value of the response model, is the average value of the experimental results. p is the number of variables, n is the sample size.

[0022] S6, the parameters are optimized by gradient ascent method, and the optimal gel formula-reservoir environment parameter combination that optimizes the gel stability period is finally determined after experimental verification, the specific steps of optimizing parameters by gradient ascent method include: S61, initialize parameters, set physical constraints and optimization parameters; S62, calculate the gradient of the response surface model; Partial derivative of response surface model to parameters: ; Gradient vector: ; S63, update the parameter value, ; Among them, alpha is the learning rate; S64, judge whether the algorithm iteration reaches convergence, if the convergence condition is met, the optimal combination of gel formula-reservoir environment parameters is obtained, and the optimal gel stability performance can be obtained by substituting the response surface model, otherwise, return to step S62.

[0023] The method provided in the embodiment is not only suitable for gel plugging agents, but also suitable for gel, organic particle, inorganic particle and other plugging agents, the difference lies in different indexes for evaluating the stability of the plugging agent.

[0024] The method of the embodiment provides a method for obtaining a regression equation of gel stability under the action of multiple factors, and can realize prediction of gel stability under multiple complex environmental conditions. According to field experience, actual environmental conditions and indoor experiment, key influencing factors and levels of gel stability are preliminarily screened, a response surface method is used to design a gel stability test scheme under multiple factors, a gel stability database is established according to experimental results, a gel stability regression equation is further established, and under the condition of a determined gel formula, environmental conditions are input into the gel stability regression equation to predict gel stability.

[0025] The method of the embodiment also provides a method for evaluating main control factors of gel for water control and oil increase in a high water cut reservoir. According to the weight of the influencing factors and the interaction in the gel stability regression equation, main control factors of gel stability are determined and interference factors without obvious influence are excluded.

[0026] The method of the embodiment also provides a method for quickly matching reservoir conditions and gel formula. On the basis of the established gel stability regression equation, a determined environmental condition is input to quickly match a gel formula meeting the stability requirement.

[0027] For the method provided in the embodiment, the following examples and comparative examples are formulated: Example 1

[0028] Taking plugging water channeling in a high-temperature and high-salt fracture-cave type reservoir as an example, the characteristics of high temperature, high salt and large size of fractures and caves may affect the gel stability. Here, the polymer is an AM and AMPS copolymer with a molecular weight of 8 million and a hydrolysis degree of 2%, the AMPS proportion is 60%, the crosslinking agent is urotropine and hydroquinone, and the mass ratio is 1:1.

[0029] According to the characteristics of the high-temperature and high-salt fracture-cave type reservoir and field construction experience, the internal influencing factors of gel stability are selected as polymer concentration and crosslinking agent concentration, and the external influencing factors are temperature, sodium chloride, calcium ion and formation water dilution ratio. A test design of 6 factors and 5 levels is determined, the polymer concentration mass fraction is 0.5%, 0.7%, 0.9%, 1.1% and 1.3%, the crosslinking agent concentration mass fraction is 0.4%, 0.6%, 0.8%, 1.0% and 1.2%, the temperature is 110℃, 120℃, 130℃, 140℃ and 150℃, the sodium chloride concentration is 100,000 mg / L, 150,000 mg / L, 200,000 mg / L, 250,000 mg / L and 300,000 mg / L, the calcium ion concentration is 2 k mg / L, 3.5 k mg / L, 5 k mg / L, 6.5 k mg / L and 8 k mg / L, and the formation water dilution ratio is 20%, 35%, 50%, 65% and 80%.

[0030] The gel strength was determined by the standard "SY-T 6296-2013 Determination of gel strength for oil production", and the results are shown in Table 1 Gel strength in the response surface method experiment results. The regression equation of the gel strength under the action of multiple factors is obtained by the response surface method (see Table 2 Regression equation of gel strength under the action of multiple factors), and the fitting accuracy R 2 = 0.9439 ( Figure 1 ). The single factor effect is shown in Figure 2 , according to the weight value sorting, among the single factors, the formation water dilution (F value is 18.57) and the polymer concentration (F value is 14.31) are the most significant; the significant interaction is A-B (polymer concentration-crosslinking agent concentration), A-C (polymer concentration-temperature), A-F (polymer concentration-formation water dilution ratio), B-F (crosslinking agent concentration-formation water dilution ratio), C-F (temperature-formation water dilution ratio). Example 2

[0031] Taking the plugging water channeling of high temperature and high salt fracture-cave type reservoir as an example, the characteristics of high temperature, high salt and large size fracture-cave may affect the stability of the gel. Here, the polymer is an AM and AMPS copolymer with a molecular weight of 10 million and a hydrolysis degree of 2%, the AMPS ratio is 40%, the crosslinking agent is urotropine and hydroquinone, and the mass ratio is 1:1.

