Carbonate gas reservoir productivity prediction method and device

Abstract

The invention discloses a carbonate rock gas reservoir productivity prediction method and device. The method comprises the following steps: establishing a first seepage equation of a quasi-steady-state seepage mode and a second seepage equation of an unsteady-state seepage mode in the carbonate rock gas reservoir in combination with a non-darcy effect and a stress sensitive effect; performing dimensionless processing and Laplace transformation to obtain a first comprehensive control differential equation and a second comprehensive control differential equation; performing Fourier cosine transform, combining a Bessel function general solution, and obtaining a pressure distribution solution in the pull-type space through point source and line source functions; a real space quasi-pressure solution is obtained through inversion according to the pressure distribution solution in the pull-type space, and then a yield prediction model under the fixed flowing bottomhole pressure condition is obtained; and based on the established productivity prediction model, predicting the yield of the target well according to the fixed bottomhole flowing pressure of the target well in the carbonate rock gas reservoir. According to the method, complex relevance among triple media of the carbonate gas reservoir is fully considered, and reasonable yield prediction is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of carbonate oil and gas reservoir development, and in particular to a carbonate gas reservoir productivity prediction method and device. Background Art

[0002] Currently, carbonate reservoirs primarily consist of three types of media: pores, caves, and natural fractures. These reservoirs are diverse and highly heterogeneous. The genesis of carbonate reservoirs varies, with pores, caves, and fractures exhibiting varying scales, connectivity, and reservoir space types, which in turn determines the complexity of fluid flow patterns within the reservoirs.

[0003] Currently, most research on the seepage mechanisms of carbonate gas reservoirs still uses the traditional triple-medium model. In reality, carbonate reservoirs consist of a triple medium: pores, vugs, and natural fractures. The interrelationships between these different scales are complex, and different correlations correspond to different fluid flow patterns. The traditional triple-medium model cannot fully describe the flow relationships among pores, vugs, and fractures. Summary of the Invention

[0004] Since the traditional method does not take into account the mutual correlation between the different scale media of pores, holes and fractures in carbonate reservoirs, its applicability to the evaluation of carbonate gas reservoir productivity under different seepage modes is affected. Based on the existing theory, the inventors consider the high-speed non-Darcy effect and stress sensitivity effect as the basis for establishing seepage equations under different modes. Due to the complex correlation between the three media of carbonate gas reservoirs, the flow process of fluid from the reservoir to the wellbore exhibits a high-speed non-Darcy seepage law; the stress sensitivity effect generated by natural fractures has a certain influence on pressure propagation, so considering the stress sensitivity effect can be closer to the actual seepage situation in the medium.

[0005] The inventors have made the present invention based on the above-mentioned understanding, and through specific implementation methods, provide a method and device for predicting the productivity of carbonate gas reservoirs, thereby realizing reasonable prediction of the productivity of carbonate gas reservoirs.

[0006] In a first aspect, an embodiment of the present invention provides a method for establishing a carbonate gas reservoir productivity prediction model, comprising:

[0007] Combining the non-Darcy effect and stress sensitivity effect, the first seepage equation of the pseudo-steady-state seepage mode and the second seepage equation of the unsteady-state seepage mode in carbonate gas reservoirs are established.

[0008] After dimensionless processing and Laplace transformation, a first comprehensive governing differential equation of the first seepage equation and a second comprehensive governing differential equation of the second seepage equation are obtained;

[0009] Performing Fourier cosine transform on the first integrated governing differential equation and the second integrated governing differential equation, combining the general solution of the Bessel function, and obtaining a pressure distribution solution in the pull-type space through point source and line source functions;

[0010] By inversion, a real space pseudo-pressure solution is obtained from the pressure distribution solution in the pull-type space;

[0011] Based on the real-space pseudo-pressure distribution solution, a productivity prediction model under a constant bottom hole flowing pressure condition is obtained.

