Method, device and equipment for determining liquid extraction parameters of oil field in medium-high water cut period and medium

Abstract

The invention discloses a method, a device and equipment for determining liquid extraction parameters of an oil field in a medium-high water cut period and a medium. The method comprises the following steps: determining candidate liquid extraction influence parameters of a historical liquid extraction well of a target oil reservoir; determining a plurality of main control parameters from the candidate extraction liquid influence parameters according to the correlation degree between the candidate extraction liquid influence parameters and the post-extraction water content rise amplitude parameters; establishing a target reservoir numerical model according to the target reservoir characteristics, and determining a plurality of numerical model simulation results according to the main control parameters and the target reservoir numerical model; performing response surface regression according to a numerical model simulation result, and constructing a response surface model between the response variable and the main control parameter; and liquid extraction parameters of the target oil reservoir are determined according to the response surface model. According to the scheme, the main control parameter is screened out from the liquid extraction parameters of the historical liquid extraction well of the target oil reservoir, and the response surface model is determined in combination with the target oil reservoir numerical model, so that the liquid extraction parameters are rapidly and accurately determined.

Description

Technical Field

[0001] This invention relates to the field of reservoir engineering technology, and in particular to a method, apparatus, equipment and medium for determining fluid extraction parameters in oilfields with medium to high water cut. Background Technology

[0002] Currently, complex oil reservoirs overseas generally exhibit strong heterogeneity, leading to significant differences in the development effects of individual oil wells. This is especially true after the reservoir enters the medium-to-high water-cut development stage, where maintaining stable oil production becomes significantly more difficult, and implementing large-scale production enhancement measures carries high risks. Increasing the fluid discharge rate of oil wells can accelerate oilfield development, delay well production decline, and improve the economic recovery rate of oil wells.

[0003] However, in some oil wells, water cut increases rapidly after fluid extraction, while in others, the effect is minimal or even worse. Therefore, the key to oil wells entering the medium-to-high water-cut stage lies in making reasonable decisions regarding fluid extraction parameters. However, using existing reservoir numerical simulation technology to perform historical fitting updates on the entire oilfield reservoir model and simulate and calculate the fluid extraction parameters for medium-to-high water-cut wells is time-consuming, labor-intensive, and computationally expensive, hindering its widespread application. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for determining fluid extraction parameters in oilfields with medium to high water cut, in order to solve the problem of high calculation costs in traditional full reservoir numerical simulation methods, and to achieve rapid and accurate optimization of oil well fluid extraction parameters.

[0005] According to one aspect of the present invention, a method for determining extraction parameters in oilfields during the medium-to-high water-cut stage is provided, the method comprising:

[0006] Determine candidate parameters affecting fluid extraction from historical fluid extraction wells in the target reservoir;

[0007] Based on the correlation between the candidate extraction influence parameters and the parameters of the increase in water content after extraction, several main control parameters are determined from the candidate extraction influence parameters;

[0008] A numerical model of the target reservoir is established based on its characteristics, and simulation results of multiple numerical models are determined based on the master control parameters and the numerical model of the target reservoir.

[0009] Response surface regression is performed based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameters; where the response variable is the increase in the extraction degree.

[0010] The extraction parameters of the target reservoir were determined based on the response surface methodology.

[0011] According to another aspect of the present invention, an apparatus for determining extraction parameters in oilfields with medium to high water cut is provided, the apparatus comprising:

[0012] The first determining module is used to determine the candidate fluid extraction influence parameters of historical fluid extraction wells in the target reservoir;

[0013] The second determining module is used to determine multiple main control parameters from the candidate extraction influence parameters based on the correlation between the candidate extraction influence parameters and the parameters of the increase in water content after extraction.

[0014] The third determination module is used to establish a numerical model of the target reservoir based on the characteristics of the target reservoir, and to determine the simulation results of multiple numerical models based on the master control parameters and the numerical model of the target reservoir.

[0015] The response surface model construction module is used to perform response surface regression based on the numerical model simulation results, and to construct a response surface model between the response variable and the master control parameters; where the response variable is the increase in the extraction degree.

[0016] The extraction parameter determination module is used to determine the extraction parameters of the target reservoir based on the response surface model.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory that is communicatively connected to at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the method for determining the extraction parameters of oilfields in the medium-to-high water-cut stage according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute a method for determining fluid extraction parameters in a medium-to-high water-cut oilfield according to any embodiment of the present invention.