[0032] According to the characteristics of high temperature and high salt reservoir and the field construction experience, the internal influencing factors of gel stability are selected as polymer concentration and crosslinking agent concentration, and the external influencing factors are temperature, sodium chloride, calcium ion and formation water dilution ratio. The test design of 6 factors and 5 levels is determined, the polymer concentration is 0.5%, 0.7%, 0.9%, 1.1% and 1.3%, the crosslinking agent concentration is 0.4%, 0.6%, 0.8%, 1.0% and 1.2%, the temperature is 110℃, 120℃, 130℃, 140℃ and 150℃, the sodium chloride concentration is 100,000, 150,000, 200,000, 250,000 and 300,000 mg / L, the calcium ion concentration is 2, 3.5, 5, 6.5 and 8 k mg / L, and the formation water dilution ratio is 20%, 35%, 50%, 65% and 80%.

[0033] The presence state of water phase was determined by low temperature DSC, and the results are shown in Table 1 Bound water ratio in the response surface method experiment results. The regression equation of the bound water ratio under the action of multiple factors is obtained by the response surface method (see Table 3 Regression equation of bound water content under the action of multiple factors), and the fitting accuracy R 2 = 0.9963 ( Figure 1 ). The single factor effect is shown in Figure 2, according to the weight value sequence, in single factor, formation water dilution (F value is 183.80) and polymer concentration (F value is 129.63) are most significant; the interaction with significant influence is A-B (polymer concentration-crosslinking agent concentration), A-C (polymer concentration-temperature), A-F (polymer concentration-formation water dilution ratio), B-C (crosslinking agent concentration-temperature), B-E (crosslinking agent concentration-calcium ion), B-F (crosslinking agent concentration-formation water dilution ratio), C-F (temperature-formation water dilution ratio). Example 3

[0034] Taking the plugging of CO2 gas channeling in low porosity and low permeability reservoirs as an example, shear, low PH of water phase and other characteristics are the reservoir characteristics that may affect the stability of the gel. Here, the polymer is HPAM with a molecular weight of 15 million and a hydrolysis degree of 10%, and the crosslinking agent is phenol type phenolic resin.

[0035] According to the characteristics of CO2 gas flooding in low porosity and low permeability reservoirs and field construction experience, the internal influencing factors of gel stability are selected as polymer concentration and crosslinking agent concentration, and the external influencing factors are shear time and PH value. The test design of 4 factors and 3 levels is determined, the polymer concentration is 0.3%, 0.4% and 0.5%, the crosslinking agent concentration is 0.8%, 1.0% and 1.2%, the shear time is 24h, 36h and 48h under the condition of shear stress 10Pa, and the PH value is 5, 5.5 and 6.

[0036] The existence state of water phase is determined by low temperature DSC, the regression equation of gel strength ratio under the action of multiple factors is obtained by response surface method, and the fitting precision R 2 =0.9675( Figure 1 ). The single factor influence effect is shown in Figure 2 , according to the weight value sequence, in single factor, polymer concentration (F value is 281.21), crosslinking agent concentration (F value is 8.02), shear time (F value is 5.0) and PH (F value is 37.65) are significant. Example 4

[0037] According to the characteristics of high-temperature and high-salt reservoirs and field construction experience, the internal influence factors of the gel stability are selected as the polymer concentration and the crosslinking agent concentration, the external influence factors are temperature, sodium chloride, calcium ions and formation water dilution ratio, a test design of 6 factors and 5 levels is determined, the polymer concentration is 0.5%, 0.7%, 0.9%, 1.1% and 1.3% by mass fraction, the crosslinking agent concentration is 0.4%, 0.6%, 0.8%, 1.0% and 1.2% by mass fraction, the temperature is 110℃, 120℃, 130℃, 140℃ and 150℃, the sodium chloride concentration is 100,000 mg / L, 150,000 mg / L, 200,000 mg / L, 250,000 mg / L and 300,000 mg / L, the calcium ion concentration is 2 k mg / L, 3.5 k mg / L, 5 k mg / L, 6.5 k mg / L and 8 k mg / L, and the formation water dilution ratio is 20%, 35%, 50%, 65% and 80%.

