Recovery factor prediction method, device and medium for offshore thin interbedded sandstone reservoirs

Abstract

The present invention relates to a method, device, medium and equipment for predicting the recovery rate of an offshore thin interbedded sandstone reservoir. The method comprises the following steps: determining whether the offshore sandstone reservoir is a thin interbedded sandstone reservoir; determining an interlayer interference correction coefficient E of the offshore sandstone reservoir based on the Lorentz coefficient; i ; Based on the density of small fault blocks and well pattern density, determine the fault block influence coefficient E f ; Determine the sand body correction coefficient E based on the well spacing and the median sand body size s ; Based on the interlayer interference correction coefficient E of offshore sandstone reservoirs i , block influence coefficient E f , sand body correction coefficient E s , determine the comprehensive calculation formula of recovery factor E R The prediction method of the present invention avoids the uncertainty of reservoir numerical simulation prediction caused by unclear reservoir knowledge, as well as the problems of complex numerical simulation process, high cost, long cycle, etc.

Description

Technical Field

[0001] The present invention relates to a method, device, medium and equipment for predicting the recovery rate of an offshore thin interbedded sandstone reservoir, and belongs to the technical field of oil development. Background Art

[0002] Offshore oil and gas field development is costly and risky, necessitating a scientifically sound development plan. The accuracy of recovery factor prediction methods directly impacts the feasibility of these plans. Conventional recovery factor prediction methods are less applicable due to the diverse distribution of sand bodies, significant interlayer interference, and the development of faults in some areas of offshore thin interbedded sandstone reservoirs. These factors should be fully considered when evaluating recovery factors to improve prediction accuracy. Currently, recovery factor prediction methods primarily rely on analogy and industry-standard formulas. Analogy methods primarily compare the physical and geological characteristics of similar reservoirs, which can be subject to uncertainty. However, according to industry standards, the Yu Qitai-type prediction method shows a rapid increase in recovery rate during the sparse well pattern phase and a slow increase in recovery rate during the dense well pattern phase, making it difficult to demonstrate the effectiveness of well pattern densification. Chen Yuanqian's prediction method shows a linear relationship between recovery rate and well pattern density, making it applicable only to a certain stage. Furthermore, Geng Li, based on CNPC's empirical formula and the Daqing dense well pattern development test, quantified the effect of sand body size on water flooding control. However, this method did not analyze the impact of interlayer interference and faults on recovery rate, nor could it determine the improvement in recovery rate due to improved heterogeneity. This poses a challenge to the efficient formulation of densification plans. Therefore, it is necessary to establish a comprehensive recovery rate prediction model based on dynamic and static data and seepage theory to accurately characterize the impact of sand body distribution, heterogeneity, and faults on recovery rate, thereby forming a set of recovery rate prediction methods for offshore thin interbedded sandstone reservoirs to provide guidance for initial recovery rate calibration and comprehensive adjustment of oilfields. Summary of the Invention

[0003] In response to the above technical problems, the present invention provides a method, device, medium and equipment for predicting the recovery rate of offshore thin interbedded sandstone reservoirs. The method can adjust the fitting coefficient in real time according to actual production data. It is simple, convenient, fast and has high prediction accuracy.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir comprises the following steps:

[0006] Determine whether an offshore sandstone reservoir is a thin interbedded sandstone reservoir;

[0007] Determination of interlayer interference correction coefficient E in offshore sandstone reservoirs based on Lorentz coefficient i ;

[0008] Determine the fault block influence coefficient E based on the small fault block density and well pattern density. f ;

[0009] Determine the sand body correction factor E based on the well spacing and the median sand body size s ;

[0010] Based on the interlayer interference correction coefficient E of offshore sandstone reservoirs i , block influence coefficient E f , sand body correction coefficient E s , determine the comprehensive calculation formula of recovery factor E R .

[0011] The recovery factor prediction method preferably determines whether the offshore sandstone reservoir is a thin interbedded sandstone reservoir as follows:

[0012] When the effective thickness of 70% of the small layers is less than 5m and the average effective thickness of the reservoir is less than 5m, it can be regarded as a thin interbedded sandstone reservoir.

[0013] The recovery factor prediction method preferably determines the interlayer interference correction coefficient E of the offshore sandstone reservoir. i , as follows:

[0014] E i =(80.73L 2 +27.23L) / 100

[0015] Where L is the Lorentz coefficient.

