A method and related device for determining dynamic reserves of a gas well in a low-permeability-compact gas reservoir

Abstract

The application discloses a kind of method and related devices for determining low-permeability-dense gas reservoir gas well dynamic reserves, belong to gas field development technical field;The method first determines that gas well enters quasi-steady state time, and the gas well with production time greater than quasi-steady state time is screened out as to-be-evaluated gas well for subsequent dynamic reserve evaluation;Determine the deviation factor and gas viscosity variation curve of to-be-evaluated gas well;According to the deviation factor and gas viscosity variation curve, calculate the pseudo pressure of to-be-evaluated gas well;Calculate the superposition yield of to-be-evaluated gas well under different production time;According to the pseudo pressure of evaluation gas well and the slope of superposition yield under different production time, calculate the slope of superposition yield normalization pseudo pressure and production time curve;Further, the dynamic reserve of to-be-evaluated gas well is calculated.The application solves the problem that the application degree of existing method is limited, and the evaluation quality is poor, improves the dynamic evaluation index level of this kind of gas reservoir, and provides reliable opinion for further realizing gas field development technical countermeasure adjustment.

Description

Technical Field

[0001] This invention belongs to the field of gas field development technology and relates to a method and related apparatus for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs. Background Technology

[0002] The dynamic reserves of a gas well can be understood as the portion of geological reserves in the formation that actually participates in seepage. It is an important dynamic evaluation indicator that comprehensively reflects the energy and production characteristics of the gas reservoir formation. The accurate evaluation of dynamic reserves is crucial because it is the direct basis for gas well production diagnosis and well network system evaluation, and the material foundation for gas field development planning and adjustments.

[0003] Traditional methods for evaluating dynamic reserves include pressure drop, mass balance, optimization fitting, and pressure recovery testing. Among these, the pressure drop method requires reliable pressure test data, the mass balance method requires stable bottomhole flowing pressure, and the optimization fitting and pressure recovery testing methods rely on production test data from intensive pressure testing. However, for low-permeability to tight gas reservoirs, firstly, the low permeability to tightness of the reservoir and the long pressure recovery time significantly conflict with production demands, resulting in a general lack of pressure test data; secondly, frequent changes in gas well production regimes and the difficulty in maintaining constant bottomhole flowing pressure both severely limit the application of traditional methods.

[0004] With continuous technological advancements, researchers have proposed production instability analysis methods based on modern production decline analysis. Compared to traditional approaches, the advantage of modern methods is that they do not rely on pressure testing. They only require the gas well to reach a quasi-steady state, and with the assistance of computer software, variable pressure / variable production data can be processed relatively quickly, enabling low-permeability tight gas reservoirs to obtain an initial dynamic reserve evaluation result. The Blasingame method is a typical example of this approach, its core being a material balance quasi-time function (essentially a superposition of times). However, this method still has technical limitations: First, in principle, the production data processed by the material balance quasi-time function will be misplaced, making it impossible to compare production and pressure at real time, thus making it difficult to identify the gas well flow regime (especially the time point of reaching the quasi-steady state). Second, when computer software is used for processing, inconsistencies in the fitting results among different technicians can lead to differences in output conclusions, or when similar conclusions are output, the presence of multiple parameter combinations weakens the evaluation quality of this method due to human influence and multiple solutions.

[0005] Therefore, it can be seen that the current dynamic reserves method has limited application and poor evaluation quality for gas wells in low-permeability and tight gas reservoirs. There is still a lack of technical methods that are simple to operate, relatively accurate in evaluation, and have good economic performance, and there is a need to carry out corresponding technical research. Summary of the Invention

[0006] The purpose of this invention is to provide a method and related apparatus for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs, so as to solve the technical problems of limited application and poor evaluation quality of existing evaluation methods.

[0007] To achieve the above objectives, the present invention employs the following technical solution:

[0008] In a first aspect, the present invention provides a method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir, comprising the following steps:

[0009] Determine the time when the gas well enters the quasi-steady state, and screen out gas wells with a production time longer than the quasi-steady state time as gas wells to be evaluated for subsequent dynamic reserves assessment;

[0010] Determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated;

[0011] The simulated pressure of the gas well to be evaluated is calculated based on the deviation factor and the gas viscosity change curve.

[0012] Calculate the cumulative production of the gas well under different production times;

[0013] The slope of the normalized pseudo-pressure versus production time curve is calculated based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times.

[0014] The dynamic reserves of the gas well to be evaluated are calculated by superimposing the slope of the production-normalized pseudo-pressure versus production-time curve and other parameters.

[0015] Furthermore, the step of determining the time it takes for a gas well to enter a quasi-steady state and selecting gas wells with a production time greater than the quasi-steady state time as gas wells to be evaluated for subsequent dynamic reserves assessment specifically includes:

[0016] S101, based on the geological conditions and dynamic characteristics of different gas fields, the gas fields where the gas wells to be evaluated are located are divided into development units.

[0017] S102, the gas wells in the development unit are divided into Class I, Class II, and Class III wells; and typical Class I, II, and III wells with long production time, no wellbore fluid accumulation during production, and high certainty of dynamic and static parameters are selected to calculate the time for typical wells in the same development unit to enter the quasi-steady state; the specific calculation method is as follows:

[0018] a. The time for a typical well to reach quasi-steady state is calculated using the seepage formula. The specific formula is as follows:

[0019]

[0020] In the formula, r is the radius of influence; K is the effective permeability of the reservoir; t is the time for the gas well to enter the pseudo-steady state; μ is the gas viscosity under the original formation pressure. C represents the effective porosity of the reservoir. t This is the overall compressibility coefficient under the original formation pressure;

[0021] The average time for typical gas wells of the same type to enter the pseudo-steady state is taken to obtain the reference time for this type of gas well to enter the pseudo-steady state in the same development unit.

[0022] b. Calculate the time for a gas well to enter a pseudo-steady state by fitting typical charts;

[0023] c. Compare the calculation results of step a and step b. If the time determined by step b is greater than the time determined by step a, and the time difference between the calculation results of step a and step b for each type of typical well meets the preset requirements, then the time to enter the pseudo-steady state determined by step b is the time to enter the pseudo-steady state for the gas well to be evaluated. If the time determined by step b is less than the time determined by step a, or the time difference between steps a and b does not meet the preset requirements, then further check the test results and remove or correct the results.