[0012] In a second aspect, an embodiment of the present invention provides a method for predicting the productivity of a carbonate gas reservoir, comprising:

[0013] Based on the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir, the dimensionless production under the constant bottom hole flowing pressure condition is determined by the corresponding relationship between the dimensionless pressure under the constant production condition and the dimensionless production under the constant bottom hole flowing pressure condition;

[0014] According to the constant bottom hole flow pressure and the dimensionless production under the constant bottom hole flow pressure condition, the gas production of the target well is predicted by the productivity prediction model under the constant bottom hole flow pressure condition, and the productivity prediction model is established by the above-mentioned carbonate gas reservoir productivity prediction model establishment method.

[0015] In a third aspect, an embodiment of the present invention provides a device for establishing a carbonate gas reservoir productivity prediction model, comprising:

[0016] A seepage equation establishment module is used to establish a first seepage equation for a pseudo-steady-state seepage mode and a second seepage equation for a non-steady-state seepage mode in a carbonate gas reservoir by combining the non-Darcy effect and the stress sensitivity effect;

[0017] a comprehensive control differential equation establishing module, configured to obtain a first comprehensive control differential equation of the first seepage equation and a second comprehensive control differential equation of the second seepage equation through dimensionless processing and Laplace transformation;

[0018] a pressure distribution solution determination module in the pull-type space, configured to perform a Fourier cosine transform on the first integrated governing differential equation and the second integrated governing differential equation, and obtain a pressure distribution solution in the pull-type space by combining the general solution of the Bessel function with point source and line source functions;

[0019] a real space pseudo-pressure solution determination module, configured to obtain a real space pseudo-pressure solution from the pressure distribution solution in the pull-type space by inversion;

[0020] The production capacity prediction model establishment module is used to obtain a production capacity prediction model under a constant bottom hole flow pressure condition based on the real space pseudo-pressure distribution solution.

[0021] In a fourth aspect, an embodiment of the present invention provides a carbonate gas reservoir productivity prediction device, comprising:

[0022] The dimensionless production determination module under the condition of constant bottom hole flowing pressure is used to determine the dimensionless production under the condition of constant bottom hole flowing pressure based on the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir by using the corresponding relationship between the dimensionless pressure under the condition of constant production and the dimensionless production under the condition of constant bottom hole flowing pressure;

[0023] The production prediction module is used to predict the gas production of the target well based on the fixed bottom hole flow pressure and the dimensionless production under the fixed bottom hole flow pressure conditions, through the production capacity prediction model under the fixed bottom hole flow pressure conditions, and the production capacity prediction model is established by the above-mentioned carbonate gas reservoir production capacity prediction model establishment method.

[0024] In a fifth aspect, an embodiment of the present invention provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the method for establishing a carbonate gas reservoir production capacity prediction model or the method for predicting carbonate gas reservoir production capacity are implemented.

[0025] In a sixth aspect, an embodiment of the present disclosure provides a server comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for establishing a carbonate gas reservoir production capacity prediction model or the method for predicting carbonate gas reservoir production capacity is implemented.

[0026] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0027] The method for establishing a carbonate gas reservoir productivity prediction model provided by an embodiment of the present invention establishes a seepage equation that considers high-speed non-Darcy effects and stress sensitivity effects under different seepage modes. Through Laplace and Fourier cosine transforms, combined with the general solution of Bessel functions, a pressure distribution solution in Laplace space is obtained using point source and line source functions. A real-space pseudo-pressure solution is obtained through inversion, and a productivity prediction model is then derived under constant bottomhole flowing pressure conditions. Based on this established productivity prediction model, reasonable productivity prediction can be achieved for carbonate gas reservoirs with complex reservoir relationships among pores, caves, and fractures.

[0028] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0029] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0031] Figure 1 This is a flow chart of a method for establishing a carbonate gas reservoir productivity prediction model in Example 1 of the present invention;

[0032] Figure 2 This is a flow chart of a carbonate gas reservoir productivity prediction method in Example 2 of the present invention;

[0033] Figure 3 A schematic diagram of the structure of a device for establishing a carbonate gas reservoir productivity prediction model according to an embodiment of the present invention;

[0034] Figure 4 Schematic diagram of the structure of a carbonate gas reservoir productivity prediction device in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0036] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0037] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of any conflict with any incorporated document, the content of this specification shall prevail.

