Characterization method of threshold pressure gradient based on heavy oil components and reservoir pore structure

Abstract

This invention discloses a method for characterizing the start-up pressure gradient based on heavy oil composition and reservoir pore structure, relating to the field of oil and gas field development technology. This method establishes multiple regression fitting formulas between different parameters and the start-up pressure gradient by considering reservoir microscopic and macroscopic properties, fluid properties, and component composition. These formulas include those for component, temperature, permeability, and start-up pressure gradient; pore radius, viscosity, and start-up pressure gradient; and formulas for component, temperature, pore radius, and start-up pressure gradient, meeting the needs for calculating the start-up pressure gradient using different parameters. To further refine the characterization of the start-up pressure gradient, the results of fitting formulas for different parameters are compared. A piecewise multiple regression fitting of the component ratio formula is proposed, using 0.50 mD / mPa·s as the dividing line. This results in high fitting accuracy, with the fitting curve closely resembling the measured data.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development technology, and in particular to a method for characterizing the start-up pressure gradient based on heavy oil components and reservoir pore structure. Background Technology

[0002] Heavy oil is a complex colloidal system composed of molecules with different structures. It possesses a three-dimensional network macromolecular structure, exhibiting both the viscosity of a fluid and the elasticity of a solid. Furthermore, different components of heavy oil exhibit varying viscoelastic properties, leading to different flow characteristics when flowing through porous media. The elasticity of heavy oil necessitates an external driving force to initiate its flow from a static state to a creeping state and finally to a flowing state. This minimum force required to initiate flow is called the yield stress. Due to the unique elasticity of heavy oil, a certain displacement pressure gradient is required for it to transition from a static to a flowing state in porous media; the limiting value that initiates flow is called the initiation pressure gradient.

[0003] The start-up pressure gradient, as a crucial parameter for reservoir fluid flow in porous media, plays a key role in production prediction, well network deployment, and development strategy formulation, significantly influencing oil and gas extraction efficiency. From the perspective of macroscopic reservoir and fluid properties, the start-up pressure gradient is affected by reservoir permeability and crude oil viscosity; from the perspective of microscopic reservoir and fluid properties, it is influenced by pore structure and crude oil composition, and it also changes with ambient temperature. Currently, the quantitative characterization method for the start-up pressure gradient mainly involves multiple regression analysis of macroscopic reservoir and fluid properties, but the regression results deviate significantly from experimentally measured values.

[0004] Therefore, there is an urgent need to study a new method for quantitative characterization of the starting pressure gradient. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention discloses a method for characterizing the starting pressure gradient based on heavy oil components and reservoir pore structure. This method uses multivariate regression of reservoir and fluid microstructure parameters.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for characterizing the initiation pressure gradient based on heavy oil composition and reservoir pore structure includes:

[0008] (1) Given the composition, temperature, and permeability, the formula for characterizing the starting pressure gradient is:

[0009] lnG=-0.00033*K+152.87*Sa+152.90*Ar+152.95*Rs+153.06*As-0.0694T-15252.13, R 2 =0.5625;

[0010] In the formula, G is the starting pressure gradient, MPa / m; Sa is saturated hydrocarbon, %; Ar is aromatic hydrocarbon, %; Rs is resin, %; As is asphaltenes, %; T is temperature, ℃; K is permeability, mD;

[0011] (2) When the component ratio, temperature, and permeability are known, the formula for characterizing the starting pressure gradient is:

[0012] lnG=-0.00037*K-2.0920*Sa / Ar-1.5069*Ar / Rs-0.0970*Ar / As-0.4318*

[0013] Rs / As-0.0775T+6.4715, R 2 =0.7069;

[0014] In the formula, Sa / Ar is the ratio of saturated hydrocarbons to aromatic hydrocarbons, a decimal; Ar / Rs is the ratio of aromatic hydrocarbons to resins, a decimal; Ar / As is the ratio of aromatic hydrocarbons to asphaltenes, a decimal; and Rs / As is the ratio of resins to asphaltenes, a decimal.