[0016] In the recovery factor prediction method, preferably, the specific process of obtaining the interlayer interference correction coefficient Ei of the offshore sandstone reservoir is as follows:

[0017] First, obtain the thickness of the small layer h i , small layer permeability K i , small layer viscosity μ i , and calculate the laminar flow coefficient K i / μ i ;

[0018] The laminar flow coefficient K i / μ i Sort in ascending order;

[0019] Calculate the thickness of the small layer h i Cumulative percentage H c , flow coefficient K i / μ i Cumulative percentage F c ,in:

[0020]

[0021]

[0022] Where n represents the total number of small layers, h n represents the effective thickness of the i-th layer, H c Indicates the percentage of cumulative thickness when accumulated to the cth sub-layer;

[0023] Draw the Lorentz curve of the flow coefficient, with H c 、H c Draw curve 1 as the horizontal and vertical coordinates, with (H c 、F c ) is used as the horizontal and vertical coordinates to draw curve 2. The area enclosed by curve 1 and curve 2 is recorded as S1, and the area enclosed by curve 1 and the coordinate axis xy is recorded as S2. Calculate the Lorentz coefficient L, L = S1 / S2;

[0024] Calculate the inter-layer interference correction coefficient E i :

[0025] E i =(80.73L 2 +27.23L) / 100.

[0026] The recovery factor prediction method preferably determines the block influence coefficient E f ; The details are as follows:

[0027]

[0028] Where n is the density of small fault blocks and s is the well pattern density.

[0029] The recovery factor prediction method preferably determines the sand body correction coefficient E s , as follows:

[0030]

[0031] Where, d is the well spacing; d m is the median value of sand body size; a, b, c are curve morphology parameters respectively.

[0032] The recovery factor prediction method is preferably based on the interlayer interference correction coefficient E of the offshore sandstone reservoir. i , block influence coefficient E f , sand body correction coefficient E s , determine the comprehensive calculation formula of recovery factor E R , as follows:

[0033] E R =E D ·(1-E i )·(1-E f )·E S ·e -a / s

[0034] Where, E D is the oil displacement efficiency, e -a / s is the sweep coefficient, s is the well controlled area, and a is the well network index.

[0035] A second aspect of the present invention provides a recovery factor prediction device for an offshore thin interbedded sandstone reservoir, comprising:

[0036] The first processing unit is used to determine whether the offshore sandstone reservoir is a thin interbedded sandstone reservoir;

[0037] The second processing unit is used to determine the interlayer interference correction coefficient E of the offshore sandstone reservoir based on the Lorentz coefficient. i ;

[0038] The third processing unit is used to determine the fault block influence coefficient E based on the small fault block density and well pattern density. f ;

[0039] The fourth processing unit is used to determine the sand body correction coefficient E based on the well spacing and the median value of the sand body size s ;

[0040] The fifth processing unit is used to correct the interlayer interference coefficient E based on the offshore sandstone reservoir i , block influence coefficient E f , sand body correction coefficient E s , determine the comprehensive calculation formula of recovery factor E R .

[0041] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir.

[0042] The fourth aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for predicting the recovery rate of offshore thin interbedded sandstone reservoirs are implemented.

[0043] The present invention has the following advantages due to the adoption of the above technical solution:

[0044] 1. When calibrating the recovery factor of a newly discovered oil field, the method of the present invention can be combined with geological reservoir data to formulate a combined / separate recovery strategy and calculate the recovery factor under different well pattern densities.

[0045] 2. When predicting the recovery rate in the comprehensive adjustment stage, the present invention can combine historical recovery rate data points to calibrate model parameters, further improve model prediction accuracy, and calculate the recovery rate value improved by the later stratum adjustment.

[0046] 3. The prediction method of the present invention avoids the uncertainty brought about by unclear understanding of the reservoir in numerical simulation prediction of oil reservoirs, as well as the problems of complex numerical simulation process, high cost and long cycle.

[0047] 4. The prediction method of the present invention can adjust the fitting coefficient in real time according to the actual production data update, which is simple, convenient, fast and has high prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is the Lorentz curve of the fluidity-thickness of the reservoir P layer;

[0049] Figure 2 is the curve of the change of fault block influence coefficient;

[0050] Figure 3 This is the analysis diagram of the fitting effect of the historical recovery factor data of reservoir P;

[0051] Figure 4 This is a comparison chart of the prediction effect of closing the high aquifer thickness in reservoir P. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by ordinary persons in this field based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0053] This invention addresses the problem of poor applicability of conventional methods for recovery rate prediction in offshore thin interbedded sandstone reservoirs, which have diverse sandbody distributions, prominent interlayer interference, and developed faults in some blocks. Therefore, a method for predicting the recovery rate of offshore thin interbedded sandstone reservoirs is proposed. When calibrating the recovery rate of newly discovered oil fields, this method can combine geological reservoir data to formulate combined / separate production strategies and calculate the recovery rate under different well pattern densities. When predicting the recovery rate during the comprehensive adjustment phase, historical recovery rate data points can be combined to calibrate model parameters, further improving model prediction accuracy and calculating the recovery rate value improved by subsequent stratum adjustments.