[0038] Example 1

[0039] The first embodiment of the present invention provides a method for establishing a carbonate gas reservoir productivity prediction model. Figure 1 As shown, the following steps are included:

[0040] Step S11: combining the non-Darcy effect and the stress sensitivity effect, establishing a first seepage equation of a pseudo-steady-state seepage mode and a second seepage equation of a non-steady-state seepage mode in the carbonate gas reservoir.

[0041] Carbonate reservoirs are highly complex, developing a triple media structure of matrix pores, caves, and natural fractures. Consequently, diverse media interactions exist. Within the reservoir medium, only natural fractures connect to the deviated wellbore. Meanwhile, caves and matrix pores are each connected to natural fractures, resulting in crossflow between the media. Consequently, the various media interactions include: fluids in pores accumulate in caves and flow through fractures to the wellbore; fluids in pores or caves flow through fractures to the wellbore due to direct communication with the fractures. Fluid flow patterns primarily include: interstitial flow within large-scale fractures, cave flow within large-scale unfilled caves, and pore flow within small- and medium-scale fracture-dissolved pores and semi-filled caves. Generally speaking, the flow of fluids from pores or caves into fracture systems can be summarized as either pseudo-steady-state flow or unsteady-state flow.

[0042] The flow of fluid in pores or caves into the fracture system is divided into two modes: pseudo-steady flow and unsteady flow. The seepage equations considering high-speed non-Darcy effect and stress sensitivity effect are established under different fluid flow modes.

[0043] Model 1: Pseudo-steady-state flow. It is assumed that the seepage mode of the matrix and natural fracture system and the seepage mode of the cave and natural fracture system are both pseudo-steady-state flow. The established seepage equation is as follows:

[0044]

[0045] in:

[0046] The pseudo-steady-state crossflow from matrix to natural fracture system and from cave to natural fracture system are:

[0047]

[0048]

[0049] The meanings of the physical parameters in the above formulas are as follows:

[0050] <![CDATA[m f --Natural fracture system pseudo-pressure, MPa;]]> <![CDATA[m m --matrix system pseudo-pressure, MPa;]]> <![CDATA[m v --Pseudo-pressure of cave system, MPa;]]> <![CDATA[k fv --Vertical permeability of natural fracture system, md;]]> <![CDATA[k fh --horizontal permeability of natural fracture system, md;]]> <![CDATA[k m --Matrix system permeability, md;]]> <![CDATA[k v --Permeability of the cave system, md;]]> <![CDATA[φ f --Natural fracture porosity, decimal;]]> <![CDATA[μ g --Natural gas viscosity, mPa·s;]]> <![CDATA[C tf --Compression coefficient of natural fracture system, MPa -1 ;]]> <![CDATA[α m --matrix shape factor, m -2 ;]]> <![CDATA[α v --Cave shape factor, m -2 ;]]> <![CDATA[α t --Unit conversion factor, 0.0864;]]> r - radial coordinate in cylindrical coordinates, m; z - vertical coordinate in cylindrical coordinates, m; <![CDATA[φ m --Porosity of the matrix system, decimal;]]> <![CDATA[φ v --Porosity of the cave system, decimal;]]> <![CDATA[C tm --Matrix system compressibility, MPa -1 ;]]> <![CDATA[C tv --Compression coefficient of cave system, MPa -1 . ]]>

[0051] Model 2: Unsteady flow. It is assumed that the gas in the matrix and the cave flows into the natural fracture system in an unsteady diffusion manner. The matrix pores and the cave are regarded as spheres, and each has independent seepage equations and boundary conditions.

