A new type of compact gas reservoir dynamic reserves calculation method and device

Abstract

The application discloses a novel method and device for calculating dynamic reserves of a gas well in a tight gas reservoir, and the method comprises the following steps: S1, acquiring daily production data of the gas well; S2, drawing a time-yield curve and a material balance time-yield curve; S3, judging whether there is a straight line with a slope greater than -1 in the time-yield curve and the material balance time-yield curve and whether the two straight lines are parallel to each other; S4, if it is judged that there is, judging whether the time-yield curve or the material balance time-yield curve is offset; S5, if it is judged that the curve is offset, extracting an offset point as a boundary flow critical time point t c ; S6, extracting a time-daily production sequence after the boundary flow critical time point t c ; S7, performing logarithmic transformation on the time and the daily production; S8, judging whether the logarithmically transformed time-daily production is in a linear relationship, and further determining a formula for calculating the dynamic reserves of the gas well. The application solves the problem that the calculation result of the dynamic reserves of the gas well in the tight gas reservoir is inaccurate.

Description

Technical Field

[0001] This invention relates to the field of dynamic reserve calculation for tight gas reservoir wells, and particularly to a novel method and apparatus for calculating dynamic reserves for tight gas reservoir wells. Background Technology

[0002] Dynamic reserves of gas wells are an important research topic in gas reservoir evaluation and development. Accurate calculation of dynamic reserves of gas wells is of great significance for the evaluation of gas reservoir potential, the formulation of effective development decisions, and the assessment of SEC value (usually, dynamic reserves of gas wells are the upper limit of SEC reserves of gas wells). The research on its methods has always been a hot topic of research for experts and scholars at home and abroad. Commonly used methods for calculating dynamic reserves of gas wells are mainly divided into three categories according to their methods and principles: (1) production decline method; (2) well testing method; (3) material balance method.

[0003] The methods for calculating dynamic gas well reserves, including the production decline method, well testing method, and material balance method and their correction methods, each have their own advantages and disadvantages and are widely used in the development of conventional gas reservoirs. However, with the deepening of gas reservoir development, unconventional gas reservoirs such as tight gas reservoirs and shale gas reservoirs are increasingly becoming key areas for evaluation and development. Compared with conventional gas reservoirs, on the one hand, the seepage mechanism and reservoir conditions of unconventional gas reservoirs such as tight gas reservoirs are complex. The well test method based on seepage theory to calculate the dynamic reserves of gas wells not only requires the gas well to enter the boundary flow state to obtain a relatively accurate dynamic reserve, but also relies on Darcy fluid as a basic premise. For the complex formation mechanism of tight gas reservoirs, non-Darcy flow of gas wells often occurs (such as turbulence effect and start-up pressure, stress sensitivity, etc.), which theoretically limits its use in tight gas reservoirs. In addition, it is necessary to carry out high-pressure fluid property experiments and rock and fluid experiments, resulting in significant human and financial costs. On the other hand, the material balance law based on formation pressure as a basic premise is also greatly limited in mining practice because the time required for the tight gas well to reach a completely static state in the tight gas reservoir reservoir is too long and the cost of dynamic monitoring of static pressure is high. Therefore, the production decline method is the optimal choice for calculating the dynamic reserves of gas wells in tight gas reservoir development. However, due to the tightness of the reservoir, the production of gas wells is low under natural conditions, and reservoir stimulation technology is often required to artificially create fractures in order to achieve profitable development. If the Arps decline model is directly used for data fitting, the hyperbolic decline model exponent is often greater than 1, resulting in abnormal physical properties in the calculated dynamic reserves of gas wells. However, production practice shows that the production decline of gas wells after reservoir stimulation in tight gas reservoirs is mainly divided into two stages: the first stage is the production decline stage dominated by fractures, and the second stage is the production decline stage dominated by boundary flow. The first stage of fitting the Arps decline model often also causes abnormal physical properties in the dynamic reserve calculation results, resulting in inaccurate results for calculating the dynamic reserves of gas wells in tight gas reservoirs. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies that directly use the Arps decline model for fitting in the first stage of production decline, resulting in inaccurate calculation results of dynamic reserves of tight gas reservoir wells, and to provide a new method and apparatus for calculating dynamic reserves of tight gas reservoir wells.

[0005] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0006] A novel method for calculating the dynamic reserves of tight gas reservoir wells includes the following steps:

[0007] S1. Obtain daily production data from gas wells and remove outlier data points;

[0008] S2. Calculate the material balance time, and plot the time-production curve and the material balance time-production curve in a double logarithmic coordinate system based on the preprocessed daily production data of the gas wells.

