A method and apparatus for analyzing gas well production data

Abstract

The application discloses a gas well production data analysis method and device. The method comprises the following steps: acquiring a first array sequence of production, bottom hole pressure and cumulative production at multiple moments, converting the bottom hole pressure into corresponding pseudo pressure to obtain a second array sequence; determining the value of the polynomial coefficient in the relationship between the product ratio of gas viscosity and comprehensive compression coefficient and the recovery degree function R according to the gas reservoir where the gas well is located and the fluid property parameters in the gas reservoir; and obtaining the optimal combination of the gas reservoir reserves and the boundary controlled flow constant based on the determined value of the polynomial coefficient and the second array sequence and the first relationship between the boundary controlled flow stage production, the corresponding pseudo pressure of the bottom hole pressure, the cumulative gas production and the gas reservoir reserves and the boundary controlled flow constant which is established in advance. The method can quickly determine the gas reservoir reserves G and the boundary controlled flow constant b, avoids iteration of material balance pseudo time t ca and can process fluctuations in production system.

Description

Technical Field

[0001] This invention relates to the field of oil and gas field development engineering technology, specifically to gas reservoir dynamic analysis and gas production data analysis (PDA), and particularly to a gas well production data analysis method and apparatus. Background Technology

[0002] Dynamic analysis of gas wells is a perennial theme in the efficient development of gas reservoirs. Reserve estimation, pressure, and production forecasting are key aspects of dynamic analysis, serving as crucial bases for well production allocation, capacity evaluation, and reservoir development planning. Because methods relying on frequent testing or well shut-in to obtain reservoir data are time-consuming, labor-intensive, and costly, low-cost methods that infer reservoir information and well operating status from daily production data have rapidly developed. Gas well production data analysis (PDA), as a powerful supplement to field testing and well trials, utilizes daily measurement data such as well production, pressure, and cumulative production to infer reservoir information such as control reserves (or reservoir reserves G) and formation pressure through theoretical or empirical models, for use in dynamic gas well prediction.

[0003] Research on production data analysis (PDA) can be traced back to Arps (1945), who proposed the law of decreasing output based on data statistics. Its application is simple and can be applied to constant flow pressure production systems, but at that time, no rigorous theoretical proof was given to explain its scope of application.

[0004] Fetkovich (1980) first introduced the concept of typical curve analysis from well testing into the analysis of production data. They combined the analytical solution of constant pressure in the unsteady flow stage with Arps empirical curves to plot a double logarithmic chart, and used typical curve fitting similar to pressure instability analysis (PTA) to analyze the production decline law. Fetkovich (1980) theoretically proved the dimensionless production q of a constant pressure system. dD and dimensionless time t dD The fact that the boundary control flow (BDF) exhibits an exponential decrease in the later stages is a landmark achievement in the field of production instability analysis (RTA). This method overcomes the empirical nature of Arps curves, but because it does not consider changes in fluid properties, its application is limited to isobaric production of microcompressible fluids.

[0005] Carter (1981, 1985) considered the changes in gas properties during the pressure drop process and introduced the variable λ = μ. i C gi / (μC g This is used to explain the changes in natural gas viscosity and compressibility to analyze gas well data; the pressure drop parameter λ in the Carter curve is a value derived from the bottom hole pressure p. wf to the original formation pressure p iThe average value on the mean formation pressure does not change with the mean formation pressure, so it is only an approximate method for analyzing data from constant flow pressure gas wells.

[0006] Blasingame (1988) introduced pseudo-pressure and pseudo-time functions, and based on the BDF liquid solution and the material balance equation for constant-volume gas reservoirs, proposed a method for iteratively calculating the pseudo-time of material balance and gas reservoir reserves using production and flowing pressure data (i.e., variable-flow edge probing). This method considers fluctuations in gas well production regimes and changes in gas properties, and its main theory has gradually become the basis for many subsequent PDA methods. The introduction of the pseudo-time function for material balance reduces the adverse effects of fluctuations in production conditions on the analysis, but its calculation requires iterative processing because it implicitly includes the average formation pressure.

[0007] Similarly, to address changes in production conditions, Blasingame (1991) proposed the concept of a constant-pressure equivalent time function. This function utilizes the conversion between constant-production and constant-pressure equivalent time during the boundary control flow stage to analyze variable pressure drop and variable-production data using constant-pressure solutions. However, for gas wells, this analytical method still requires the iterative process of Blasingame (1988)'s variable-flow edge probing test to determine the slope parameter m. bdf and the boundary control flow constant b.

[0008] Palacio and Blasingame (1993) developed the previous BDF pressure-flow relationship into a typical curve analysis method that can handle variable flow pressure-variable production data, proving that the dimensionless production q of the decreasing curve defined by the pseudo-pressure of the gas and the pseudo-time of the mass balance. dD and dimensionless time t dD The BDF stage follows a harmonic decreasing law. Blasingame curve analysis is another representative work of RTA methods. In addition, other typical curve analysis methods, such as Agarwal-Gardner curves and normalized pressure integral (NPI) curves, are actually variations in the solution formula and plotting function, but their analytical principles are roughly the same.

[0009] Canard and Schenekerk (1994, 1995) used typical production-cumulative production curves to analyze production data and first proposed the concept of viscosity-compressibility normalized cumulative production for gas wells. They pointed out that the solution of microcompressible liquids could be applied to gas well data analysis by using pseudo-pressure and viscosity-compressibility normalization coefficients.

[0010] Mattar (1995, 1998) proposed using the ratio of pressure to deviation factor (p / Z) under bottomhole pressure for gas wells with constant production in constant-volume gas reservoirs. wf The value (p / Z) is used instead of the mean formation pressure. ave Furthermore, the method for estimating gas reservoir reserves (i.e., the fluid mass balance method) is based on the premise that the pressure drop rate at all points in the gas reservoir is approximately equal during the "quasi-steady-state" stage. In reality, the function p / Z and the cumulative gas production G... p The slope of the relationship line may not be equal to the result when the reference pressure is the bottomhole flowing pressure, which is the same as the result when the reference pressure is the mean formation pressure. Therefore, this method has certain theoretical defects and is not applicable to gas wells with variable production or strong compression effects. Later, some researchers made corrections, but they still cannot meet the requirements for the analysis of variable production / variable flowing pressure data.

[0011] Ansah et al. (1996, 2000) innovatively multiplied the gas viscosity-compressibility coefficient product μC. g It is related to p / Z, and the correspondence between the dimensionless quantities of the two is approximated by first-order polynomial, exponential polynomial and general polynomial respectively for three cases: low pressure, high pressure and general case. The "stabilized flow equation" is reinterpreted in the form of typical curves and applied to the dynamic analysis of gas wells.