[0052] For the matrix, the pseudo-pressure is introduced. In spherical coordinates, the governing equation (seepage equation + boundary conditions) is:

[0053]

[0054] m m (r m ,0)=m m (6)

[0055]

[0056]

[0057] For the cave system, the pseudo-pressure is introduced and the governing equation in spherical coordinates is:

[0058]

[0059] m v (r v ,0)=m v (10)

[0060]

[0061]

[0062] For the natural fracture system, in cylindrical coordinates, the governing equation is:

[0063]

[0064] The meanings of the physical parameters in the above formulas are as follows:

[0065] <![CDATA[r m --Radius of the matrix microcircle, m;]]> <![CDATA[r v --Cave microcircle radius, m;]]> <![CDATA[R m --matrix radius, m;]]> <![CDATA[R v --Cave radius, m;]]> μ--fluid viscosity.

[0066] Step S12: After dimensionless processing and Laplace transformation, a first comprehensive governing differential equation of the first seepage equation and a second comprehensive governing differential equation of the second seepage equation are obtained.

[0067] By making equations (1), (3) and (4) dimensionless, we obtain the mathematical model:

[0068]

[0069]

[0070]

[0071] Among them, ω f --storage capacity ratio of natural fracture system, decimal;

[0072] ω m --matrix system storage capacity ratio, decimal;

[0073] ω v --storage capacity ratio of cave system, decimal;

[0074] λ m --The crossflow coefficient between the matrix and the natural fractures, dimensionless;

[0075] λ v --The crossflow coefficient between caves and natural fractures, dimensionless.

[0076] After dimensionless processing, the governing equations of the matrix and cave are:

[0077]

[0078] m jD (r jD ,0)=0 (18)

[0079]

[0080]

[0081] After dimensionless processing, the governing equation of the natural fracture system is:

[0082]

[0083] The boundary conditions of the natural fracture system in the pseudo-steady-state model and the unsteady-state model are the same, as follows:

[0084] Initial conditions:

[0085] m j (r,t=0)=m i ,(j=f,m,v) (22)

[0086] The lateral outer boundary is a closed boundary:

[0087]

[0088] where r e —Reservoir detection radius, m;

[0089] When a point source (x w ,y w ,z w ) produces at a fixed output q on the inner boundary, its inner boundary condition is expressed as:

[0090]

[0091] Among them, x w ,y w ,z w are the coordinates of the wellbore center respectively; ε is the infinitesimal length; B gi is the gas volume coefficient under original formation conditions.

[0092] According to the assumptions, the top and bottom of the reservoir are impermeable boundaries, and the top and bottom boundaries are expressed as:

[0093]

[0094] Where h is the effective thickness of the reservoir, m.

[0095] After dimensionless processing,

[0096] The initial conditions are:

[0097] m jD (r D ,t D =0)=0,(j=f,m,v) (26)

[0098] The lateral external boundary conditions are:

[0099]

[0100] The inner boundary conditions are:

[0101]

[0102] Top and bottom closed boundary conditions:

[0103]

[0104] Laplace transform:

[0105] 1) Pseudo-steady-state flow. Perform Laplace transform on the dimensionless mathematical model and boundary conditions to obtain:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] Where: s--Laplace variable;

[0113] --- represents the Laplace space variable.

[0114] Combining Equations (31) and (32), Equation (30) can be rearranged and transformed to obtain the first comprehensive control differential equation of the pseudo-steady-state model:

[0115]

[0116] in:

[0117] 2) Unsteady flow. The dimensionless matrix and cave control equations are subjected to Laplace transformation to obtain:

[0118]

[0119]

[0120]

[0121]

[0122] Where j=m,v.

[0123] After solving, the pressure distribution solution of the unsteady flow matrix and the cave in the Laplace space can be obtained:

[0124]

[0125] Perform Laplace transformation on the dimensionless natural fracture system control equation to obtain:

[0126]

[0127] For formula (41), find the mD and r vD The derivative of , and the result is substituted into Equation (42). After deformation and arrangement, the second comprehensive control differential equation of the unsteady model can be obtained:

[0128]

[0129] in:

[0130]

[0131] In summary, it can be seen that the structures of the comprehensive control differential equations of the quasi-steady-state and unsteady-state models are similar, the difference lies in the different definitions of f(s).

[0132] Step S13: Perform Fourier cosine transform on the first integrated control differential equation and the second integrated control differential equation, combine with the general solution of Bessel function, and obtain the pressure distribution solution in the pull-type space through point source and line source functions.