[0022] The technical solution of this invention, by determining candidate extraction influencing parameters of historical extraction wells in the target reservoir, achieves evaluation using extraction parameters from historical extraction wells in the target reservoir, clarifying the reasons for differences in extraction performance. Based on the correlation between candidate extraction influencing parameters and parameters related to the increase in water cut after extraction, multiple main control parameters are determined from the candidate extraction influencing parameters. This facilitates the rapid and accurate determination of reasonable extraction parameters for extraction wells in the target reservoir, avoiding the impact of extraction parameters with low correlation on the predicted extraction effect, thereby saving time and costs. A numerical model of the target reservoir is established based on its characteristics, and the main control parameters and the target reservoir... Numerical models have determined the simulation results of multiple models, enabling the identification of development characteristics of the target reservoir and providing data support for the subsequent screening of potential wells for enhanced recovery. Response surface regression has been performed based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameters. The response variable is the increase in recovery rate. Based on the response surface model, the enhanced recovery parameters for the target reservoir have been determined. This allows for the determination of the actual values ​​of multiple main control parameters using statistical analysis software, maximizing the enhanced recovery rate of the target reservoir and improving the efficiency of implementing enhanced recovery measures. This lays the foundation for oilfield production planning and measure deployment.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for determining fluid extraction parameters in a medium-to-high water-cut oilfield, provided in Embodiment 1 of the present invention.

[0026] Figure 2 This is a reservoir dimensionless production index curve of a target reservoir provided in Embodiment 1 of the present invention;

[0027] Figure 3 This is a surface diagram illustrating the influence of different main control parameters on the recovery rate of a target reservoir, provided in Embodiment 1 of the present invention.

[0028] Figure 4 A surface diagram illustrating the influence of different main control parameters on the target reservoir's recovery rate, as provided in Embodiment 1 of the present invention.

[0029] Figure 5 This is a schematic diagram of a device for determining fluid extraction parameters in a medium-to-high water-cut oilfield, provided in Embodiment 2 of the present invention.

[0030] Figure 6 This is a schematic diagram of the electronic device used in an embodiment of the present invention to illustrate a method for determining extraction parameters in oilfields during periods of high water cut. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0033] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0034] Example 1

[0035] Figure 1 This is a flowchart illustrating a method for determining fluid extraction parameters in a medium-to-high water-cut oilfield, as provided in Embodiment 1 of the present invention. This embodiment of the invention is applicable to determining the fluid extraction parameters of any fluid extraction well in a medium-to-high water-cut oilfield. The method can be executed by a device for determining fluid extraction parameters in a medium-to-high water-cut oilfield. This device can be implemented in hardware and / or software and can be configured in an electronic device that implements the method for determining fluid extraction parameters in a medium-to-high water-cut oilfield. Figure 1 As shown, the method includes:

[0036] S101. Determine the candidate fluid extraction influence parameters for historical fluid extraction wells in the target reservoir.

[0037] In this embodiment of the invention, the target reservoir refers to any reservoir in the actual oilfield used for research that has entered the medium-to-high water-cut stage. Historical fluid extraction wells refer to wells in the target reservoir where a series of measures were taken to increase fluid extraction in order to improve production. Candidate fluid extraction influence parameters refer to a series of parameters that can affect the production of historical fluid extraction wells in the target reservoir.

[0038] Candidate parameters affecting fluid extraction include geological characteristic parameters and development dynamic data. Geological characteristic parameters include at least one of the following: reservoir thickness, oil-to-thickness ratio, permeability, permeability gradient, oil-water viscosity ratio, and water-avoidance thickness. Development dynamic data includes the magnitude and timing of fluid extraction. Geological characteristic parameters are static parameters describing the geology of the extraction well. Reservoir thickness refers to the thickness of the oil-producing portion of the reservoir in the extraction well. Oil-to-thickness ratio is the ratio of the oil-producing layer thickness to the total thickness in the extraction well. Permeability quantifies the capacity of the reservoir rock to allow oil and gas flow in the extraction well. Permeability gradient is the ratio of the maximum to the minimum permeability in the extraction well, characterizing the heterogeneity of the reservoir. Oil-water viscosity ratio is the ratio of the viscosity of crude oil in the extraction well to the viscosity of formation water. Water-avoidance thickness is the distance maintained between the oil layer and the bottom water layer in the extraction well to prevent premature water intrusion into the oil-producing layer and its impact on well production.

[0039] Specifically, any reservoir in the oilfield that has entered the medium-to-high water-cut development stage is selected as the target reservoir for studying reasonable parameters affecting fluid extraction. Actual measurements are conducted on historical fluid extraction wells in the target reservoir to obtain geological characteristic parameters, including at least one of the following: oil layer thickness, oil-to-thickness ratio, permeability, permeability gradient, oil-water viscosity ratio, and water-avoidance thickness. Simultaneously, development dynamic data of the fluid extraction wells are determined based on their development status, including the extraction amplitude and timing. Furthermore, the geological characteristic parameters and development dynamic data of the historical fluid extraction wells in the target reservoir are used as candidate parameters affecting fluid extraction.

[0040] As an optional step, before determining the candidate fluid extraction influence parameters for historical fluid extraction wells in the target reservoir, the method further includes the following steps A1-A2:

[0041] Step A1: Determine the dimensionless production index curve of the target reservoir.