[0054] The technical solution of the present invention is explained in detail below in conjunction with specific implementation methods.

[0055] The method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir provided by the present invention comprises the following steps:

[0056] 1. Determine whether the sandstone reservoir is a thin interbedded sandstone reservoir: For a sandstone reservoir, if 70% of the small layers have an effective thickness of less than 5m and the average effective reservoir thickness is less than 5m, it can be regarded as a thin interbedded sandstone reservoir.

[0057] 2. Determine the reservoir interlayer interference correction coefficient E i , which is the recovery loss caused by interlayer interference.

[0058] (1) First obtain the thickness of the small layer h i , small layer permeability K i , small layer viscosity μ i , and calculate the laminar flow coefficient K i / μ i

[0059] (2) The laminar flow coefficient K i / μ i Sort in ascending order;

[0060] (3) Calculate the thickness of the small layer h i Cumulative percentage H c , flow coefficient K i / μ i Cumulative percentage F c ,in:

[0061]

[0062]

[0063] Where n represents the total number of small layers, h n represents the effective thickness of the i-th layer, H c Indicates the cumulative thickness ratio when accumulated to the cth sub-layer.

[0064] (4) Draw the Lorentz curve of the flow coefficient, (H c 、H c ) as the horizontal and vertical coordinates to draw curve 1, with (H c 、F c ) are used as the horizontal and vertical coordinates to draw curve 2. The area enclosed by curve 1 and curve 2 is recorded as S1, and the area enclosed by curve 1 and the coordinate axis xy is recorded as S2. Calculate the Lorentz coefficient L, L = S1 / S2.

[0065] (5) Calculate the inter-layer interference correction coefficient E i

[0066] E i =(80.73L 2 +27.23L) / 100

[0067] 3. Determine the block influence coefficient E f , satisfying the following formula:

[0068]

[0069] Where n is the density of small fault blocks, that is, the number of small fault blocks per unit area, per km 2 ; s is the well pattern density, wells / km 2 .

[0070] 4. Determine the sand body correction coefficient E s , which characterizes the effect of sand body size on recovery efficiency.

[0071] The influence of sand body on water flooding control satisfies the following formula:

[0072]

[0073] Where, d is the well spacing, m; d m is the median sandbody size, m; a, b, and c are curve morphology parameters. The median sandbody sizes and parameter values for different sandbody types are shown in Table 1. For sandbodies of different median sizes, interpolation calculations were performed using the data in Table 1.

[0074] Table 1 Sand body median size and parameter values

[0075]

[0076] 5. Determine the comprehensive calculation formula for recovery factor ER:

[0077] E R =E D ·(1-E i )·(1-E f )·E S ·e -a / s

[0078] Where, E D is the oil displacement efficiency, e -a / s is the sweep coefficient, s is the well controlled area, ha / well.

[0079] For different fluidities, the calculation method is the empirical formula in industry standard 3, see Table 2.

[0080] Table 2 Comprehensive calculation formula parameter expression

[0081]

[0082] 6. The recovery rate is predicted based on the comprehensive calculation formula in step (5), which is mainly divided into the following two cases.

[0083] (1) For new oil fields, the median size of the known sand body d m .

[0084] a. After obtaining the parameters in steps 1 to 4, the expression of block recovery factor with well pattern density satisfies:

[0085] E R =E D ·(1-E i )·(1-E f )·E S ·e -a / s

[0086] b. In the later stage of stratum adjustment, close the high aquifer and recalculate the interlayer interference correction coefficient E' in the production layer according to the method in step 2. i , the block recovery calculation formula becomes:

[0087] E' R =E D ·(1-E' i )·(1-E f )·E S ·e -a / s

[0088] Increased recovery factor ΔE due to formation adjustment R satisfy:

[0089] ΔE R =E' R -E R

[0090] (2) For developed oil fields, the historical recovery rate of the block is known, but the size of the sand body is unknown.

[0091] a. Use E R =E D ·(1-E i )·(1-E f )·E S ·e -a / s Fit known recovery factor data points to obtain the median size d of the sand body m and morphological parameters a, b, and c.

