Oil reservoir pressure prediction model optimization method and system

Abstract

The invention provides an oil reservoir pressure prediction model optimization method and system, and the method comprises the steps: collecting the geological development data of a target oil reservoir, and building a target oil reservoir numerical simulation model; target reservoir pressure field condition evaluation research is carried out, and the pressure field state of a research area before adjustment is determined; based on the pressure field distribution condition of the research area and the screened pressure field characterization parameters, key parameters influencing the pressure field of the research area are determined; analyzing the change rule of the pressure coefficient over time, and obtaining the relationship between the pressure recovery duration and the change of the pressure coefficient under the influence of the radial flow pressure conduction rule; on the basis of pressure field characterization parameter sensitivity analysis, a pressure optimization prediction model is established in combination with production dynamic data and dynamic monitoring data of a research area; and comprehensively considering the well pattern condition and the current development situation of the target area, and applying the pressure field optimization model to determine the injection-production policy of the pressure field of the target area. And comprehensively considering and determining an optimal injection-production adjustment strategy.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas field development, and particularly to a method and system for optimizing an oil reservoir pressure prediction model. Background Art

[0002] The formation pressure directly reflects the magnitude of the formation energy and determines the development effect and development life of the oilfield. How to scientifically and reasonably obtain the formation pressure has become a problem that must be solved by the majority of development workers. After years of development by various means, the pressure field distribution in the oil reservoir is relatively complex, and the pressure field is an important influencing factor that leads to a significant increase in the cost and safety risks of drilling and downhole operations. At the same time, high drilling fluid density in oil reservoirs with a high pressure coefficient will cause formation pollution.

[0003] At present, there are many research methods for determining the formation pressure of oil and water wells in the oilfield, including logarithmic straight-line extrapolation of the pressure (P*) value, high-point pressure method, calculating the formation pressure using the empirical formula unique to the block, etc. However, these methods have limitations and are restricted by objective conditions (such as low accuracy of pressure gauges, backward well test analysis methods, and changes in the geological conditions of the research block). At present, there is no pressure field characterization formula suitable for the current situation of Gudong Oilfield, and the differential distribution of the current pressure field cannot be clearly understood. The pressure field distribution in the test area is directly related to the optimization of drilling costs. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide an oil reservoir pressure prediction model optimization method and system that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of the present invention, an oil reservoir pressure prediction model optimization method is provided. The optimization method includes:

[0006] Step 1: Collect geological development data of the target oil reservoir and establish a numerical simulation model of the target oil reservoir;

[0007] Step 2: Conduct an evaluation study on the pressure field status of the target oil reservoir to clarify the pressure field state of the study area before adjustment;

[0008] Step 3: Based on the pressure field distribution in the study area, screen out the pressure field characterization parameters and determine the key parameters affecting the pressure field in the study area;

[0009] Step 4: Analyze the variation law of the pressure coefficient over time, and affected by the radial flow pressure conduction law, obtain the relationship between the pressure recovery duration and the change of the pressure coefficient;

[0010] Step 5: Based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area, establish a pressure optimization prediction model;

[0011] Step 6: Considering the well pattern condition and development status of the target area comprehensively, apply the pressure field optimization model to determine the injection-production policy of the pressure field in the target area.

[0012] Optionally, in Step 1, the geological and development data collected for the target reservoir include: reservoir structure, permeability, porosity, effective thickness, saturation, pressure measurement data, water phase density and viscosity, oil phase density and viscosity, relative permeability curve, well trajectories of injection wells and production wells, perforation positions, well pattern form, monthly oil production of production wells, monthly water production, monthly injection volume of injection wells, production time step, injection-production ratio, liquid production rate, and dynamic monitoring data.

[0013] Optionally, Step 1 further includes: establishing a data volume file for the numerical simulation model of the target reservoir.

[0014] Optionally, in Step 2, conduct an evaluation study on the pressure field condition of the target reservoir to clarify the pressure field state of the study area before adjustment, specifically including: conducting an evaluation study on the pressure field condition of the target reservoir to clarify the pressure field distribution condition and the pressure field optimization and adjustment direction of the study area before adjustment.