[0009] S3. Determine whether there exists a straight line with a slope greater than -1 in both the time-yield curve and the material balance time-yield curve, and whether the two lines are parallel to each other.

[0010] S4. If it is determined that there are no two parallel lines with a slope greater than -1 in the time-production curve and the material balance time-production curve, then return to step S1; if it is determined that there is one parallel line with a slope greater than -1 in both the time-production curve and the material balance time-production curve, then determine whether the time-production curve or the material balance time-production curve is offset.

[0011] S5. If a shift is detected in the time-yield curve or the material balance time-yield curve, the shift point is extracted as the critical time point t for the boundary flow. c ;

[0012] S6. Extract the critical time point t of the boundary flow. c Subsequent time-daily production sequence;

[0013] S7 performs logarithmic transformations on time and daily output and plots the results.

[0014] S8. Determine whether the time-daily production after logarithmic transformation is linear. If it is linear, select the exponentially decreasing model to calculate the dynamic reserves of the gas well. If it is nonlinear, select the harmonic decreasing model or the hyperbolic decreasing model to calculate the dynamic reserves of the gas well.

[0015] Preferably, in step S1, abnormal data points include points where the daily productivity is less than 1.

[0016] Preferably, in step S8, when a linear relationship is determined, the content of selecting the exponential decline model to calculate the dynamic reserves of the gas well includes: the exponential decline model is expressed by the formula as follows: And solve for the exponential model parameters q0 and D0, and according to the formula Calculate the dynamic reserves of the gas well, where q0 is the initial gas production of the gas well, 10 4 m 3 / d, D0 is the initial decrease rate, / d The critical time for the boundary flow is t = t c The cumulative production of the gas well at time q′0, where G is the dynamic reserves of the well and q′0 is the initial production at the boundary flow stage. 4 m 3 / d, where D′0 is the initial decline rate of the boundary flow stage, / d.

[0017] Preferably, in step S8, when a nonlinear relationship is determined, the content of selecting the harmonic decline model or the hyperbolic decline model to calculate the dynamic reserves of the gas well includes: the harmonic decline model is expressed by the formula q=q0(1+D0t). -1 The hyperbolic decreasing model is expressed by the formula: b is the hyperbolic decline exponent, which is dimensionless. The parameter b of the hyperbolic decline model is solved, and the method of calculating the dynamic reserves of gas wells is selected based on whether the difference between the absolute value of b and 1 is less than 0.01.

[0018] Preferably, the method for selecting a formula to calculate the dynamic reserves of a gas well based on whether the absolute difference between the value of b and 1 is less than 0.01 includes: selecting a formula when the absolute difference between the value of b and 1 is less than 0.01. Calculate the dynamic reserves of a gas well, where t a This refers to the time it takes for a gas well to reach its abandoned production level.

[0019] Preferably, the method for calculating the dynamic reserves of a gas well based on whether the absolute difference between the value of b and 1 is less than 0.01 further includes: selecting the formula when the absolute difference between the value of b and 1 is greater than or equal to 0.01. Calculate the dynamic reserves of the gas well, where b2 is the hyperbolic decline index of the second stage of the tight gas reservoir gas well, which is dimensionless and takes values ​​in the range of [0, 1].

[0020] Preferably, solving for the exponential model parameters q0 and D0 includes the following steps:

[0021] 1) Logarithmize the exponential model to obtain ln(q) = ln(q0) - D0t;

[0022] 2). Let y = ln(q), A = ln(q0), B = -D0, then we get y = A + Bt;

[0023] 3) Use linear regression to find the slope B and intercept A of y, and then obtain the parameters q0 = e of the exponentially decreasing model. A ,D0=-B.

[0024] Preferably, solving for the parameter b of the hyperbolic decreasing model includes the following steps:

[0025] 1) When solving for the parameter b of the hyperbolic decreasing model, the hyperbolic decreasing model is logarithmically transformed to obtain...

[0026]

[0027] 2) Let y(t) = ln(q), a = ln(q0), We obtain y(t) = a + f(b, D0, t);

[0028] 3) Let n be the logarithm of the data points fitted to the parameters, and construct the error function as follows:

[0029]

[0030] 4) Based on the optimization principle and using the gradient descent method, solve for parameter b, expressed by the formula:

[0031] In the formula, j represents the number of iterations, c represents the iteration step size with a value of 0.5, and q0 = e a .