[0024] S103, calculate the average value of the time when typical gas wells of the same type in the same development unit enter the pseudo-steady state, and use this average value as the time when gas wells of this type enter the pseudo-steady state;

[0025] S104. If the production time of the gas well to be evaluated is greater than the quasi-steady-state time, then subsequent dynamic reserves evaluation will be carried out; otherwise, subsequent dynamic reserves evaluation will not be carried out.

[0026] Furthermore, the steps of determining the deviation factor and gas viscosity variation curve of the gas well to be evaluated specifically include:

[0027] S201. If the gas well to be evaluated has test data for deviation factor and gas viscosity, then plot the curves of its variation with bottom hole flowing pressure as the abscissa and deviation factor and gas viscosity as the ordinate, respectively.

[0028] S202, If the gas well to be evaluated does not have deviation factor and gas viscosity test data, then:

[0029] By comparing the test data of typical gas wells of the same type with deviation factors and viscosity in the same development unit where the gas well to be evaluated is located, a reasonable model is determined based on the comparison between the measured data and the model calculation data.

[0030] If the gas well to be evaluated has pre-production formation pressure, reservoir temperature, and gas composition test data, then the deviation factor and viscosity variation of the gas well to be evaluated are directly calculated using the aforementioned reasonable model. The curves showing the variation of the deviation factor and gas viscosity with bottom hole flowing pressure are plotted with the bottom hole flowing pressure as the abscissa and the deviation factor and gas viscosity as the ordinates, respectively. If the gas well to be evaluated has pre-production formation pressure and reservoir temperature test data but lacks gas composition test data, then the average gas composition of typical wells of the same type in the same development unit is first used as the average value of the gas composition of the gas well to be evaluated. Then, the deviation factor and viscosity variation of the gas well to be evaluated are calculated using the aforementioned reasonable model. The curves showing the variation of the deviation factor and gas viscosity with bottom hole flowing pressure are plotted with the bottom hole flowing pressure as the abscissa and the deviation factor and gas viscosity as the ordinates, respectively.

[0031] Furthermore, the step of calculating the pseudo-pressure of the gas well to be evaluated based on the deviation factor and the gas viscosity change curve specifically includes:

[0032] Based on the deviation factor and the shape of the gas viscosity change curve, different functions were selected for fitting, including polynomial and exponential fitting functions, with Ri being one of them. 2 The function with the highest value corresponds to the deviation factor and the gas viscosity curve. Substituting these values ​​into the pseudo-pressure calculation formula, the pseudo-pressure variation characteristics of the gas well to be evaluated are calculated. The pseudo-pressure calculation formula is as follows:

[0033]

[0034] In the formula, m(p) is the pseudo-pressure; p is the formation pressure; z(p) is the deviation factor under varying formation pressure; and μ(p) is the gas viscosity under varying formation pressure.

[0035] Furthermore, the step of calculating the superimposed production rate of the gas well under different production times specifically includes:

[0036] The cumulative production of the gas well under different production times is calculated using the production data of the gas well to be evaluated. The specific calculation formula is as follows:

[0037]

[0038] In the formula, q 叠加 To superimpose output q i t represents the daily gas production on day i; n This refers to the nth day of production.

[0039] Furthermore, the step of calculating the slope of the normalized pseudo-pressure versus production time curve based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times specifically includes:

[0040] Using the superimposed production at different production times as the denominator and the difference between the pseudo-pressure under the original formation pressure and the pseudo-pressure at the bottom hole flowing pressure under different production times as the numerator, the two are divided at different production times to obtain the normalized pseudo-pressure of the superimposed production at different production times.

[0041] Plot the corresponding curve with the normalized pseudo-pressure of the superimposed production of the gas well to be evaluated as the vertical axis and the production time as the horizontal axis, and regress its slope by taking the obvious straight line segment; the specific formula for calculating the slope is as follows:

[0042]

[0043] In the formula, q is the slope of the straight line segment; 叠加 To accumulate output; This is a simulated pressure difference.

[0044] Furthermore, the step of calculating the dynamic reserves of the gas well to be evaluated by superimposing the slope of the production-normalized pseudo-pressure versus production-time curve and other parameters specifically includes:

[0045] The dynamic reserves of the gas well are calculated based on the deviation factor, gas viscosity, and overall compressibility coefficient. The specific calculation formula is as follows:

[0046]

[0047] In the formula, p i S represents the original formation pressure. g Z represents the gas saturation level. i The deviation factor under the original formation pressure conditions; (μC) t ) i The gas viscosity μ and the overall compressibility C under the original formation pressure conditions t product.

[0048] Secondly, the present invention provides a system for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs, comprising:

[0049] The pseudo-steady-state time calculation module is used to determine the time when a gas well enters the pseudo-steady-state state and to screen out gas wells with a production time greater than the pseudo-steady-state time as gas wells to be evaluated for subsequent dynamic reserves evaluation.

[0050] The deviation factor and gas viscosity variation curve determination module is used to determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated.

[0051] The pseudo-pressure calculation module is used to calculate the pseudo-pressure of the gas well to be evaluated based on the deviation factor and the gas viscosity change curve.

[0052] The superimposed production calculation module is used to calculate the superimposed production of the gas well under different production times.

[0053] The curve slope calculation module is used to calculate the slope of the superimposed production normalized pseudo-pressure versus production time curve based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times.

[0054] The dynamic reserves evaluation module is used to calculate the dynamic reserves of the gas well to be evaluated by superimposing the slope of the production-normalized pseudo-pressure and production-time curves and other parameters.

[0055] Thirdly, the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0056] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] This invention discloses a method and related apparatus for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs. First, by dividing the reservoir into development units, the time it takes for the gas well to enter a quasi-steady state is calculated. An optimal and reasonable model is then used to determine the deviation factor and gas viscosity change curve of the gas well, thereby calculating the quasi-pressure of the gas well. Then, the dynamic reserves of the low-permeability tight gas well are calculated using superimposed production data. In contrast, existing technologies often use production data processed with superimposed time, which is misaligned and cannot be compared with production and pressure at real time, making it difficult to identify the flow regime of the gas well (especially the time point of entering the quasi-steady state). The production data processed with superimposed production data proposed in this invention conforms to the real time axis, making flow regime identification (especially the time point of entering the quasi-steady state) clearer and more intuitive. Furthermore, this invention differs from the scattered stretching of production data at the middle and end points in the superimposed time processing method, resulting in higher slope normalization and reducing the ambiguity among different evaluators, significantly improving the overall evaluation quality. In the Jingbian and Sulige gas fields, using the same process, the accuracy of evaluating the dynamic reserves of gas wells using superimposed time compared to the pressure drop method is 71.2%, which can be increased to 88.3% using superimposed production data. This invention addresses the limitations of current dynamic reserves methods for low-permeability tight gas reservoirs, which suffer from limited application or poor evaluation quality. It improves the evaluation quality of dynamic reserves in such gas reservoirs and further enhances the reliability of this indicator in guiding gas field development adjustments.

