Starting pressure gradient characterization method based on heavy oil component and reservoir pore structure

Abstract

The invention discloses a starting pressure gradient characterization method based on a thickened oil component and a reservoir pore structure, and relates to the technical field of oil and gas field development. A multiple regression fitting formula between different parameters and a starting pressure gradient is established by considering microscopic and macroscopic properties, fluid properties, component composition and the like of a reservoir; comprising a component, temperature, permeability and starting pressure gradient fitting formula; a gradient fitting formula of pore radius, viscosity and starting pressure is established; the component, temperature, pore radius and starting pressure gradient fitting formula can meet the requirement for calculating the starting pressure gradient by using different parameters. In order to characterize the starting pressure gradient more finely, different parameter fitting formula results are compared, segmentation multiple regression fitting is carried out on a component ratio formula by taking 0.50 mD / mPa.s as a boundary, the fitting precision is high, and a fitting curve is very close to actually measured data.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and particularly to a method for characterizing the starting pressure gradient based on heavy oil components and reservoir pore structure. Background Art

[0002] Heavy oil is a complex colloidal system composed of molecules with different structures, and there is a macromolecular structure with a three-dimensional network structure in the heavy oil system. The heavy oil system with this structure has both the viscous action of a fluid and the elastic action of a solid. Moreover, due to different component structures of heavy oil, the viscoelastic properties also vary, which will also cause different seepage characteristics when heavy oil with different component structures flows in a porous medium. The elastic action of heavy oil will cause an external driving force to be required during its flow process to make the heavy oil change from a static state to a creep state and then to a flowing state. This minimum force that causes the fluid to start flowing is called the yield stress. It is precisely due to the special elastic action of heavy oil that a certain displacement pressure gradient is required for heavy oil to change from a static state to a flowing state in a porous medium, and the limit value that causes the heavy oil to start flowing is called the starting pressure gradient.

[0003] As one of the important parameters for the seepage of reservoir fluids in a porous medium, the starting pressure gradient plays a key role in production capacity prediction, well pattern deployment, development plan formulation, etc., and greatly affects the oil and gas production efficiency. From the perspective of the macroscopic properties of the reservoir and the fluid, the starting pressure gradient is affected by the reservoir permeability and crude oil viscosity; from the perspective of the microscopic properties of the reservoir and the fluid, the starting pressure gradient is affected by the pore structure and crude oil components, and the starting pressure gradient will also change with the change of the environmental temperature. At present, the main method for quantitatively characterizing the starting pressure gradient is to perform multiple regression through the macroscopic property parameters of the reservoir and the fluid, and there is a large deviation between the regression result and the experimental measurement value.

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

[0005] To solve the above technical problems, the present invention discloses a method for characterizing the starting pressure gradient based on heavy oil components and reservoir pore structure, and this method performs multiple regression through the microscopic property parameters of the reservoir and the fluid.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for characterizing the starting pressure gradient based on heavy oil components and reservoir pore structure, comprising:

[0008] (1) When the components, temperature, and permeability are known, the starting pressure gradient characterization formula 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 asphaltene, %; T is temperature, °C; K is permeability, mD;

[0011] (2) When the component ratio, temperature, and permeability are known, the characterization formula for 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 content ratio of saturated hydrocarbon to aromatic hydrocarbon, in decimals; Ar / Rs is the content ratio of aromatic hydrocarbon to resin, in decimals; Ar / As is the content ratio of aromatic hydrocarbon to asphaltene, in decimals; Rs / As is the content ratio of resin to asphaltene, in decimals;

[0015] (3) When the pore radius and viscosity are known, the characterization formula for 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 components, temperature, and pore radius are known, the characterization formula for 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 characterization formula for 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, the component ratio formula is segmented and multiple regression fitting is performed with 0.50 mD / mPa·s as the dividing line. When the mobility is less than or equal to 0.50 mD / mPa·s, the formula for characterizing the starting pressure gradient 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] where, R 2 = 0.8045。

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

[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 the 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] Wherein, R 2 = 0.9414.