[0012] Knowles (1999) et al., based on Ansah et al. (1996), characterized λ~(p / Z) / (p i / Z i The first-order polynomial function of the relationship was used to derive the corresponding pressure-time, production-time, and production-cumulative production relationships. These relationships are for relatively low-pressure gas reservoirs (p i The boundary control flow approximation solution (<6000psi) can be used for explicit analysis of constant flow compressed gas wells.

[0013] Buba (2003) and Blasingame and Rushing (2005) focused on analyzing the quadratic polynomial relationship between production and cumulative production proposed by Knowles (1999). They constructed three new plotting functions based on this binomial relationship by introducing a "cumulative production averaging production function" for linear data analysis. It is important to note that these explicit analytical methods rely on the assumption of constant-pressure production in low-pressure gas reservoirs.

[0014] Mattar (2005, 2006) modified the previous flow material balance method to address the possibility of fluctuations in production well output, proposing the dynamic material balance method (or variable production flow material balance method). The dynamic material balance relationship in this method can be derived from the "steady flow" solution of the gas seepage model and the material balance equation of the constant volume gas reservoir. It depends on the determination of the material balance pseudo-time and the boundary control flow constant, so the reserve estimation also requires an iterative process.

[0015] To avoid iterative calculations of the pseudo-time (mass balance), Ye and Ayala (2012) used a pseudo-time factor (β) defined by the viscosity-compressibility ratio λ and a density function (ρ, which is equivalent to ignoring the viscosity term in the pseudo-pressure) to linearize the governing equations. They used the overlap between the liquid solution and gas well data in the early stages (when gas properties changed relatively little) to estimate the dimensionless reservoir radius r. eD Then, the time series values ​​of λ and β are determined by using the difference between the two in BDF, and then a mass equilibrium plot is constructed using the λ-ρ approximation relationship (i.e., λ). 1 / B ~G p The curve is used to determine the gas reservoir reserves. This method assumes the viscosity-compressibility product μC. g The slope of the double logarithmic curve with respect to density ρ is approximately constant -B, and it mainly emphasizes the case of constant flow pressure production in gas wells.

[0016] Mohammed and Enty (2013) proposed the concept of "pseudocumulative" based on the normalized cumulative yield defined by Canard (1994, 1995), and transformed the traditional material equilibrium pseudo-time into "rate normalized pseudocumulative," but the two are essentially the same; they pointed out that if the actual cumulative yield is used instead of the pseudocumulative, q / Δp p ~G p / Δp p The relationship curve will show two straight lines during the boundary control flow stage. The early straight line can be used to preliminarily infer the gas reservoir reserves. Then, the mean formation pressure can be estimated using the mass balance relationship, and then the viscosity-compressibility product μC can be calculated. t and proposed cumulative output G pn Finally, q / Δp is used. p ~G pn / Δp p The parameters are redefined using the relationship curve.

[0017] Molokwu and Onyekonwu (2016), based on the work of Ansah et al. (1996, 2000), semi-analyzed the gas viscosity-compressibility product ratio λ as the cumulative gas production G.p A nonlinear flow mass balance method for estimating gas reservoir reserves and property parameters was proposed using polynomial functions. However, its derivation process implicitly assumes that the bottom hole pressure remains constant, making it difficult to handle situations where the production regime changes. In addition, the choice of polynomial degree has a significant impact on the calculation results.

[0018] Stumpf and Ayala (2016) derived an analytical expression for the decline exponent n under constant flow pressure conditions by utilizing the relationship between the average pseudo-pressure and the bottom hole pseudo-pressure during the boundary control flow stage, combined with the Arps hyperbolic decline model. Based on this, they proposed a method using production data (q) within the "hyperbolic window (hyperbolic decline period)". 1-n ~G p * A method for determining reserves using linear relationships. While this method avoids the calculation of material balance pseudo-time, it requires curve fitting to determine the time range of the hyperbolic window and is only applicable to gas wells producing gas at constant flow pressure.

[0019] Alom et al. (2017) adopted the same concept of "pseudo-cumulative production" as Mohammed (2013) and Canard (1994, 1995) (which is actually based on cumulative gas production G). p They defined a material balance pseudo-time and used a method similar to the Blasingame typical curve for gas well data analysis; they pointed out that their analysis process avoided iteration and data extrapolation. However, calculating the pseudo-cumulative production actually requires first determining the mean formation pressure, and when the mean formation pressure is unknown, using polynomial fitting of μ... i C gi / (μC g )~G p Curves, however, cannot be created.

[0020] Wang and Ayala (2020) extended Stumpf's (2016) hyperbolic decline model of constant pressure to the case where bottom hole pressure changes according to a specific law. They re-derived the decline exponent in a similar manner and provided the same linear analysis method as Stumpf (2016), namely, fitting a harmonic canonical curve and q 1-n ~G p *A linear relationship is used to determine natural gas reserves. This method requires a constant ratio between the bottomhole pseudo-pressure and the average pseudo-pressure, and can be applied to all data in the boundary control flow stage, reducing the difficulty of identifying hyperbolic windows in the previous Stumpf (2016) method. It should be noted that both Stumpf (2016) and Wang (2020) used the integral average from the bottomhole pseudo-pressure to the initial pseudo-pressure when calculating the decline exponent, but in fact, according to their derived analytical formula, the decline exponent should be a function of the average formation pressure; in addition, although these two methods avoid pseudo-time iteration, they are limited by the bottomhole pressure condition assumptions and also lose the advantage of pseudo-time in handling variable production / variable flowing pressure conditions.

[0021] Jongkittinarukorn et al. (2021) based their work on the viscosity-compressibility ratio λ[λ = μ] defined by Carter (1981, 1985). i C gi / (μC g The "steady flow" equation proposed by Ansah et al. (1996, 2000) also derived expressions for the decline rate D and the decline exponent n, thus proposing a method based on the change in gas properties [λ ~ p]. D p D =(p / Z) / (p i / Z i This method estimates gas reservoir reserves by relating production, decline rate, and decline exponent; this explicit method takes into account the variation of the decline exponent under constant flow pressure conditions; however, inferring the decline rate and decline exponent profiles based on production-time data (obtained by polynomial fitting) may introduce significant errors.