[0059] Furthermore, this invention eliminates the need for shut-in pressure testing and utilizes only daily production data for dynamic reserve evaluation of this type of gas well, adapting to the low-cost development needs of low-permeability carbonate gas reservoirs and tight sandstone gas reservoirs. Moreover, this evaluation method primarily relies on small-scale reservoir homogenization, selection of reliable models, and the establishment of new data processing approaches to achieve data quality control. By applying relatively mature commercial software, it achieves near-optimized model and data format, making operation relatively simple and highly applicable in the field. In addition, this method allows for the formation of a connection between static indicators (reservoir properties, fluids, etc.) and dynamic indicators (dynamic reserves, daily gas production, etc.) based on development units. New wells in the same development unit can achieve rapid indicator evaluation in the early stages, particularly suitable for the rapid advancement of production and construction work in low-permeability carbonate and tight sandstone reservoirs. Fourthly, it proposes a "self-renewal" requirement based on development unit understanding, realizing the periodic evaluation requirements from single wells to development units to gas fields, which has significant positive implications for deepening gas field understanding.

[0060] Furthermore, this invention selects R from polynomial and exponential fitting functions. 2 The function with the highest value corresponds to the deviation factor and the gas viscosity curve, thus calculating the pseudo-pressure of the gas well. Compared to existing technologies that typically use binomial fitting, which often overlooks the fact that the binomial fitting function begins to deviate from the actual test data under high pressure conditions in deeply buried low-permeability-tight gas reservoirs, this invention uses a trinomial fitting function R... 2 The highest value, which is closest to the optimal model, helps improve the evaluation accuracy when the gas reservoir has high compressibility characteristics, and is especially suitable for low-permeability to tight gas reservoirs. Attached Figure Description

[0061] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 In embodiment A1-1 of this invention, the Blasingame plot in RTA is used to identify the quasi-steady state.

[0063] Figure 2 This is a comparison chart of the deviation factor and measured data under different calculation models for well A1-1 in embodiment A of the present invention;

[0064] Figure 3 This is a comparison chart of gas viscosity and measured data under different calculation models for well A1-1 in embodiment A of the present invention;

[0065] Figure 4-1The variation of the deviation factor of well M and the trinomial fitting curve are shown in the embodiment of the present invention.

[0066] Figure 4-2 The variation of the deviation factor of well M and the binomial fitting curve are shown in the embodiment of the present invention.

[0067] Figure 5-1 The following is an example of the gas viscosity variation and trinomial fitting curve in well M according to an embodiment of the present invention;

[0068] Figure 5-2 The gas viscosity variation in well M and the binomial fitting curve;

[0069] Figure 6 This is a simulated pressure change curve of well M in block A of the Jingbian gas field, according to an embodiment of the present invention.

[0070] Figure 7 This is a simulated pressure curve of well M in Block A of Jingbian Gas Field under different production times / overlapping times, with production / production normalization.

[0071] Figure 8 This is a flowchart of the method of the present invention;

[0072] Figure 9 This is a schematic diagram of the system of the present invention;

[0073] Figure 10 This is a schematic diagram of the computer device structure of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0075] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0076] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0077] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0078] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0079] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.

[0080] The present invention will now be described in further detail with reference to the accompanying drawings:

[0081] See Figure 8 This invention discloses a method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir, comprising the following steps:

[0082] Step 1) Divide the gas field containing the gas well of the low-permeability gas reservoir to be evaluated into development units.

[0083] Considering the large-scale distribution of low-permeability tight gas reservoirs, their strong reservoir heterogeneity, and the large number of development wells, development units are divided according to the geological conditions and dynamic characteristics of different gas fields. The aim is to minimize the differences in reservoir properties, fluid properties, and high-pressure properties within a certain range, thus laying the foundation for rapid and accurate evaluation.

[0084] Given the relative consistency of reservoir properties, fluid properties, and high-pressure properties, the number of development units should not be too few or too many. For technically recoverable reserves less than 25 × 10⁻⁶, 8 m 3 Small and micro gas fields can be divided into 1 to 2 categories; for technically recoverable reserves of 25 to 250 × 10⁻⁶ m³ / h⁻¹, the remaining areas can be further subdivided. 8 m3 The number of medium-sized gas fields should be controlled at 4 to 6; for technically recoverable reserves of 250 to 500 million cubic meters per second... 8 m 3 The number of large gas fields should be controlled to 6-8; for those with technically recoverable reserves greater than 500×10⁻⁶... 8 m 3 Large gas fields require first dividing development blocks, and then further dividing each development block into an appropriate number of development units according to the technically recoverable ranges mentioned above.

[0085] The development blocks are divided according to different gas field division principles. They are usually divided based on a rough division of sedimentary, diagenetic, reservoir properties, and fluid properties, and the results are determined by comprehensively considering administrative boundaries, mining rights, gas field management and other factors.

[0086] Step 2) Determine the dynamic and static parameters of typical gas wells in development units I, II, and III where the gas well to be evaluated is located, as well as the time to reach the quasi-steady state.

[0087] The dynamic and static parameters of typical gas wells in different development units (Types I, II, and III) and the time to reach quasi-steady state are determined according to the following steps.

[0088] Step 2-1: Optimization of typical wells of types I, II, and III

[0089] The preferred development units are typical gas wells of types I, II, and III, which have a longer production time, no wellbore fluid accumulation during production, and high certainty of dynamic and static parameters.

[0090] Furthermore, the requirements for longer production times are as follows: for medium-low permeability gas reservoirs with an effective permeability of 5.0–10.0 md, gas wells require 0.5 years or more; for low permeability gas reservoirs with an effective permeability of 3.0–5.0 md, gas wells require 1.0 year or more; for low permeability gas reservoirs with an effective permeability of 1.0–3.0 md, gas wells require 1.5 years or more; for low permeability gas reservoirs with an effective permeability of 0.1–1.0 md, gas wells require 2.0 years or more; and for tight gas reservoirs with an effective permeability of 0.01–0.1 md, gas wells require 3.0 years or more.

