Quantitative evaluation method for connectivity degree between river channel sand oil-water wells based on connectivity factors

Abstract

The invention relates to the field of oil reservoir engineering research, in particular to a method for quantitatively evaluating the connectivity degree between river sand oil-water wells based on connectivity factors. The method mainly solves the problems that an existing connectivity evaluation method is not comprehensive enough, evaluation parameters are not easy to obtain, and evaluation objects are limited. Comprising the following steps: acquiring a deposition unit boundary database and logging curve data, and establishing a small-layer hierarchical database; calculating the blending average permeability, the connectivity coefficient and the stage difference of the oil-water well; normalizing the harmonic average permeability, the connectivity coefficient and the stage difference; the influence weights of the relative permeability, the relative connectivity coefficient and the relative stage difference after normalization on the connectivity degree are determined, and a connectivity factor formula is established; and determining a connectivity factor, and evaluating the connectivity of the oil-water well. According to the method, evaluation parameters are easy to obtain, the connectivity of the sand bodies between the oil wells and the water wells in spatial distribution and the connectivity of seepage parameters are comprehensively considered, the seepage connectivity condition of fluid in the sand bodies between the wells can be accurately indicated, and evaluation factors are more comprehensively considered.

Description

Technical Field

[0001] This invention relates to the field of reservoir engineering research, specifically a quantitative evaluation method for the degree of connectivity between river sand oil and water wells based on connectivity factors. Background Technology

[0002] Inter-well sand body connectivity is a major factor determining the distribution of fluids between oil and water wells, especially for reservoirs with well-developed channel sands. Channel sands are formed under strong hydrodynamic forces, generally resulting in large vertical and horizontal scales with interconnected and overlapping sand bodies, exhibiting high overall connectivity. However, significant variations in hydrodynamic conditions across different directions lead to heterogeneity within the channel sand reservoir, manifested in differences in the distribution of sand body lithology and physical properties within the reservoir space. This results in extremely complex fluid flow between oil and water wells. Furthermore, the displacement ratios of high and low permeability layers vary considerably during long-term water injection development, further exacerbating the complexity of flow parameter distribution within the sand bodies. Therefore, evaluating inter-well sand body connectivity requires a comprehensive approach that considers both the spatial distribution of sand bodies and the connectivity of flow parameters, developing a quantitative evaluation method for the degree of connectivity between channel sand and oil-water wells. This method is crucial for identifying the distribution of remaining oil between wells and for targeted tapping of remaining oil potential.

[0003] Based on a review of relevant literature, most existing methods for evaluating sand body connectivity primarily focus on the spatial connectivity of sand bodies. For example, patent CN202310958006.9 discloses a "Multi-Factor Evaluation Method for Channel Sand Body Connectivity in Tight Gas Reservoirs," which categorizes channel sand body connectivity into three types—smooth, partially smooth, and impassable—based on parameters such as sand-to-mud ratio, width-to-depth ratio, sand body thickness, interlayer thickness, and connectivity coefficient, combined with channel formation. Patent CN202310124152.1 discloses a "Qualitative Characterization Method for Channel Sand Body Connectivity," which mainly utilizes well logging and seismic data to identify different levels of interfaces within composite channels, achieving a qualitative characterization of meandering river connectivity. Patent CN202110188607.7 discloses a "Quantitative Characterization Method for Connected Sand Bodies in Any Two Wells of Conglomerate Reservoirs," which uses Petrel for reservoir modeling and generates reservoir net-to-gross ratio data, calculating the connectivity between any two wells based on this data. The patent CN201811002234.4, entitled "A method, apparatus and system for determining sand body connectivity", mainly extracts sand body thickness data from digital outcrop profiles and determines the sand body connectivity of the target work area based on the extracted sand body thickness data and well logging sand body thickness data.