[0022] In summary, based on the methods used for gas reservoir estimation and dynamic analysis, current PDA methods can be divided into two main categories: implicit and explicit. Implicit methods primarily rely on the material balance pseudo-time t. ca The iteration, even if t ca The integral variable is replaced with the cumulative gas production, and μ·C in the integrand function. t The evaluation still needs to be conducted under mean formation pressure; therefore, iterative calculation of t ca Mean formation pressure and BDF constant are often unavoidable. On the other hand, explicit methods do not require multiple calculations in pseudo-time, but are limited by specific gas well production conditions and are difficult to apply to actual variable production-variable flow pressure production data; in addition, they often do not consider the compressibility effect of rocks and bound water in abnormally high-pressure gas reservoirs. Summary of the Invention

[0023] In view of the above problems, this invention is proposed to provide a gas well production data analysis method and apparatus that overcomes or at least partially solves the above problems, enabling rapid determination of gas reservoir reserves G and boundary control flow constant b, while avoiding the need for material balance pseudo-time t. ca The iteration can handle fluctuations in the production system and also takes into account the effects of porosity and the compressibility of bound water.

[0024] In a first aspect, embodiments of the present invention provide a method for analyzing gas well production data, including:

[0025] A first array sequence of production, bottom hole pressure and cumulative production at multiple time points is obtained. The bottom hole pressure in the first array sequence is converted into the corresponding pseudo pressure to obtain a second array sequence.

[0026] Based on the gas reservoir where the gas well is located and the fluid property parameters within the gas reservoir, determine the values ​​of the polynomial coefficients in the relationship between the product ratio of gas viscosity and comprehensive compressibility coefficient λ and the recovery degree function R, where the degree of the polynomial is greater than 2.

[0027] Based on the determined values ​​of the polynomial coefficients and the second array sequence, the optimal combination of gas reservoir reserves and boundary control flow constant is obtained through the pseudo-pressure corresponding to the gas well production and bottom hole pressure, the cumulative production and the first relationship between gas reservoir reserves and boundary control flow constant. The first relationship is pre-established based on the relationship between pressure and flow rate in the boundary control flow stage, the static material balance equation of the gas reservoir under isothermal conditions and the relationship between λ and R.

[0028] In a second aspect, embodiments of the present invention provide a gas well production data analysis device, comprising:

[0029] The production data array sequence acquisition module is used to acquire a first array sequence of production, bottom hole pressure and cumulative production at multiple times, and convert the bottom hole pressure in the first array sequence into the corresponding pseudo pressure to obtain a second array sequence;

[0030] The module for determining the relationship between λ and R is used to determine the values ​​of polynomial coefficients in the formula relating the product ratio of gas viscosity and comprehensive compressibility coefficient λ to the production degree function R, based on the gas reservoir where the gas well is located and the fluid property parameters within the gas reservoir. The degree of the polynomial is greater than 2.

[0031] The solution module is used to obtain the optimal combination of gas reservoir reserves and boundary control flow constant based on the determined values ​​of polynomial coefficients and the second array sequence, through the pseudo-pressure corresponding to gas well production and bottom hole pressure, the cumulative production and the first relationship between gas reservoir reserves and boundary control flow constant. The first relationship is pre-established based on the pressure and flow rate relationship of the boundary control flow stage, the gas reservoir static material balance equation under isothermal conditions and the relationship between λ and R.

[0032] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described gas well production data analysis method.

[0033] Fourthly, this disclosure provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described gas well production data analysis method.

[0034] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0035] The gas well production data analysis method provided in this invention pre-establishes a first relationship between the gas well production, the pseudo-pressure corresponding to the bottom hole pressure, the cumulative production, and the gas reservoir reserves and boundary control flow constant during the boundary control flow stage; based on the gas reservoir where the gas well is located and the fluid property parameters within the reservoir, it determines the polynomial coefficient c in the relationship between λ and R. i The value of c; i The array sequence of values, production, and bottom hole pressure corresponding to the pseudo-pressure and cumulative production is substituted into the first relation, and the optimal combination of gas reservoir reserves and boundary control flow constant is obtained through optimization. This method can quickly determine the gas reservoir reserves G and boundary control flow constant b, assisting in dynamic analysis and prediction, thereby reducing production costs and simplifying the analysis process. The method is simple to operate, requiring no iteration, overcoming the shortcomings of current implicit methods such as typical curve analysis, which involve repeated calculations of pseudo-time and cumbersome fitting. It can handle fluctuations in production regimes, compensating for the limitations of current explicit methods that are restricted to specific production conditions and gas reservoir types, and is applicable to both high-pressure and atmospheric-pressure gas reservoirs.

[0036] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0037] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 The relative density of the gas γ g The effect on the λ-R relationship curve;

[0040] Figure 2 The effect of reservoir temperature T on the λ-R relationship curve;

[0041] Figure 3 The original formation pressure p i The effect on the λ-R relationship curve;

[0042] Figure 4 This is a flowchart of the gas well production data analysis method in Embodiment 1 of the present invention;

[0043] Figure 5 In the numerical calculation example of Embodiment 2 of the present invention, 1 / (μC) t () ~ p relationship;

[0044] Figure 6 p in the numerical example of Embodiment 2 of the present invention p The relationship between g(p) and pressure p;

[0045] Figure 7 This illustrates the relationship between λ and ε as a function of R in the numerical example of Embodiment 2 of the present invention.

[0046] Figure 8 In the numerical example of Embodiment 2 of the present invention, B g The relationship between μ and pressure p;

[0047] Figure 9 This is the production and flowing pressure profile of the numerical calculation example of Embodiment 2 of the present invention;

[0048] Figure 10 This illustrates the variation of the observation error E(Y) with time t in the numerical example of Embodiment 2 of the present invention.

[0049] Figure 11 p is an example of field application in Embodiment 3 of the present invention. p The relationship between g(p) and pressure p;

[0050] Figure 12 This illustrates the relationship between λ and ε as a function of R in a field example of Embodiment 3 of the present invention.

[0051] Figure 13 This is the production history of gas well A in the field example of Embodiment 3 of the present invention;

[0052] Figure 14 This is an example of how E(Y) changes with t when all data points are used in a field instance of Embodiment 3 of the present invention;

[0053] Figure 15 This is an example of how E(Y) changes with t when 44 data points are used in a field instance of Embodiment 3 of the present invention;

[0054] Figure 16This is an example of the variation of E(Y) with t using 44 data points after the bottom hole pressure changes in a field instance of Embodiment 3 of the present invention;

[0055] Figure 17 This is a schematic diagram of the gas well production data analysis device in an embodiment of the present invention. Detailed Implementation

[0056] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0057] It should be understood that the terminology used herein is merely for describing particular embodiments and is not intended to limit the invention. Unless otherwise stated, 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 invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0058] To address the problem in existing gas well production data analysis technologies where implicit methods rely on pseudo-time iterations and explicit methods struggle to handle varying flow pressure and production conditions, this invention provides a gas well production data analysis method and apparatus. This method can rapidly determine the gas reservoir reserves G and the boundary control flow constant b, avoiding the need for pseudo-time iterations in material balance analysis. ca It can iterate and also handle fluctuations in the production system.