[0091] The production time requirement is primarily based on the post-evaluation results of the dynamic indicators of gas wells in the Jingbian low-permeability carbonate gas reservoir and the Sulige tight sandstone gas reservoir, which have a production time of over 10 years (well opening time of over 6 years), possess data from 3 or more pressure tests, and have data from 2 or more production capacity tests. The post-evaluation of these dynamic indicators shows that only by meeting this production time requirement can the corresponding dynamic indicators be effectively evaluated. If the gas field where the well to be evaluated is located has similar statistical constraints, its own constraints can be used. If these constraints cannot be established, they can be directly applied under conditions of similar reservoir types.

[0092] Furthermore, the requirement for high certainty in dynamic and static parameters is that the selected gas wells must have pre-production formation pressure testing, 1-2 static pressure tests during production, gas composition, fluid, and high-pressure property testing data, and production capacity test data. Considering the characteristics of low-permeability-tight gas reservoirs—numerous wells, low production capacity, long pressure recovery time, significant contradiction between shut-in testing and production needs, and low-cost development—if it is difficult to meet all requirements, it can be further simplified to require pre-production formation pressure testing and 1-2 static pressure tests during production, while the total number of tests for each individual item (gas composition, fluid, high-pressure property, and production capacity test) should meet 30%-50% of the number of wells selected within the range, and it is not required that the same gas well simultaneously meet all requirements. For example, if a Class II well requires 10 wells to be selected, then all 10 wells must have pre-production formation pressure testing, 1-2 static pressure tests during production, and at least 3 well tests for each individual item (gas composition, fluid, high-pressure property, and production capacity test).

[0093] Furthermore, the requirements for the number of typical gas wells are as follows: Considering the characteristics of low-permeability tight gas reservoirs—numerous wells, low production capacity, long pressure recovery time, significant contradiction between shut-in testing and production needs, and low-cost development—there may be a situation where the number of wells meeting the typical well selection requirements is relatively small. In principle, the total number of typical wells should not be less than 10% of the total number of wells. The number of typical wells for each of categories I, II, and III is calculated by multiplying the total number of typical wells by the proportion of category I, II, and III wells in the total number of wells. Taking a development unit with a total of 300 wells as an example, if the proportions of category I, II, and III gas wells are 30%, 40%, and 30% respectively, then the total number of typical wells selected should not be less than 30, and the proportions of category I, II, and III typical gas wells should also be 30%, 40%, and 30%, respectively, which translates to no less than 9, 12, and 9 wells. If it is indeed difficult to meet this requirement, it can be reduced to 5% of the total number of wells.

[0094] It should be noted that the requirements for each of the above items are derived from the production experience of the Jingbian Gas Field (low-permeability carbonate gas reservoir) and the Sulige Gas Field (tight sandstone gas reservoir), which demonstrated engineering accuracy in conducting relevant typicality evaluations. If the gas field where the well to be evaluated is located meets the corresponding typicality evaluation requirements, its own constraints can be used; if these cannot be established, the constraints can be directly applied under conditions of similar gas reservoir types.

[0095] Step 2-2: Calculate the time for a typical gas well to enter a pseudo-steady state.

[0096] a. Calculation of the time for a gas well to reach pseudo-steady state using the seepage formula:

[0097] ①Refer to the calculation formula for the radius of influence under ideal conditions in seepage mechanics (1)

[0098]

[0099] Therefore, the formula for calculating the time for a gas well to enter a quasi-steady state under ideal conditions is derived (2).

[0100]

[0101] In the formula, r is the radius of influence, which can be replaced by the actual well spacing (m) for the operability of the formula; K is the effective permeability of the reservoir (mD); t is the time for the gas well to enter the pseudo-steady state (h); and μ is the gas viscosity under the original formation pressure. C represents the effective porosity of the reservoir, a decimal. t This is the overall compressibility coefficient under the original formation pressure, in MPa. -1 The parameters above can be used to calculate the time required for gas wells within the same development unit's well network to reach a quasi-steady state.

[0102] ② By directly substituting the above parameters of a typical gas well (excluding the pseudo-steady-state time t) into equation (2), t can be calculated. This t can be used as the reference time for the typical well to enter the pseudo-steady-state. After calculating the reference time for other typical wells, the reference time for the typical gas wells of the same type to enter the pseudo-steady-state can be averaged to obtain the reference time for the gas wells of this type to enter the pseudo-steady-state in the same development unit.

[0103] It should be noted that this formula is an evolution of the Darcy flow formula under ideal conditions. The ideal conditions refer to the absence of consideration for non-Darcy, heterogeneous, and low-permeability to tight reservoir conditions. Therefore, the calculation results are relatively ideal (the time to enter the quasi-steady state is smaller than the actual time). Thus, this calculation time is required to be the minimum time required for the gas well to enter the quasi-steady state.

[0104] b. Typical chart fitting calculation of gas well entry into pseudo-steady state time:

[0105] The time when the normalized rate curve in the Blasingame plot of the RTA software shows a monotonically decreasing straight line is used to determine the time when a typical gas well enters a pseudo-steady state. It should be noted that other plots in the same or similar software (AG, NPI, etc.) can also be used, as long as the respective plot identification conditions are followed and the constraints in the identification below are applied.

[0106] Furthermore, the requirements for using charts to determine the time when a gas well enters a quasi-steady state are as follows: within the initial time range when the normalized rate curve on the Blasingame chart shows a monotonically decreasing straight line, select three or more data points to confirm its actual production time (five or more are required in cases of abnormally tight reservoirs or large fluctuations in gas well production). The earliest and latest actual production times of the three or more data points should be within 0.5 years. Then, the latest time should be selected as the time when the well enters a quasi-steady state.

[0107] c. Determine the time to reach the quasi-steady state.

[0108] Comparing the calculation results of steps a and b, if the time determined in step b is greater than the time determined in step a, and the difference is within 0.5 years for Class I wells, within 0.8 years for Class II wells, and within 1.3 years for Class III wells, then the time to reach the quasi-steady state determined in step b is the required time to reach the quasi-steady state for the gas well to be evaluated. If the time determined in step b is less than the time determined in step a, or the difference between steps a and b does not meet the gap requirement, then it is necessary to further verify the reliability and causes of each test result, and decide whether to use this result as an outlier for removal or correction.

