Water invasion oil and gas well productivity prediction method and system based on well test interpretation and seepage theoretical calculation

Abstract

The invention discloses a water invasion oil and gas well productivity prediction method and system based on well test interpretation and seepage theoretical calculation, and relates to the technical field of oil and gas reservoir development. The method comprises the following steps: establishing a gas well test interpretation model based on well test data; performing historical data fitting on the gas well test interpretation model to obtain a productivity well test indication curve; analyzing the characteristics of a productivity well testing indication curve, and introducing additional pressure loss coefficient and laminar flow item coefficient correction based on a seepage mechanics theory to obtain a plane radial non-Darcy seepage productivity evaluation model of the water-invaded gas well; and building a block well type and stratified system correlation coefficient correction chart based on a non-Darcy seepage productivity evaluation model, and guiding reasonable production allocation and injection allocation of the oil and gas field / gas storage well. Rapid and reasonable production allocation and injection allocation of the water-invaded gas well can be achieved, the cost is reduced, the efficiency is improved, the number of well testing times is greatly reduced, and reasonable and efficient operation of oil-gas field development / gas storage injection and production is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of oil and gas reservoir development technology, specifically to a method and system for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations. Background Technology

[0002] During oil and gas field development and gas storage construction, as extraction progresses, the gradual decrease in reservoir pressure is often accompanied by edge and bottom water intrusion and reverse condensation. These factors lead to a slowdown in wellbore pressure and natural gas flow rate, making it difficult for produced water or condensate in the reservoir to be carried out of the wellbore with the natural gas. Consequently, it remains and accumulates at the bottom of the well, forming a liquid column, such as... Figure 2 As shown in the image, this liquid column not only increases the hydrostatic back pressure but also significantly weakens the gas well's self-flowing capability, potentially killing the well and severely impacting the gas field's recovery rate.

[0003] Gas well productivity analysis is fundamental to studying gas well dynamics and reservoir characteristics, playing a crucial role in guiding oil and gas field development and gas storage facility operation. Therefore, research on productivity evaluation methods for water-encroached gas wells is essential for dynamic prediction and project implementation in oil and gas fields / storages. A survey of the main controlling factors influencing the injection and production capacity of oil and gas fields / storages reveals that gas well injection and production capacity is primarily affected by formation seepage capacity, wellbore flow capacity, tubing erosion flow rate, critical sand production in the formation, and minimum back pressure at the wellhead. Among these, formation seepage capacity has the greatest impact, accounting for as much as 48%.

[0004] Currently, the evaluation of production capacity in water-infiltrated gas wells typically employs inter-well analogy or the planar radial Darcy flow production capacity equation, neglecting the pressure loss caused by non-Darcy flow. This approach is detrimental to the rational production and injection allocation of water-infiltrated gas wells. Therefore, developing an accurate, reliable, simple, and rapid planar radial non-Darcy flow production capacity model for water-infiltrated gas wells, by fully integrating test production dynamics data and the geological characteristics of oil and gas reservoirs, is a pressing issue that needs to be addressed in actual production. This model would guide the rational production of gas wells with severe water infiltration and condensation phenomena, ultimately achieving stable and efficient gas field development. Summary of the Invention

[0005] The purpose of this invention is to propose a method and system for predicting the production capacity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations. This method enables rapid and rational production and injection allocation for water-invaded gas wells, significantly reducing the number of well tests while ensuring the rational and efficient operation of oil and gas field development and gas storage injection and production.

[0006] According to a first aspect of the present disclosure, a method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculations is provided, comprising the following steps:

[0007] Based on well test data, a well test interpretation model for gas wells is established;

[0008] Historical data was used to fit the gas well test interpretation model to obtain the production capacity test indication curve;

[0009] By analyzing the characteristics of the production capacity test indicator curve, and based on the seepage mechanics theory, an additional pressure loss coefficient and laminar flow term coefficient are introduced for correction, and a planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells is obtained.

[0010] Based on the non-Darcy flow productivity evaluation model, a modified chart of block-specific well types and stratified correlation coefficients is constructed to guide the rational allocation of production and injection in oil and gas fields / gas storage wells.

[0011] According to a second aspect of the present disclosure, a system for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations is provided, comprising:

[0012] The interpretation model construction module establishes a gas well test interpretation model based on well test data;

[0013] The indicator curve fitting module uses historical data to fit the gas well test interpretation model to obtain the production capacity test indicator curve.