[0015] (3) Given the pore radius and viscosity, the formula for characterizing the starting pressure gradient is:

[0016] lnG=-0.2695r+0.00017μ+3.7006, R 2 =0.6372;

[0017] In the formula, r is the pore radius, μm; μ is the viscosity, mPa·s;

[0018] (4) When the composition, temperature, and pore radius are known, the formula for characterizing the starting pressure gradient is:

[0019] lnG=-0.1372*r+179.50*Sa+179.53*Ar+179.58*Rs+179.70*As-0.0694T-17954.96, R 2 =0.5742;

[0020] (5) When the component ratio, temperature, and pore radius are known, the formula for characterizing the starting pressure gradient is:

[0021] lnG=-0.1552*r-2.1444*Sa / Ar-1.5001*Ar / Rs-0.1084*Ar / As-0.4386*

[0022] Rs / As-0.0774T+6.9932, R 2 =0.7332.

[0023] Optionally, when the component ratio, temperature, and permeability are known, a piecewise multiple regression fitting is performed on the component ratio formula with 0.50 mD / mPa·s as the cutoff line. When the mobility is less than or equal to 0.50 mD / mPa·s, the starting pressure gradient characterization formula is:

[0024] lnG=0.00016*K-1.8860*Sa / Ar-1.3709*Ar / Rs-0.4051*Ar / As-0.1165*

[0025] Rs / As -0.1032T + 6.6907;

[0026] When the mobility is greater than 0.50 mD / mPa·s, the formula for characterizing the starting pressure gradient is:

[0027] lnG=-0.00042*K-1.2075*Sa / Ar-1.2179*Ar / Rs+0.1144*Ar / As-0.4633*

[0028] Rs / As -0.0551T + 2.8361;

[0029] Among them, R 2 =0.8045.

[0030] Optionally, when the component ratio, temperature, and pore radius are known, a piecewise multiple regression fitting is performed on the component ratio formula with 0.50 mD / mPa·s as the cutoff line. The multiple regression formula for mobility less than or equal to 0.50 mD / mPa·s is as follows:

[0031] lnG=0.0680*r-1.8603*Sa / Ar-1.3452*Ar / Rs-0.4163*Ar / As+0.1017*

[0032] Rs / As -0.1033T + 6.4305;

[0033] The multiple regression formula for mobility greater than 0.50 mD / mPa·s is:

[0034] lnG=-0.2422*r-1.6717*Sa / Ar-1.2183*Ar / Rs+0.0515*Ar / As-0.3752*

[0035] Rs / As -0.0476T + 3.9026;

[0036] Among them, R 2 =0.9414.

[0037] The beneficial effects of this invention are that, by considering reservoir microscopic and macroscopic properties, fluid properties, and component composition, the method establishes multiple regression fitting formulas between different parameters and the starting pressure gradient. These include fitting formulas for component, temperature, permeability, and starting pressure gradient; fitting formulas for pore radius, viscosity, and starting pressure gradient; and fitting formulas for component, temperature, pore radius, and starting pressure gradient, which can meet the needs of calculating the starting pressure gradient using different parameters. Furthermore, to more precisely characterize the starting pressure gradient, the results of fitting formulas for different parameters are compared. A piecewise multiple regression fitting of the component ratio formula is proposed, using 0.50 mD / mPa·s as the dividing line. This results in high fitting accuracy, and the fitting curves closely approximate the measured data. Attached Figure Description

[0038] Figure 1 A bar chart comparing the measured values ​​of the starting pressure gradient with the calculated results;

[0039] Figure 2 A comparison chart of the calculated results and measured results for components, temperature, and permeability using Formula 1;

[0040] Figure 3 A comparison chart of the calculated results and measured results for component ratio, temperature, and permeability using Formula 2;

[0041] Figure 4 A comparison chart of the calculated results and measured results of the component segmentation formula;

[0042] Figure 5 A comparison chart of measured values ​​and calculated results of the starting pressure gradient;

[0043] Figure 6 A comparison chart of the calculated results and measured results for components, temperature, and pore radius using Formula 1;