[0059] The implementation of this invention relies on a pre-established first relationship between the gas well production, the pseudo-pressure corresponding to the bottom hole pressure, the cumulative production, the gas reservoir reserves, and the boundary control flow constant during the boundary control flow stage.

[0060] Based on gas flow theory and the principle of material balance, and using the relationship between the static material balance equation of a gas reservoir under isothermal conditions and the product ratio λ of gas viscosity and comprehensive compressibility coefficient and the recovery rate function R, a second relationship between the pseudo-time of material balance and production and gas reservoir reserves is obtained. Based on the relationship between pressure and production in the boundary control flow stage and the second relationship, a first relationship is established between the pseudo-pressure corresponding to the well production and bottom hole pressure in the boundary control flow stage, the cumulative production, and the gas reservoir reserves and boundary control flow constant. The specific derivation process is as follows:

[0061] I. Gas Well Variable Production Solution

[0062] In solving gas seepage problems, pseudo-pressure and pseudo-time functions are often introduced to handle the nonlinear terms in the governing equations, characterizing the pressure dependence of gas viscosity, compressibility, and deviation factor (or density). This allows classical liquid solutions to describe pressure changes under constant production conditions or flow rate changes under constant pressure conditions. In reality, gas well production and flowing pressure are often not constant. However, when the severity of production and flowing pressure fluctuations is weaker in the production data than the boundary effect, the production system is said to have entered the boundary-controlled flow (BDF) stage (Blasingame, 1986, 1988). At this point, the following pressure-flow relationship holds (Zhang Lixia, 2019, 2021):

[0063]

[0064] in,

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072] Where: p—pressure, Pa; p i —Original formation pressure, Pa; p wf — Bottom hole pressure, Pa; p p —Pseudo-pressure, Pa; ——p i The corresponding pseudo-pressure, Pa; ——p wf Corresponding pseudo-pressure, Pa; q—output, m 3 / s;t a —Pseudo-time, s; t ca —Pseudo-time of mass equilibrium, s; (Δp) p ) i-wf ——p i With p wf The corresponding pseudo-pressure difference, Pa; m bdf ——(Δp p ) i-wf / q~t ca The slope of the relationship line, Pa / m3 b——(Δp) p ) i-wf / q~t ca The intercept of the relational line (i.e., the boundary control flow constant), Pa·s / m 3 G – Gas reservoir reserves, m 3 S wc —Bound water saturation, decimal; S wci —Initial bound water saturation, decimal; C t —Comprehensive compressibility factor, Pa -1 C ti —Initial overall compression factor, Pa -1 t—time, s; μ—gas viscosity, Pa·s; μ i —Initial gas viscosity, Pa·s; Z —Gas deviation factor, decimal; Z i — Deviation factor under original stratigraphic conditions, decimal; p ave —Mean formation pressure, Pa; B gi —Natural gas volume factor under original formation conditions, m 3 / m 3 K – Effective penetration rate, m 2 h—effective thickness of the gas reservoir, in meters; r w — Well diameter, m; r e — Boundary length, m; r eD ——r e / r w λ is dimensionless. n — Equation J1(r eD λ)Y1(λ)-Y1(r eD The roots of λ)J1(λ)=0; J1—a first-order Bessel function of the first kind; C g —Natural gas compressibility factor, Pa -1 C w — Formation water compressibility, Pa -1 C φ — Rock compressibility coefficient, Pa -1 .

[0073] In equation (1), b is the boundary control flow constant. In reality, this value changes slowly over time, but it can be approximated as a constant within a certain evaluation period of the boundary control flow stage. 1 / b can be interpreted as the "production capacity index" under the pseudo-pressure definition, which reflects the production capacity of the gas well.

[0074] Material equilibrium pseudo-time t ca The mean formation pressure p ave Defined as a reference condition, due to p aveSince it is unknown, it is necessary to use the static mass balance equation (SMBE) to iteratively calculate t under the assumed reserves. ca This process is necessary for current production data analysis (PDA) methods capable of handling variable flow pressure and variable production conditions, hence these methods are implicit. Current explicit PDA methods are limited by specific production or flow pressure assumptions, making them difficult to apply to gas well production systems under variable flow pressure and variable production conditions.

[0075] However, this invention cleverly constructs a quasi-time transformation process for the material balance by introducing the product ratio λ of gas viscosity and comprehensive compressibility coefficient and by using appropriate transformations of the static material balance equation. This transforms the pressure-flow relationship in the boundary control flow stage into an optimization problem controlled by the gas reservoir reserves G and the boundary control flow constant b, thus avoiding direct calculation of the quasi-time. This combines the advantages of both explicit and implicit methods, which will be introduced below.

[0076] II. Pseudo-time conversion of mass equilibrium

[0077] Under isothermal conditions, the static mass balance equation (SMBE) for a gas reservoir can be expressed as:

[0078]

[0079] in,

[0080]

[0081]

[0082] In the formula: g(p) — compression effect function, Pa; g D (p) — Dimensionless compression effect function; G p —Cumulative gas production, m 3 .

[0083] The extraction degree function R(p) is defined as follows:

[0084] R(p) = 1 - g D (p)(12)

[0085] Therefore, equation (9) can be transformed into:

[0086]

[0087] Based on cumulative production G p The relationship with time t, equation (3) can be written as:

[0088]

[0089] Introducing the product ratio λ of gas viscosity and overall compressibility:

[0090]

[0091] Equation (14) can be further written as:

[0092]

[0093] Under formation pressure p ave By p i As the recovery rate decreases to near 0, the recovery rate R increases from 0 to near 1, while λ decreases from 1 to near 0. According to equations (12) and (15), as long as the properties of the gas reservoir and natural gas are given, λ(p) and R(p) can be linked by pressure p. This relationship is determined by the inherent properties of the gas well production system and is independent of the gas well production conditions and its response.