[0038] The gel strength is determined according to the standard "SY-T 6296-2013 Determination of Gel Strength for Oil Production", the water phase existing state is determined by low-temperature DSC, the bound water proportion regression equation under the action of multiple factors is obtained by using the response surface method, and the fitting precision R 2 =0.9502( Figure 1 ). The single-factor influence effect is shown in Figure 2 According to the weight value sorting, among the single factors, the polymer concentration (F value is 181.43) and PH (F value is 20.59) are the most significant.

[0039] Application Example 1 The application provides a method for quickly determining a gel formula according to reservoir conditions and gel stability requirements.

[0040] According to the characteristics of high-temperature and high-salt reservoirs and field construction experience, under the typical reservoir conditions of Tahe Oilfield, 130℃, 20Wmg / L, 5kmg / L Ca 2+ , 35% formation water dilution, the gel formula with a gel strength of >5Pa and >10Pa is determined as polymer concentration 1.0% and crosslinking agent concentration 0.9%, and polymer concentration 1.1% and crosslinking agent concentration 1.0%; and the gel formula with a bound water proportion of >10% is determined as polymer concentration 0.9% and crosslinking agent concentration 0.6%.

[0041] It is tested that under the conditions of 130℃, 20Wmg / L, 5kmg / L Ca 2+ , 35% formation water dilution, the gel strengths of the above formulas are 5.4Pa and 10.6Pa, and the bound water proportions are 10.8% (the response surface prediction is 7.33915Pa, 10.5467Pa and 12.5967% respectively). Application Example 2 (according to the formulas in Tables 2 and 3) The application provides a method for quickly determining an application reservoir environment according to a gel formula and gel stability requirements.

[0042] Polymer 0.9%+Crosslinker 0.6, the reservoir conditions for determining the gel strength >5Pa are: 140℃, 15Wmg / L, 4kmg / L Ca2+, 35% formation water dilution.

[0043] The gel strength of the above formula is 5.4Pa, 10.6Pa, and the irreducible water ratio is 10.8% under the conditions of 130℃, 20Wmg / L, 5kmg / L Ca2+, 35% formation water dilution. 2+ , 35% formation water dilution. Polymer 0.9%+Crosslinker 0.6, the reservoir conditions for determining the irreducible water >10% are: 130℃, 20Wmg / L, 4.5kmg / L Ca2+, 40% formation water dilution. 2+ , 40% formation water dilution.

[0044] The gel strength of the above formula is 5.4Pa, 10.6Pa, and the irreducible water ratio is 10.8% under the conditions of 130℃, 20Wmg / L, 5kmg / L Ca2+, 35% formation water dilution. 2+ , 35% formation water dilution. Application Example 3 (according to the formulas in Tables 2 and 3) The present application provides a method for quickly predicting the gel stability according to the gel formula and the reservoir environment.

[0045] The gel strength and the irreducible water ratio of polymer 0.9%+crosslinker 0.6% under the reservoir conditions of 140℃, 15Wmg / L, 2kmg / L Ca2+, 10% formation water dilution are 8.45466Pa and 47.055%. 2+

[0046] The gel strength of polymer 0.9%+crosslinker 0.6% under the reservoir conditions of 140℃, 15Wmg / L, 2kmg / L Ca2+, 10% formation water dilution is 8.2Pa, and the irreducible water ratio is 42.6%.

[0047] Application Example 4 (according to the formulas in Tables 4 and 5) The present application provides a method for quickly determining the gel formula according to the reservoir conditions and the gel stability requirements.