[0032] A novel device for calculating the dynamic reserves of tight gas reservoir wells includes at least one processor and at least one memory communicatively connected to the processor. The memory stores instructions that are executed by the at least one processor to enable the at least one processor to perform any step of the method.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] 1. This invention determines the critical time point t of boundary flow by plotting time-yield curves and material balance time-yield curves in a double logarithmic coordinate system, and determining whether there exists a straight line with a slope greater than -1 and parallel to each other in both curves. c Extract the critical time point t of the boundary flow cThe time-daily production sequence was then processed, and the extracted time-daily production was subjected to a base logarithmic transformation. Based on whether the transformation relationship was linear, the Arps decline model type was determined, and the formula for dynamic gas well reserves was determined. This improved the accuracy of the dynamic gas well reserve calculation results by directly using the Arps decline model for fitting during the production decline stage, thus providing effective data support for gas reservoir development decisions and reserve analysis.

[0035] 2. The method for calculating dynamic reserves provided by this invention mainly relies on gas well production data, without the need for dynamic monitoring of gas wells. It is simple and efficient, thereby effectively reducing the cost of gas reservoir evaluation and development, and playing a role in improving the quality and efficiency of gas field development. Attached Figure Description

[0036] Figure 1 This is a flowchart of the present invention;

[0037] Figure 2 This is a flowchart of the first part of the embodiment;

[0038] Figure 3 This is a flowchart of the method in the second part of the embodiment;

[0039] Figure 4 A schematic diagram illustrating anomalies in a gas well;

[0040] Figure 5 A double logarithmic plot of time-production and material balance time-production for tight gas reservoir wells;

[0041] Figure 6 Schematic diagram for determining the critical time point of boundary flow in tight gas reservoir wells;

[0042] Figure 7 A chart analyzing the daily production data of gas wells using horizontal well volumetric fracturing in tight gas reservoirs;

[0043] Figure 8 This is a schematic diagram illustrating data extraction based on the critical time point of the boundary flow.

[0044] Figure 9 This is a schematic diagram illustrating model fitting with the boundary flow as the boundary. Detailed Implementation

[0045] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0046] In this invention, / d means daily.

[0047] Example 1

[0048] like Figure 1 As shown, a novel method for calculating the dynamic reserves of tight gas reservoir wells includes the following steps:

[0049] S1. Obtain daily production data from gas wells and remove outlier data points;

[0050] S2. Calculate the material balance time, and plot the time-production curve and the material balance time-production curve in a double logarithmic coordinate system based on the preprocessed daily production data of the gas wells.

[0051] S3. Determine whether there exists a straight line with a slope greater than -1 in both the time-yield curve and the material balance time-yield curve, and whether the two lines are parallel to each other.

[0052] S4. If it is determined that there are no two parallel lines with a slope greater than -1 in the time-production curve and the material balance time-production curve, then return to step S1; if it is determined that there is one parallel line with a slope greater than -1 in both the time-production curve and the material balance time-production curve, then determine whether the time-production curve or the material balance time-production curve is offset.

[0053] S5. If a shift is detected in the time-yield curve or the material balance time-yield curve, the shift point is extracted as the critical time point t for the boundary flow. c ;

[0054] S6. Extract the critical time point t of the boundary flow. c Subsequent time-daily production sequence;

[0055] S7 performs logarithmic transformations on time and daily output and plots the results.

[0056] S8. Determine whether the time-daily production after logarithmic transformation is linear. If it is linear, select the exponentially decreasing model to calculate the dynamic reserves of the gas well. If it is nonlinear, select the harmonic decreasing model or the hyperbolic decreasing model to calculate the dynamic reserves of the gas well.

[0057] The specific process of steps S1 to S8 is as follows: Figure 2 and 3 As shown, the specific content is as follows:

[0058] In step S1, the daily production sequence of gas wells is extracted from the established gas well dynamic database in chronological order, and dynamic data of daily production of gas wells is collected. Data points where the daily production of gas wells enters a period of decline are extracted, and production anomalies in the data are removed, including points where the daily production rate is less than 1 and points where gas well fluctuations are caused by the implementation of process measures, such as... Figure 4 As shown.

[0059] In step S2, time-yield curves and material balance time-yield curves are plotted in a double logarithmic coordinate system, as follows: Figure 5 As shown.