[0042] Step A2: Determine the reservoir fluid supply capacity of the target reservoir based on the dimensionless fluid production index curve, and determine whether to increase fluid supply based on the reservoir fluid supply capacity.

[0043] In this embodiment of the invention, the dimensionless production index curve of the reservoir refers to a curve representing the change of the dimensionless production index of the reservoir with the water cut of the reservoir. The dimensionless production index of the reservoir is the ratio between the production index when the reservoir has water and the production index when the water cut is zero, representing the production capacity of the oil well under different water cuts. The reservoir's fluid supply capacity refers to the amount of fluid that the reservoir can supply to the oil well per unit time under certain conditions.

[0044] Specifically, actual measurements are taken of the target reservoir to obtain its dimensionless production index curve. Based on the obtained curve, the change in the dimensionless production index of the target reservoir at medium to high water cuts is determined to assess the reservoir's fluid supply capacity and thus determine whether the target reservoir is suitable for fluid extraction and enhancement development. If the target reservoir is suitable for fluid extraction and enhancement development, the fluid extraction parameters are further determined. If the target reservoir is not suitable for fluid extraction and enhancement development, other measures are selected to increase the oil production of the target reservoir.

[0045] For example, the dimensionless production index curve of a high water-cut sandstone reservoir is as follows: Figure 2 As shown, after the reservoir enters the medium-high water cut stage, that is, after the water cut of the reservoir is greater than 0.6, the dimensionless production index of the reservoir increases rapidly, indicating that the reservoir has good reservoir production capacity and is suitable for liquid extraction and amplification development.

[0046] As an option, determining the dimensionless production index curve of the target reservoir includes:

[0047] Based on the oil-water relative permeability curves, crude oil viscosity, and water viscosity data of the target reservoir, the dimensionless production index curve of the reservoir is determined.

[0048] In this embodiment of the invention, the oil-water relative permeability curve refers to the curve describing the ratio of effective permeability to absolute permeability of the oil and water phases in the target reservoir as a function of water saturation, reflecting the flow capacity of the oil and water phases. Crude oil viscosity refers to the magnitude of internal friction when crude oil flows in the target reservoir, reflecting the fluidity of the crude oil. Water viscosity refers to the magnitude of internal friction when water flows in the target reservoir, reflecting the fluidity of the water.

[0049] Specifically, actual measurements are taken of the target reservoir to obtain the relative permeability curves when the oil and water phases coexist. Simultaneously, the viscosity data of the crude oil and water in the target reservoir are measured. By combining the relative permeability data of oil and water, the viscosity data of crude oil and water in the target reservoir, the curve of the dimensionless production index of the target reservoir as a function of water cut is determined, and this curve is used as the dimensionless production index curve of the target reservoir.

[0050] S102. Based on the correlation between the candidate extraction influence parameters and the parameters of the increase in water content after extraction, determine multiple main control parameters from the candidate extraction influence parameters.

[0051] In this embodiment of the invention, the increase in water cut after fluid extraction refers to the increase in water cut in the produced fluid of historical fluid extraction wells in the target reservoir after fluid extraction measures are implemented, reflecting the impact of fluid extraction measures on the water cut produced by historical fluid extraction wells. The correlation degree refers to the degree of correlation between each candidate fluid extraction influence parameter of historical fluid extraction wells in the target reservoir and the parameter of the increase in water cut after fluid extraction, reflecting the influence of different candidate fluid extraction influence parameters on the parameter of the increase in water cut after fluid extraction of historical fluid extraction wells. The main control parameter refers to the parameter among the candidate fluid extraction influence parameters of historical fluid extraction wells that has a high degree of correlation with the parameter of the increase in water cut after fluid extraction, and can better influence the fluid extraction effect of the fluid extraction well.

[0052] Specifically, using the grey relational analysis method, a quantitative study was conducted on the interaction between candidate fluid extraction influencing parameters and post-extraction water cut increase parameters for historical fluid extraction wells in the target reservoir. The correlation degree corresponding to each candidate fluid extraction influencing parameter was calculated. These parameters were then ranked according to their correlation degree, and several candidate fluid extraction influencing parameters with high correlation degrees were selected as the main control parameters for the fluid extraction wells in the target reservoir.