[0015] Optionally, in Step 3, based on the pressure field distribution condition of the study area, the pressure field characterization parameters are screened out, and the key parameters affecting the pressure field of the study area are determined, specifically including:

[0016] Based on the pressure field distribution condition of the study area, conduct an evaluation of the influencing factors of the pressure coefficient in the target area, screen out the pressure field characterization parameters, conduct a sensitivity analysis of the variation law of the pressure field in the study area, and determine the key parameters affecting the pressure field of the study area.

[0017] Optionally, in Step 3, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area, determine the injection-production ratio and liquid production rate that affect the pressure field.

[0018] Optionally, in Step 4, analyze the variation law of the pressure coefficient over time. Affected by the radial flow pressure conduction law, obtain the relationship between the pressure recovery duration and the change of the pressure coefficient, specifically including:

[0019] Based on the numerical simulation model and the sensitivity analysis of the pressure field characterization parameters;

[0020] Study and analyze the variation law of the pressure coefficient over time. Affected by the radial flow pressure conduction law, study the relationship between the pressure recovery duration and the change of the pressure coefficient.

[0021] Optionally, in Step 5, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area, establish a pressure optimization prediction model, specifically including:

[0022] Based on the laws and influence sensitivities of the change value of the pressure coefficient in the study area with respect to the injection-production ratio, liquid production rate, and pressure recovery duration, a pressure optimization prediction model is established.

[0023] Optionally, in step 6, considering the well pattern status and development status of the target area comprehensively, apply the pressure field optimization model to design pressure field adjustment plans for different regions.

[0024] Optionally, in step 6, compare the change value of the pressure coefficient and the recovery duration in the study area of different adjustment plans, and optimize and determine the injection-production policy of the pressure field in the target area.

[0025] The present invention also provides an optimization system for an oil reservoir pressure prediction model, which applies the above-mentioned optimization method for an oil reservoir pressure prediction model. The optimization system includes:

[0026] A numerical simulation model establishment module, which is used to collect geological development data of the target oil reservoir and establish a numerical simulation model of the target oil reservoir;

[0027] A pressure field state clarification module, which is used to conduct an evaluation study on the pressure field status of the target oil reservoir and clarify the pressure field state of the study area before adjustment;

[0028] A key parameter determination module, which is used to screen out pressure field characterization parameters based on the pressure field distribution status of the study area and determine the key parameters affecting the pressure field of the study area;

[0029] A change law analysis module, which is used to analyze the change law of the pressure coefficient over time, affected by the radial flow pressure conduction law, and obtain the relationship between the pressure recovery duration and the change of the pressure coefficient;

[0030] An optimization prediction model establishment module, which is used to establish a pressure optimization prediction model based on the sensitivity analysis of pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area;

[0031] A pressure field injection-production policy determination module, which is used to comprehensively consider the well pattern status and development status of the target area, apply the pressure field optimization model, and determine the injection-production policy of the pressure field in the target area.

[0032] An optimization method and system for a reservoir pressure prediction model provided by the present invention. The optimization method includes: Step 1, collecting geological development data of the target reservoir and establishing a numerical simulation model of the target reservoir; Step 2, conducting an evaluation study on the pressure field condition of the target reservoir to clarify the pressure field state of the study area before adjustment; Step 3, based on the pressure field distribution in the study area, screening out pressure field characterization parameters and determining the key parameters affecting the pressure field in the study area; Step 4, analyzing the variation law of the pressure coefficient over time, affected by the radial flow pressure conduction law, to obtain the relationship between the pressure recovery duration and the change of the pressure coefficient; Step 5, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area, establishing a pressure optimization prediction model; Step 6, comprehensively considering the well pattern condition and development status of the target area, applying the pressure field optimization model to determine the injection-production policy of the pressure field in the target area. Comprehensively consider and determine the optimal injection-production adjustment countermeasure.