[0044] Figure 7 A comparison chart of the calculated results and measured results for component ratio, temperature, and pore radius using Formula 2;

[0045] Figure 8 A comparison chart of the calculated results and measured results of the component segmentation formula;

[0046] Figure 9 The curve showing the relationship between measured and calculated pressure gradient values ​​and flow rate is used to initiate the process. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] The method for characterizing the initiation pressure gradient based on heavy oil composition and reservoir pore structure is as follows:

[0049] 1.1 Quantitative characterization of permeability, viscosity and starting pressure gradient

[0050] Multiple regression analysis was performed using MATLAB software to investigate the influencing factors of the starting pressure gradient of heavy oil, considering permeability, viscosity, and starting pressure gradient. The formulas are as follows:

[0051]

[0052] In the formula, G is the starting pressure gradient, MPa / m; K is the permeability, mD; and μ is the viscosity, mPa·s.

[0053] pass Figure 1 It can be seen that there is a certain correlation between the starting pressure gradient and the mobility. The results calculated by the empirical formula have a consistent trend with the experimental results. The fitting effect is relatively low in areas with lower mobility.

[0054] 1.2 Quantitative characterization of components, temperature, permeability and starting pressure gradient

[0055] The relationships between different components and component ratios, temperature, permeability, and starting pressure gradient were investigated using MATLAB software to perform multiple regression analysis on the factors affecting the starting pressure gradient of heavy oil. Formula 1 is the multiple regression formula for component, temperature, permeability, and starting pressure gradient, and Formula 2 is the multiple regression formula for component ratio, temperature, permeability, and starting pressure gradient. The fitting effects of different fitting formulas were compared.

[0056] The regression formulas for the two different multiple regression methods are as follows:

[0057] Formula 1:

[0058] lnG=-0.00033*K+152.87*Sa+152.90*Ar+152.95*Rs+153.06*As-0.0694T-15252.13, R 2 =0.5625 (2);

[0059] Formula 2:

[0060] lnG=-0.00037*K-2.0920*Sa / Ar-1.5069*Ar / Rs-0.0970*Ar / As-0.4318*

[0061] Rs / As-0.0775T+6.4715, R 2 =0.7069 (3);

[0062] In the formula, Sa is saturated hydrocarbon, %; Ar is aromatic hydrocarbon, %; Rs is resin, %; As is asphaltenes, %; Sa / Ar is the ratio of saturated hydrocarbon to aromatic hydrocarbon content, decimal; Ar / Rs is the ratio of aromatic hydrocarbon to resin content, decimal; Ar / As is the ratio of aromatic hydrocarbon to asphaltenes content, decimal; Rs / As is the ratio of resin to asphaltenes content, decimal; T is temperature, ℃; K is permeability, mD.

[0063] Comparison of calculation results and measured results using the two formulas: Figure 2 , Figure 3 As shown, the calculation results of Formula 1 and Formula 2 are somewhat worse than the empirical formula for mobility regression, but they still demonstrate a certain correlation between heavy oil components and the starting pressure gradient. Formula 2 has a better fitting effect than Formula 1. The overall trend of the fitting results of the two sets of formulas shows that the fitting effect is relatively poor when the mobility is less than 0.50 mD / mPa·s. Therefore, to improve the fitting accuracy of the formulas, a piecewise multiple regression fitting of the component ratio formula is performed with 0.50 mD / mPa·s as the dividing line.

[0064] The results of the piecewise formula are shown below:

[0065] Multiple regression formula for mobility less than or equal to 0.50 mD / mPa·s:

[0066] lnG=0.00016*K-1.8860*Sa / Ar-1.3709*Ar / Rs-0.4051*Ar / As-0.1165*

[0067] Rs / As-0.1032T+6.6907(4);

[0068] Multiple regression formula for mobility greater than 0.50 mD / mPa·s:

[0069] lnG=-0.00042*K-1.2075*Sa / Ar-1.2179*Ar / Rs+0.1144*Ar / As-0.4633*

[0070] Rs / As-0.0551T+2.8361(5);

[0071] Among them, R 2 =0.8045.