[0004] However, the above technologies have at least the following problems: the parameters used in these patents are mostly various thickness parameters, that is, the connectivity they divide emphasizes the connectivity of lithology in space. The degree of connectivity is related to lithological parameters, but more so to seepage parameters. Therefore, they lack a reflection of the distribution of seepage parameters inside the sand body, and thus cannot accurately determine the degree of connectivity or indicate the distribution pattern of residual oil in the sand body between oil and water wells. Summary of the Invention

[0005] To overcome the shortcomings of existing connectivity evaluation methods, such as incompleteness, difficulty in obtaining evaluation parameters, and limited evaluation objects, this invention provides a quantitative evaluation method for the connectivity between river sand oil-water wells based on connectivity factors. This method provides easily obtainable evaluation parameters, comprehensively considers the spatial connectivity of sand bodies between oil and water wells as well as the connectivity of seepage parameters, and can accurately indicate the seepage connectivity of fluids within the sand bodies between wells. The evaluation factors are more comprehensive.

[0006] The technical solution of this invention is: a method for quantitatively evaluating the connectivity between river sand oil-water wells based on connectivity factors, comprising the following steps:

[0007] S1. Obtain the sedimentary unit boundary database and well logging curve data, and establish a layered database of sub-layers;

[0008] S2. Calculate the blended average permeability, connectivity coefficient, and gradient of oil and water wells;

[0009] S3. Normalize the harmonic average permeability, connectivity coefficient, and gradient to obtain the relative permeability, relative connectivity coefficient, and relative gradient;

[0010] S4. Determine the influence weights of the normalized relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity, and establish the connectivity factor formula.

[0011] S5. Determine the connectivity factor based on the connectivity factor formula and evaluate the connectivity of oil and water wells.

[0012] Furthermore, in step S2, the harmonic average permeability is:

[0013]

[0014] In the formula: To harmonize the average permeability, ×10 -3 μm 2 Ko represents the well permeability, multiplied by 10. -3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 ;

[0015] The connectivity coefficient of oil and water wells is:

[0016]

[0017] In the formula: Ltxs is the connectivity coefficient, dimensionless; Ho is the formation thickness of the oil well unit, m; Hw is the formation thickness of the water well unit, m; h is the effective thickness; ho is the effective thickness of the oil well, m; hw is the effective thickness of the water well, m; hmin is the smaller effective thickness between the oil and water wells, i.e., hmin=min(ho,hw);

[0018] The difference between oil and water well grades is:

[0019]

[0020] In the formula: JC represents the permeability difference between oil and water wells; Ko represents the permeability of the oil well, ×10 -3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 .

[0021] Furthermore, in step S3, the normalized relative permeability is:

[0022]

[0023] Where: K 01 K represents relative permeability, dimensionless; max The maximum permeability within the study unit layer of the study area, ×10 -3 μm 2 ; To harmonize the average permeability, ×10 -3 μm 2 ;

[0024] The normalized relative range is:

[0025]

[0026] In the formula: JC 01 JC represents the relative grade difference, dimensionless; JC represents the permeability grade difference between oil and water wells; JC max This represents the maximum level difference within the research unit layer of the study area.

[0027] Furthermore, the normalized relative connectivity coefficient in step S3 is the same as the oil-water well connectivity coefficient in step S2.

[0028] Furthermore, in step S4, the weighting coefficients for the influence of relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity are as follows:

[0029]

[0030]

[0031] In the formula: W1 is the weighting coefficient of the influence of relative permeability on connectivity; W2 is the weighting coefficient of the influence of relative connectivity coefficient on connectivity; W3 is the weighting coefficient of the influence of relative gradient on connectivity; R y，x1(x2，x3) R is the partial correlation coefficient between relative permeability and the change in water saturation of oil wells. y，x2(x1，x3) R is the partial correlation coefficient between the relative connectivity coefficient and the change in water saturation of the oil well; y，x3(x2，x1) This is the partial correlation coefficient between the relative grade difference and the change in water saturation of the oil well.