[0109] It should be noted that the time difference is determined by the actual test data and production experience of the Sulige and Jingbian gas fields. Specifically, it is determined by substituting the tested limit dynamic parameters (effective permeability, viscosity under the original bottom conditions of the gas field, etc.), geological parameters (effective porosity, etc.), and well spacing into formula (2) and the time difference determined by the established RTA model according to step c. If the gas field where the gas well to be evaluated is located can establish a corresponding constraint relationship, it can use its own constraint relationship. If it cannot be established, the constraint relationship can be directly used under the condition of similar gas reservoir type.

[0110] Steps 2-3: Calculate the time for the development unit containing a typical gas well to enter a quasi-steady state.

[0111] Following step 2-2, identify the time when all typical gas wells in the same development unit enter the quasi-steady state. Then, calculate the average time when typical gas wells of the same type enter the quasi-steady state, and use this average value as the time when gas wells of that type enter the quasi-steady state.

[0112] Furthermore, if the number of typical wells screened in the development unit increases and the richness of production test data improves, the time for different types of typical gas wells in the development unit to enter the quasi-steady state should be updated according to the latest data in accordance with the above steps, and periodic "self-update" should be carried out. Among them, the evaluation period for low-permeability carbonate gas reservoirs is 2 years and the evaluation period for tight sandstone gas reservoirs is 1 year.

[0113] It should be noted that this step provides support in two main aspects. First, in situations where the evaluation time is tight, simply determining the well type (Class I, II, or III) allows for a rapid assessment of the well's time to reach a quasi-steady state, thus deciding whether to continue dynamic reserve evaluation (if the well's production time exceeds the quasi-steady state time, dynamic reserve evaluation can continue; if it hasn't reached a quasi-steady state, subsequent evaluation cannot proceed). Second, it provides relatively reliable dynamic (effective permeability K) and static (comprehensive compressibility coefficient under original formation pressure conditions, etc.) parameters within the same development unit, offering parameter support for wells lacking corresponding testing conditions.

[0114] Step 3) Determine the time when the gas well to be evaluated enters the pseudo-steady state.

[0115] Calculating the time for a gas well to reach a quasi-steady state involves two scenarios: First, if the well meets the typical well screening requirements, proceed directly to step 2-2 to determine whether it has reached a quasi-steady state or entered a quasi-steady state. Second, if the well's test data is incomplete or lacks corresponding dynamic and static parameters, use the dynamic and static parameters from the same development unit and type of gas well in step 2-3, and still follow step 2-2 to determine whether it has reached a quasi-steady state or entered a quasi-steady state. Regardless of the scenario, if the well's production time exceeds the quasi-steady state time, dynamic reserve evaluation can continue according to the following steps; otherwise, subsequent dynamic reserve evaluation cannot proceed.

[0116] It should be noted that most gas wells in low-permeability-tight gas reservoirs fall into the second category due to poor reservoir properties, long pressure recovery time, and the requirement for low-cost development.

[0117] Step 4) Determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated.

[0118] Depending on whether the gas well to be evaluated has deviation factor and gas viscosity test data, the calculation can be divided into the following two scenarios.

[0119] Scenario 1: The gas well to be evaluated has deviation factor (PVT) and gas viscosity test data. In this case, the deviation factor and gas viscosity test data are directly used, and the curves of their variation with bottom hole flowing pressure are plotted with the deviation factor and gas viscosity as the ordinates, respectively.

[0120] Scenario 2: The gas well to be evaluated does not have deviation factor (PVT) or viscosity test data, but it is further divided into the following two scenarios based on whether gas component test data is available.

[0121] Scenario 1: The gas well to be evaluated has pre-production test data on formation pressure, gas layer temperature, and gas composition;

[0122] Scenario 2: The gas well to be evaluated has pre-production formation pressure and gas layer temperature test data, but does not have gas component test data.

[0123] If either of the above two scenarios applies, proceed to the next steps for further determination.

[0124] a. Optimize the deviation factor and gas viscosity calculation model for gas wells of the same type and within the same development unit.

[0125] Select typical gas wells of the same type and with test data of deviation factor and viscosity in the same development unit where the gas well to be evaluated is located (step 2-1). Using the Editors module in RTA software, input the basic parameters (original formation pressure, temperature, effective reservoir thickness, effective porosity, gas saturation, gas specific gravity, gas composition, critical temperature, and critical pressure), calculate the deviation factor and viscosity data under the commonly used calculation models, and then compare them with the measured data curves. The curve with the highest similarity is determined as the reasonable model for a single well. Then, the reasonable model for a single well that appears most frequently is determined as the reasonable model for the same development unit and the same type of gas well.

[0126] b. Calculate the deviation factor and gas viscosity change curve of the gas well to be evaluated.

[0127] b-1 For case ① in scenario 2, input the original formation pressure, temperature, reservoir properties (effective reservoir thickness, effective porosity, gas saturation), gas composition, and other test data of the well to be evaluated into the Editors module of the RTA software. Using the reasonable model selected in step a, directly calculate the deviation factor and viscosity change of the gas well to be evaluated, and plot the curves of its variation with bottom hole flowing pressure as the abscissa and deviation factor and gas viscosity as the ordinates.

[0128] b-2 For case 2, case ②, it is necessary to first use the average value of the gas composition of typical wells of the same development unit and the same type as the average value of the well to be evaluated, and then proceed according to step b-1.

[0129] It should be noted that steps a and b can be performed using other software with similar RTA functions, following the same approach to achieve the same calculation purpose.

[0130] Furthermore, if the number of typical wells screened in the development unit increases and the production test data is abundant, the model optimization of deviation factors and gas viscosity for different types of gas wells in the development unit should be updated according to the above steps based on the latest data, and periodic "self-update" should be carried out. Among them, the evaluation cycle for low-permeability carbonate gas reservoirs is 2 years and the evaluation cycle for tight sandstone gas reservoirs is 1 year.

[0131] Step 5) Calculate the simulated pressure of the gas well to be evaluated.

[0132] Based on the deviation factor of the development unit where the gas well to be evaluated is located and the shape of the gas viscosity curve calculated in step 4), different functions are selected for fitting, and R is selected. 2 The function with the higher value is the deviation factor and the function corresponding to the gas viscosity curve. Substituting it into the pseudo-pressure calculation formula (Equation (3)) will allow us to calculate the pseudo-pressure change characteristics of the gas well to be evaluated.