[0053] For example, Table 1 shows the correlation and ranking results of various candidate parameters affecting fluid extraction and parameters of the increase in water cut after fluid extraction for a historical fluid extraction well in a high water-cut sandstone reservoir:

[0054] Table 1. Correlation and Ranking Results of Influencing Parameters of Candidate Extracts

[0055] Influence parameters of candidate extract correlation Close correlation Sorting results Liquor extraction amplitude 1.235 1.000 1 oil layer thickness 1.133 0.917 2 Oil thickness ratio 1.014 0.821 3 timing of liquid extraction 0.838 0.679 4 Waterproof thickness 0.724 0.586 5 Penetration level difference 0.721 0.584 6 Penetration 0.616 0.499 7 oil-water viscosity ratio 0.553 0.448 8

[0056] The correlation degree is calculated by taking the maximum correlation value as the benchmark and comparing the correlation degree of each candidate extraction parameter to the maximum correlation degree. It can be seen that the correlation degree of extraction amplitude is the highest, so the correlation degree of extraction amplitude is 1. The correlation degree of oil layer thickness is approximately 1.133 / 1.235 ≈ 0.917. Based on the correlation degree ranking, extraction amplitude, oil layer thickness, and oil thickness ratio are identified as parameters with high correlation degrees among the candidate extraction parameters. Therefore, extraction amplitude, oil layer thickness, and oil thickness ratio are considered as the main control parameters for the extraction well in this reservoir.

[0057] Alternatively, based on the correlation between the candidate extraction influence parameters and the parameter of the increase in water content after extraction, multiple main control parameters are determined from the candidate extraction influence parameters, including the following steps B1-B3:

[0058] Step B1: Based on the correlation analysis method, determine the correlation parameters between the influence parameters of each candidate extract and the parameters of the increase in water content after extraction.

[0059] Step B2: Determine the degree of correlation between the influence parameters of each candidate extract and the parameters of the increase in water content after extraction based on the correlation parameters.

[0060] Step B3: Sort the candidate extract influencing parameters according to their degree of closeness, and determine multiple main control parameters based on the sorting results.

[0061] In this embodiment of the invention, the correlation parameter refers to the numerical value of the correlation between each candidate extract influence parameter and the parameter of the increase in water content after extraction, calculated using correlation analysis. The value typically ranges from 0 to 1, where 1 represents perfect correlation and 0 represents no correlation. The degree of closeness refers to the degree of correlation as defined by different numerical ranges of the correlation parameter. For example, the numerical ranges of the correlation parameter can be divided into 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1, where 0.8–1 represents the highest degree of closeness, and 0–0.2 represents the lowest degree of closeness.

[0062] Specifically, using Pearson correlation coefficient analysis, the correlation parameters between each candidate fluid extraction influence parameter and the parameter of water cut increase after fluid extraction for historical fluid extraction wells in the target reservoir are calculated. Based on the numerical range of the correlation parameters corresponding to each candidate fluid extraction influence parameter, each candidate fluid extraction influence parameter is classified into a corresponding degree of closeness. Furthermore, the candidate fluid extraction influence parameters are ranked according to their degree of closeness, and the candidate fluid extraction influence parameters included in the highest degree of closeness are selected as multiple main control parameters for the fluid extraction wells in the target reservoir.

[0063] S103. Establish a numerical model of the target reservoir based on its characteristics, and determine the simulation results of multiple numerical models based on the master control parameters and the numerical model of the target reservoir.

[0064] In this embodiment of the invention, the target reservoir numerical model refers to a conceptual numerical model that can characterize the features of the target reservoir. Combined with multiple key control parameters of the target reservoir's fluid extraction wells, multiple numerical simulation models can be formed. The numerical model simulation result is the increase in the recovery rate of the target reservoir's fluid extraction wells, calculated by the exponential model, reflecting the effectiveness of the numerical simulation model in the fluid extraction development of the target reservoir's fluid extraction wells.

[0065] Specifically, experimental simulations are conducted on the target reservoir to establish a conceptual numerical model that characterizes its features, serving as the target reservoir numerical model. Multiple key control parameters of the target reservoir's fluid extraction wells are combined with the target reservoir numerical model to obtain multiple target reservoir numerical models. The enhancement of the recovery rate of each target reservoir numerical model for the fluid extraction wells is calculated, and this calculation represents the simulation results of the multiple target reservoir numerical models.

[0066] As an option, a numerical model of the target reservoir is established based on its characteristics, and multiple numerical model simulation results are determined based on the master control parameters and the numerical model of the target reservoir, including the following steps C1-C3:

[0067] Step C1: Establish a numerical model of the target reservoir based on the reservoir fluid properties and reservoir distribution characteristics.

[0068] Step C2: Determine multiple combinations of main control parameter values ​​based on the candidate value range of the main control parameters.

[0069] Step C3: Based on the numerical model of the target reservoir, determine the numerical model simulation results corresponding to the combination of main control parameter values.

[0070] In this embodiment of the invention, reservoir fluid physical properties refer to the physical properties of the fluids in the target reservoir, including at least the groundwater viscosity and crude oil viscosity. Reservoir distribution characteristics refer to the reservoir geological parameters, rock physical properties, fluid properties, and their interrelationships within the target reservoir. The candidate value range refers to the pre-set value range for each main control parameter of the fluid extraction well in the target reservoir, which can be represented by discrete values. Multiple main control parameter value combinations are different combinations of values ​​for each main control parameter within the candidate value range.