[0094] Here we take S wci =0.21, C φ =5.335×10 -4 MPa -1 C w =4.002×10 -4 MPa -1 Example to illustrate the relative density γ of gas g Reservoir temperature T, original formation pressure p i The effects on the λ-R relationship curve are as follows: Figures 1-3 As shown. Wherein, the relative density of the gas γ g The pseudocritical temperature and pseudocritical pressure of the gas are determined by the Sutton (2005, 2007) relation; the gas deviation factor Z and compressibility coefficient C are also determined. g The Hall-Yarborough (1973) relation can be used to determine the gas viscosity μ; the Londono et al. (2005, 2007) relation can be used to determine the gas viscosity μ.

[0095] Depend on Figures 1 to 3 It can be seen that λ decreases monotonically with R, and its nonlinear relationship is predetermined by the formation and fluid properties. This relationship can be characterized by an nth-degree polynomial:

[0096]

[0097] Substituting equations (17) and (13) into equation (16), we get:

[0098]

[0099] Right now

[0100]

[0101] Substituting equations (19) and (2) into equation (1), we get:

[0102]

[0103] Right now

[0104]

[0105] Equation (21) is the pseudo-pressure p corresponding to the production rate q and bottom hole pressure during the boundary control flow stage of the gas well. pwf Cumulative gas production G p The first relationship between gas reservoir reserves G and boundary control flow constant b, where gas reservoir reserves G and boundary control flow constant b are constants to be determined.

[0106] Example 1

[0107] Embodiment 1 of this invention provides a gas well production data analysis method. Based on the first relationship derived in the above process, and based on the array sequence of production, bottom hole pressure corresponding to pseudo-pressure, and cumulative production at multiple times, the optimal combination of gas reservoir reserves and boundary control flow constant is obtained. The process is as follows: Figure 4 As shown, it includes the following steps:

[0108] Step S41: Obtain a first array sequence of production, bottom hole pressure and cumulative production at multiple time points, and convert the bottom hole pressure in the first array sequence into the corresponding pseudo-pressure to obtain a second array sequence.

[0109] According to the definition of pseudo-pressure (Equation (7)), the bottom hole pressure in the first array sequence is converted into the corresponding pseudo-pressure:

[0110]

[0111] The core of equation (22) lies in the calculation of the definite integral term, which requires determining the relationship between μ and Z and pressure p. This integral calculation can be completed by the Romberg quadrature formula. Since the calculation methods of viscosity μ and deviation factor Z have been explained in the previous text, they will not be repeated here.

[0112] Step S42: Based on the gas reservoir where the gas well is located and the fluid property parameters within the gas reservoir, determine the values ​​of the polynomial coefficients in the formula relating the product ratio λ of gas viscosity and comprehensive compressibility coefficient to the production degree function R.

[0113] The relationship between the product ratio λ of gas viscosity and overall compressibility coefficient and the recovery rate function R is given by equation (17) above: Generally, n=4 is accurate enough.

[0114] (1) The relationship between λ and p is determined.

[0115] Based on the definition of the product ratio of gas viscosity and overall compressibility coefficient λ (Equation (15)), a third relationship between λ and pressure p is established.

[0116] Specifically, the viscosity μ is determined by the relationship between Londono et al. (2005, 2007), and the comprehensive compressibility coefficient C... t Defined by equation (8), then μ·C t The ~p relation can then be obtained, that is, the λ ~p relation can be obtained.

[0117] (2) The R-p relationship is determined.

[0118] Based on the definition of the extraction degree function R, a fourth relationship between R and pressure is established.

[0119] The degree of extraction function R(p) is defined by equation (12), g(p), g D (p) are shown in equations (11) and (10) respectively, where the deviation factor Z is calculated using the Hall-Yarborough (1973) relation. Then g(p), g D The relationship between (p) and p can be obtained, that is, the relationship between R and p can be obtained.

[0120] (3) Determining the λ~R relationship

[0121] Based on the third and fourth relationships, the relationship between the product ratio λ of gas viscosity and overall compressibility coefficient and the recovery degree function R is determined. The polynomial coefficients c i The value of .

[0122] If the same p-sequence value is set, then the λ-R relationship can be obtained, i.e., c i The value can be obtained.

[0123] The λ-R relationship is determined by reservoir and fluid property parameters (such as reservoir temperature T, initial pressure p). i gas relative density γ g and S wci C w C φ The relationship is determined by factors such as (etc.) and generally does not change over time in a short period of time, so it can be considered fixed within a certain period of time.

[0124] Step S43: Based on the determined polynomial coefficients c i The optimal combination of gas reservoir reserves and boundary control flow constant is obtained by using the values ​​of the second array sequence and the pseudo-pressure corresponding to the bottom hole pressure, the cumulative production, and the first relationship between gas well production, bottom hole pressure, gas reservoir reserves, and boundary control flow constant in the pre-established boundary control flow stage.

[0125] The first relationship is specifically the above equation (21), which is pre-established based on the pressure and flow relationship in the boundary control flow stage, the static material balance equation of the gas reservoir under isothermal conditions, and the relationship between λ and R, and includes the polynomial coefficients c. i .

[0126] Using the selected optimization algorithm, the combination of gas reservoir reserves and boundary control flow constant that makes the difference between the left and right expressions of the first relation satisfy the set error threshold is obtained, which is taken as the optimal combination of gas reservoir reserves and boundary control flow constant.

[0127] The optimization algorithm can be a multivariate nonlinear fitting algorithm, a nonlinear least squares algorithm, or a constrained optimization algorithm, etc.

[0128] Specifically, the left side of equation (21) above is denoted as the observation vector Y(p). wf ,q), that is

[0129]

[0130] The right side is denoted as the estimated vector. Right now:

[0131]

[0132] In the observation vector Alternatively, it can be determined by the definition of pseudo-pressure (Equation (7)) based on p i get:

[0133]

[0134] Since all parameters in the observation vector are known, they are considered a known vector. The gas reservoir capacity G and the boundary control flow constant b in the estimation vector are unknowns. Therefore, the problem of determining the gas reservoir capacity and the boundary control flow constant can be transformed into solving for a set of (G,b) combinations that make the observed value Y consistent with its estimated value. The optimization problem that minimizes the overall difference between them is:

[0135]

[0136] The error E(Y) of the observation Y at each data point can be expressed as:

[0137]

[0138] The above method can analyze the error of the observation Y at each data point, thereby determining the data range in the boundary control flow stage and improving the reliability of the analysis results.