[0060] Determine whether there exists a straight line with a slope greater than -1 in both the time-to-yield curve and the mass balance time-to-yield curve, and that the two lines are parallel to each other. Also, determine the critical time point t for the boundary flow. c The process is as follows:

[0061] Domestic and international experts and scholars generally believe that tight gas reservoirs often require engineering intervention to achieve production due to the tightness of the reservoir. Production decline mainly occurs in two stages. The first stage is controlled by linear or transitional flow, with production decline following Arps decline, and the decline exponent b1 being greater than 1. The second stage is primarily controlled by boundary flow, following three Arps decline models: exponential, harmonic, and hyperbolic decline, with hyperbolic decline being the dominant model (since exponential and harmonic decline are special cases where the hyperbolic decline exponent tends to 0 and equal to 1 respectively, this patent description uniformly uses the hyperbolic decline model for description), and the decline exponent b2 is usually greater than 0 and less than 1. According to the Arps decline rate formula:

[0062]

[0063] Combining the three decreasing models proposed by Arps, including the exponential decreasing model:

[0064]

[0065] Hyperbolic decreasing model:

[0066]

[0067] Harmonic diminishing model,

[0068] q = q0(1 + D0t) -1 (4)

[0069] In the formula, t represents time, in days (d); D(t) is the rate of decrease at time t, / d; q is the daily gas production of the gas well, 10 4 m 3 / d; q0 is the initial gas well production rate, 10 4 m 3 / d; b is the hyperbolic decreasing exponent, dimensionless; D0 is the initial decreasing rate, / d.

[0070] because,

[0071]

[0072]

[0073] It is evident that the Arps exponential decline and harmonic decline models are special cases of the hyperbolic decline model; therefore, this patent uniformly uses hyperbolic decline for description. Since the production decline of tight gas reservoir wells is divided into two stages, its decline rate is a function of time. Combining this with formula (3), we obtain...

[0074]

[0075] Where b(t) is the hyperbolic decreasing exponent at time t, and let t be the time it takes for the gas well to transition from linear flow or transitional flow to boundary flow. c Then, combining formulas (1), (3), and (7), we get...

[0076]

[0077] Where, b1 is the first-stage hyperbolic decline index of tight gas reservoirs, dimensionless, and its value range is greater than 1 for tight gas reservoirs; b2 is the second-stage hyperbolic decline index of tight gas reservoir wells, dimensionless, and its value range is between 0 and 1 (inclusive). According to formula (8)

[0078]

[0079]

[0080] make

[0081]

[0082] Among them, t m Let d be the time of mass equilibrium.

[0083]

[0084] According to the definition of material equilibrium time, and combined with formula (12),

[0085]

[0086] In the formula G p (t) represents the cumulative gas production at time t, 10 4 m 3 Combining equations (3), (7), and (13), we get:

[0087]

[0088] It is easy to prove that k1>k2 always holds true based on equations (9) and (14).

[0089] When t <t c At that time, since both k1 and k2 are monotonically decreasing functions,

[0090]

[0091]

[0092] When t c When large enough,

[0093]

[0094] Right now

[0095]

[0096] In summary, the slope of the fracture-dominated production decline time-production and material balance time-production curves of a gas well before reaching boundary flow is approximately a parallel line on the double logarithmic curves. Since the production behavior of a gas well is declining and b1>1, the slope of this parallel line is greater than -1.

[0097] When t>t c And when t is sufficiently large (t→∞), according to formulas (9) and (14),

[0098]

[0099] Similarly, since gas well production behavior is decreasing and 0 < b2 < 1, the slope of the logarithmic time-production and mass balance time-production curves after the gas well enters the boundary flow stage is smaller than that during the fracture-dominated stage, resulting in a steeper graph, such as... Figure 6 As shown.

[0100] Therefore, in a log-log coordinate system, a set of straight lines with a slope less than -1 can be used to calibrate the graph. The point where the time-yield or material balance time-yield deviates from the parallel line is the critical point t at the start of the boundary flow. c .

[0101] Critical time point t of boundary flow c The subsequent daily production data of gas wells, taking the daily production data of a gas well in a tight gas reservoir in western Sichuan using horizontal well volumetric fracturing as an example, involves logarithmic transformation of the time-production data and plotting it, as shown in the figure. Figure 5 As shown, to determine the gas well production decline model, if the logarithmic time-logarithmic production after the double logarithmic change of time-production is a straight line, then the gas well production decline model is an exponential decline model; otherwise, it is a hyperbolic decline model or a harmonic decline model. The basic principle is as follows: based on formulas (2), (3), and (4), a logarithmic change is performed, then...