[0037] The beneficial effects of the present invention are as follows. By considering parameters such as reservoir microscopic and macroscopic properties, fluid properties, and component compositions, the present invention method establishes multiple regression fitting formulas between different parameters and the starting pressure gradient, including the fitting formulas of components, temperature, permeability, and the starting pressure gradient; the fitting formulas of pore radius, viscosity, and the starting pressure gradient; the fitting formulas of components, temperature, pore radius, and the starting pressure gradient, which can meet the requirements of calculating the starting pressure gradient using different parameters. In addition, in order to more precisely characterize the starting pressure gradient, the results of different parameter fitting formulas are compared, and it is proposed to perform piecewise multiple regression fitting on the component ratio formula with 0.50 mD / mPa·s as the demarcation line, with high fitting accuracy and the fitting curve being very close to the measured data. Description of the Drawings

[0038] Figure 1 It is a bar chart comparing the measured value of the starting pressure gradient with the calculation result of the formula;

[0039] Figure 2 It is a comparison chart of the calculation result and the measured result of the formula one of components, temperature, and permeability;

[0040] Figure 3 It is a comparison chart of the calculation result and the measured result of the formula two of component ratio, temperature, and permeability;

[0041] Figure 4 It is a comparison chart of the calculation result and the measured result of the piecewise formula of component ratio;

[0042] Figure 5 It is a comparison chart of the measured value of the starting pressure gradient with the calculation result of the formula;

[0043] Figure 6 It is a comparison chart of the calculation result and the measured result of the formula one of components, temperature, and pore radius;

[0044] Figure 7 It is a comparison chart of the calculation result and the measured result of the formula two of component ratio, temperature, and pore radius;

[0045] Figure 8 It is a comparison chart of the calculation result and the measured result of the piecewise formula of component ratio;

[0046] Figure 9 It is the relationship curve of the measured value of the starting pressure gradient, the calculation value of the formula, and the mobility. Detailed Embodiment

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0048] A method for characterizing the starting pressure gradient based on the components of heavy oil and the pore structure of the reservoir is as follows:

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

[0050] For permeability, viscosity, and starting pressure gradient respectively, multivariate regression is performed on the influencing factors of the starting pressure gradient of heavy oil using Matlab software. The formula is:

[0051]

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

[0053] Through Figure 1 It can be seen that there is a certain correlation between the starting pressure gradient and mobility. The calculation results of the empirical formula and the experimental results have a consistent trend, and the fitting effect in the area with smaller mobility is relatively low.

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

[0055] For the relationships between different components and component ratios, temperature, permeability, and starting pressure gradient respectively, multivariate regression is performed on the influencing factors of the starting pressure gradient of heavy oil using Matlab software. Formula 1 is the multivariate regression formula for components, temperature, permeability, and starting pressure gradient, and Formula 2 is the multivariate regression formula for component ratio, temperature, permeability, and starting pressure gradient. The fitting effects of different fitting formulas are compared.

[0056] The regression formulas under two different multivariate 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] Wherein, Sa is saturated hydrocarbon, %; Ar is aromatic hydrocarbon, %; Rs is resin, %; As is asphaltene, %; Sa / Ar is the content ratio of saturated hydrocarbon to aromatic hydrocarbon, in decimals; Ar / Rs is the content ratio of aromatic hydrocarbon to resin, in decimals; Ar / As is the content ratio of aromatic hydrocarbon to asphaltene, in decimals; Rs / As is the content ratio of resin to asphaltene, in decimals; T is temperature, °C; K is permeability, mD.

[0063] The comparison between the calculation results of the two formulas and the measured results is as Figure 2 、 Figure 3 shown. It can be seen that the calculation results of Formula 1 and Formula 2 are somewhat different from the empirical formula of mobility regression, but it can still show that there is a certain correlation between the heavy oil components and the starting pressure gradient, and the fitting effect of Formula 2 is better than that of Formula 1. From the overall trend of the fitting results of the two groups of formulas, when the mobility is less than 0.50 mD / mPa·s, the fitting effect of the formula is relatively poor. Therefore, in order to improve the fitting accuracy of the formula, the component ratio formula is segmented and multiple regression fitting is carried out with 0.50 mD / mPa·s as the dividing line.

[0064] The results of the segmented formula are as follows:

[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] where, R 2 = 0.8045.