[0133] Performing Fourier cosine transform on the quasi-steady-state and unsteady-state comprehensive differential control equations, we can obtain:

[0134]

[0135]

[0136]

[0137] in:

[0138] After rearranging Equation (47), it can be transformed into the Bessel function of the zero-order imaginary quantity:

[0139]

[0140] Based on the Bessel function general solution expression of the zero-order imaginary quantity and the internal and external boundary conditions, the Fourier inversion is performed to obtain the bottom hole pressure point source solution:

[0141]

[0142] Among them, I0, I1, K0, and K1 are the first-kind zero-order modified Bessel function, the first-kind first-order modified Bessel function, the second-kind zero-order modified Bessel function, and the second-kind first-order modified Bessel function, respectively.

[0143] A well is composed of an infinite number of point sources. Considering the wellbore as a line source, integrating and summing along the wellbore, we can obtain the pressure distribution solution in the pull space:

[0144]

[0145] in:

[0146]

[0147]

[0148]

[0149]

[0150] D represents dimensionless processing, and -- represents the Laplace space variable.

[0151] Step S14: Obtain the real space pseudo-pressure solution from the pressure distribution solution in the pull-type space through inversion.

[0152] The solutions for the triple medium pressure distribution under quasi-steady and unsteady conditions (there are two solutions, the difference being the different definitions of f(s)) are in Laplace space, so they need to be converted to real space through inversion for easier calculation and analysis. The Stehfest numerical method can be used for inversion:

[0153] set up is the Latent space function, f(t) is the original function, then:

[0154]

[0155]

[0156] Where N is an even number, ranging from 10 to 30. For the previously derived Laplace space pseudo-pressure solution, we only need to replace the s variable in m(s) with (i represents the i-th number) and we can get the real-space pseudo-pressure solution at a certain moment.

[0157] Step S15: Based on the real-space pseudo-pressure distribution solution, a productivity prediction model under a constant bottom hole flowing pressure condition is obtained.

[0158] Taking the bottom hole as the target, based on the real-space pseudo-pressure distribution solution, the Duhamel superposition principle is used to solve the production expression under the condition of constant bottom hole flowing pressure, and the production capacity prediction model is obtained.

[0159] Based on the Duhamel superposition principle, the dimensionless pressure under constant production conditions and the dimensionless production under constant bottomhole pressure conditions have the following relationship:

[0160]

[0161] According to the definition of dimensionless production, the formula for gas well production is:

[0162]

[0163] Where q sc is the gas production of the gas well, m 3 / s;q D is the dimensionless production under constant bottom hole pressure; k fi is the initial permeability of the natural fracture system, m 2 ; h is the formation thickness, m; Δψ is the pressure drop, Pa; p sc is the constant bottom hole pressure, Pa; T is the reservoir temperature, K; T sc is the wellbore temperature, K.

[0164] The first embodiment of the present invention provides a method for establishing a carbonate gas reservoir productivity prediction model. This method establishes a seepage equation that considers high-speed non-Darcy effects and stress-sensitivity effects under different seepage modes. Through Laplace and Fourier cosine transforms, combined with the general solution of Bessel functions, a pressure distribution solution in Laplace space is obtained using point and line source functions. Inversion then yields a real-space pseudo-pressure solution, which in turn provides a productivity prediction model under constant bottomhole flow pressure. Based on this established productivity prediction model, it is possible to reasonably predict the productivity of carbonate gas reservoirs with complex reservoir relationships among pores, caves, and fractures.

[0165] In some embodiments, based on the traditional expression for production decline under variable bottom hole pressure, material balance pseudo-time can be introduced to obtain the expression for production decline under variable bottom hole pressure.