[0033] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, it can be implemented according to the content of the description. And in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention are specifically exemplified below. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a flow block diagram of an optimization method for a reservoir pressure prediction model provided by an embodiment of the present invention;

[0036] Figure 2 It is a chart of the pressure coefficient decline value over time under different injection-production ratios provided by an embodiment of the present invention;

[0037] Figure 3 It is a chart of the pressure coefficient decline value over time under different liquid production rates provided by an embodiment of the present invention;

[0038] Figure 4 It is a curve of the pressure coefficient change with the pressure recovery time provided by an embodiment of the present invention;

[0039] Figure 5 It is a graph of the pressure coefficient decline over time under different injection-production adjustment schemes provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0041] In the description of the embodiments of the present invention, the terms "including" and "having" and any variations thereof in the claims and the drawings are intended to cover non-exclusive inclusions. For example, a series of steps or units are included.

[0042] The technical solutions of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] As Figure 1 shown, a reservoir pressure optimization prediction model considering the pressure recovery duration includes:

[0044] Step S10, the geological development data collected includes: reservoir structure, permeability, porosity, effective thickness, saturation, pressure measurement data, water phase density and viscosity, oil phase density and viscosity, relative permeability curve, well trajectories of injection wells and production wells, perforation positions, well pattern forms, monthly oil production of production wells, monthly water production, monthly injection volume of injection wells, production time step, injection-production ratio, liquid production rate, and dynamic monitoring data. Based on the basic data preparation of the target reservoir study area, a data volume file of the target reservoir numerical simulation model is established.

[0045] Step S20, conduct an evaluation study on the pressure field status of the target reservoir, and clarify the pressure field distribution status and the pressure field optimization adjustment direction in the study area before adjustment.

[0046] Step S30, based on the pressure field distribution status in the study area, conduct an evaluation of the influencing factors of the pressure coefficient in the target area, screen out the pressure field characterization parameters, conduct a sensitivity analysis of the variation law of the pressure field in the study area, and determine the key parameters affecting the pressure field in the study area. Combining the production dynamic data and dynamic monitoring data in the study area, it is determined that the injection-production ratio has a greater impact on the pressure field, followed by the liquid production rate;

[0047] Step S40, based on the numerical simulation model and the sensitivity analysis of the pressure field characterization parameters, study and analyze the variation law of the pressure coefficient over time. Affected by the radial flow pressure conduction law, study the relationship between the pressure recovery duration and the change of the pressure coefficient;

[0048] Step S50, based on the variation law and influence sensitivity of the pressure coefficient change value in the study area with respect to the injection-production ratio, liquid production rate, and time, establish a pressure optimization prediction model;

[0049] Pressure optimization prediction model:

[0050] ΔP f = 0.00606197 × IPR - 0.00251718 × V l -1.33795 × 10 -6 × T 2 -0.000168667 × T + 0.03471 (injection-production ratio < 0.3, T < 150 days)

[0051] ΔP f = 0.111369 × IPR - 0.0168533 × V l -0.000490737 × T + 0.17536 (injection-production ratio < 0.3, T > 150 days)

[0052] ΔP f = 0.0104579 × IPR - 0.00100535 × V l -0.000217297 × T + 0.01365 (injection-production ratio > 0.3, T < 150 days, V l < 10%)

[0053] ΔP f = 0.082951 × IPR - 0.00721289 × V l -0.000215251 × T + 0.02224 (injection-production ratio > 0.3, T > 150 days, V l < 10%)

[0054] ΔP f = 0.0224029 × IPR - 0.00100096 × V l -1.32553 × 10 -6 × T 2 -0.000167283 × T + 0.01413 (injection-production ratio > 0.3, T < 150 days, 10% < V l < 12%)

[0055] ΔP f = 0.118352 × IPR - 0.0060039 × V l -0.000384058 × T + 0.02981 (injection-production ratio > 0.3, T > 150 days, 12% < V l < 14%)

[0056] ΔP f = 0.162274 × IPR - 0.00617875 × V l -0.000550335 × T + 0.04596 (injection-production ratio > 0.3, T > 150 days, 14% < V l < 20%)

[0057] Where: Rip-injection-production ratio, dimensionless;

[0058] V l -Liquid collection rate, %;

[0059] T - production time, days;

[0060] ΔP f - Pressure coefficient drop value, decimal.