[0072] The comparison between the calculation results of the component ratio segmented formula and the measured results is as Figure 4 shown. It can be seen from the fitting results of the component ratio segmented formula 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] In order to study the influence of reservoir microscopic pore structure on the starting pressure gradient, while quantitatively characterizing the starting pressure gradient by components, pore radius is used instead of permeability, and the influencing factors of the starting pressure gradient of heavy oil are subjected to multiple regression using matlab software for pore radius, viscosity and starting pressure gradient respectively. The formula is:

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

[0076] It can be seen through Figure 5 that there is a certain correlation between the starting pressure gradient and the pore radius, and the calculation results of the formula are consistent with the experimental results. From the overall trend, when the mobility is less than 0.50 mD / mPa·s, the fitting result of the mobility formula is better than that when the mobility is greater than 0.50 mD / mPa·s.

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

[0078] The relationships between different components and component ratios and temperature, pore radius, and starting pressure gradient are respectively subjected to multiple regression using matlab software for the influencing factors of the starting pressure gradient of heavy oil. Formula 1 is the multiple regression formula for components, 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, and the fitting effects of different fitting formulas are compared.

[0079] The regression formulas under two different multiple regression methods are as follows:

[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] The comparison between the calculation results of the two formulas and the measured results is as Figure 6 、 7 shown. It can be seen that the calculation results of Formula 1 and Formula 2 are slightly worse than those of the mobility formula, but it can still show that there is a certain correlation between the heavy oil components and the starting pressure gradient, and the fitting effect of Formula 2 is better than that of Formula 1. From the overall trend of the fitting results of the two groups of formulas, when the mobility is less than 0.50 mD / mPa·s, the fitting effect of the formula is relatively poor. Therefore, in order to improve the fitting accuracy of the formula, a piecewise multiple regression fitting of the component ratio formula is carried out with 0.50 mD / mPa·s as the dividing line.

[0086] The results of the piecewise formula are as follows:

[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 between the calculation results of the component ratio segmented formula and the measured results Figure 8 As shown, it can be seen from the fitting results of the component ratio segmented formula 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] Verify the pore component ratio segmented formula selected based on the above comparison of formula calculation results. The selected heavy oil samples and basic core data are shown in Tables 1 and 2 below, and the simulation verification results are as Figure 9 shown.

[0096] Table 1 Basic parameters of heavy oil seepage law experiment samples

[0097]

[0098] Table 2 Basic parameters of heavy oil seepage law experiment cores

[0099]

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

[0101] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by those skilled in the art within the scope of the essence of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for characterizing the starting pressure gradient based on heavy oil components and reservoir pore structure, characterized in that: include: (1) When the composition, temperature, and permeability are known, 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; Where, G is the starting pressure gradient, MPa / m; Sa is saturated hydrocarbon, %; Ar is aromatic hydrocarbon, %; Rs is colloid, %; As is asphaltene, %; T is temperature, ℃; K is permeability, 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; Wherein, Sa / Ar is the ratio of saturated hydrocarbon to aromatic hydrocarbon, decimal; Ar / Rs is the ratio of aromatic hydrocarbon to colloid, decimal; Ar / As is the ratio of aromatic hydrocarbon to asphaltene, decimal; Rs / As is the ratio of colloid to asphaltene, decimal; (3) When the pore radius and viscosity are known, the formula for characterizing the starting pressure gradient is: lnG=-0.2695r+0.00017μ+3.7006,R 2 =0.6372; Where r is the pore radius, μm; μ is the viscosity, mPa·s; (4) When the composition, temperature and pore radius are known, the starting pressure gradient characterization formula 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 starting pressure gradient based on heavy oil components and reservoir pore structure according to claim 1, characterized in that: When the component ratio, temperature and permeability are known, the component ratio formula is fitted by piecewise multivariate regression with 0.50 mD / mPa·s as the dividing line. When the mobility is less than or equal to 0.50 mD / mPa·s, the start-up pressure gradient characterization formula is: 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 fluidity is greater than 0.50mD / mPa·s, the starting pressure gradient characterization formula 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 starting pressure gradient based on heavy oil components and reservoir pore structure according to claim 1, characterized in that: When the component ratio, temperature, and pore radius are known, the component ratio formula is fitted by piecewise multivariate regression with 0.50 mD / mPa·s as the dividing line. The multivariate regression formula for mobility less than or equal to 0.50 mD / mPa·s is: 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 fluidity greater than 0.50mD / 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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