[0014] The evaluation model acquisition module analyzes the characteristics of the production capacity test indicator curve, and introduces additional pressure loss coefficient and laminar flow term coefficient correction based on seepage mechanics theory to obtain the planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells.

[0015] The revised chart module constructs a revised chart of block-specific well types and stratified correlation coefficients based on the non-Darcy flow productivity evaluation model, guiding the rational allocation of production and injection in oil and gas fields / gas storage wells.

[0016] According to a third aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the memory. When the processor executes the program, it implements the aforementioned method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations.

[0017] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the aforementioned method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations.

[0018] Compared with existing technologies, the technical solutions adopted in this invention have the following advantages: The production capacity prediction method and system proposed in this invention are specifically designed for gas wells facing water intrusion challenges in oil and gas fields and gas storage facilities. They aim to achieve rapid and rational production and injection allocation, thereby ensuring the efficient and stable operation of oil and gas field development activities and gas storage facility injection and production operations. Compared with traditional methods, this method not only provides accurate and reliable prediction results but also is simple and quick to operate, demonstrating excellent performance and adaptability, especially in gas wells where water intrusion and condensation phenomena are significant. Attached Figure Description

[0019] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0020] Figure 1 This is a flowchart of a method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory.

[0021] Figure 2 Diagram of a liquid-gas well with bottom water intrusion;

[0022] Figure 3 A schematic diagram of a gas well interpretation model;

[0023] Figure 4 This is a production test indicator curve for an abnormal gas well.

[0024] Figure 5 A double logarithmic diagnostic plot of the gas well pressure recovery test analysis curve;

[0025] Figure 6 A comparison curve of IPR between the corrected capacity equation and the single-phase capacity equation;

[0026] Figure 7 A comparison curve of the IPR of the modified production capacity equation under different gas-liquid ratios;

[0027] Figure 8 To study the TPC curve intersection diagram of vertical well gas production in the block;

[0028] Figure 9 Intersection diagram of TPC curves for gas production from horizontal wells in the block;

[0029] Figure 10 A comparison chart of gas production capacity under different formation pressures in the block;

[0030] Figure 11 Corrected charts for laminar flow term coefficients and pressures in vertical wells of the block;

[0031] Figure 12 This is a chart showing the correction of flow term coefficients and pressure for horizontal wells in the block. Detailed Implementation

[0032] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and systems according to various embodiments of this disclosure. It should be noted that each block in a flowchart or block diagram may represent a module, segment, or portion of code, which may include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0036] Example 1:

[0037] like Figure 1 As shown in the figure, this embodiment provides a method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations, including the following steps:

[0038] S1. Based on well test data, establish a well test interpretation model for gas wells;

[0039] Specifically, such as Figure 3 As shown, based on the dynamic and static characteristics of oil and gas reservoirs and gas wells, wellbore models, well models, reservoir models, and boundary models are constructed. In conjunction with relevant data from system well testing and production testing, a gas well test interpretation model is established.

[0040] S2. Historical data are fitted to the gas well test interpretation model to obtain the production capacity test indication curve;

[0041] Specifically, by adding normalized pressure, normalized pressure derivative, and normalized pressure derivative integral of the material balance time to the gas well test interpretation model, the double logarithmic pressure guide curve, semi-logarithmic pressure difference curve, and historical fitting of production capacity test data are completed to obtain the corresponding pseudo-pressure binomial / exponential production capacity test indicator curve.

[0042] like Figure 4-5 As shown, after analyzing the production capacity test indicator curve, the slope is negative and the curve shape is abnormal; at the same time, the double logarithmic pressure guide curve has a significant hump effect; combined with the current production characteristics of the gas well, such as gas production with liquid, violent oil pressure fluctuations, and accelerated production decline, it is comprehensively judged that this is caused by water intrusion and liquid accumulation at the bottom of the well, and further production capacity model correction is required.

[0043] S3. Analyze the characteristics of the production capacity test indicator curve, and based on the seepage mechanics theory, introduce the additional pressure loss coefficient and laminar flow term coefficient for correction to obtain the plane radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells.