[0133]

[0134] It should be noted that:

[0135] First, we choose a relatively simple function for fitting instead of directly using the preferred model in step a of step 4) in order to simplify the calculation of equation (3) and improve the efficiency of equation (3).

[0136] Secondly, the variation curves of the deviation factor and gas viscosity are selected using R... 2 The purpose of using high-value functions is to restore the optimal model performance as much as possible while simplifying it. Furthermore, if polynomial fitting is used, the higher the number of terms, the better the fit. Therefore, when computational resources permit, it is desirable to use R-squared values ​​that are high enough to achieve the best fit. 2 The function with the highest value is substituted into the calculation (usually the number of terms does not exceed 4). It should be noted that in current gas reservoir engineering calculations, people often directly choose binomial fitting, frequently neglecting the fact that in deeply buried, low-permeability, tight gas reservoirs under high-pressure conditions, the binomial fitting function begins to deviate from the actual test data. This example shows that the trinomial fitting function R... 2 The highest value, and the closest to the optimal model, helps improve evaluation accuracy when the gas reservoir has high compressibility characteristics. Furthermore, if a fast and simple calculation process is desired, a binomial regression relationship can still be used. If the high-pressure properties of the gas reservoir in the same development unit are unclear, or if there is sufficient test evidence indicating large variations in high-pressure properties or deep burial of the gas layer, it is required to select R0. 2 The function with the highest value (recommended trinomial for low-permeability tight gas reservoirs).

[0137] Step 6) Calculate the superimposed production of the gas well to be evaluated under different production times.

[0138] Using the concept of superimposed time (a process that divides a continuous production curve into multiple small time intervals and treats the production as a constant in each close segment, only compressing or extending the time), the superimposed time definition (4) is obtained. Referring to the definition of superimposed time, the superimposed production definition (5) is further extended, that is, keeping the length of time constant, only one equilibrium production is used to express the fluctuating production change. Using methods such as VBA in Excel or MATLAB programming, the production data of the gas well to be evaluated can be substituted into the formula (5) to calculate the superimposed production of the gas well to be evaluated under different production times. Furthermore, from the connotation of the formula, that is, the production curve of continuous production is required, it can be seen that the production time does not include the shut-in time, that is, the pressure and production data of the day when the daily gas production is 0 need to be deleted. Furthermore, in order to use the imperial seepage formula in the subsequent steps, the daily gas production unit needs to be converted to mscf / d.

[0139]

[0140] In the formula, q i Let be the daily gas production on day i, 10 4 m3 / d;t n For production day n, d; t 叠加 For the superposition time, d; q 叠加 For the cumulative output, mscf / d.

[0141] It should be noted that the advantage of superimposed production functions is that they can handle situations where gas well production fluctuates continuously and significantly, especially suitable for low-permeability to tight gas reservoirs with large production fluctuations. However, this type of function has a large error when applied to depleted gas reservoirs with high compressibility (variation of deviation factor). Therefore, when applying this method, it is required that the deviation factor fitting be as close as possible to the optimal model, which is also the requirement in step 5) for R... 2 The key reason for substituting functions with higher values ​​instead of directly using binomials.

[0142] Step 7) Calculate the slope of the superimposed normalized pressure versus production time curve of the gas well to be evaluated.

[0143] Step 7-1 Calculate the superimposed output normalization pseudo-pressure under different production times.

[0144] Superimposed production normalization pseudo-pressure refers to a mathematical processing method that uses superimposed production to process pseudo-pressure. That is, the superimposed production at different production times is used as the denominator, and the difference between the pseudo-pressure under the original formation pressure and the pseudo-pressure at the bottom hole flowing pressure under different production times is used as the numerator. The result is the result of dividing the two at different production times.

[0145] Step 7-2: Calculate the slope of the superimposed production output normalized pseudo-pressure versus production time curve.

[0146] Plot the corresponding curve with the normalized pseudo-pressure of the superimposed production of the gas well to be evaluated as the vertical axis and the production time as the horizontal axis. Take the slope of the obvious straight line segment to obtain the result. The slope of the straight line segment of the curve is expressed as shown in Equation (6). Furthermore, in order to use the imperial seepage formula in the subsequent steps, the daily gas production unit is required to be converted to mscf / d and the pressure data is converted to psi.

[0147]

[0148] In the formula, The slope of the straight line segment (psi) 2 / (mscf*cp); q 叠加 For the cumulative output, mscf / d; The simulated pressure difference is expressed in psi.

[0149] The main advantages of superimposed output functions are further explained.

[0150] Without using the superposition function, the curve of production normalization pressure versus production time is multi-segmented, with several small fluctuations in the relatively flat middle segment, making it difficult to determine a definite slope and causing significant calculation errors.

[0151] Using a superimposed production function instead of a superimposed time function has two advantages. First, when superimposed time is used as the horizontal axis, the "superimposed time" curve in the graph shows that the actual production data is stretched on the time axis (the curve processed by superimposed time is significantly longer than the actual production time). This means that the production data no longer follows the original order, and the time axis loses its physical meaning. The identification of the key flow state (quasi-steady state) needs to be recalculated. However, after using the superimposed production function, the length of the curve processed by superimposed production is consistent with the actual production time. This means that the production data is no longer "misplaced" in time. The accurate presentation of production data plays a positive role in the determination of the gas well flow state. The time to enter the quasi-steady state can be directly read from the beginning of the monotonically decreasing straight line (the timing of the quasi-steady state is particularly critical to the stability and reliability of the evaluation of low-permeability-tight gas reservoir indicators, and the subsequent changes in the flow state are of great significance for deepening the understanding of the gas well seepage mechanism and the reliable identification of the timing of measures). Secondly, when using superimposed time, the actual production data is stretched on the time axis, and the data in the middle and later stages will be scattered, which is particularly prominent when the production time is short (early stage of quasi-steady state). This causes multiple solutions (multiple linear slopes) in the slope identification, which seriously affects the accuracy of the evaluation.

[0152] Step 8) Calculate the dynamic reserves of the gas well to be evaluated.

[0153] By substituting the parameters of the gas well to be evaluated into equation (7), the dynamic reserves of the gas well can be calculated. Furthermore, if test results are available for the deviation factor, gas viscosity, and comprehensive compressibility coefficient under the original conditions, these parameters can be directly applied.

[0154] If it is not present, it can be divided into two main situations.