[0061] Step S60, comprehensively consider the well network conditions and development status of the target area, apply the pressure field optimization model, and design pressure field adjustment plans for different areas. Compare the pressure coefficient change values ​​and recovery time of the study area under different adjustment plans, and optimize and determine the injection and production policy of the target area pressure field.

[0062] Application Examples

[0063] Gudong 7th District West 5 4 -6 1 The pilot area is located in the southern part of the unit, with simple and gentle structures and braided river deposits. 4 , 5 5 , 6 1 Three small layers, 5 4 It is the main layer. The oil-bearing area is 0.94km 2 , geological reserves 277×10 4 t, reservoir burial depth 1261-1294m, porosity 34%, permeability 2560md, average effective thickness 13.6m. The pilot area currently has high comprehensive water content (99.1%), high recovery degree (51.22%), low liquid and oil production rates (9.46% / 0.10%), and low single well production capacity (0.7t / d).

[0064] A numerical simulation model of the pilot area was established with a model size of 65×62×37, a model pressure of 13.04MPa, an original formation pressure of 12.9MPa, and a simulation period of August 2021-August 2031. There were 11 oil wells and 7 water wells in the model. In order to better analyze the differential distribution of formation pressure field in the study area and formulate injection and production policies for the next step of the study area, the model was balanced and partitioned: 5 4 Layer (1-16), 5 5 Layer (18-30), 6 1 Layer (32-37)

[0065] Using the method described in the embodiments of the present invention, numerical simulation means are adopted to evaluate the pressure field condition in the study area. Based on the pressure field distribution in the study area, the influencing factors of the pressure coefficient in the target area are evaluated. The pressure field characterization parameters are screened out, and the sensitivity analysis of the variation law of the pressure field in the study area is carried out to determine the key parameters affecting the pressure field in the study area. Through analysis, the injection-production ratio, liquid production rate and pressure coefficient show a linear relationship. Affected by the radial flow pressure conduction law, with the passage of development time, the pressure coefficient is related to the deficit volume, showing a non-linear decline in the initial stage and an approximately linear decline trend in the later stage.

[0066] Through single-factor analysis, the sensitivity ranking of the influencing factors of the pressure coefficient is determined as injection-production ratio > liquid production rate > time. According to the condition limits of different times, liquid production rates and injection-production ratios, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data, dynamic monitoring data, etc. of the study area, a pressure optimization prediction model is established. According to the injection-production ratio, liquid production rate and the time condition required for the pressure coefficient to decline in the actual production of the block, different pressure prediction formulas are selected, and the pressure coefficient decline charts under different injection-production ratios and liquid production rates are drawn. By looking up the charts, the injection-production adjustment plan for the study area is determined. For example Figure 2 is the chart of the pressure coefficient decline value over time under different injection-production ratio conditions. Considering the well pattern condition, development status, etc. of the target area comprehensively, the pressure field optimization model is applied to design the pressure field adjustment plan for different regions.

[0067] First, the pressure prediction model is applied to draw the chart of the pressure coefficient decline over time under different injection-production ratio conditions at the current liquid production rate. According to the requirement of the pressure reduction time, the lower limit of the injection-production ratio is found; at the same time, the chart of the pressure coefficient decline over time under different liquid production rates at this injection-production ratio is drawn. According to the requirement of the pressure reduction time, the lower limit of the liquid production rate is found. The pressure prediction model is applied to draw the charts in this study area ( Figure 2 、 Figure 3 ). By looking up the charts, the time requirement for the pressure coefficient to decline by 0.1 is 300 days, the lower limit of the injection-production ratio is 0.3, and the lower limit of the liquid production rate is 10%.