[0032] Furthermore, when calculating the partial correlation coefficient, relative permeability, connectivity coefficient, and relative gradient are set as independent variables x1, x2, and x3, respectively, and the change in water saturation of the oil well is the dependent variable y. Then, the partial correlation coefficient R between relative permeability x1 and the change in water saturation of the oil well y is... y，x1(x2，x3) for:

[0033]

[0034] The partial correlation coefficient R between the relative connectivity coefficient and the change in water saturation of oil wells y，x2(x1，x3) for:

[0035]

[0036] The partial correlation coefficient R between relative grade difference and change in water saturation of oil wells y，x3(x2，x1) for:

[0037]

[0038] In the formula: R y，x1(x2，x3) Let x2 and x3 remain constant, and let x1 be the partial correlation coefficient between x1 and y.

[0039] R y，x2(x1，x3) Let x1 and x3 remain constant, and let x2 be the partial correlation coefficient between x2 and y.

[0040] R y，x3(x2，x1) Let x2 be the partial correlation coefficient between x3 and y when x1 remains constant.

[0041] r y，x1 Let r be the correlation coefficient between y and x1. y，x2 Let r be the correlation coefficient between y and x². y，x3 The correlation coefficient between y and x3;

[0042] r x2，x1 Let r be the correlation coefficient between x2 and x1. x1，x3 Let r be the correlation coefficient between x1 and x3. x2，x3Let x2 and x3 be the correlation coefficients.

[0043] Furthermore, in formulas (7-1), (7-2), and (7-3),

[0044]

[0045] In the formula: n is the number of samples, and i is the current sample; It is the average of n x1 values; It is the average of n x2; It is the average of n x3; Let y be the average of n y values.

[0046] Furthermore, the connectivity factor Ltyz in step S5 is:

[0047] Ltyz=W1·K 01 +W2·Ltxs+W3·JC 01 (9)

[0048] Furthermore, the larger the connectivity factor in step S5, the higher the degree of connectivity between the oil and water wells.

[0049] The present invention has the following beneficial effects:

[0050] (1) This invention proposes the concept of connectivity factor, which integrates three parameters: permeability, connectivity coefficient, and grade difference. These parameters respectively represent the magnitude of planar permeability, the degree of connectivity of thickness in the vertical direction, and the difference in permeability between oil and water wells. Compared with previous methods, this invention can comprehensively characterize the geometric morphology of sand bodies and the connectivity of seepage between oil and water wells.

[0051] (2) Based on connectivity factors, the connectivity of different levels of well groups, namely “development section-sedimentary unit-subdivided layer”, can be evaluated, as well as the connectivity between wells of different grades of river sand-oil-water within the well group. This more accurately reflects the differences in connectivity between wells, clarifies the seepage characteristics of different displacement directions and different planar locations, and can guide the accurate prediction of remaining oil.

[0052] (3) Based on the research results of this invention, the connectivity factor parameter is used to quantitatively evaluate the degree of connectivity between oil and water wells in the well group, which can analyze the dominant direction of injection and guide the implementation of fracturing measures.

[0053] This invention is applicable to hydroflooding development blocks, and can provide technical support for the preparation of oil recovery schemes and planning schemes in tertiary oil recovery blocks. Attached Figure Description

[0054] Figure 1 This is a flowchart of the present invention;

[0055] Figure 2 This is a diagram showing the morphological characteristics of the core well curve;

[0056] Figure 3 This is a schematic diagram for calculating connectivity coefficients;

[0057] Figure 4 This is a connectivity factor characterization diagram of unit P133a in well group X9-3-P234 in Example 1;

[0058] Figure 5 This is a characterization diagram of the water saturation change in unit P133a of well group X9-3-P234 in Example 1;

[0059] Figure 6 This is the well history production curve of well X8-4-SP331 in Example 2;

[0060] Figure 7 This is the connectivity factor analysis diagram of well group X8-4-SP331 in Example 2;

[0061] Figure 8 This is a diagram showing the effect of fracturing measures in well X8-4-SP331 in Example 2. Detailed Implementation

[0062] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. The technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0063] Depend on Figure 1 As shown, a quantitative evaluation method for the connectivity between oil and water wells in river sands based on connectivity factors includes the following steps:

[0064] S1. Collect the sedimentary unit boundary database and well logging curve data, establish a comparative work area, and vertically subdivide the sedimentary unit; export the subdivided sedimentary unit boundaries into a database, split the small layer layer database, including parameters such as oil and water well permeability, formation thickness, effective thickness, and porosity.