[0048] According to the characteristics of CO2 gas flooding in low-porosity and low-permeability oil reservoirs and the field construction experience, under the conditions of continuous shearing for 30h and PH=5.8, the gel formulas for determining the gel strength >5Pa and >10Pa under the typical reservoir conditions in Shengli Oilfield are polymer concentration 0.3% and crosslinker concentration 0.9%, and polymer concentration 0.4% and crosslinker concentration 1.0%, respectively; and the gel formula for determining the irreducible water ratio >10% is polymer concentration 0.35% and crosslinker concentration 1.0%.​

[0049] The tested gel strength of the above formula under the condition of continuous shearing for 30h and PH=5.8 is 5.42Pa, 14.26Pa, and the bound water ratio is 15.9%. (The response surface prediction is 7.73775Pa, 15.8519Pa; 14.7451%) Application Example 5 (according to the formula in Tables 4 and 5) The application provides a method for quickly determining the application reservoir environment according to a gel formula and gel stability requirements.

[0050] The polymer 0.4%+crosslinking agent 1.1% determines the reservoir condition of gel strength >12Pa: continuous shearing for 25h and PH=5.2.

[0051] The tested gel strength of the above formula under the condition of continuous shearing for 30h and PH=5.8 is 5.4Pa, 10.6Pa, and the bound water ratio is 20.4%. (The response surface prediction under 25h-5.2 is 14.2992Pa; 19.1247%) The polymer 0.45%+crosslinking agent 1.2% determines the reservoir condition of bound water >10%: continuous shearing for 35h and PH=5.2.

[0052] The tested gel strength of the above formula under the condition of continuous shearing for 30h and PH=5.8 is 5.4Pa, 10.6Pa, and the bound water ratio is 10.8%. (The response surface prediction under 35h-5.2 is 16.8941Pa, 23.0415%) Application Example 6 (according to the formula in Tables 4 and 5) The application provides a method for quickly predicting the gel stability of a gel formula and a reservoir environment.

[0053] The predicted polymer 0.35%+crosslinking agent 0.9% has a gel strength and a bound water ratio of 12.1471Pa and 14.4216% under the condition of continuous shearing for 30h and PH=5.8.

[0054] The tested gel strength of the polymer 0.35%+crosslinking agent 0.9% under the condition of continuous shearing for 30h and PH=5.8 is 13.9Pa, and the bound water ratio is 13.6%.

[0055] Comparative Example 1 According to the characteristics of high-temperature and high-salt reservoirs and field construction experience, the typical reservoir conditions of Tahe Oilfield are 130℃, 20Wmg / L, 5kmg / L Ca 2+, 35% formation water dilution, the gel strength > 5Pa is determined according to the empirical method, the formula is: polymer concentration 0.8-1.6%, crosslinking agent concentration 0.9-1.2%. This method needs a lot of experimental evidence, waste time and money, and the results obtained are similar to the present application.

[0056] According to the characteristics of CO2 gas drive of low porosity and low permeability reservoir and the field construction experience, under the condition of continuous shearing for 30h and PH=5.8, the gel formula with gel strength > 5Pa under the typical reservoir conditions of Shengli Oilfield is determined as polymer concentration 0.3-0.5% and crosslinking agent concentration 0.6-1.0%. This method needs a lot of experimental evidence, waste time and money, and the results obtained are similar to the present application.

[0057] Comparative Example 2 Under the confirmation of a large amount of experimental data, the formula of polymer 0.9%+crosslinking agent 0.6% is determined, and the reservoir conditions suitable for gel strength > 5Pa are: 140℃, 15Wmg / L, 4kmg / L Ca 2+ , 35% formation water dilution.

[0058] Under the confirmation of a large amount of experimental data, the formula of polymer 0.45%+crosslinking agent 1.2% is determined, and the reservoir conditions suitable for bound water > 10% are: continuous shearing for 35h and PH=5.2.

[0059] Comparative Example 3 The gel strength required under the condition of continuous shearing for 48h and PH=5.0 is > 10Pa, if the formula of polymer 0.45%+crosslinking agent 1.2% is directly used, the gel failure time cannot be predicted, which leads to the failure to timely and stably plug CO2 channeling, resulting in the waste of plugging agent.

[0060] At present, there is no method for quickly and accurately predicting the stability of gel according to the gel formula and reservoir environment. Only the experience recognized by researchers and the field can be used as a basis, and the prediction results are relatively wide.