[0071] Specifically, based on the reservoir fluid properties and reservoir distribution characteristics of the target reservoir, a five-point well network conceptual numerical model of the target reservoir is established. Injection wells are located at the center of the model, and production wells are located at the four corners. This five-point well network conceptual numerical model is used as the numerical model for the target reservoir. For example, the basic parameters of the target reservoir numerical model established for a high water-cut sandstone reservoir, identical to those in Table 1, are shown in Table 2.

[0072] Table 2. Basic parameters of the numerical model for the target reservoir.

[0073]

[0074] The candidate value ranges for each main control parameter of the target reservoir fluid extraction well were determined. Based on the Box-Behnken experimental design method, different combinations of the candidate value ranges for each main control parameter were obtained, resulting in multiple combinations of main control parameter values. These multiple combinations of main control parameter values ​​were then combined with the target reservoir numerical model to form multiple target reservoir numerical models. The recovery rate increase for each target reservoir numerical model was calculated, serving as the simulation results for the multiple numerical models. For example, the candidate value ranges for the multiple main control parameters of the reservoir fluid extraction well determined in Table 1 are shown in Table 3.

[0075] Table 3. Main Control Parameters and Corresponding Candidate Value Ranges

[0076]

[0077] Based on the Box-Behnken experimental design method, multiple control parameters were combined within the candidate value range. Combined with the target reservoir numerical models given in Table 2, multiple target reservoir numerical models were obtained, and the recovery rate increase of each model was calculated as shown in Table 4.

[0078] Table 4. Combinations of multiple main control parameters and simulation results of the numerical model.

[0079]

[0080]

[0081] S104. Based on the numerical model simulation results, perform response surface regression to construct a response surface model between the response variable and the master control parameter; where the response variable is the increase in the extraction rate.

[0082] In this embodiment of the invention, response surface regression refers to using regression analysis to establish an approximate surrogate mathematical model between multiple main control parameters of the target reservoir fluid extraction well and the increase in the recovery rate of the target reservoir fluid extraction well.

[0083] Specifically, statistical analysis software was used to perform response surface regression analysis on the simulation results of multiple numerical models. An approximate surrogate mathematical model was established between the response variables and the main control parameters of the target reservoir fluid extraction well, which served as the response surface model of the target reservoir fluid extraction well. The response variable represents the increase in the degree of recovery as shown in the numerical model simulation results.

[0084] As an option, response surface regression can be performed based on the numerical model simulation results to construct a response surface model between the response variables and the main control parameters, including the following steps D1-D2:

[0085] Step D1: Use multiple candidate models to perform response surface regression on the numerical model simulation results to obtain multiple candidate response surface models.

[0086] Step D2: Based on the results of the analysis of variance, determine the target response surface model between the response variables and the master control parameters from multiple candidate response surface models.

[0087] In this embodiment of the invention, multiple candidate models refer to various experimental models selected during the construction of the response surface model, such as the mean value model, linear model, 2FI model, quadratic equation model, and cubic equation model. The candidate response surface model is a response surface model established based on regression analysis of multiple candidate models, relating the response variables and master control parameters of the target reservoir's fluid extraction well. The analysis of variance results refer to the calculation of the corresponding variances for each of the multiple candidate response surface models, and the comparison results of the variance analysis between different models.

[0088] Specifically, different candidate models are selected, and multiple numerical model simulation results are combined with statistical analysis software to establish multiple candidate response surface models representing the relationship between the response variables and master control parameters of the target reservoir's fluid extraction well. Then, analysis of variance is performed on the established multiple candidate response surface models using statistical analysis software, including comparing the sum of squares, degrees of freedom, mean, F-value, and R-value among different candidate response surface models. 2 Comprehensive analysis, etc. Then, based on the comparison results of the analysis of variance, the optimal model is selected from multiple candidate response surface models as the target response surface model between the response variable and the main control parameters.

[0089] For example, the simulation results of multiple numerical models obtained in Table 4 are combined with multiple candidate models to establish multiple candidate response surface models. The results of the analysis of variance comparison of multiple candidate response surface models are shown in Tables 5 and 6:

[0090] Table 5. Analysis of Variance for Multiple Candidate Response Surface Models

[0091]

[0092] Table 6. Multiple candidate response surface models R 2 Comprehensive Analysis Table

[0093] Candidate response surface model Standard deviation <![CDATA[R 2 ]]> <![CDATA[R 2 Correction value <![CDATA[R 2 Predicted value linear model 0.16 0.4690 0.3465 -0.0591 2FI model 0.13 0.7318 0.5709 -0.1598 Biscalar model 0.055 0.9662 0.9228 0.4599 Recommended cubic equation model 0 1 1 Poor

[0094] Choose the one with the largest R 2 Correction value and R 2 The quadratic equation model of the predicted values ​​is used as the target response surface model, and the calculation formula is as follows:

[0095] EOR=1.826-0.0046*A-0.056*B-2.175*C+0.0028*A*B

[0096] +0.016*A*C+0.1356*B*C-0.0002*A 2 -0.0076*B 2

[0097] +0.8937*C 2

[0098] Where EOR represents the increase in production level, A represents the increase in fluid extraction, B represents the oil layer thickness, and C represents the oil thickness ratio.