[0065] S2. Calculate the blended average permeability, connectivity coefficient, and level difference of oil and water wells. These three parameters can reflect the degree of connectivity between wells.

[0066] The harmonic average permeability is:

[0067]

[0068] In the formula: To harmonize the average permeability, ×10 -3 μm 2 Ko represents the well permeability, multiplied by 10.-3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 .

[0069] The connectivity coefficient of oil and water wells is:

[0070]

[0071] In the formula: Ltxs is the connectivity coefficient, dimensionless; Ho is the formation thickness of the oil well unit, m; Hw is the formation thickness of the water well unit, m; h is the effective thickness; ho is the effective thickness of the oil well, m; hw is the effective thickness of the water well, m; hmin is the smaller effective thickness between the oil and water wells, i.e., hmin = min(ho, hw).

[0072] The difference between oil and water well grades is:

[0073]

[0074] In the formula: JC represents the permeability difference between oil and water wells; Ko represents the permeability of the oil well, ×10 -3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 .

[0075] S3. Normalize the harmonic average permeability, connectivity coefficient, and grade difference to obtain the relative permeability, relative connectivity coefficient, and relative grade difference, so as to eliminate the influence of different distribution ranges of each parameter.

[0076] The normalized relative penetration rate is:

[0077]

[0078] Where: K 01 K represents relative permeability, dimensionless; max The maximum permeability within the study unit layer of the study area, ×10 -3 μm 2 ; To harmonize the average permeability, ×10 -3 μm 2 .

[0079] The normalized relative range is:

[0080]

[0081] In the formula: JC 01 JC represents the relative grade difference, dimensionless; JC represents the permeability grade difference between oil and water wells; JC max This represents the maximum level difference within the research unit layer of the study area.

[0082] Since the connectivity coefficient itself is a number between 0 and 1, the normalized relative connectivity coefficient is the same as the oil-water well connectivity coefficient in step S2, that is, no normalization is required.

[0083] S4. Determine the influence weights of the normalized relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity, and establish the connectivity factor formula.

[0084] First, calculate the partial correlation coefficients between three parameters—relative permeability, relative connectivity coefficient, and relative gradient—and the change in water saturation of the oil well. The larger the change in water saturation of the oil well, the better the water drive effect and the better the connectivity of sand bodies within the oil and water wells. The larger the partial correlation coefficient between the three parameters and the change in water saturation of the oil well, the greater the influence of that parameter on connectivity.

[0085] The partial correlation coefficient refers to a measure of the close relationship between two parameters when studying the influence or correlation of a certain parameter Xi on Y in a multi-parameter system Y = f(X1, X2, ..., Xn), while treating the influence of other parameters as constants (remaining unchanged).

[0086] The independent variables in this application include relative permeability, relative connectivity coefficient, and relative gradient, while the dependent variable is the change in water saturation. Here, relative permeability is set as x1, connectivity coefficient as x2, and relative gradient as x3, and the change in water saturation of the oil well is set as the dependent variable y.

[0087] The partial correlation coefficient R between relative permeability x1 and the change in water saturation y of the oil well y,x1(x2，x3) for:

[0088]

[0089] The partial correlation coefficient between the relative connectivity coefficient and the change in water saturation of oil wells, Ry,x2(x1,x3) ) for:

[0090]

[0091] The partial correlation coefficient R between relative grade difference and change in water saturation of oil wells y，x3(x2，x1) for:

[0092]

[0093] In the formula: R y，x1(x2，x3) Let x2 and x3 remain constant, and let x1 be the partial correlation coefficient between x1 and y.

[0094] R y，x2(x1，x3) Let x1 and x3 remain constant, and let x2 be the partial correlation coefficient between x2 and y.