[0072] Comparison of the calculated results of the component segment formula with the measured results: Figure 4 As shown in the figure, the fitting results of the component ratio segmented formula can be seen that the fitting accuracy of the component ratio segmented formula is significantly improved compared with the component ratio formula, and the overall fitting curve is very close to the measured data.

[0073] 1.3 Quantitative characterization of pore radius, viscosity and starting pressure gradient

[0074] To investigate the influence of reservoir micropore structure on the start-up pressure gradient, while quantitatively characterizing the start-up pressure gradient using components, pore radius was used instead of permeability. Multiple regression analysis was performed on the influencing factors of the start-up pressure gradient in heavy oil using MATLAB software, with the following formula:

[0075] lnG=-0.2695r+0.00017μ+3.7006, R 2 =0.6372 (6);

[0076] pass Figure 5 It can be seen that there is a certain correlation between the starting pressure gradient and the pore radius, and the calculated results and experimental results show a consistent trend. Overall, the fitting results of the flow rate formula are better when the flow rate is less than 0.50 mD / mPa·s than when the flow rate is greater than 0.50 mD / mPa·s.

[0077] 1.4 Quantitative characterization of composition, temperature, pore radius and starting pressure gradient

[0078] The relationships between different components and component ratios, temperature, pore radius, and starting pressure gradient were investigated using MATLAB software to perform multiple regression analysis on the factors affecting the starting pressure gradient of heavy oil. Formula 1 is the multiple regression formula for component, temperature, pore radius, and starting pressure gradient, and Formula 2 is the multiple regression formula for component ratio, temperature, pore radius, and starting pressure gradient. The fitting effects of different fitting formulas were compared.

[0079] The regression formulas for the two different multiple regression methods are shown below:

[0080] Formula 1:

[0081] lnG=-0.1372*r+179.50*Sa+179.53*Ar+179.58*Rs+179.70*As-0.0694T-17954.96, R 2 =0.5742 (7);

[0082] Formula 2:

[0083] lnG=-0.1552*r-2.1444*Sa / Ar-1.5001*Ar / Rs-0.1084*Ar / As-0.4386*

[0084] Rs / As-0.0774T+6.9932, R 2 =0.7332 (8);

[0085] Comparison of calculation results and measured results using the two formulas: Figure 6 , 7 As shown, the calculation results of Formula 1 and Formula 2 are somewhat worse than those of the mobility formula, but they still demonstrate a certain correlation between heavy oil components and the starting pressure gradient. Formula 2 shows a better fit than Formula 1. The overall trend of the two sets of formula fitting results shows that the formula fitting effect is relatively poor when the mobility is less than 0.50 mD / mPa·s. Therefore, to improve the accuracy of the formula fitting, a piecewise multiple regression fitting of the component ratio formula is performed with 0.50 mD / mPa·s as the dividing line.

[0086] The results of the piecewise formula are shown below:

[0087] Multiple regression formula for mobility less than or equal to 0.50 mD / mPa·s:

[0088] lnG=0.0680*r-1.8603*Sa / Ar-1.3452*Ar / Rs-0.4163*Ar / As+0.1017*

[0089] Rs / As-0.1033T+6.4305(9);

[0090] Multiple regression formula for mobility greater than 0.50 mD / mPa·s:

[0091] lnG=-0.2422*r-1.6717*Sa / Ar-1.2183*Ar / Rs+0.0515*Ar / As-0.3752*

[0092] Rs / As-0.0476T+3.9026(10);

[0093] Among them, R 2 =0.9414.

[0094] Comparison of the calculated results of the component segment formula with the measured results: Figure 8 As shown in the figure, the fitting results of the component ratio segmented formula can be seen that the fitting accuracy of the component ratio segmented formula is significantly improved compared with the component ratio formula, and the overall fitting curve is very close to the measured data.

[0095] The calculation results of the above formulas were compared with the optimized segmented formula for pore component ratio to verify the results. The selected heavy oil samples and core data are shown in Tables 1 and 2 below. The simulation verification results are as follows: Figure 9 As shown.