[0166] In stress-sensitive reservoirs, the dimensionless time of each spatial unit at different times is defined as:

[0167]

[0168] In dimensionless time, permeability and time are both variables. To eliminate the influence of permeability on dimensionless time, it can be further defined as:

[0169]

[0170] Using Fetkovich's decreasing variable definition, dimensionless time is:

[0171]

[0172] The dimensionless yield defined by Fetkovich is:

[0173]

[0174] The dimensionless cumulative yield is defined as:

[0175]

[0176] The dimensionless normalized yield integral is defined as:

[0177]

[0178] The meanings of the physical parameters in the above formulas are as follows:

[0179] <![CDATA[t Di --dimensionless time of each spatial unit i at different moments]]> <![CDATA[k i --Permeability of the i-th spatial unit at different times, 10 -3 μm 2 ]]> φ--porosity, % μ--fluid viscosity, mPa·s <![CDATA[C t --Comprehensive compression coefficient, 1 / MPa]]> <![CDATA[k o --Initial permeability of oil and gas reservoir, 10 -3 μm 2 ]]> <![CDATA[r w --well radius, m <![CDATA[r ed --dimensionless boundary radius]]>

[0180] Example 2

[0181] The second embodiment of the present invention provides a carbonate gas reservoir production capacity prediction method, the process of which is as follows Figure 2 As shown, the following steps are included:

[0182] Step S21: According to the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir, the dimensionless production under the constant bottom hole flowing pressure condition is determined by the correspondence between the dimensionless pressure under the constant production condition and the dimensionless production under the constant bottom hole flowing pressure condition.

[0183] According to the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir, the dimensionless pressure under the constant production condition is determined, and the dimensionless production under the constant bottom hole flowing pressure condition is determined by formula (60) in the above embodiment 1.

[0184] Step S22: predicting the gas production of the target well using a productivity prediction model under the constant bottom hole flowing pressure condition according to the constant bottom hole flowing pressure and the dimensionless production under the constant bottom hole flowing pressure condition.

[0185] The productivity prediction model is established by the carbonate gas reservoir productivity prediction model establishment method in the above-mentioned embodiment 1, as specifically shown in formula (61).

[0186] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a carbonate gas reservoir productivity prediction model establishment device, the structure of which is as follows: Figure 3 Shown, including:

[0187] A seepage equation establishment module 31 is used to establish a first seepage equation for a pseudo-steady-state seepage mode and a second seepage equation for a non-steady-state seepage mode in a carbonate gas reservoir by combining a non-Darcy effect and a stress sensitivity effect;

[0188] a comprehensive governing differential equation establishing module 32, configured to obtain a first comprehensive governing differential equation of the first seepage equation and a second comprehensive governing differential equation of the second seepage equation through dimensionless processing and Laplace transformation;

[0189] a pressure distribution solution determination module 33 in the pull-type space, configured to perform a Fourier cosine transform on the first integrated governing differential equation and the second integrated governing differential equation, and obtain a pressure distribution solution in the pull-type space by combining the general solution of the Bessel function with point source and line source functions;

[0190] a real space pseudo-pressure solution determining module 34, configured to obtain a real space pseudo-pressure solution from the pressure distribution solution in the pull-type space by inversion;

[0191] The production capacity prediction model establishing module 35 is used to obtain a production capacity prediction model under a constant bottom hole flowing pressure condition based on the real space pseudo-pressure distribution solution.

[0192] In some embodiments, the real-space pseudo-pressure solution determining module 34 obtains the real-space pseudo-pressure solution from the pressure distribution solution in the pull-space by inversion, and is used to:

[0193] The pressure distribution solution in the pull-type space is inverted using the Stehfest numerical method to obtain the pseudo-pressure solution in the real space.

[0194] In some embodiments, the production capacity prediction model building module 35 obtains a production capacity prediction model under a constant bottom hole flow pressure condition based on the real-space pseudo-pressure distribution solution, which is used to:

[0195] Based on the real-space pseudo-pressure distribution solution, the Duhamel superposition principle is used to obtain a production capacity prediction model under constant bottom hole flowing pressure conditions.