[0095] Ry，x3(x2，x1) Let x2 be the partial correlation coefficient between x3 and y when x1 remains constant.

[0096] r y，x1 Let r be the correlation coefficient between y and x1. y，x2 Let r be the correlation coefficient between y and x². y，x3 The correlation coefficient between y and x3;

[0097] r x2,x1 Let F be the correlation coefficient between x2 and x1. x1,x3 Let r be the correlation coefficient between x1 and x3. x2,x3 Let x2 and x3 be the correlation coefficients.

[0098] In formulas (7-1), (7-2), and (7-3),

[0099]

[0100] In the formula: n is the number of samples, and i is the current sample; It is the average of n x1 values; It is the average of n x2; It is the average of n x3; Let y be the average of n y values.

[0101] The weighting coefficients for the influence of relative penetration, relative connectivity coefficient, and relative gradient on connectivity are as follows:

[0102]

[0103] In the formula: W1 is the weighting coefficient of the influence of relative penetration rate on connectivity; W2 is the weighting coefficient of the influence of relative connectivity coefficient on connectivity; W3 is the weighting coefficient of the influence of relative gradient on connectivity.

[0104] S5. Determine the connectivity factor according to the connectivity factor formula and evaluate the connectivity of oil and water wells. The connectivity factor Ltyz is:

[0105] Ltyz=W1·K 01 +W2·Ltxs+W3·JC 01 (9)

[0106] The connectivity of oil and water wells is determined by the calculated connectivity factor; the larger the connectivity factor, the higher the degree of connectivity between the oil and water wells.

[0107] Example 1:

[0108] Taking Block B of Xingjiu District in Daqing Oilfield as an example, the present invention will be described in detail.

[0109] S1. Collect sedimentary unit boundary databases and well logging data. Using GPTlog software, establish a comparative study area. Within the sedimentary unit, based on the characteristics of the core well logging curves, including low values, curve steps, and bottom sediment retention, determine that the study area can be further subdivided into four segments within the original sedimentary unit. See [link to relevant documentation]. Figure 2 The vertical grape I21a-33b layer of 519 wells in Block B of Xingjiu District was divided into 29 sub-layers.

[0110] The boundaries of the subdivided sedimentary units were exported to the database, and the database was divided into small layers, including parameters such as oil and water well permeability, formation thickness, effective thickness, and porosity. Taking the subdivided layer units P121a-33b between oil well X8-4-30 and water well X8-4-P229 as an example, see Table 1.

[0111] Table 1

[0112]

[0113]

[0114] S2. Calculate the blended average permeability, connectivity coefficient, and gradient of all oil and water wells in the study area. The connectivity coefficient calculation is detailed in [link to calculation]. Figure 3 With well group X9-3-P234 P13 3a -3 3b For example, see Table 2 for the unit calculation results.

[0115] Table 2

[0116]

[0117]

[0118] S3. Normalize the harmonic average permeability, connectivity coefficient, and gradient to obtain the relative permeability, relative connectivity coefficient, and relative gradient. The normalized parameters, including relative permeability, connectivity coefficient, and relative gradient, are shown in Table 3.

[0119] Table 3

[0120]

[0121]

[0122] S4. Determine the influence weights of the normalized relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity, and establish the connectivity factor formula.

[0123] The partial correlation coefficients of relative permeability to the change in water saturation of oil wells were calculated using the formulas to be 0.145, relative connectivity coefficient to the change in water saturation of oil wells to be 0.227, and relative gradient to the change in water saturation of oil wells to be 0.097.

[0124] Calculate the weights of the relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity using formulas (6-1), (6-2), and (6-3), respectively.

[0125]

[0126] According to formula (9), the connectivity factor formula is:

[0127] Ltyz = 0.31·K 01 +0.48·Ltxs+0.21·JC 01

[0128] S5. Substitute the relative permeability, relative connectivity coefficient, and relative grade difference calculated in step S3 into the above connectivity factor formula to calculate and determine the connectivity factor, using well group X9-3-P234, P13. 3a -3 3b For example, the calculation results of each subdivided layer unit are shown in Table 3.