[0068] Based on the pressure imbalance in the study area, the study area is divided into three different regions: the high injection-production ratio area, the low injection-production ratio area, and the area with clustered water wells (imperfect injection-production). For different regions, the pressure prediction model is used respectively to formulate injection-production adjustment plans. The area with clustered water wells (imperfect well pattern) is located at the edge of the study area, with a relatively high pressure level, but the well pattern is imperfect, and it is difficult to increase liquid production. According to the chart and actual field situation, the designed injection-production ratio of water wells is 0.287, and the designed liquid production rates of oil wells are 3.39%, 6.11%, 4.34%, and 6.79% in total. The time required for the pressure coefficient to decrease is calculated through the prediction model, and the liquid production rates of 3.39% and 6.11% are optimized. Using the same method, the injection-production ratio of the high injection-production ratio area is determined to be 0.15, and the liquid production rates are 13.42% and 10.74%. The injection-production ratio of the low injection-production ratio area is 0.1, and the liquid production rates are 12.30% and 12.43%. The combined injection-production optimization plans for the three well areas are 8 plans. The time required for the pressure coefficient to decrease is calculated through the prediction model. By comparing the optimized plan 5 ( Figure 5 ), that is, the injection-production ratio is 0.15, the liquid production rate is 11.62%, and it takes 186 - 206 days for the pressure coefficient to decrease by 0.1.

[0069] Beneficial effects: In the method described in the embodiments of the present invention, not only the development effect is considered, but also the current well pattern situation and development status of the oil reservoir are combined, and the time required for the pressure coefficient to decrease is considered. The optimal injection-production adjustment countermeasures are determined through comprehensive consideration; this method is more reasonable, and the optimization result is more effective.

[0070] Based on the pressure field distribution in the study area, an evaluation of the influencing factors of the pressure coefficient in the target area is carried out, the pressure field characterization parameters are screened out, a sensitivity analysis of the variation law of the pressure field in the study area is carried out, and the key parameters affecting the pressure field in the study area are determined. The variation law of the pressure coefficient over time is studied and analyzed. Affected by the radial flow pressure conduction law, the relationship between the pressure recovery duration and the change of the pressure coefficient is studied. Based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data, dynamic monitoring data, etc. of the study area, a pressure optimization prediction model is established. Considering the well pattern situation, development status, etc. of the target area comprehensively, the pressure field optimization model is applied to determine the adjustment range of relevant parameters, and the injection-production plan is designed by region to determine the optimal pressure field injection-production policy for the target area. The present invention considers various influencing factors of the pressure field and the variation law of the oil reservoir pressure drop with the pressure recovery time. The method is more reasonable and effective, can be directly used for quickly optimizing the pressure field of the oil reservoir, can solve the problems of formation pollution and high cost caused by high drilling fluid density in oil reservoirs with a high pressure coefficient, and at the same time, this model can be used for injection-production allocation to determine the best pressure field injection-production policy.

[0071] The evaluation method described in the present invention can be directly used for quickly optimizing the pressure field of the oil reservoir. At the same time, this model can be used for injection-production allocation, which has important guiding significance for the design of the development adjustment plan of the oil reservoir.

[0072] In the above specific embodiments, the purpose, technical solution and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method for reservoir pressure prediction model, characterized in that, the optimization method includes: Step 1, collect geological development data of the target reservoir and establish a numerical simulation model of the target reservoir; Step 2, conduct an evaluation study on the pressure field status of the target reservoir to clarify the pressure field state in the study area before adjustment; Step 3, based on the pressure field distribution in the study area, screen out pressure field characterization parameters and determine the key parameters affecting the pressure field in the study area; Step 4, analyze the variation law of the pressure coefficient over time, affected by the radial flow pressure conduction law, and obtain the relationship between the pressure recovery duration and the change of the pressure coefficient; Step 5, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data in the study area, establish a pressure optimization prediction model; Step 6, comprehensively consider the well pattern status and development status in the target area, apply the pressure field optimization model, and determine the injection-production policy of the pressure field in the target area.

2. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, in Step 1, the geological development data collected for the target reservoir includes: reservoir structure, permeability, porosity, effective thickness, saturation, pressure measurement data, water phase density and viscosity, oil phase density and viscosity, relative permeability curve, well trajectories of injection wells and production wells, perforation positions, well pattern form, monthly oil production and water production of production wells, monthly water injection of injection wells, production time step, injection-production ratio, liquid production rate, and dynamic monitoring data.

3. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, Step 1 further includes: establishing a data volume file of the numerical simulation model of the target reservoir.

4. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, in Step 2, conducting an evaluation study on the pressure field status of the target reservoir to clarify the pressure field state in the study area before adjustment specifically includes: conducting an evaluation study on the pressure field status of the target reservoir to clarify the pressure field distribution and the pressure field optimization adjustment direction in the study area before adjustment.

5. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, in Step 3, based on the pressure field distribution in the study area, screening out pressure field characterization parameters and determining the key parameters affecting the pressure field in the study area specifically includes: based on the pressure field distribution in the study area, conduct an evaluation of the influencing factors of the pressure coefficient in the target area, screen out pressure field characterization parameters, conduct a sensitivity analysis of the variation law of the pressure field in the study area, and determine the key parameters affecting the pressure field in the study area.

6. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, in Step 3, based on the sensitivity analysis of the pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data in the study area, determine the injection-production ratio and liquid production rate affecting the pressure field.

7. The optimization method for reservoir pressure prediction model according to claim 1, characterized in that, in Step 4, analyzing the variation law of the pressure coefficient over time, affected by the radial flow pressure conduction law, and obtaining the relationship between the pressure recovery duration and the change of the pressure coefficient specifically includes: Based on numerical simulation models and sensitivity analysis of pressure field characterization parameters; Study and analyze the variation law of pressure coefficient over time. Affected by the pressure conduction law of radial flow, study the relationship between pressure build-up duration and the change of pressure coefficient.

8. A method for optimizing an oil reservoir pressure prediction model according to claim 1, characterized in that, in step 5, based on the sensitivity analysis of pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area, establishing a pressure optimization prediction model specifically includes: Based on the laws and sensitivity of the change value of the pressure coefficient in the study area with respect to injection-production ratio, liquid production rate, and pressure build-up duration, establish a pressure optimization prediction model.

9. A method for optimizing an oil reservoir pressure prediction model according to claim 1, characterized in that, in step 6, comprehensively considering the well pattern condition and development status of the target area, apply the pressure field optimization model to design pressure field adjustment schemes for different regions.

10. A method for optimizing an oil reservoir pressure prediction model according to claim 1, characterized in that, in step 6, compare the change value of the pressure coefficient and the build-up duration in the study area of different adjustment schemes, and optimize and determine the injection-production policy of the pressure field in the target area.

11. An oil reservoir pressure prediction model optimization system applying the method for optimizing an oil reservoir pressure prediction model according to any one of claims 1-10, characterized in that, the optimization system includes: A numerical simulation model establishment module, used to collect geological development data of the target oil reservoir and establish a numerical simulation model of the target oil reservoir; A pressure field state determination module, used to conduct an evaluation study on the pressure field condition of the target oil reservoir and clarify the pressure field state of the study area before adjustment; A key parameter determination module, used to screen out pressure field characterization parameters based on the pressure field distribution condition of the study area and determine the key parameters affecting the pressure field of the study area; A variation law analysis module, used to analyze the variation law of the pressure coefficient over time. Affected by the pressure conduction law of radial flow, obtain the relationship between pressure build-up duration and the change of pressure coefficient; An optimization prediction model establishment module, used to establish a pressure optimization prediction model based on the sensitivity analysis of pressure field characterization parameters, combined with the production dynamic data and dynamic monitoring data of the study area; A pressure field injection-production policy determination module, used to comprehensively consider the well pattern condition and development status of the target area, apply the pressure field optimization model to determine the injection-production policy of the pressure field in the target area.

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