[0092] b. After obtaining the sand body size and morphological parameters in the previous step, a comprehensive recovery rate prediction model is established.

[0093] c. Using the model from the previous step, calculate the recovery factor E when the well pattern density is s1 R1 .

[0094]

[0095] d. Using the model from the previous step, calculate the interlayer interference coefficient E after the layer system is adjusted (the high aquifer is closed) according to step 2. i2 Calculate the recovery factor E at this time R2 .

[0096]

[0097] e. Calculate the increase in recovery factor ΔE due to well pattern densification and stratum adjustment R2

[0098] ΔE R2 =E R -E R2 .

[0099] The technical solution of the present invention is described in detail below with reference to specific examples.

[0100] The density of P small fault blocks in known sandstone reservoirs is 1 per km. 2 The reservoir has 30 vertical sub-layers with an average thickness of 4.02 m and a thickness-weighted mobility of 57.64 mD / (mP·s). See the Sub-Layer Parameters Statistics Table for details. Early development employed multi-layer commingled production, and in later stages, five high-aquifer layers were closed, improving vertical sweep.

[0101] Small layer parameter statistics table

[0102]

[0103] According to step 1: the thickness of the P22 layer in the reservoir is less than 5m, accounting for 73.3%, which is a thin interbedded sandstone reservoir.

[0104] According to steps 2.1 to 2.4, the reservoir Lorenz curve is drawn, as shown in Figure 1 , and the Lorentz coefficient L = 0.16 is calculated.

[0105] According to step 2.5, substitute L = 0.16 into the inter-layer interference correction coefficient expression E i =(80.73L 2 +27.23L) / 100: Calculate the inter-layer interference correction coefficient E i It is 6.4%.

[0106] According to step 3, the density of small fault blocks n is 1 / km 2 , block interference coefficient E f The changing trend of well pattern density is as follows Figure 2 , and satisfy:

[0107]

[0108] According to step 4, determine the sand body correction coefficient E s , which characterizes the effect of sand body size on recovery efficiency.

[0109] The influence of sand body on water flooding control satisfies the following formula:

[0110]

[0111] Where, d is the well spacing, m; d m is the median sandbody size, m; a, b, and c are curve morphology parameters. The median sandbody sizes and parameter values for different sandbody types are shown in Table 1. For sandbodies of different median sizes, interpolation calculations were performed using the data in Table 1.

[0112] Table 1 Sand body median size and parameter values

[0113]

[0114] According to steps 2 to 5, and using the comprehensive calculation formula E in step 5 R Perform optimal fitting on the early recovery factor data points to determine the comprehensive calculation formula E for recovery factor R :

[0115] E R =E D ·(1-E i )·(1-E f )·E S ·e -a / s

[0116] Where, E D is the oil displacement efficiency, e -a / s is the sweep coefficient, s is the well controlled area, ha / well.

[0117] For different fluidities, the calculation method is the empirical formula in industry standard 3, see Table 2.

[0118] Table 2 Comprehensive calculation formula parameter expression

[0119]

[0120] The fitting situation is as follows Figure 3 , it can be seen that the fitting degree of this model is higher than 90%, and the effect is much better than the fitting effect of the empirical formula in the industry standard, which shows that this method has higher accuracy and stronger adaptability in characterizing the recovery rate of thin interbedded reservoirs. At the same time, the median size of the P sand body in the reservoir d m It is 224m, and the coefficients a, b, and c are 3.44, 0.648, and 65, respectively, belonging to the striped channel sand body model.

[0121] According to step 6, this belongs to the second case in step 6. After closing the five high-water-bearing layers in the late development, the Lorenz curve of the reservoir P layer mobility-thickness was redrawn according to step 2, and the interlayer interference coefficient E was recalculated. i2 , calculate E i2 It drops to 2.5%, which indicates that the recovery factor prediction curve is lifted as a whole based on step 5. Figure 4The adjusted prediction model is used to carry out subsequent recovery rate prediction and compared with the actual recovery rate data points, such as Figure 4 .exist Figure 4 After adjustment, under two different well pattern densities, the actual recovery factor data were 36.85% and 38.34% respectively. The recovery factors predicted by this model were 36.43% and 37.60% respectively, which has a high accuracy.