[0139] The gas well production data analysis method provided in this invention pre-establishes a first relationship between the gas well production, the pseudo-pressure corresponding to the bottom hole pressure, the cumulative production, and the gas reservoir reserves and boundary control flow constant during the boundary control flow stage; and determines the relationship between λ and R based on the gas reservoir where the gas well is located and the fluid property parameters within the reservoir. The polynomial coefficients c iThe value of c; i The array sequence of values, production, and bottom hole pressure corresponding to the pseudo-pressure and cumulative production is substituted into the first relation, and the optimal combination of gas reservoir reserves and boundary control flow constant is obtained through optimization. This method can quickly determine the gas reservoir reserves G and boundary control flow constant b, assisting in dynamic analysis and prediction, thereby reducing production costs and simplifying the analysis process. The method is simple to operate, requiring no iteration, overcoming the shortcomings of current implicit methods such as typical curve analysis, which involve repeated calculations of pseudo-time and cumbersome fitting. It can handle fluctuations in production regimes, compensating for the limitations of current explicit methods that are restricted to specific production conditions and gas reservoir types, and is applicable to both high-pressure and atmospheric-pressure gas reservoirs.

[0140] This explicit PDA method, based on the pressure-flow relationship and pseudo-time transformation of the BDF stage, can determine gas reservoir (well-controlled) reserves and boundary control flow constants without any iterative process, and is applicable to variable flow rate-variable pressure production conditions. Compared with existing PDA methods, this method significantly simplifies the analysis process and improves the efficiency of dynamic analysis and prediction.

[0141] Furthermore, once the reserves G and the boundary control flow constant b are determined, the cumulative production G can be calculated according to equation (9). p Inferring g D (p ave The function is used to obtain the mean formation pressure profile; if the production plan is determined, the level of pressure decline in the gas reservoir can be estimated based on this. Conversely, if the rate of pressure decline in the gas reservoir, i.e., p, is to be controlled... ave Since the profile is known, the corresponding cumulative production profile can also be calculated based on equation (9), thereby rationally planning the gas production target. In addition, if the production plan is determined, the future bottom hole flowing pressure changes can also be determined according to equation (1) or (21).

[0142] (1) Determine the future bottom hole flowing pressure change according to equation (1).

[0143] Since the reserves G and the boundary control flow constant b have been determined from historical data, then in equation (2), m bdf Given; in the dynamic forecasting phase, if the production plan (output) is determined, then q(t) and G are known; p The change of (t) with time t is known, and the cumulative output G can be obtained from equation (9). p Inferring g D (p ave The mean formation pressure p can be obtained from equation (11) using the function. ave Profile; then calculate the material equilibrium pseudo-time function t using equation (3). ca , from equation (1) establish (Δp) p ) i-wf / q~t ca relation:

[0144]

[0145] Finally, according to equation (7), p pwf Convert to p wf That's all.

[0146] (2) Determine the future bottom hole pressure change according to equation (21).

[0147] In the dynamic forecasting phase, if the production plan (output) is determined, then q(t) and G... p The change of (t) with time t is known; only p in equation (21) is known. wf For an unknown quantity, it can be written as:

[0148]

[0149] Similarly, from equation (29) we obtain Then, according to equation (7), it is converted to p. wf .

[0150] This invention introduces the product ratio λ of gas viscosity and overall compressibility coefficient with the recovery rate function R, and correlates these two through pressure p. This transforms the pseudo-time of the mass balance into a polynomial function of cumulative gas production, thereby converting the reserve estimation problem into an optimization problem based on the boundary control rheological flow solution. Using production data and gas properties, the gas reservoir reserves and boundary control flow constant can be accurately determined, and the error of the observation vector can be given. Compared with similar technologies or methods, the main advantages of this innovative achievement are:

[0151] (1) This method has a rigorous theoretical basis, low data requirements, and the analysis can be completed by an internal program, which can significantly reduce production costs and simplify the analysis process.

[0152] (2) This method combines the advantages of both implicit and explicit PDA methods, effectively processing variable flow and pressure data while avoiding curve fitting and various iterative processes.

[0153] (3) This method can quickly and effectively determine the gas reservoir reserves (well-controlled reserves) and boundary control flow constant under constant flow pressure, constant production and variable production and variable flow pressure conditions, thereby providing insights into the control range and production capacity of gas wells.

[0154] (4) This method can analyze the error of the observation Y at each data point, thereby determining the data range in the boundary control flow stage and improving the reliability of the analysis results.

[0155] (5) This method takes into account the changes in fluid properties and the compression effect of rock pores and bound water, and is applicable to gas reservoirs of various pressure types.

[0156] Example 2

[0157] Embodiment 2 of the present invention provides an application example of gas well production data analysis, wherein the data is experimental simulation data.

[0158] Initial pressure p of the gas reservoir i The pressure is 60 MPa, the formation temperature is T = 100 °C, and the molar mass of natural gas is M. g The quasi-critical temperature T is 18.66 g / mol, obtained from the Sutton (2005, 2007) relationship. pc =205.76K, pseudocritical pressure p pc =4.59 MPa; C is determined using the empirical formula for compressibility coefficient. φ =5.335×10 -4 MPa -1 C w =4.002×10 -4 MPa -1 Z and C were calculated using the method of Hall et al. (1973). g The gas viscosity μ was calculated using the method of Londono et al. (2005, 2007). Given S... wci =0.21, can output 1 / (μC) t The variation of pressure p, such as Figure 5 As shown.

[0159] According to 1 / (μC) t The λ-p relationship can be used to intuitively determine the change of λ during the pressure drop process and to calculate the λ-p relationship; Figure 5 It can be seen that when the pressure is p i During the process of decreasing to near 0, 1 / (μC) t ) by 1 / (μ i C ti Since λ decreases to near 0, it can be inferred that λ decreases from 1 to near 0.

[0160] The λ-R relationship is obtained by bridging the λ-p and R-p relationships using pressure p. As natural gas is extracted from the reservoir, if the pressure increases from p... i As the pressure decreases to near zero, the recovery rate function R increases from 0 to near 1. Therefore, as the pressure decreases, R increases from 0 to near 1, and λ decreases from 1 to near 0. (Note: Formation pressure cannot truly decrease to near zero, and the recovery rate cannot reach 1.)

[0161] Figure 6 The pseudo-pressure p was shown p And the variation of the compression effect function g(p) with pressure. Figure 7This reveals the relationship between the product ratio λ of viscosity and overall compressibility and the extraction degree function R, where the dashed line represents the polynomial fitting line and ε is the fitting error:

[0162]

[0163] λ≈λ pol = -9.399657 × 10 -1 R 4 +7.882297×10 -1 R 3 +1.119501R 2 -1.969778R+1 (31) Where: ε——λ pol The difference from λ, %; λ pol — A polynomial function in R, in decimal form.