[0196] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a carbonate gas reservoir productivity prediction device, the structure of which is as follows: Figure 4 Shown, including:

[0197] A dimensionless production determination module 41 under a constant bottom hole flowing pressure condition is configured to determine the dimensionless production under a constant bottom hole flowing pressure condition based on the constant bottom hole flowing pressure of a target well in a carbonate gas reservoir by using the corresponding relationship between the dimensionless pressure under the constant production condition and the dimensionless production under the constant bottom hole flowing pressure condition;

[0198] The production prediction module 42 is used to predict the gas production of the target well based on the fixed bottom hole flow pressure and the dimensionless production under the fixed bottom hole flow pressure conditions through the production capacity prediction model under the fixed bottom hole flow pressure conditions, and the production capacity prediction model is established by the above-mentioned carbonate gas reservoir production capacity prediction model establishment method.

[0199] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0200] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the method for establishing a carbonate gas reservoir production capacity prediction model or the method for predicting carbonate gas reservoir production capacity are implemented.

[0201] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the above-mentioned method for establishing a carbonate gas reservoir production capacity prediction model, or implements the above-mentioned method for predicting the production capacity of carbonate gas reservoirs.

[0202] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0203] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0204] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0205] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0206] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0207] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0208] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A method for establishing a carbonate gas reservoir productivity prediction model, characterized in that: include: Combining the non-Darcy effect and stress sensitivity effect, the first seepage equation of the pseudo-steady-state seepage mode and the second seepage equation of the unsteady-state seepage mode in carbonate gas reservoirs are established. After dimensionless processing and Laplace transformation, a first comprehensive governing differential equation of the first seepage equation and a second comprehensive governing differential equation of the second seepage equation are obtained; Performing Fourier cosine transform on the first integrated governing differential equation and the second integrated governing differential equation, combining the general solution of the Bessel function, and obtaining a pressure distribution solution in the pull-type space through point source and line source functions; By inversion, a real space pseudo-pressure solution is obtained from the pressure distribution solution in the pull-type space; Based on the real-space pseudo-pressure distribution solution, a productivity prediction model under a constant bottom hole flowing pressure condition is obtained.

2. The method according to claim 1, characterized in that The productivity prediction model under the condition of constant bottom hole pressure is: In formula (1), q sc is the gas production of the gas well, q D is the dimensionless production under constant bottom hole pressure, k fi is the initial permeability of the natural fracture system, h is the formation thickness, Δψ is the pressure drop, and p sc is the constant bottom hole pressure, T is the reservoir temperature, T sc is the wellbore temperature.

3. The method according to claim 1, characterized in that The first seepage equation of the pseudo-steady-state seepage model in carbonate gas reservoirs is established, including: It is determined that the seepage mode of the matrix and natural fracture system in carbonate gas reservoirs, and the seepage mode of the cave and natural fracture system are both pseudo-steady-state flow, and the seepage equation is established as follows: In formula (2), r is the radial coordinate in cylindrical coordinates, z is the vertical coordinate in cylindrical coordinates, and m is f is the pseudo pressure of the natural fracture system, m m is the pseudo-pressure of the matrix system, m v is the pseudo pressure of the cave system, k fv is the vertical permeability of the natural fracture system, k fh is the horizontal permeability of the natural fracture system, k m is the matrix system permeability, k v is the permeability of the cave system, φ f is the porosity of the natural fracture system, μ g is the viscosity of natural gas, C tf is the compression coefficient of the natural fracture system, α t is the unit transformation coefficient, α m is the matrix shape factor, α v is the cave shape factor, and t is the seepage time.

4. The method according to claim 3, characterized in that The second seepage equation of the unsteady seepage mode in carbonate gas reservoirs is established, including: The second seepage equations for establishing the unsteady-state seepage model of the matrix, caves and natural fracture systems in carbonate gas reservoirs are: In formulas (3)-(5), r m is the radius of the matrix microcircle, μ is the fluid viscosity, φ m is the porosity of the matrix system, C tm is the matrix system compressibility coefficient, r v is the micro-circle radius of the cave, φ v is the porosity of the cave system, C tv is the compression coefficient of the cave system, R m is the matrix radius, R v is the cave radius.