[0102] ln(q)=ln(q0)-D0t (20)

[0103]

[0104] ln(q)=ln(q0)-ln(1+D0t) (22)

[0105] According to formulas (20), (21), and (22), if the time-output ratio after logarithmic transformation is a straight line, it is exponentially decreasing; otherwise, it is harmonic or hyperbolic decreasing.

[0106] Based on the model diagnosis, linear regression is used to obtain the parameters of the exponentially decreasing model, and gradient descent is used to obtain the hyperbolic or harmonic decreasing parameters. The basic principle is as follows: according to formula (20), let...

[0107] y=ln(q),a=ln(q0),b=-D0 (23)

[0108] but

[0109] y = a + bt (24)

[0110] The slope b and intercept a are obtained by using linear regression, and then the parameters of the exponentially decreasing model are solved inversely.

[0111] q0 = e a ,D0=-b (25)

[0112] According to formula (21), let

[0113]

[0114] but

[0115] y(t)=a+f(b,D0,t) (27)

[0116] Let n be the logarithm of the data points fitted to the parameters, and construct the error function.

[0117]

[0118] Based on the principle of optimization, the gradient descent method is used to solve for the parameters.

[0119]

[0120] Where j represents the number of iterations, and c represents the iteration step size, typically taken as 0.5. The model parameters are then obtained, where...

[0121] q0 = e a (30)

[0122] Since the harmonic decreasing model is a special case of hyperbolic decreasing exponent 1, it can be solved using formula (29). In the solution, the value of b is approximately equal to 1. The criterion for being approximately equal to 1 is whether the absolute value of the difference between the value of b and 1 is less than 0.01. If it is less than 0.01, it is determined to be approximately equal to 1.

[0123] Based on the solution of the model parameters, the dynamic reserves of the gas well are calculated. Let the critical time for boundary flow be t = t_0. c The cumulative production of the gas well at that moment was When the gas well production decline model in the boundary flow stage follows an exponential decline model, the dynamic reserves G of the gas well satisfy the following condition based on the differential and integral relationship between production and cumulative production:

[0124]

[0125] In the formula, q′0 is the initial production of the boundary flow stage, 10 4 m 3 / d; D′0 is the initial decline rate in the boundary flow stage, / d, when the gas well production decline model in the boundary flow stage follows a hyperbolic decline model.

[0126]

[0127] When the gas well production decline model in the boundary flow stage is a harmonic decline model (when parameter b in equation (29) is approximately equal to 1), the time t for the gas well to reach abandoned production can be obtained using equation (4) of the harmonic decline model. a Then the dynamic reserves G of the gas well is:

[0128]

[0129] Figure 7 The daily production data analysis chart of a gas well in a tight gas reservoir in western Sichuan using horizontal well volumetric fracturing is shown. The traditional Arps decline model is directly adopted, but it is a hyperbolic decline model with an exponent greater than 1. According to formula (32), the calculated reserves are negative, indicating a physical anomaly in the well. Figure 6 As can be seen from the present invention, it is used for stage division and diagnosis, in t c =At 635 days, the gas well reaches the boundary flow state. Figure 8 for Figure 7 According to the critical time t of the boundary flow in gas wells c Data was extracted over a period of 635 days. For periods t greater than or equal to 635, the logarithm of daily gas production was taken for model diagnostics. Figure 8 The data shows a linear relationship between logarithmic time and logarithmic output, indicating an exponentially declining model. Figure 9 for Figure 7 and Figure 8For medium-sized gas wells, equations (23), (29), and (30) are used to fit the model with the boundary flow as the boundary, respectively. Figure 9 The data shows that before the boundary flow, the well exhibited a fracture-dominated hyperbolic decline with a decline exponent greater than 1. After the boundary flow, the well exhibited a boundary flow-dominated exponential decline, with an initial decline rate of 0.01 / day and an initial production of 12,000 cubic meters / day. The well reached the boundary flow critical time point t... c The cumulative gas production at the well is 23.53 million cubic meters. Using formula (32), the dynamic reserves of the well are calculated to be 35.53 million cubic meters.