[0164] Figure 8 The required volume factor B for the digital model is shown. g The gas viscosity μ data and other parameters are shown in Table 1. The operating regime of the gas well was periodically changed to simulate continuous fluctuations in production and flowing pressure, and its production history is shown in Table 1. Figure 9 As shown. Using the explicit PDA method of this invention, G = 2.743294 × 10⁻⁶ was obtained. 8 m 3 b = 7.983647 MPa·(10 4 m 3 / d) -1 The error between the observed value Y and its estimated value is smallest at this time, and the reserve estimation error is 1.116%. Figure 10 The estimation error E(Y) of Y under the (G,b) combination is shown.

[0165] Under this complex production regime, the numerical example shows a reserve calculation error of less than 2%, and the estimation error of vector Y is less than 1%. This demonstrates that the explicit method presented in this paper can quickly and accurately estimate gas reservoir reserves and boundary control flow constants, and is applicable to various gas well production systems. This method requires only production data and gas property parameters to complete the calculation, avoiding iterations of quasi-time functions and greatly simplifying the calculation process. The effectiveness of the method is further illustrated below with a frequently cited field example from the literature.

[0166] Table 1. Property parameters of digital model settings

[0167]

[0168] Example 3

[0169] Embodiment 3 of the present invention provides an application example of gas well production data analysis, wherein the data is actual gas reservoir data.

[0170] This field example is taken from field data of a gas well A in West Virginia, provided by Fetkovich et al. (1987). The original formation pressure of the gas reservoir was 28.785612 MPa (=4175 psi), and the formation temperature was 71.11 °C. Fetkovich et al. (1987) gave a bottomhole pressure of 3.447379 MPa (=500 psi). Fraim and Wattenbarger (1987) listed the production-time data for this well and adjusted the bottomhole pressure to 4.895278 MPa (=710 psi) for curve fitting to represent the average value over the entire production period. Mohammed et al. (2013) and Alom et al. (2017) also provided its cumulative production data.

[0171] According to γ g =0.57 or M g =16.509480 g / mol. Based on the Sutton (2005, 2007) relation, the pseudocritical properties of natural gas are deduced as follows: p pc = 4.605256 MPa, T pc =191.218772K; The reservoir parameters of gas well A are shown in Table 2.

[0172] Figure 11 It demonstrates the pseudo-pressure p p The relationship between the compression effect function g(p) and pressure p. The relationship between the product ratio of gas viscosity and the overall compressibility coefficient λ and the recovery rate function R is as follows. Figure 12 As shown, the polynomial represented by the dashed line is:

[0173] λ≈λ pol = -3.840680 × 10 -1 R 4 -1.851623×10 -1 R 3 +9.185172×10 -1 R 2 -1.348872R+1(32)

[0174] Table 2. Gas reservoir and natural gas property parameters from field examples.

[0175]

[0176] The production and cumulative production data of gas well A are as follows: Figure 13 As shown. When p is taken wf When the pressure is 4.895278 MPa, G is obtained by solving using all 46 data points: G = 7.618829 × 10⁻⁶. 7 m 3 b = 3.199891 MPa·(10 4m 3 / d) -1 , Figure 14 The estimation error of Y is shown; it can be seen that the estimation error of the first two points exceeds 20%, indicating that the gas well production before this point has not reached the boundary control flow, so the first two points should be removed in the analysis.

[0177] Take p wf =4.895278MPa, and using the last 44 data points, we get G = 7.667620 × 10 7 m 3 b = 3.266598 MPa·(10 4 m 3 / d) -1 , Figure 15 The corresponding estimation error for Y is shown.

[0178] Similarly, when p is taken wf When the pressure is 3.447379 MPa, the reserves and boundary control flow constant obtained by solving the method in this paper based on 44 data points become: G = 7.401173 × 10 7 m 3 b = 3.300964 MPa·(10 4 m 3 / d) -1 The estimation error of Y is as follows: Figure 16 As shown.

[0179] Table 3 lists the analytical results of previous researchers using different methods for this example. It can be seen that the results in this paper differ slightly from, but are basically consistent with, those in this paper. It should be noted that since most literature does not provide the compressibility coefficients of the rock and bound water, the estimates of G and b have some uncertainty. In terms of error analysis, the method in this paper has higher reliability.

[0180] Table 3 Comparison of calculation results for field examples using various methods

[0181]

[0182] Based on the inventive concept of this invention, embodiments of this invention also provide a gas well production data analysis device, the structure of which is as follows: Figure 17 As shown, it includes:

[0183] The production data array sequence acquisition module 171 is used to acquire a first array sequence of production, bottom hole pressure and cumulative production at multiple times, and convert the bottom hole pressure in the first array sequence into the corresponding pseudo pressure to obtain a second array sequence.

[0184] The module 172 for determining the relationship between λ and R is used to determine the value of the polynomial coefficient in the formula for the relationship between the product ratio of gas viscosity and comprehensive compressibility coefficient λ and the production degree function R, based on the gas reservoir where the gas well is located and the fluid property parameters in the gas reservoir. The degree of the polynomial is greater than 2.

[0185] The solver module 173 is used to obtain the optimal combination of gas reservoir reserves and boundary control flow constant based on the determined values ​​of polynomial coefficients and the second array sequence, through the pseudo-pressure corresponding to gas well production and bottom hole pressure, the cumulative production and the first relationship between gas reservoir reserves and boundary control flow constant. The first relationship is pre-established based on the pressure and flow rate relationship of the boundary control flow stage, the gas reservoir static material balance equation under isothermal conditions and the relationship between λ and R.

[0186] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0187] Based on the inventive concept of the present invention, the embodiments of the present invention also provide a computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions, when executed by a processor, implement the above-mentioned gas well production data analysis method.

[0188] 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 executable on the processor, wherein the processor executes the program to implement the above-mentioned gas well production data analysis method.