[0061] In the above examples and comparative examples, the state of water phase is determined by differential scanning calorimetry; the rheometer is purchased from Haake Company, and the model is Haake Rheostree RS75; the scanning electron microscope is purchased from Hitachi Ltd., and the model is Hitachi S-4800.

[0062] Table 1 Experimental results of response surface method Number Polymer concentration Crosslinker concentration Temperature Sodium chloride Calcium ion Formation water dilution Gel strength (Pa) Bound water fraction (%) 1 1.1 1 140 25 3.5 35 12.5 42.6 2 1.1 1 140 15 6.5 35 12.1 40.3 3 1.1 1 120 25 3.5 65 2.8 6.6 4 1.1 0.6 140 15 6.5 65 2.3 5.8 5 0.7 1 120 25 6.5 65 0 0.34 6 1.1 0.6 140 25 6.5 65 2.4 5.18 7 0.7 1 140 15 3.5 35 4.02 13.8 8 1.1 1 120 15 6.5 65 0.22 0.91 9 1.1 0.6 120 25 3.5 35 0.18 0.78 10 0.7 0.6 140 15 3.5 65 0.02 0.39 11 0.7 1 120 15 3.5 65 0 0.36 12 1.1 0.6 120 15 6.5 35 0.16 0.44 13 0.7 0.6 120 25 6.5 35 0.08 0.33 14 0.7 0.6 140 25 6.5 65 0.01 0.05 15 0.7 1 140 25 6.5 35 0.03 0.19 16 0.7 0.6 120 15 3.5 35 0.014 0.06 17 0.5 0.8 130 20 5 50 1.2 3.2 18 1.3 0.8 130 20 5 50 8.1 32.4 19 0.9 0.4 130 20 5 50 2.7 6.4 20 0.9 1.2 130 20 5 50 3.2 10.4 21 0.9 0.8 110 20 5 50 2.4 3.82 22 0.9 0.8 150 20 5 50 1.9 2.71 23 0.9 0.8 130 10 5 50 2.6 3.69 24 0.9 0.8 130 30 5 50 2.65 4.26 25 0.9 0.8 130 20 2 50 2.72 6.82 26 0.9 0.8 130 20 8 50 2.36 0.89 27 0.9 0.8 130 20 5 20 8.5 36.6 28 0.9 0.8 130 20 5 80 0.64 1.83 29 0.9 0.8 130 20 5 50 3.03 9.12 30 0.9 0.8 130 20 5 50 3.05 9.11 31 0.9 0.8 130 20 5 50 3.02 9.13 Table 2 Regression formula of gel strength under the action of multiple factors Factor A - Polymer concentration B - Crosslinker concentration C - Temperature D - Sodium chloride E - Calcium ion F - Formation water dilution AB AC AD AE AF BC BD BE BF CD CE CF DE DF EF Constant term Coefficient -98.3377 57.3674 0.3269 0.5273 1.8757 1.8479 29.1781 0.6867 0.3484 0.2112 -0.2734 -0.3164 -0.0729 -1.4346 -0.6647 -0.00768 -0.008108 -0.010203 -0.00755 0.005128 0.004539 -53.2762 Influence degree 14.31 0.0751 0.0751 0.0008 0.0389 18.57 13.1 18.14 0.389 0.0129 6.47 1.28 0.0511 1.78 12.75 1.42 0.1423 7.51 0.0103 1.42 0.1003 Table 3 Regression formula of bound water content under the action of multiple factors Factor A - Polymer concentration B - Crosslinker concentration C - Temperature D - Sodium chloride E - Calcium ion F - Formation water dilution AB AC AD AE AF BC BD BE BF CD CE CF DE DF EF A² B² C² D² E² F² Constant term Coefficient -366.517 318.839 6.5546 4.2536 7.6409 4.891 100.5469 2.244 -0.144 1.7229 -1.091 -1.88156 -0.3306 -4.548 -2.144 -0.023 -0.041 -0.032 0.2476 0.0128 0.029 44.276 -14.474 -0.0186 -0.067 -0.762 0.0094 -562.72 Influence degree 129.63 2.43 0.1873 0.0494 5.35 183.8 78.69 98 0.0338 0.4332 52.16 22.96 0.5318 9.06 67.11 6.54 1.84 36.66 5.59 4.51 2.12 28.72 3.07 31.77 26.01 26.94 41.35 Table 4 experimental results of response surface method Number Polymer concentration Crosslinker concentration Shear time PH Gel strength (Pa) Bound water