[0044] Specifically, according to the binomial production capacity equation, water intrusion at the bottom of the well will contaminate the near-wellbore reservoir, reducing effective permeability and causing changes in the laminar flow coefficient A. Simultaneously, the change from single-phase to multi-phase flow at the bottom of the well will affect the accuracy of flowing pressure test data and the gas phase production pressure differential under stable conditions. Therefore, it is necessary to correct the laminar flow coefficient A and production pressure differential of water-intruded gas wells to improve the production capacity evaluation model.

[0045] m(P e )-m(P wf ) = AQ + BQ 2

[0046]

[0047]

[0048] Where m(P) e ) represents the pseudo-pressure value corresponding to the formation pressure; m(P) wf ) represents the simulated pressure value corresponding to the wellbore flowing pressure; Q represents the tested gas production value corresponding to the production capacity test; K represents the effective permeability value of the near-wellbore zone of the test well; h represents the formation thickness value; r e To test the well control radius value; r w γ is the wellbore radius; S is the skin coefficient near the wellbore; β is the turbulence velocity coefficient; γ g The relative density of natural gas is T; the formation temperature is μ. g The value represents the viscosity of the natural gas layer. In the above binomial productivity equation, A is the laminar flow coefficient, which is mainly affected by effective permeability, well control radius, skin surface, deviation factor, etc. In the binomial productivity equation, B is the turbulence coefficient, which is mainly affected by rock type and cementation.

[0049] In this embodiment, the actual gas phase flow pressure P under steady-state conditions is assumed. wf Compared with the measured bottom hole flowing pressure P wf'With an error ΔPs, the laminar flow term coefficient changes from A to At. Through a series of transformations using pseudo-pressure, J function, and phase permeability relationship, combined with the high-pressure fluid property fitting equations for the study section (high-pressure seepage stage / transitional seepage stage / low-pressure seepage stage), the relationship between test yield and pressure is obtained:'

[0050] P wf ′=P wf +ΔPs

[0051] AA t

[0052] Bottom hole flowing pressure P wf After transforming the laminar flow term coefficient A, we have:

[0053] m(P e )-m((P wf (′-ΔPs)=A t Q+BQ 2

[0054]

[0055] Among them, P wf This represents the actual gas phase flow pressure; P wf ' is the measured bottomhole flowing pressure value in the gas phase; ΔPs is the error between the actual and measured flowing pressure values ​​in the gas phase; A t The corrected laminar flow term coefficient; P is the test pressure; This represents the average viscosity of natural gas. This represents the average compressibility factor of natural gas.

[0056] In this embodiment, the high-pressure fluid property fitting equations for the study area are fitting equations for the formation pressure, deviation factor, viscosity, etc., corresponding to the high-pressure gas phase seepage stage, the transitional seepage stage, and the low-pressure seepage stage of the target well.

[0057] PV = NRT

[0058] f(u, z, P) = f(P)

[0059] Where P is the test pressure; V is the volume of natural gas; N is the number of moles of natural gas; R is the gas constant; and T is the formation temperature.

[0060] In the high-pressure seepage stage of the fluid in the study area, when the formation pressure is greater than N1 (e.g., 21 MPa), the fitting equations for formation pressure, deviation factor, viscosity, etc. show a linear relationship between "deviation factor * viscosity" and formation pressure, thus obtaining the pseudo-pressure of the water-invaded gas well.

[0061] In the fluid transitional seepage stage of the study area, when the formation pressure is greater than N2 (e.g., 13.5 MPa) and less than N1 (e.g., 21 MPa), the fitting equations for formation pressure, deviation factor, viscosity, etc., show a polynomial relationship between "deviation factor * viscosity" and formation pressure, thus obtaining the pseudo-pressure of the water-invaded gas well.

[0062] In the low-pressure seepage stage of the fluid in the study area, when the formation pressure is less than N2 (e.g., 13.5 MPa), the "deviation factor * viscosity" is a constant in the fitting equation of formation pressure, deviation factor, and viscosity, thus obtaining the pseudo-pressure of the water-invaded gas well.

[0063]

[0064] Where P e This refers to formation pressure.

[0065] In this embodiment, based on the relationship between test production and pressure, and combined with relevant well test / production test data points, a system of linear equations about parameters At, n, and B is established. By solving the system of linear equations, a series of At, n, and B values ​​are obtained, leading to the corrected production capacity prediction model for the well.