[0096] Table 1 Basic parameters of experimental samples for heavy oil seepage behavior

[0097]

[0098] Table 2 Basic parameters of experimental core samples for heavy oil seepage behavior.

[0099]

[0100] By verifying formulas (9) and (10), it can be seen from the calculation results that the calculated value of the starting pressure gradient has good consistency with the measured value.

[0101] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for characterizing the initiation pressure gradient based on heavy oil components and reservoir pore structure, characterized in that, include: (1) Given the composition, temperature, and permeability, the formula for characterizing the starting pressure gradient is: lnG=-0.00033*K+152.87*Sa+152.90*Ar+152.95*Rs+153.06*As-0.0694T-15252.13,R 2 =0.5625; In the formula, G is the starting pressure gradient, in MPa / m; Sa is saturated hydrocarbon; Ar is aromatic hydrocarbon; Rs is resin; As is asphaltenes; T is temperature, in °C; and K is permeability, in mD. (2) When the component ratio, temperature, and permeability are known, the formula for characterizing the starting pressure gradient is: lnG=-0.00037*K-2.0920*Sa / Ar-1.5069*Ar / Rs-0.0970*Ar / As-0.4318* Rs / As-0.0775T+6.4715,R 2 =0.7069; In the formula, Sa / Ar is the ratio of saturated hydrocarbons to aromatic hydrocarbons; Ar / Rs is the ratio of aromatic hydrocarbons to resins; Ar / As is the ratio of aromatic hydrocarbons to asphaltenes; and Rs / As is the ratio of resins to asphaltenes. (3) Given the pore radius and viscosity, the formula for characterizing the starting pressure gradient is: lnG=-0.2695r+0.00017μ+3.7006,R 2 =0.6372; In the formula, r is the pore radius in μm; μ is the viscosity in mPa·s; (4) When the composition, temperature, and pore radius are known, the formula for characterizing the starting pressure gradient is: lnG=-0.1372*r+179.50*Sa+179.53*Ar+179.58*Rs+179.70*As-0.0694T-17954.96,R 2 =0.5742; (5) When the component ratio, temperature, and pore radius are known, the formula for characterizing the starting pressure gradient is: lnG=-0.1552*r-2.1444*Sa / Ar-1.5001*Ar / Rs-0.1084*Ar / As-0.4386* Rs / As-0.0774T+6.9932,R 2 =0.7332。 2. The method for characterizing the start-up pressure gradient based on heavy oil components and reservoir pore structure as described in claim 1, characterized in that, Given the component ratio, temperature, and permeability, a piecewise multiple regression fitting is performed on the component ratio formula with 0.50 mD / mPa·s as the cutoff line. When the mobility is less than or equal to 0.50 mD / mPa·s, the starting pressure gradient characterization formula is as follows: lnG=0.00016*K-1.8860*Sa / Ar-1.3709*Ar / Rs-0.4051*Ar / As-0.1165* Rs / As -0.1032T + 6.6907; When the mobility is greater than 0.50 mD / mPa·s, the formula for characterizing the starting pressure gradient is: lnG=-0.00042*K-1.2075*Sa / Ar-1.2179*Ar / Rs+0.1144*Ar / As-0.4633* Rs / As -0.0551T + 2.8361; Among them, R 2 =0.8045.

3. The method for characterizing the start-up pressure gradient based on heavy oil components and reservoir pore structure as described in claim 1, characterized in that, Given the component ratio, temperature, and pore radius, a piecewise multiple regression fitting is performed on the component ratio formula with 0.50 mD / mPa·s as the cutoff line. The multiple regression formula for mobility less than or equal to 0.50 mD / mPa·s is as follows: lnG=0.0680*r-1.8603*Sa / Ar-1.3452*Ar / Rs-0.4163*Ar / As+0.1017*Rs / As-0.1033T+6.4305; The multiple regression formula for mobility greater than 0.50 mD / mPa·s is: lnG=-0.2422*r-1.6717*Sa / Ar-1.2183*Ar / Rs+0.0515*Ar / As-0.3752*Rs / As-0.0476T+3.9026; Among them, R 2 =0.9414.

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