5. The method according to claim 1, characterized in that The method of obtaining a real space pseudo-pressure solution from the pressure distribution solution in the pull-type space by inversion includes: The pressure distribution solution in the pull-type space is inverted using the Stehfest numerical method to obtain the pseudo-pressure solution in the real space.

6. The method according to claim 1, characterized in that The method of obtaining a production capacity prediction model under a constant bottom hole flow pressure condition based on the real-space pseudo-pressure distribution solution includes: Based on the real-space pseudo-pressure distribution solution, the Duhamel superposition principle is used to obtain a production capacity prediction model under constant bottom hole flowing pressure conditions.

7. A carbonate gas reservoir productivity prediction method, characterized in that: include: Based on the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir, the dimensionless production under the constant bottom hole flowing pressure condition is determined by the corresponding relationship between the dimensionless pressure under the constant production condition and the dimensionless production under the constant bottom hole flowing pressure condition; According to the constant bottom hole flow pressure and the dimensionless production under the constant bottom hole flow pressure condition, the gas production of the target well is predicted by a production capacity prediction model under the constant bottom hole flow pressure condition, and the production capacity prediction model is established by the carbonate gas reservoir production capacity prediction model establishment method according to any one of claims 1 to 6.

8. The method according to claim 7, characterized in that The method of determining the dimensionless production under the constant bottom hole flowing pressure condition based on the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir by the corresponding relationship between the dimensionless pressure under the constant production condition and the dimensionless production under the constant bottom hole flowing pressure condition includes: According to the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir, the dimensionless pressure under the constant production condition is determined. The dimensionless production under the constant bottom hole flowing pressure condition is determined by formula (1): In formula (6), q D is the dimensionless production under constant bottom hole pressure, ψ wD is the dimensionless pressure under constant production conditions, and s is the Laplace variable.

9. A device for establishing a carbonate gas reservoir productivity prediction model, characterized in that: The device comprises: A seepage equation establishment module is used to establish a first seepage equation for a pseudo-steady-state seepage mode and a second seepage equation for a non-steady-state seepage mode in a carbonate gas reservoir by combining the non-Darcy effect and the stress sensitivity effect; a comprehensive control differential equation establishment module, configured to obtain a first comprehensive control differential equation of the first seepage equation and a second comprehensive control differential equation of the second seepage equation through dimensionless processing and Laplace transformation; a pressure distribution solution determination module in the pull-type space, configured to perform a Fourier cosine transform on the first integrated governing differential equation and the second integrated governing differential equation, and obtain a pressure distribution solution in the pull-type space by combining the general solution of the Bessel function with point source and line source functions; a real space pseudo-pressure solution determination module, configured to obtain a real space pseudo-pressure solution from the pressure distribution solution in the pull-type space by inversion; The production capacity prediction model establishment module is used to obtain a production capacity prediction model under a constant bottom hole flow pressure condition based on the real space pseudo-pressure distribution solution.

10. A carbonate gas reservoir productivity prediction device, characterized in that: The device comprises: The dimensionless production determination module under the condition of constant bottom hole flowing pressure is used to determine the dimensionless production under the condition of constant bottom hole flowing pressure based on the constant bottom hole flowing pressure of the target well in the carbonate gas reservoir by using the corresponding relationship between the dimensionless pressure under the condition of constant production and the dimensionless production under the condition of constant bottom hole flowing pressure; A production prediction module is used to predict the gas production of the target well based on the fixed bottom hole flow pressure and the dimensionless production under the fixed bottom hole flow pressure conditions, through a production capacity prediction model under the fixed bottom hole flow pressure conditions, and the production capacity prediction model is established by the carbonate gas reservoir production capacity prediction model establishment method described in any one of claims 1 to 6.

11. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed by a processor, implement the method for establishing a carbonate gas reservoir productivity prediction model according to any one of claims 1 to 6, or implement the carbonate gas reservoir productivity prediction method according to claim 7 or 8.

12. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for establishing a carbonate gas reservoir productivity prediction model according to any one of claims 1 to 6 is implemented, or the method for predicting the productivity of a carbonate gas reservoir according to claim 7 or 8 is implemented.

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