fraction (%) 1 0.4 1.2 36 6 18 25 2 0.5 1.2 36 5.5 19 26.6 3 0.4 1 48 5 9 6.4 4 0.3 1.2 36 5.5 7.2 4.9 5 0.4 1 24 6 17.8 24.6 6 0.4 1 36 5.5 16 22 7 0.4 0.8 36 5 12 17.4 8 0.4 1.2 36 5 15 20 9 0.4 0.8 48 5.5 11.6 16.3 10 0.3 1 36 6 7.8 5.9 11 0.4 0.8 24 5.5 13.2 17.2 12 0.4 1 24 5 13.1 17 13 0.5 1 24 5.5 18.5 25.8 14 0.3 0.8 36 5.5 6.2 4.1 15 0.3 1 24 5.5 6.8 4.7 16 0.5 0.8 36 5.5 16.8 23.6 17 0.5 1 36 6 19.5 28 18 0.4 1.2 24 5.5 14.3 18.6 19 0.5 1 48 5.5 17.3 24.2 20 0.3 1 48 5.5 6.3 4.2 21 0.3 1 36 5 5.7 3.7 22 0.4 1 48 6 16.6 22.9 23 0.4 0.8 36 6 16.2 22.1 24 0.4 1 36 5.5 13.8 18.2 25 0.4 1.2 48 5.5 13.9 18.4 26 0.5 1 36 5 16.4 22.5 27 0.4 1 36 5.5 13.7 18.2 Table 5 regression formula of gel strength under the action of multiple factors Factor A - Polymer concentration B - Crosslinker concentration C - Temperature D - Sodium chloride E - Calcium ion F - Formation water dilution AB AC AD AE AF BC BD BE BF CD CE CF DE DF EF Constant term Coefficient -98.3377 57.3674 0.3269 0.5273 1.8757 1.8479 29.1781 0.6867 0.3484 0.2112 -0.2734 -0.3164 -0.0729 -1.4346 -0.6647 -0.00768 -0.008108 -0.010203 -0.00755 0.005128 0.004539 -53.2762 Influence degree 14.31 0.0751 0.0751 0.0008 0.0389 18.57 13.1 18.14 0.389 0.0129 6.47 1.28 0.0511 1.78 12.75 1.42 0.1423 7.51 0.0103 1.42 0.1003 Table 6 regression formula of bound water content under the action of multiple factors Factor A - Polymer concentration B - Crosslinker concentration C - Shear time D - PH AB AC AD BC BD CD A² B² C² D² Constant term Coefficient 336.33 -22.625 -1.3618 -29.9167 27.5 -0.229 16.5 0.073 0.75 0.371 -429.58 5.104 -0.0106 1.467 30.0125 Influence degree 181.43 1.96 2.87 20.59 0.1736 0.0434 0.3905 0.0176 0.0032 2.84 14.12 0.0319 1.8 0.1028 In summary, the application discloses a gel main control factor evaluation and optimization method for channeling sealing, wherein, based on stability test experiments, factors and levels affecting gel stability performance are screened, a multi-target evaluation system including gel strength, bound water content, polymer molecular weight, gel network density, dehydration rate and the like is formed, different weight coefficients are configured to determine target responses; a spatial sample database is formed, a response surface function of gel stability performance suitable for reservoir environment is established by using stepwise regression analysis, main control factors of gel stability performance and their action mechanisms are determined; a gradient ascent method is used to determine formation conditions under which the gel system can maintain stability at a fixed aging time, or to determine a gel system that can maintain stability within a fixed aging time under certain formation conditions, and gel stability capacity under optimal parameters. The method can determine main control factors affecting gel stability, determine the best adaptability of the gel to the reservoir conditions on this basis, and can form durable and effective plugging for water channeling channels.