[0066] D T =(P wf1 ′ 2 -P wf2 ′ 2 P wf2 ′ 2 -P wf3 ′ 2 , ..., P wfn-1 ′ 2 -P wfn ′ 2 )

[0067] β T =(A t n, B, Q AOF )

[0068] Therefore, the revised capacity forecasting model is as follows:

[0069] m(P e )-m((1-n)P wf ′)=A t Q+BQ 2

[0070] Where n is the additional pressure loss coefficient.

[0071] In this embodiment, based on the modified production capacity prediction model and combined with the gas-liquid two-phase pseudo-pressure function, gas-liquid motion equation, and phase permeability relationship equation, it can be seen that the key parameters At and n are functions of the test pressure and GWR. Through a series of well test / test data of liquid-accumulated gas wells, the influence law between the variables is obtained:

[0072] m(P e )-m((n+1)P wf ′)=A t Q+BQ 2

[0073] The pseudo-pressure function for the gas-liquid two-phase system is:

[0074]

[0075] The equations of motion for gas and liquid are as follows:

[0076]

[0077] The gas-liquid mass flow rate is:

[0078]

[0079] A t n = f(P, GWR)

[0080] Where m(p) is the pseudo-pressure function of the gas-water two-phase system; γ is the ratio of water to gas mass flow rate; GWR is the gas-liquid ratio; n is the additional pressure loss coefficient; ρ g ρ is the density of natural gas. w For water density, Q g For natural gas production, Q w For water production; μ g This represents the viscosity value of natural gas; μ w K represents the water viscosity value. rg K represents the relative permeability of natural gas. rw The relative permeability of water; m w The mass flow rate of water; m g This represents the mass flow rate of natural gas.

[0081] In this embodiment, the research approach combining the fitting of the gas well system test interpretation model and the gas well seepage mechanics theoretical model is applied to the production capacity prediction of liquid-filled gas wells in gas storage facilities, enriching the method for predicting the production capacity of liquid-filled gas wells in gas storage facilities; its dimensionless pseudo-pressure-time expression for well testing is:

[0082]

[0083] The gas well seepage mechanics equation is:

[0084]

[0085] Among them, P D t is a dimensionless pressure. D t is dimensionless time; C is the test time; i φ is the overall compressibility coefficient; φ is the formation porosity value; Q SC For testing production volume;

[0086] The planar radial non-Darcy flow productivity evaluation model for water-infiltrated gas wells improves the scientific validity and rationality of conventional evaluation methods such as inter-well analogy and planar radial Darcy flow. Its non-Darcy flow productivity evaluation model is as follows:

[0087]

[0088] This equation is the planar radial Darcy flow equation for the high-pressure seepage stage of a gas well.

[0089]

[0090] This equation is the planar radial Darcy flow equation for the low-pressure seepage stage of a gas well.

[0091] This invention realizes the transformation of oil and gas field / gas storage water-inundated / reverse condensate gas well productivity evaluation from single-phase non-Darcy / multi-phase Darcy flow to multi-phase non-Darcy flow. The multi-phase Darcy flow method involves converting water production into gas production for productivity prediction; the single-phase non-Darcy flow method does not consider liquid production, only analyzes gas production data, and then processes it using the single-phase method.

[0092] S4. Based on the non-Darcy flow productivity evaluation model, construct a block-specific well type and stratified correlation coefficient correction chart to guide the rational allocation of production and injection of oil and gas field / gas storage wells.

[0093] Specifically, such as Figure 11-12 As shown, based on the established planar radial non-Darcy flow productivity evaluation model for water-invaded gas wells, a series of laminar flow term coefficients At and additional pressure loss coefficients n were obtained. According to different gas-liquid ratios and pressure ranges, correlation coefficient charts for well types and strata in this block were established and verified with actual production data, which can quickly guide the rational production allocation of water-invaded / condensate gas wells in gas storage facilities.

[0094] like Figure 6 As shown, the IPR curves comparing the corrected production capacity equation and the single-phase production capacity equation indicate that the IPR curve for gas wells considering water intrusion is lower, resulting in a smaller unobstructed flow rate. Ignoring water intrusion would inevitably lead to an overestimation of the gas well's production capacity.

[0095] like Figure 7As shown, the IPR curves of the modified production capacity equation under different gas-liquid ratios indicate that as the gas-liquid ratio decreases, the IPR curve gradually shifts to the lower left, indicating a decline in gas well production capacity. This is mainly due to the increasing influence of the liquid phase on gas phase seepage.