[0130] A novel dynamic reserve calculation device for tight gas reservoir wells uses a Core i7-12700 processor and a Samsung 980PRO 1T solid-state drive for memory.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A novel method for calculating the dynamic reserves of gas wells in tight gas reservoirs, characterized in that, Includes the following steps: S1. Obtain daily production data from gas wells and remove outlier data points; S2. Calculate the material balance time, and plot the time-production curve and the material balance time-production curve in a double logarithmic coordinate system based on the preprocessed daily production data of the gas wells. S3. Determine whether there exists a straight line with a slope greater than -1 in both the time-yield curve and the material balance time-yield curve, and whether the two lines are parallel to each other. S4. If it is determined that there are no two parallel lines with a slope greater than -1 in the time-production curve and the material balance time-production curve, then return to step S1; if it is determined that there is one parallel line with a slope greater than -1 in both the time-production curve and the material balance time-production curve, then determine whether the time-production curve or the material balance time-production curve is offset. S5. If a shift is detected in the time-yield curve or the material balance time-yield curve, the shift point is extracted as the critical time point t for the boundary flow. c ; S6. Extract the critical time point t of the boundary flow. c Subsequent time-daily production sequence; S7 performs logarithmic transformations on time and daily output and plots the results. S8. Determine whether the time-daily production after logarithmic transformation is linear. If it is linear, select the exponentially decreasing model to calculate the dynamic reserves of the gas well. If it is nonlinear, select the harmonic decreasing model or the hyperbolic decreasing model to calculate the dynamic reserves of the gas well.

2. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 1, characterized in that, In step S1, abnormal data points include those with a daily productivity of less than 1.

3. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 1, characterized in that, In step S8, when a linear relationship is determined, the content of calculating the dynamic reserves of gas wells using the exponential decline model includes: the exponential decline model is expressed by the formula as follows: And solve for the parameters of the exponential model. and And according to the formula To calculate the dynamic reserves of a gas well, the formula is as follows: This represents the initial gas production of the gas well. The initial decrease rate, Critical time of boundary flow The cumulative production of the gas well at any given time, where G represents the dynamic reserves of the well. This represents the initial output for the boundary flow phase. This represents the initial decline rate during the boundary flow phase.

4. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 3, characterized in that, In step S8, when a nonlinear relationship is determined, the selection of either the harmonic decline model or the hyperbolic decline model for calculating the dynamic reserves of the gas well includes: the harmonic decline model is expressed by the formula... The hyperbolic decreasing model is expressed by the formula: Let b be the hyperbolic decline exponent, and solve for the parameter b of the hyperbolic decline model. Based on whether the difference between the absolute value of b and 1 is less than 0.01, select the formula to calculate the dynamic reserves of the gas well.

5. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 4, characterized in that, The method for calculating the dynamic reserves of gas wells based on whether the absolute difference between the value of b and 1 is less than 0.01 includes: selecting the formula when the absolute difference between the value of b and 1 is less than 0.

01. Calculate the dynamic reserves of a gas well, where t a This refers to the time it takes for a gas well to reach its abandoned production level.

6. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 4, characterized in that, The method for calculating dynamic gas well reserves based on whether the absolute difference between the value of b and 1 is less than 0.01 also includes: when the absolute difference between the value of b and 1 is greater than or equal to 0.01, the formula is selected... Calculate the dynamic reserves of a gas well, where is the hyperbolic decline index for the second stage of tight gas reservoir wells, with a value range of [0, 1].

7. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 3, characterized in that, Solving for the parameters of the exponential model and This includes the following steps: 1) Logarithmize the exponential model to obtain ; 2). Settings ,get ; 3) Use linear regression to find the slope B and intercept A of y, and then obtain the parameters of the exponentially decreasing model. .

8. The novel method for calculating the dynamic reserves of tight gas reservoir wells according to claim 4, characterized in that, Solving for the parameter b of the hyperbolic decreasing model includes the following steps: 1) When solving for the parameter b of the hyperbolic decreasing model, the hyperbolic decreasing model is logarithmically transformed to obtain... ; 2) Settings get ; 3) Let n be the logarithm of the data points fitted to the parameters, and construct the error function as follows: ; 4) Based on the optimization principle and using the gradient descent method, solve for parameter b, expressed by the formula: In the formula, j represents the number of iterations, and c represents the iteration step size, with a value of 0.

5. .

9. A novel device for calculating the dynamic reserves of a tight gas reservoir well, implementing the method according to any one of claims 1 to 8, characterized in that, The method includes at least one processor and at least one memory communicatively connected to the processor, the memory storing instructions executable by the at least one processor to enable the at least one processor to perform any step of the method.

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