[0099] S105. Determine the extraction parameters of the target reservoir based on the response surface model.

[0100] In this embodiment of the invention, the fluid extraction parameter refers to the optimal parameter value of multiple main control parameters for the fluid extraction well in the target reservoir, which corresponds to the largest increase in the recovery degree of the fluid extraction well, and is used to implement reasonable fluid extraction measures for the fluid extraction well.

[0101] Specifically, based on the simulation results of multiple numerical models and the regression analysis performed using statistical analysis software, a response surface model is established to determine the optimal values ​​of multiple main control parameters for the target reservoir's extraction wells. These parameters are then used as extraction parameters for the target reservoir to maximize the recovery rate of the extraction wells.

[0102] For example, based on the target response surface models obtained in Tables 5 and 6, the influence of different master control parameters on the production recovery rate of the target reservoir's wells can be plotted as follows: Figure 3 and Figure 4 As shown in the figure, when the oil layer thickness is 6–8 m and the oil-to-thickness ratio is 0.6–0.8, the extraction rate can be controlled at 70%–80%, achieving the optimal extraction effect; when the oil layer thickness is 6–8 m and the oil-to-thickness ratio is 0.4–0.6, the extraction rate can be controlled at 60%–70%; when the oil layer thickness is 4–6 m and the oil-to-thickness ratio is 0.6–0.8, the extraction rate can be controlled at 50%–60%; and when the oil layer thickness is 4–6 m and the oil-to-thickness ratio is 0.4–0.6, the extraction rate can be controlled at 30%–50%.

[0103] As an alternative, since the geological feature parameters included in the multiple master control parameters are static parameters, determining the fluid extraction parameters of the target reservoir based on the response surface model can be transformed into finding the development dynamic data with the largest increase in production rate among the master control parameters. For example, in the calculation formulas of the target response surface model obtained in Tables 5 and 6, the reservoir thickness and oil-to-thickness ratio are geological feature parameters, and the fluid extraction amplitude is development dynamic data. Therefore, differentiating the fluid extraction amplitude on both sides of the formula yields dEOR = -0.0046 + 0.0028*B + 0.016*C - 0.0004*A

[0104] Where dEOR represents the derivative of the increase in production rate with respect to the fluid extraction amplitude. Setting dEOR = 0, we have A = 7*B + 40*C - 11.5. Given the oil layer thickness B and the oil thickness ratio C, the optimal fluid extraction amplitude A can be obtained. The results of the fluid extraction parameter optimization are shown in Table 7.

[0105] Table 7. Verification of Extraction Parameter Optimization Results

[0106]

[0107] Among them, wells N1, N6, N7, and N8 have reasonable and actual liquid extraction ranges that are close to each other, and their water cut increases are all within an acceptable range; wells N2, N3, and N5 have reasonable liquid extraction ranges that are greater than actual liquid extraction ranges, and their water cut increases are also relatively small; well N4 has an actual liquid extraction range that is significantly greater than reasonable liquid extraction ranges, and its water cut increase is significantly higher than the other wells.

[0108] The technical solution of this invention, by determining candidate extraction influencing parameters of historical extraction wells in the target reservoir, achieves evaluation using extraction parameters from historical extraction wells in the target reservoir, clarifying the reasons for differences in extraction performance. Based on the correlation between candidate extraction influencing parameters and parameters related to the increase in water cut after extraction, multiple main control parameters are determined from the candidate extraction influencing parameters. This facilitates the rapid and accurate determination of reasonable extraction parameters for extraction wells in the target reservoir, avoiding the impact of extraction parameters with low correlation on the predicted extraction effect, thereby saving time and costs. A numerical model of the target reservoir is established based on its characteristics, and the main control parameters and the target reservoir... Numerical models have determined the simulation results of multiple models, enabling the identification of development characteristics of the target reservoir and providing data support for the subsequent screening of potential wells for enhanced recovery. Response surface regression has been performed based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameters. The response variable is the increase in recovery rate. Based on the response surface model, the enhanced recovery parameters for the target reservoir have been determined. This allows for the determination of the actual values ​​of multiple main control parameters using statistical analysis software, maximizing the enhanced recovery rate of the target reservoir and improving the efficiency of implementing enhanced recovery measures. This lays the foundation for oilfield production planning and measure deployment.