[0096] Based on the above methods, the gas storage facility studied was converted from a gas-cap-oil-ring-edge-bottom-water oil-gas reservoir. The reservoir has a burial depth of 1900-2500m, a thickness of 100-200m, a porosity of 18.2%, a permeability of 279mD, and an original formation pressure of 26.01MPa.

[0097] The evaluation model for non-Darcy flow productivity of vertical wells in Block A of the study area is as follows:

[0098] m(P e -0.837m(P wf = 0.019Q + 0.004Q 2

[0099] The non-Darcy flow productivity evaluation model for horizontal wells in Block B of the study area is as follows:

[0100] m(P e -0.791m(P wf = 0.033Q + 0.003Q 2

[0101] like Figure 8-9 As shown, based on the established non-Darcy flow production capacity evaluation model and modified chart, combined with erosion and sand production flow limitations, the reasonable injection and production capacity of a single well in a gas storage facility was evaluated. The average daily injection and production of vertical wells in the studied gas storage facility is 730,000 cubic meters, while that of horizontal wells is 1.29 million cubic meters. The injection and production capacity of horizontal wells is twice that of vertical wells.

[0102] like Figure 10 As shown, based on the theoretical calculation of single-well gas production capacity and taking into account factors such as sand production and erosion, the maximum daily gas production capacity of the studied gas storage facility is between 8.5 million and 46 million cubic meters under operating pressures of 8–26 MPa.

[0103] The accuracy of the single-well injection-production capacity design results was further verified using actual production data. The actual single-well injection-production capacity during production was determined by monitoring daily oil production, daily gas production, and formation pressure. The design results for the single-well injection-production capacity were consistent with the actual production data. This further validated the reliability and practicality of the water-inundated / condensate gas well productivity evaluation method.

[0104] Example 2:

[0105] This embodiment provides a water-invaded oil and gas well productivity prediction system based on well test interpretation and seepage theory calculations, including:

[0106] The interpretation model construction module establishes a gas well test interpretation model based on well test data;

[0107] The indicator curve fitting module uses historical data to fit the gas well test interpretation model to obtain the production capacity test indicator curve.

[0108] The evaluation model acquisition module analyzes the characteristics of the production capacity test indicator curve, and introduces additional pressure loss coefficient and laminar flow term coefficient correction based on seepage mechanics theory to obtain the planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells.

[0109] The revised chart module constructs a revised chart of block-specific well types and stratified correlation coefficients based on the non-Darcy flow productivity evaluation model, guiding the rational allocation of production and injection in oil and gas fields / gas storage wells.

[0110] Example 3:

[0111] An electronic device includes a memory, a processor, and a computer program stored in the memory and running thereon. When the processor executes the program, it implements the aforementioned method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculations, comprising:

[0112] Based on well test data, a well test interpretation model for gas wells is established;

[0113] Historical data was used to fit the gas well test interpretation model to obtain the production capacity test indication curve;

[0114] By analyzing the characteristics of the production capacity test indicator curve, and based on the seepage mechanics theory, an additional pressure loss coefficient and laminar flow term coefficient are introduced for correction, and a planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells is obtained.

[0115] Based on the non-Darcy flow productivity evaluation model, a modified chart of block-specific well types and stratified correlation coefficients is constructed to guide the rational allocation of production and injection in oil and gas fields / gas storage wells.

[0116] Example 4:

[0117] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for predicting the productivity of water-intruded oil and gas wells based on well test interpretation and seepage theory calculations, comprising:

[0118] Based on well test data, a well test interpretation model for gas wells is established;

[0119] Historical data was used to fit the gas well test interpretation model to obtain the production capacity test indication curve;

[0120] By analyzing the characteristics of the production capacity test indicator curve, and based on the seepage mechanics theory, an additional pressure loss coefficient and laminar flow term coefficient are introduced for correction, and a planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells is obtained.

[0121] Based on the non-Darcy flow productivity evaluation model, a modified chart of block-specific well types and stratified correlation coefficients is constructed to guide the rational allocation of production and injection in oil and gas fields / gas storage wells.

[0122] This invention efficiently utilizes test data from water-embedded gas wells to conduct accurate production capacity assessments, thereby enabling rapid and rational configuration of production and injection volumes for these wells. The aim is to reduce costs, improve efficiency, and significantly decrease the frequency of well testing. While ensuring the rational and efficient operation of oil and gas field development activities and gas storage injection and production operations, it achieves the goal of cost reduction and efficiency improvement.