[0063] The above comparative examples 1, 2 and 3 have the following problems relative to the embodiments: 1. Comparative example 1 has great subjectivity in terms of selecting concentrations according to the characteristics of high-temperature and high-salt reservoirs and field construction experience, which may lead to too low concentrations due to incomplete consideration of influencing factors, or too high costs due to excessive caution, resulting in waste, and being not conducive to gel injection and forming effective plugging; 2. Comparative example 2 obtains reasonable results through gel optimization based on a large amount of experimental data, but a large number of construction wells and various reservoir conditions may lead to serious time waste and economic losses due to corresponding experimental screening for each construction; and 3. Comparative example 3 lacks a reasonable method to match gel formulations and reservoir environments, and to realize reasonable matching and effective gel stability prediction.

[0064] It is apparent for those skilled in the art that the present application is not limited to the details of the foregoing exemplary embodiments, but can be implemented in other concrete forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all aspects as exemplary and non-restrictive, and the scope of the present application is defined by the appended claims rather than the foregoing description, and all changes falling within the meaning and range of equivalent elements of the claims are intended to be embraced in the present application. Any reference signs in the claims should not be considered as limiting the claims involved.

Claims

1. A method for evaluating and optimizing a gel master for chase, characterized in that: The method comprises the following steps: S1, screening the factors and levels affecting the stability of the gel based on rheological test or differential scanning calorimetry test experiment; S2, selecting the gel strength, bound water content, polymer molecular weight, gel network density and dehydration rate as the target response evaluation indexes, and configuring weights for the response indexes to form a comprehensive response dependent variable considering multiple evaluation standards; S3, carrying out a gel stability experiment based on the designed space points to obtain comprehensive response values under multiple test schemes, which together constitute the basic data of the response surface model; S4, establishing a gel stability performance response surface function by a stepwise regression fitting method; S5, verifying the effectiveness of the model by variance analysis, determining the significance of the first-order terms, square terms and interaction terms of the key factors, and analyzing the interaction between variables by contour and response surface graphs; S6, optimizing the parameters by the gradient ascent method, and finally determining the optimal gel formulation-reservoir environment parameter combination that optimizes the gel stability period after experimental verification.

2. A method of evaluating and optimizing a gel master according to claim 1, characterized in that: In the step S2, the comprehensive response dependent variable: ； wherein y k is a target response evaluation index including gel strength, bound water content, polymer molecular weight, gel network density, dehydration rate, δ k is the weight of each response index, and the weight of each index is determined according to the emphasis of actual application requirements.

3. A method of evaluating and optimizing a gel master according to claim 1, characterized in that: In the step S3, the central composite design or Box-Benhnken design is used to carry out the gel stability test scheme to obtain the response values, and the influence factor levels and response results together constitute the basic data of the response surface model.

4. The method of claim 1, wherein the method is used for gel control factor evaluation and optimization. In the step S4, the most significant variables are added forwardly and the insignificant variables are checked and removed backwardly by two-way stepwise regression, and when no new variable can be added and no variable can be removed, the response surface function is determined: ； Wherein, Y is a comprehensive response target representing the stability of the gel, β0 is a constant term, x is a key influencing factor, β i is a coefficient of a linear term, β ij is a coefficient of a two-factor interaction term, β ii is a coefficient of a square term of a factor.

5. The method of claim 1, wherein: the gel master control factor is evaluated and optimized for channeling. In the step S5, the p value of the response surface model is less than 0.05, which proves that the model has statistical significance, the determination coefficient and the adjusted determination coefficient of the model are higher than 90%, which proves that the model has high accuracy, reliability and prediction ability, and the p values of the first-order terms, square terms and interaction terms are less than 0.05, which indicates that the influence of the key terms on the gel stability is significant, Determination coefficient: ； Adjusted determination coefficient: ； wherein, is the experimental value, is the response model calculated value, is the average of experimental results. p is the number of variables, n is the number of test samples.

6. A method of evaluating and optimizing a gel master according to claim 1, wherein: In the step S6, the specific steps of optimizing the parameters by the gradient ascent method include: S61, initializing the parameters, setting the physical constraints and optimizing the parameters; S62, calculating the gradient of the response surface model; The partial derivative of the response surface model to the parameter: ； Gradient vector: ； S63, updating the parameter value, ; Wherein, α is the learning rate; S64, judging whether the algorithm iteration reaches convergence, if the convergence condition is met, the optimal combination of the gel formulation-reservoir environment parameters is obtained, and the optimal gel stability performance can be obtained by substituting the response surface model, otherwise, returning to step S62.

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