[0122] A second aspect of the present invention provides a recovery factor prediction device for an offshore thin interbedded sandstone reservoir, comprising:

[0123] The first processing unit is used to determine whether the offshore sandstone reservoir is a thin interbedded sandstone reservoir;

[0124] The second processing unit is used to determine the interlayer interference correction coefficient E of the offshore sandstone reservoir based on the Lorentz coefficient. i ;

[0125] The third processing unit is used to determine the fault block influence coefficient E based on the small fault block density and well pattern density. f ;

[0126] The fourth processing unit is used to determine the sand body correction coefficient E based on the well spacing and the median value of the sand body size s ;

[0127] The fifth processing unit is used to correct the interlayer interference coefficient E based on the offshore sandstone reservoir i , block influence coefficient E f , sand body correction coefficient E s , determine the comprehensive calculation formula of recovery factor E R .

[0128] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir.

[0129] The fourth aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned method for predicting the recovery rate of offshore thin interbedded sandstone reservoirs are implemented.

[0130] The present invention is described in terms of flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to specific embodiments. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir, characterized in that: The steps include: Determine whether an offshore sandstone reservoir is a thin interbedded sandstone reservoir; Determination of interlayer interference correction coefficients in offshore sandstone reservoirs based on the Lorentz coefficient ; Determine the fault block influence coefficient based on the small fault block density and well pattern density ; Determine the sand body correction factor based on the well spacing and the median sand body size ; Interlayer interference correction coefficient based on offshore sandstone reservoirs , block influence coefficient , sand body correction coefficient , determine the comprehensive calculation formula for recovery factor ; Interlayer interference correction coefficient for offshore sandstone reservoirs The specific process of obtaining is as follows: First obtain the thickness of the small layer , small layer permeability , small layer viscosity , and calculate the laminar flow coefficient ; Laminar flow coefficient Sort in order from smallest to largest; Calculate the thickness of the small layer Cumulative percentage , flow coefficient Cumulative percentage ; Draw the Lorentz curve of the flow coefficient to H c 、 H c Draw curve 1 for the horizontal and vertical coordinates, with H c 、 F c Draw curve 2 for the horizontal and vertical coordinates. The area enclosed by curve 1 and curve 2 is recorded as S1. The area enclosed by curve 1 and the coordinate axis xy is recorded as S2. Calculate the Lorentz coefficient L , L=S 1 / S 2; Calculate inter-layer interference correction coefficient .

2. The method for predicting oil recovery according to claim 1, wherein: To determine whether an offshore sandstone reservoir is a thin interbedded sandstone reservoir, the details are as follows: When the effective thickness of 70% of the small layers is less than 5m and the average effective thickness of the reservoir is less than 5m, it can be regarded as a thin interbedded sandstone reservoir.

3. The method for predicting oil recovery according to claim 1, wherein: Determining the Interlayer Interference Correction Factor for Offshore Sandstone Reservoirs , as follows: Where, L is the Lorentz coefficient 。 4. The method for predicting oil recovery according to claim 3, wherein: Interlayer interference correction coefficient for offshore sandstone reservoirs The specific process of obtaining: Where n represents the total number of small layers, h i represents the effective thickness of the i-th layer, H c Indicates the percentage of cumulative thickness when accumulated to the cth sub-layer; Calculate inter-layer interference correction coefficient : 。 5. The method for predicting oil recovery according to claim 1, wherein: Determine the block influence coefficient ; The details are as follows: Where n is the density of small fault blocks and s is the well pattern density.

6. The method for predicting oil recovery according to claim 1, wherein: Determine the sand body correction factor , as follows: Where, d is the well spacing; d m is the median value of sand body size; a, b, c are curve morphology parameters respectively.

7. The method for predicting oil recovery according to claim 1, wherein: Interlayer interference correction coefficient based on offshore sandstone reservoirs , block influence coefficient , sand body correction coefficient , determine the comprehensive calculation formula for recovery factor , as follows: Where, E D is the oil displacement efficiency, is the sweep coefficient, s is the well control area, a is the well network index.

8. A prediction device for the method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir according to claim 1, characterized in that: include: The first processing unit is used to determine whether the offshore sandstone reservoir is a thin interbedded sandstone reservoir; The second processing unit is used to determine the interlayer interference correction coefficient of the offshore sandstone reservoir based on the Lorentz coefficient ; The third processing unit is used to determine the fault block influence coefficient based on the small fault block density and well pattern density. ; The fourth processing unit is used to determine the sand body correction coefficient based on the well spacing and the median value of the sand body size ; The fifth processing unit is used to correct the interference coefficient between layers of offshore sandstone reservoirs , block influence coefficient , sand body correction coefficient , determine the comprehensive calculation formula for recovery factor .

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for predicting the recovery rate of an offshore thin interbedded sandstone reservoir according to any one of claims 1 to 7 are implemented.

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