[0123] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, which can then be stored in a storage device for execution by a computer device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.

[0124] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0125] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations, characterized in that, Includes the following steps: Based on well test data, a well test interpretation model for gas wells is established; Historical data was used to fit the gas well test interpretation model to obtain the production capacity test indication curve; By analyzing the characteristics of the production capacity test indicator curve, and based on the seepage mechanics theory, an additional pressure loss coefficient and laminar flow term coefficient are introduced for correction, and a planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells is obtained. Based on the non-Darcy flow productivity evaluation model, a modified chart of block-specific well types and stratified correlation coefficients is constructed to guide the rational allocation of production and injection in oil and gas fields / gas storage wells.

2. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 1, characterized in that, Based on the dynamic and static characteristics of oil and gas reservoirs and gas wells, wellbore models, well models, reservoir models, and boundary models are constructed. In conjunction with relevant data from system well testing and production testing, a gas well test interpretation model is established.

3. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 1, characterized in that, By adding normalized pressure, normalized pressure derivative, and normalized pressure derivative integral of material balance time to the gas well test interpretation model, the double logarithmic pressure guide curve, semi-logarithmic pressure difference curve, and historical fitting of production capacity test data are completed, and the corresponding pseudo-pressure binomial / exponential production capacity test indicator curve is obtained.

4. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 1, characterized in that, According to the binomial productivity equation, the laminar flow coefficient A and the turbulent flow coefficient B of water-entrapped gas wells are respectively: m(P e )-m(P wf )=AQ+BQ 2 Where m(P) e ) represents the pseudo-pressure value corresponding to the formation pressure; m(P) wf ) represents the simulated pressure value corresponding to the wellbore flowing pressure; Q represents the tested gas production value corresponding to the production capacity test; K represents the effective permeability value of the near-wellbore zone of the test well; h represents the formation thickness value; r e To test the well control radius value; r w γ is the wellbore radius; S is the skin coefficient near the wellbore; β is the turbulence velocity coefficient; γ g The relative density of natural gas is T; the formation temperature is μ. g A represents the viscosity of natural gas; B represents the laminar flow coefficient and C represents the turbulent flow coefficient.

5. The method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 4, characterized in that, Assuming the actual gas-phase flow pressure P under steady-state conditions wf Compared with the measured bottom hole flowing pressure P wf 'There is an error ΔPs, and the laminar flow term coefficient changes from A to A' t By transforming the pseudo-pressure, J-function, and phase permeability relationship, and combining the fitting equations of the high-pressure fluid properties in the study area, the relationship between test output and pressure is obtained: P wf ’=P wf +ΔPS A=A t Bottom hole flowing pressure P wf After transforming the laminar flow term coefficient A, we have: m(P e )-m)(P wf '-ΔPs)=A t Q+BQ 2 Among them, P wf This represents the actual gas phase flow pressure; P wf ' is the measured bottomhole flowing pressure value in the gas phase; ΔPs is the error between the actual and measured flowing pressure values ​​in the gas phase; A t The corrected laminar flow term coefficient; P is the test pressure; This represents the average viscosity of natural gas. This represents the average compressibility factor of natural gas.

6. The method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 5, characterized in that, The fitting equations for the high-pressure fluid properties in the study area are the fitting equations for the formation pressure, deviation factor, and viscosity corresponding to the high-pressure gas phase seepage stage, the transitional seepage stage, and the low-pressure seepage stage of the target well. PV = NRT f(u, z, P) = f(P) Where P is the test pressure; V is the volume of natural gas; N is the number of moles of natural gas; R is the gas constant; and T is the formation temperature.

7. The method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculations as described in claim 6, characterized in that, In the high-pressure seepage stage of the fluid in the study area, when the formation pressure is greater than N1, the fitting equation of formation pressure, deviation factor, and viscosity shows that "deviation factor * viscosity" has a linear relationship with the formation pressure, thus obtaining the pseudo-pressure of the water-invaded gas well. In the fluid transitional seepage stage of the study area, when the formation pressure is greater than N2 and less than N1, the fitting equations for formation pressure, deviation factor, and viscosity show a polynomial relationship between "deviation factor * viscosity" and formation pressure, thus obtaining the pseudo-pressure of the water-invaded gas well. In the low-pressure seepage stage of the fluid in the study area, when the formation pressure is less than N2, the fitting equation of formation pressure, deviation factor, and viscosity has "deviation factor * viscosity" as a constant, thus obtaining the pseudo-pressure of the water-invaded gas well. Where P e This refers to formation pressure.