[0109] Example 2

[0110] Figure 5 This is a schematic diagram of a device for determining fluid extraction parameters in a medium-to-high water-cut oilfield, provided in Embodiment 2 of the present invention. This embodiment of the invention is applicable to determining the fluid extraction parameters of any fluid extraction well in a medium-to-high water-cut oilfield. The device can be implemented in hardware and / or software and can be configured in an electronic device that implements the method for determining fluid extraction parameters in a medium-to-high water-cut oilfield. Figure 5 As shown, the device includes:

[0111] The first determining module 201 is used to determine the candidate fluid extraction influence parameters of historical fluid extraction wells in the target reservoir;

[0112] The second determining module 202 is used to determine multiple main control parameters from the candidate extraction influence parameters based on the correlation between the candidate extraction influence parameters and the parameters of the increase in water content after extraction.

[0113] The third determination module 203 is used to establish a numerical model of the target reservoir based on the characteristics of the target reservoir, and to determine the simulation results of multiple numerical models based on the main control parameters and the numerical model of the target reservoir.

[0114] The response surface model construction module 204 is used to perform response surface regression based on the numerical model simulation results and construct a response surface model between the response variable and the main control parameters; wherein, the response variable is the increase in the extraction degree;

[0115] The extraction parameter determination module 205 is used to determine the extraction parameters of the target reservoir based on the response surface model.

[0116] As an optional feature, before determining candidate fluid extraction influence parameters for historical fluid extraction wells in the target reservoir, the device also includes:

[0117] Determine the reservoir dimensionless production index curve for the target reservoir;

[0118] The reservoir's fluid supply capacity is determined based on the dimensionless fluid production index curve, and whether to increase fluid production is determined based on the reservoir's fluid supply capacity.

[0119] As an option, determining the dimensionless production index curve of the target reservoir includes:

[0120] Based on the oil-water relative permeability curves, crude oil viscosity, and water viscosity data of the target reservoir, the dimensionless production index curve of the reservoir is determined.

[0121] As an option, candidate extraction parameters include geological feature parameters and development dynamic data; wherein, the geological feature parameters include at least one of the following: oil layer thickness, oil-to-thickness ratio, permeability, permeability gradient, oil-water viscosity ratio, and water-avoidance thickness, and the development dynamic data include extraction amplitude and extraction timing.

[0122] Alternatively, based on the correlation between the candidate extraction influence parameters and the parameter of the increase in water content after extraction, several main control parameters can be determined from the candidate extraction influence parameters, including:

[0123] Based on correlation analysis, the correlation parameters between each candidate extract influence parameter and the parameter of the increase in water content after extraction were determined.

[0124] The degree of correlation between the influence parameters of each candidate extract and the parameters of the increase in water content after extraction is determined based on the correlation parameters.

[0125] The candidate extract parameters were ranked according to their degree of influence, and several key control parameters were determined based on the ranking results.

[0126] As an option, a numerical model of the target reservoir is established based on its characteristics, and multiple numerical model simulation results are determined based on the master control parameters and the numerical model of the target reservoir, including:

[0127] A numerical model of the target reservoir is established based on reservoir fluid properties and reservoir distribution characteristics.

[0128] Multiple combinations of main control parameter values ​​are determined based on the candidate value range of the main control parameters;

[0129] Based on the numerical model of the target reservoir, the numerical model simulation results corresponding to the combination of main control parameter values ​​are determined.

[0130] As an option, response surface regression can be performed based on the numerical model simulation results to construct a response surface model between the response variables and the principal control parameters, including:

[0131] Multiple candidate models are used to perform response surface regression on the numerical model simulation results to obtain multiple candidate response surface models;

[0132] Based on the results of analysis of variance, the target response surface model is determined from multiple candidate response surface models to determine the relationship between the response variables and the master control parameters.

[0133] The technical solution of this invention, by determining candidate extraction influencing parameters of historical extraction wells in the target reservoir, achieves evaluation using extraction parameters from historical extraction wells in the target reservoir, clarifying the reasons for differences in extraction performance. Based on the correlation between candidate extraction influencing parameters and parameters related to the increase in water cut after extraction, multiple main control parameters are determined from the candidate extraction influencing parameters. This facilitates the rapid and accurate determination of reasonable extraction parameters for extraction wells in the target reservoir, avoiding the impact of extraction parameters with low correlation on the predicted extraction effect, thereby saving time and costs. A numerical model of the target reservoir is established based on its characteristics, and the main control parameters and the target reservoir... Numerical models determine the simulation results of multiple models, enabling the identification of development characteristics of the target reservoir and providing data support for the subsequent screening of potential fluid extraction wells. Response surface regression is performed based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameters, where the response variable is the increase in recovery rate. The fluid extraction parameters for the target reservoir are determined based on the response surface model, enabling the determination of the actual values ​​of multiple main control parameters using statistical analysis software. This maximizes the recovery rate of the target reservoir, improves the efficiency of implementing enhanced oil recovery measures, and lays the foundation for oilfield production planning and measure deployment. The device for determining fluid extraction parameters in medium-to-high water-cut oilfields provided in this invention can execute the method for determining fluid extraction parameters in medium-to-high water-cut oilfields provided in any embodiment of this invention, possessing the corresponding functional modules and beneficial effects of the method.