[0129] The connectivity factor is used to evaluate the connectivity between oil and water wells. The connectivity factor can assess the connectivity of a well group across different levels, from the development interval to the sedimentary unit to the subdivided layer. Taking the X9-3-P234 well group as an example (due to the large amount of data, only P133a-33b are shown), see... Figure 4 , Figure 5 Table 3 shows that the development zone of this well group refers to the polymer injection target layer P121a-33b, with a connectivity factor of 0.64. The sedimentary units refer to P133a and P133b in the diagram. For example, the connectivity factor of unit P133a between water well X9-3-P234 and oil well X9-3-SP140 is 0.6, while the connectivity factor of unit P133b is 0.35. The connectivity factor of different sedimentary units between the two wells can be used to quantitatively characterize the degree of connectivity; a higher value indicates better connectivity, meaning that unit P133a has a better connectivity than unit P133b.

[0130] Within a sedimentary unit, further subdivisions are made, for example, the P133a unit is subdivided into P133a-1, P133a-2, P133a-3, and P133a-4. Comparing the connectivity factors of the same subdivided layers across different wells allows for the evaluation of the connectivity degree of each subdivided layer. For instance, the P133a-1 subdivision layer of oil well X9-3-SP339 has the lowest connectivity factor (0.03), corresponding to the smallest change in water saturation (0%), indicating the lowest connectivity degree. Conversely, the P133a-1 subdivision layer of oil well X9-30-P334 has the highest connectivity factor (0.56), corresponding to a relatively large change in water saturation (20.43%), indicating the highest connectivity degree. The P133a-1 subdivision layer connectivity factors of oil wells X9-30-SP134 and X9-3-SP140 are in the middle range. Based on the above analysis, taking the water well X9-3-P234 as the center, and analyzing the connectivity factor and water saturation variation values ​​of the four oil wells connected to it, the P133a-1 sub-layer shows that X9-30-P334 has the best connectivity, followed by X9-3-SP140 and X9-30-SP134, while the oil well X9-3-SP339 has the worst connectivity. Therefore, this demonstrates that the connectivity factor calculated using this method can quantitatively characterize the degree of connectivity between oil and water wells.

[0131] Example 2:

[0132] After polymer injection in well X8-4-SP331, the production rate decreased significantly, with a production intensity of 2.3. (See...) Figure 6 Calculate the connectivity factor values ​​of the four surrounding wells, see Figure 7 Among the four wells, the connectivity factor of well X8-4-SP233 was 0.35, and that of well X9-D1-P233 was 0.43. These high connectivity factors indicate a high degree of connectivity with the production wells. Therefore, pre-injection cultivation was conducted on these two injection wells to increase the injection rate by 30-35 m³. 3 The fracturing effect was good, with a significant increase in fluid production. Figure 8 Initially, the daily increase in liquid production was 17.6 tons, and the daily increase in oil production was 4.3 tons. The cumulative increase in oil production is expected to be 400 tons, with a dry powder surplus of 108 tons, generating economic benefits of 2.009 million yuan.

[0133] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A quantitative evaluation method for the connectivity between river sand oil-water wells based on connectivity factors, characterized in that... Includes the following steps: S1. Obtain the sedimentary unit boundary database and well logging curve data, and establish a layered database of sub-layers; S2. Calculate the blended average permeability, connectivity coefficient, and gradient of oil and water wells; S3. Normalize the harmonic average permeability, connectivity coefficient, and gradient to obtain the relative permeability, relative connectivity coefficient, and relative gradient; S4. Determine the influence weights of the normalized relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity, and establish the connectivity factor formula. S5. Determine the connectivity factor based on the connectivity factor formula and evaluate the connectivity of oil and water wells.

2. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 1, characterized in that: In step S2, the harmonic average permeability is: In the formula: To harmonize the average permeability, ×10 -3 μm 2 Ko represents the well permeability, multiplied by 10. -3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 ; The connectivity coefficient of oil and water wells is: In the formula: Ltxs is the connectivity coefficient, dimensionless; Ho is the formation thickness of the oil well unit, m; Hw is the formation thickness of the water well unit, m; h is the effective thickness; ho is the effective thickness of the oil well, m; hw is the effective thickness of the water well, m; hmin is the smaller effective thickness between the oil and water wells, i.e., hmin=min(ho,hw); The difference between oil and water well grades is: In the formula: JC represents the permeability difference between oil and water wells; Ko represents the permeability of the oil well, ×10 -3 μm 2 Kw represents the water well permeability, ×10⁻¹⁰ -3 μm 2 .

3. The method for quantitatively evaluating the connectivity between river sand oil-water wells based on connectivity factors according to claim 2, characterized in that: In step S3, the normalized relative permeability is: Where: K 01 K represents relative permeability, dimensionless; max The maximum permeability within the study unit layer of the study area, ×10 -3 μm 2 ; To harmonize the average permeability, ×10 -3 μm 2 ; The normalized relative range is: In the formula: JC 01 JC represents the relative grade difference, dimensionless; JC represents the permeability grade difference between oil and water wells; JC max This represents the maximum level difference within the research unit layer of the study area.

4. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 3, characterized in that: The normalized relative connectivity coefficient in step S3 is the same as the oil-water well connectivity coefficient in step S2.

5. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 4, characterized in that: In step S4, the weighting coefficients for the influence of relative permeability, relative connectivity coefficient, and relative gradient on the degree of connectivity are as follows: In the formula: W1 is the weighting coefficient of the influence of relative permeability on connectivity; W2 is the weighting coefficient of the influence of relative connectivity coefficient on connectivity; W3 is the weighting coefficient of the influence of relative gradient on connectivity; R y,x1(x2,x3) R is the partial correlation coefficient between relative permeability and the change in water saturation of oil wells. y,x2(x1,x3) R is the partial correlation coefficient between the relative connectivity coefficient and the change in water saturation of the oil well; y,x3(x2,x1) This is the partial correlation coefficient between the relative grade difference and the change in water saturation of the oil well.

6. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 5, characterized in that: When calculating the partial correlation coefficient, relative permeability, connectivity coefficient, and relative gradient are set as independent variables x1, x2, and x3, respectively, and the change in water saturation of the oil well is the dependent variable y. The partial correlation coefficient R between relative permeability x1 and the change in water saturation of the oil well y is then calculated. y,x1(x2,x3) for: The partial correlation coefficient R between the relative connectivity coefficient and the change in water saturation of oil wells y,x2(x1,x3) for: The partial correlation coefficient R between relative grade difference and change in water saturation of oil wells y,x3(x2,x1) for: In the formula: R y,x1(x2,x3) Let x1 and y be the partial correlation coefficients when x2 and x3 remain constant. R y,x2(x1,x3) Let x2 be the partial correlation coefficient between x and y when x1 and x3 remain constant. R y,x3(x2,x1) Let x2 and x1 remain constant, and let x3 be the partial correlation coefficient between x3 and y. r y,x1 Let r be the correlation coefficient between y and x1. y,x2 Let r be the correlation coefficient between y and x². y,x3 The correlation coefficient between y and x3; r x2,x1 Let r be the correlation coefficient between x2 and x1. x1,x3 Let r be the correlation coefficient between x1 and x3. x2,x3 Let x2 and x3 be the correlation coefficients.

7. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 6, characterized in that: In formulas (7-1), (7-2), and (7-3), In the formula: n is the number of samples, and i is the current sample; It is the average of n x1 values; It is the average of n x2; It is the average of n x3; Let y be the average of n y values.

8. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 7, characterized in that: The connectivity factor Ltyz in step S5 is: Ltyz=W1·K 01 +W2·Ltxs+W3·JC 01 (9)。 9. The quantitative evaluation method for the degree of connectivity between river sand oil-water wells based on connectivity factors according to claim 8, characterized in that: The larger the connectivity factor in step S5, the higher the degree of connectivity between the oil and water wells.

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