8. The method for predicting the productivity of water-infiltrated oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 5, characterized in that, Based on the relationship between test production and pressure, and combined with relevant well test / production test data points, a system of linear equations about parameters At, n, and B is established. By solving the system of linear equations, a series of At, n, and B values ​​are obtained, leading to the corrected production capacity prediction model for the well. D T =(P wf1 ' 2 -P wf2 ' 2 ,P wf2 ' 2 -P wf3 ' 2 ,…,P wfn-1 ' 2 -P wfn ' 2 ) b T =(A t ,n,B,Q AOF ) Therefore, the revised capacity forecasting model is as follows: m(P e )-m((1-n)P wf ')=A t Q+BQ 2 Where n is the additional pressure loss coefficient.

9. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 8, characterized in that, Based on the modified production capacity prediction model, combined with the pseudo-pressure function of the gas-liquid two-phase system, the gas-liquid motion equation, and the phase permeability relationship equation, the key parameters At and n are functions of the test pressure and GWR. Through a series of well test / test data of liquid-accumulated gas wells, the influence relationship between the variables is obtained: m(P e )-m((n+1)P wf ')=A t Q+BQ 2 The pseudo-pressure function for the gas-liquid two-phase system is: The equations of motion for gas and liquid are as follows: The gas-liquid mass flow rate is: A t ,n=f(P,GWR) Where m(p) is the pseudo-pressure function of the gas-water two-phase system; γ is the ratio of water to gas mass flow rate; GWR is the gas-liquid ratio; n is the additional pressure loss coefficient; ρ g ρ is the density of natural gas. w For water density, Q g For natural gas production, Q w For water production; μ g This represents the viscosity value of natural gas; μ w K represents the water viscosity value. rg K represents the relative permeability of natural gas. rw The relative permeability of water; m w The mass flow rate of water; m g This represents the mass flow rate of natural gas.

10. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 9, characterized in that, The dimensionless pseudo-pressure-time expression for well testing is: The gas well seepage mechanics equation is: Among them, P D t is a dimensionless pressure. D t is dimensionless time; C is the test time; i φ is the overall compressibility coefficient; φ is the formation porosity value; Q SC This is for testing production volume.

11. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 10, characterized in that, The planar radial non-Darcy flow productivity evaluation model for water-infiltrated gas wells is as follows: This equation is the planar radial Darcy flow equation for the high-pressure seepage stage of a gas well. This equation is the planar radial Darcy flow equation for the low-pressure seepage stage of a gas well. Among them, the multiphase Darcy flow method is a method of predicting production capacity by converting water production into gas production; the single-phase non-Darcy flow method does not consider the liquid production, only analyzes the gas production data, and then processes it using the single-phase method.

12. The method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculation as described in claim 11, characterized in that, Based on the planar radial non-Darcy flow productivity evaluation model for water-infiltrated gas wells, a series of laminar flow term coefficients At and additional pressure loss coefficients n were obtained. According to different gas-liquid ratios and pressure ranges, a modified chart of block-specific well types and layer-specific correlation coefficients was constructed.

13. A system for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations, characterized in that, include: The interpretation model construction module establishes a gas well test interpretation model based on well test data; The indicator curve fitting module uses historical data to fit the gas well test interpretation model to obtain the production capacity test indicator curve. The evaluation model acquisition module analyzes the characteristics of the production capacity test indicator curve, and introduces additional pressure loss coefficient and laminar flow term coefficient correction based on seepage mechanics theory to obtain the planar radial non-Darcy seepage production capacity evaluation model for water-infiltrated gas wells. The revised chart module constructs a revised chart of block-specific well types and stratified correlation coefficients based on the non-Darcy flow productivity evaluation model, guiding the rational allocation of production and injection in oil and gas fields / gas storage wells.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running thereon, characterized in that, When the processor executes the program, it implements the method for predicting the production capacity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations as described in any one of claims 1-12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for predicting the productivity of water-invaded oil and gas wells based on well test interpretation and seepage theory calculations, as described in any one of claims 1-12.

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