[0155] First, if basic data such as original formation pressure, temperature, reservoir properties (effective reservoir thickness, effective porosity, gas saturation), and gas composition are available, but deviation factors, gas viscosity, and comprehensive compressibility coefficient are not available, then according to the reasonable model of deviation factors and gas viscosity selected in step 4 for the selected development unit and similar gas wells, the basic data such as original formation pressure, temperature, and reservoir properties (effective reservoir thickness, effective porosity, gas saturation) of the well to be evaluated are input into the Editors module of the RTA software to calculate the original condition deviation factor, gas viscosity, and comprehensive compressibility coefficient of the gas well to be evaluated.

[0156] Second, if the well possesses the original formation pressure and temperature but lacks reservoir properties (effective reservoir thickness, effective porosity, gas saturation), gas composition, deviation factor, gas viscosity, and overall compressibility coefficient, then the dynamic and static parameters of the same type as the development unit determined in step 2 are first used as the parameters of the gas well to be evaluated. Then, according to the reasonable model of deviation factor and gas viscosity selected for the same type of gas well in the development unit in step 4, the basic data such as the original formation pressure, temperature, and reservoir properties (effective reservoir thickness, effective porosity, gas saturation) of the gas well to be evaluated are input into the Editors module of the RTA software. The original condition deviation factor, gas viscosity, and overall compressibility coefficient of the gas well to be evaluated are then calculated.

[0157]

[0158] In this formula, p i Original formation pressure, psi; S g Z represents the gas saturation level, a decimal. i The deviation factor under the original formation pressure conditions, a decimal; (μC) t ) i The product of the gas viscosity μ and the overall compressibility coefficient under the original formation pressure conditions, in psi. -1 .

[0159] See Figure 9 This invention discloses a system for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs, comprising:

[0160] The pseudo-steady-state time calculation module is used to determine the time when a gas well enters the pseudo-steady-state state and to screen out gas wells with a production time greater than the pseudo-steady-state time as gas wells to be evaluated for subsequent dynamic reserves evaluation.

[0161] The deviation factor and gas viscosity variation curve determination module is used to determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated.

[0162] The pseudo-pressure calculation module is used to calculate the pseudo-pressure of the gas well to be evaluated based on the deviation factor and the gas viscosity change curve.

[0163] The superimposed production calculation module is used to calculate the superimposed production of the gas well under different production times.

[0164] The curve slope calculation module is used to calculate the slope of the superimposed production normalized pseudo-pressure versus production time curve based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times.

[0165] The dynamic reserves evaluation module is used to calculate the dynamic reserves of the gas well to be evaluated by superimposing the slope of the production-normalized pseudo-pressure and production-time curves.

[0166] Example:

[0167] This embodiment takes the M well (Class II well) of the A1 development unit in the A development block of the Sulige Gas Field, the largest tight gas reservoir in China, as an example, and provides a method for evaluating the dynamic reserves of tight gas reservoirs to improve the quality of dynamic reserve evaluation for this type of gas reservoir, including the following steps.

[0168] The following will provide further explanation in conjunction with the appendix and figures.

[0169] (The reason for choosing tight gas reservoirs as an example is that tight sandstone gas reservoirs have lower reservoir permeability than low-permeability carbonate gas reservoirs, have lower production capacity, larger production fluctuations, and are more difficult to linearize. Under the circumstances where tight gas reservoirs can be applied, low-permeability gas reservoirs usually have better application results.)

[0170] Step 1) Divide the gas field containing the gas well of the low-permeability gas reservoir to be evaluated into development units.

[0171] Considering the large-scale distribution of low-permeability tight gas reservoirs, their strong reservoir heterogeneity, and the large number of development wells, development units are divided according to the geological conditions and dynamic characteristics of different gas fields. The aim is to minimize the differences in reservoir properties, fluid properties, and high-pressure properties within a certain range.

[0172] Given the relative consistency of reservoir properties, fluid properties, and high-pressure properties, the number of development units should not be too few or too many. For technically recoverable reserves less than 25 × 10⁻⁶, 8 m 3 Small and micro gas fields can be divided into 1 to 2 categories; for technically recoverable reserves of 25 to 250 × 10⁻⁶ m³ / h⁻¹, the remaining areas can be further subdivided. 8 m 3 The number of medium-sized gas fields should be controlled at 4 to 6; for technically recoverable reserves of 250 to 500 million cubic meters per second... 8 m 3 The number of large gas fields should be controlled to 6-8; for those with technically recoverable reserves greater than 500×10⁻⁶... 8 m 3 Large gas fields require first dividing development blocks, and then further dividing each development block into an appropriate number of development units according to the technically recoverable ranges mentioned above.

[0173] The development blocks are divided according to different gas field division principles. They are usually divided based on a rough division of sedimentary, diagenetic, reservoir properties, and fluid properties, and the results are determined by comprehensively considering administrative boundaries, mining rights, gas field management and other factors.

[0174] Step 2) Determine the dynamic and static parameters of typical gas wells in development units I, II, and III where the gas well to be evaluated is located, as well as the time to reach the quasi-steady state.

[0175] The dynamic and static parameters of typical gas wells in different development units (Types I, II, and III) and the time to reach quasi-steady state are determined according to the following steps.

[0176] Step 2-1) Optimization of typical wells of types I, II, and III

[0177] The preferred development units are typical gas wells of types I, II, and III, which have a longer production time, no wellbore fluid accumulation during production, and high certainty of dynamic and static parameters.

[0178] Furthermore, the requirements for longer production times are as follows: for medium-low permeability gas reservoirs with an effective permeability of 5.0–10.0 md, gas wells require 0.5 years or more; for low permeability gas reservoirs with an effective permeability of 3.0–5.0 md, gas wells require 1.0 year or more; for low permeability gas reservoirs with an effective permeability of 1.0–3.0 md, gas wells require 1.5 years or more; for low permeability gas reservoirs with an effective permeability of 0.1–1.0 md, gas wells require 2.0 years or more; and for tight gas reservoirs with an effective permeability of 0.01–0.1 md, gas wells require 3.0 years or more.

[0179] The production time requirement is primarily based on the post-evaluation results of the dynamic indicators of gas wells in the Jingbian low-permeability carbonate gas reservoir and the Sulige tight sandstone gas reservoir, which have a production time of over 10 years (well opening time of over 6 years), possess data from 3 or more pressure tests, and have data from 2 or more production capacity tests. The post-evaluation of these dynamic indicators shows that only by meeting this production time requirement can the corresponding dynamic indicators be effectively evaluated. If the gas field where the well to be evaluated is located has similar statistical constraints, its own constraints can be used. If these constraints cannot be established, they can be directly applied under conditions of similar reservoir types.