[0134] Example 3

[0135] Figure 6This is a schematic diagram of an electronic device used in an embodiment of the present invention to provide a method for determining extraction parameters in oilfields during periods of high water cut. The electronic device is intended to represent various forms of digital computers, such as laptops, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0136] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0137] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0138] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining fluid extraction parameters in oilfields with medium to high water cut.

[0139] In some embodiments, the method for determining fluid extraction parameters in oilfields with medium to high water cut can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining fluid extraction parameters in oilfields with medium to high water cut described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining fluid extraction parameters in oilfields with medium to high water cut by any other suitable means (e.g., by means of firmware).

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0145] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0146] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining extraction parameters in oilfields during the medium-to-high water-cut stage, characterized in that, The method includes: Determine candidate parameters affecting fluid extraction from historical fluid extraction wells in the target reservoir; Based on the correlation between the candidate extract influencing parameters and the parameters of the increase in water content after extraction, multiple main control parameters are determined from the candidate extract influencing parameters; A numerical model of the target reservoir is established based on the characteristics of the target reservoir, and the simulation results of multiple numerical models are determined based on the main control parameters and the numerical model of the target reservoir. Response surface regression is performed based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameter; wherein, the response variable is the increase in the extraction degree; The extraction parameters of the target reservoir are determined based on the response surface model.

2. The method according to claim 1, characterized in that, Before determining the candidate fluid extraction influence parameters for historical fluid extraction wells in the target reservoir, the method further includes: Determine the reservoir dimensionless production index curve of the target reservoir; The reservoir fluid supply capacity of the target reservoir is determined based on the dimensionless fluid production index curve of the reservoir, and whether to increase fluid production is determined based on the reservoir fluid supply capacity.

3. The method according to claim 2, characterized in that, Determining the dimensionless production index curve of the target reservoir includes: Based on the oil-water relative permeability curve, crude oil viscosity, and water viscosity data of the target reservoir, the dimensionless production index curve of the reservoir is determined.

4. The method according to claim 1, characterized in that, The candidate extraction parameters include geological characteristic parameters and development dynamic data; wherein, the geological characteristic parameters include at least one of the following: oil layer thickness, oil-to-thickness ratio, permeability, permeability gradient, oil-water viscosity ratio, and water-avoidance thickness, and the development dynamic data includes extraction amplitude and extraction timing.

5. The method according to claim 1 or 4, characterized in that, Based on the correlation between the candidate extract influencing parameters and the parameters of the increase in water content after extraction, several main control parameters are determined from the candidate extract influencing parameters, including: Based on correlation analysis, the correlation parameters between each candidate extract influence parameter and the parameter of the increase in water content after extraction were determined. The degree of correlation between the influence parameters of each candidate extract and the parameters of the increase in water content after extraction is determined based on the correlation parameters. The candidate extract parameters are sorted according to their degree of closeness, and multiple main control parameters are determined based on the sorting results.

6. The method according to claim 1, characterized in that, A numerical model of the target reservoir is established based on its characteristics, and multiple numerical model simulation results are determined based on the master control parameters and the numerical model of the target reservoir, including: A numerical model of the target reservoir is established based on reservoir fluid properties and reservoir distribution characteristics. Multiple combinations of main control parameter values ​​are determined based on the candidate value range of the main control parameters; Based on the numerical model of the target reservoir, the numerical model simulation results corresponding to the combination of main control parameter values ​​are determined.

7. The method according to claim 1, characterized in that, Based on the numerical model simulation results, response surface regression is performed to construct a response surface model between the response variable and the main control parameter, including: Response surface regression is performed on the numerical model simulation results using multiple candidate models to obtain multiple candidate response surface models; Based on the results of the analysis of variance, the target response surface model between the response variable and the master control parameter is determined from the plurality of candidate response surface models.

8. A device for determining extraction parameters in oilfields during medium-to-high water cut stages, characterized in that, The device includes: The first determining module is used to determine the candidate fluid extraction influence parameters of historical fluid extraction wells in the target reservoir; The second determining module is used to determine multiple main control parameters from the candidate extract influencing parameters based on the correlation between the candidate extract influencing parameters and the parameters of the increase in water content after extraction. The third determination module is used to establish a numerical model of the target reservoir based on the characteristics of the target reservoir, and to determine the simulation results of multiple numerical models based on the main control parameters and the numerical model of the target reservoir. The response surface model construction module is used to perform response surface regression based on the numerical model simulation results to construct a response surface model between the response variable and the main control parameter; wherein, the response variable is the increase in the degree of extraction; The extraction parameter determination module is used to determine the extraction parameters of the target reservoir based on the response surface model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the extraction parameters of oilfields in the medium-to-high water-cut stage as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining fluid extraction parameters in oilfields with medium to high water cut as described in any one of claims 1-7.

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