[0189] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0190] It should be understood that the specific order or hierarchy of steps in the disclosed process 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 may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0191] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0192] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0193] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0194] 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. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0195] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term “comprising” as used in the specification or claims is interpreted in a manner similar to the term “including,” as it is understood when used as a conjunction in the claims. Additionally, the use of any term “or” in the specification of the claims is intended to mean “non-exclusive or.” The terms “first,” “second,” etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

Claims

1. A method for analyzing gas well production data, characterized in that, include: A first array sequence of production, bottom hole pressure and cumulative production at multiple time points is obtained. The bottom hole pressure in the first array sequence is converted into the corresponding pseudo pressure to obtain a second array sequence. Based on the gas reservoir where the gas well is located and the fluid property parameters within the gas reservoir, determine the values ​​of the polynomial coefficients in the relationship between the product ratio of gas viscosity and comprehensive compressibility coefficient λ and the recovery degree function R, where the degree of the polynomial is greater than 2. Based on the determined values ​​of the polynomial coefficients and the second array sequence, the optimal combination of gas reservoir reserves and boundary control flow constant is obtained through the first relationship between gas well production, the pseudo-pressure corresponding to bottom hole pressure, cumulative production and gas reservoir reserves and boundary control flow constant. The first relationship is pre-established based on the relationship between pressure and flow rate in the boundary control flow stage, the gas reservoir static material balance equation under isothermal conditions and the relationship between λ and R. The product ratio λ of gas viscosity and overall compressibility is defined as follows: , C represents the viscosity of a gas. t The overall compression factor is μ. i For the initial gas viscosity, C ti Here, p is the initial overall compressibility coefficient, and p is the pressure. The first relationship is established in advance in the following manner: The static mass balance equation of a gas reservoir under isothermal conditions and the aforementioned relationship The second relationship between material balance pseudotime and production and gas reservoir reserves was obtained: ； ； The first relationship is established based on the pressure-flow relationship during the boundary control flow stage and the second relationship: ； Where q is the output, G p G represents the cumulative production, G represents the gas reservoir reserves, and b represents the boundary control flow constant. The original formation pressure p i The corresponding pseudo-pressure, For the bottom hole pressure p wf The corresponding pseudo-pressure, S wci C represents the initial bound water saturation. ti c is the initial overall compression factor. i λ represents the polynomial coefficient in the relationship between the product ratio of gas viscosity and overall compressibility coefficient and the extraction degree function R, where i is the monomial number in the polynomial and n is the degree of the polynomial.

2. The method as described in claim 1, characterized in that, The extraction degree function R is defined as follows: g D (p) is the dimensionless compression effect function. g(p) is the compression effect function. Z i p is the gas deviation factor under the original formation conditions. i Where Z is the original formation pressure, and C is the gas deviation factor. φ C is the rock compressibility coefficient. w S is the formation water compressibility coefficient. wci The initial bound water saturation.

3. The method as described in claim 2, characterized in that, The determination of the polynomial coefficients in the formula relating the product ratio λ of gas viscosity and comprehensive compressibility coefficient to the recovery rate function R, based on the gas reservoir where the gas well is located and the fluid property parameters within the reservoir, specifically includes: Based on the definition of the product ratio λ of gas viscosity and overall compressibility, a relationship between λ and pressure is established. A third relationship between them; Based on the definition of the extraction degree function R, a fourth relationship between R and pressure is established; Based on the third and fourth relationships, the relationship between the product ratio λ of gas viscosity and overall compressibility coefficient and the recovery degree function R is determined. The polynomial coefficients c i The value of , where i is the index of the monomial in the polynomial, and n is the degree of the polynomial.

4. The method as described in claim 1, characterized in that, The static mass balance equation for the gas reservoir under isothermal conditions is as follows: ； Among them, g(p ave ) is determined by the mean formation pressure p ave The dimensionless compression effect function, G, is characterized. p G represents cumulative production, and G represents gas reservoir reserves. g D (p) is the dimensionless compression effect function. Let g(p) be the pressure, and g(p) be the compression effect function. Z i p is the gas deviation factor under the original formation conditions. i Where Z is the original formation pressure, and C is the gas deviation factor. φ C is the rock compressibility coefficient. w S is the formation water compressibility coefficient. wci The initial bound water saturation.

5. The method as described in claim 1, characterized in that, The pressure-flow relationship in the boundary control flow stage is as follows: ； in, The original formation pressure p i The corresponding pseudo-pressure, For the bottom hole pressure p wf The corresponding pseudo-pressure, q is the production rate, G is the gas reservoir reserves, and S is the gas reservoir capacity. wci C represents the initial bound water saturation. ti Let t be the initial overall compression coefficient. ca Let b be the pseudo-time for mass equilibrium, and b be the boundary control flow constant.

6. The method as described in claim 1, characterized in that, The optimal combination of gas reservoir reserves and boundary control flow constant is obtained by specifically including: Using a selected optimization algorithm, the combination of gas reservoir reserves and boundary control flow constant that satisfies the set error threshold between the left and right expressions of the first relationship is obtained, which is taken as the optimal combination of gas reservoir reserves and boundary control flow constant.

7. The method as described in claim 6, characterized in that, The optimization algorithm is a multivariate nonlinear fitting algorithm, a nonlinear least squares algorithm, or a constrained optimization algorithm.

8. The method as described in claim 1, characterized in that, The step of converting the bottom hole pressure in the first array sequence into the corresponding pseudo-pressure specifically includes: According to the definition of pseudo-pressure The bottom hole pressure in the first array sequence is converted into the corresponding pseudo-pressure; where p p Let p be the pseudo-pressure corresponding to pressure p. i μ represents the original formation pressure. i Z represents the initial gas viscosity. i Z represents the gas deviation factor under the original formation conditions, μ is the gas viscosity, and Z is the gas deviation factor. It is the integral variable.

9. A gas well production data analysis device, characterized in that, The apparatus is used to perform the gas well production data analysis method according to claim 1, and the apparatus includes: The production data array sequence acquisition module is used to acquire a first array sequence of production, bottom hole pressure and cumulative production at multiple times, and convert the bottom hole pressure in the first array sequence into the corresponding pseudo pressure to obtain a second array sequence; The module for determining the relationship between λ and R is used to determine the values ​​of polynomial coefficients in the formula relating the product ratio of gas viscosity and comprehensive compressibility coefficient λ to the production degree function R, based on the gas reservoir where the gas well is located and the fluid property parameters within the gas reservoir. The degree of the polynomial is greater than 2. The solution module is used to obtain the optimal combination of gas reservoir reserves and boundary control flow constant based on the determined values ​​of polynomial coefficients and the second array sequence, through the pseudo-pressure corresponding to gas well production and bottom hole pressure, the cumulative production and the first relationship between gas reservoir reserves and boundary control flow constant. The first relationship is pre-established based on the pressure and flow rate relationship of the boundary control flow stage, the gas reservoir static material balance equation under isothermal conditions and the relationship between λ and R.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the gas well production data analysis method according to any one of claims 1 to 8.

11. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the gas well production data analysis method according to any one of claims 1 to 8.

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