[0180] Furthermore, the requirement for high certainty in dynamic and static parameters is that the selected gas wells must have pre-production formation pressure testing, 1-2 static pressure tests during production, gas composition, fluid, and high-pressure property testing data, and production capacity test data. Considering the characteristics of low-permeability-tight gas reservoirs—numerous wells, low production capacity, long pressure recovery time, significant contradiction between shut-in testing and production needs, and low-cost development—if it is difficult to meet all requirements, it can be further simplified to require pre-production formation pressure testing and 1-2 static pressure tests during production, while the total number of tests for each individual item (gas composition, fluid, high-pressure property, and production capacity test) should meet 30%-50% of the number of wells selected within the range, and it is not required that the same gas well simultaneously meet all requirements. For example, if a Class II well requires 10 wells to be selected, then all 10 wells must have pre-production formation pressure testing, 1-2 static pressure tests during production, and at least 3 well tests for each individual item (gas composition, fluid, high-pressure property, and production capacity test).

[0181] Furthermore, the requirements for the number of typical gas wells are as follows: Considering the characteristics of low-permeability tight gas reservoirs—numerous wells, low production capacity, long pressure recovery time, significant contradiction between shut-in testing and production needs, and low-cost development—there may be a situation where the number of wells meeting the typical well selection requirements is relatively small. In principle, the total number of typical wells should not be less than 10% of the total number of wells. The number of typical wells for each of categories I, II, and III is calculated by multiplying the total number of typical wells by the proportion of category I, II, and III wells in the total number of wells. Taking a development unit with a total of 300 wells as an example, if the proportions of category I, II, and III gas wells are 30%, 40%, and 30% respectively, then the total number of typical wells selected should not be less than 30, and the proportions of category I, II, and III typical gas wells should also be 30%, 40%, and 30%, respectively, which translates to no less than 9, 12, and 9 wells. If it is indeed difficult to meet this requirement, it can be reduced to 5% of the total number of wells.

[0182] The classification of low-permeability-tight gas reservoirs into categories I, II, and III is suggested in Table 1. It should be noted that the classification of wells into categories I, II, and III can be based on the specific development characteristics of the gas field, utilizing existing dynamic and static parameter standards. The main requirement is to comprehensively reflect reservoir properties and formation energy as much as possible. If these standards are not available, this suggestion can be used as a reference.

[0183] Table 1 Classification criteria for typical gas wells in low-permeability-tight gas reservoirs (Classes I, II, and III)

[0184]

[0185] It should be noted that the requirements for each of the above items are derived from the production experience of the Jingbian Gas Field (low-permeability carbonate gas reservoir) and the Sulige Gas Field (tight sandstone gas reservoir), which demonstrated engineering accuracy in conducting relevant typicality evaluations. If the gas field where the well to be evaluated is located meets the corresponding typicality evaluation requirements, its own constraints can be used; if these cannot be established, the constraints can be directly applied under conditions of similar gas reservoir types.

[0186] Taking the A1 development unit of Block A in the Sulige Gas Field as an example, there are 173 gas wells in production in this development unit. The proportions of wells evaluated as Class I, II, and III are 31.5%, 42.1%, and 26.3%, respectively. A total of 19 typical wells of Class I, II, and III were selected with a long production time (more than 3 years), no wellbore fluid accumulation during production, and high certainty of dynamic and static parameters (more than 10% of the total number of wells). The typical wells of Class I, II, and III are 6, 8, and 5, respectively (Table 2).

[0187] Table 2. Screening of typical wells in development unit A1 and their time to reach pseudo-steady state.

[0188]

[0189]

[0190] Step 2-2) Calculate the time for a typical gas well to enter a pseudo-steady state.

[0191] a. Calculation of the time for a gas well to reach pseudo-steady state using the seepage formula:

[0192] ①Refer to the calculation formula for the radius of influence under ideal conditions in seepage mechanics (1)

[0193]

[0194] Therefore, the formula for calculating the time for a gas well to enter a quasi-steady state under ideal conditions is derived (2).

[0195]

[0196] In the formula, r is the radius of influence, which can be replaced by the actual well spacing in meters for the operability of the formula; K is the effective permeability of the reservoir in meters; t is the time for the gas well to enter the pseudo-steady state in hours; μ is the gas viscosity under the original formation pressure in cp. C represents the effective porosity of the reservoir, a decimal. t This is the overall compressibility coefficient under the original formation pressure, in MPa. -1 The parameters above can be used to calculate the time required for gas wells within the same development unit's well network to reach a quasi-steady state.

[0197] ② By directly substituting the above parameters of a typical gas well (excluding the pseudo-steady-state time t) into equation (2), t can be calculated. This t can be used as the reference time for the typical well to enter the pseudo-steady-state. After calculating the reference time for other typical wells, the reference time for the typical gas wells of the same type to enter the pseudo-steady-state can be averaged to obtain the reference time for the gas wells of this type to enter the pseudo-steady-state in the same development unit.

[0198] It should be noted that this formula is an evolution of the Darcy flow formula under ideal conditions. The ideal conditions refer to the absence of consideration for non-Darcy, heterogeneous, and low-permeability to tight reservoir conditions. Therefore, the calculation results are relatively ideal (the time to enter the quasi-steady state is smaller than the actual time). Thus, this calculation time is required to be the minimum time required for the gas well to enter the quasi-steady state.

[0199] Taking well A1-1, a typical Class II well selected from development unit A1 in block A of the Sulige gas field, as an example, this well has relatively stable production, no wellbore fluid accumulation, and has undergone pressure testing and pressure recovery testing, with its dynamic and static parameters determined. The effective permeability K obtained under the testing conditions of well A1-1 is 0.065 mD, and the viscosity μ under the original formation pressure is... i With a compressibility coefficient of 0.0208 cp, an effective porosity φ of 7.0%, and an original formation pressure, the comprehensive compressibility coefficient C... ti It is 0.0276 MPa -1 Substituting the well spacing r of 600m into (2), the time t to reach the pseudo-steady state can be calculated to be 1.8 years.

[0200] b. Typical chart fitting calculation of gas well entry into pseudo-steady state time:

[0201] The time when the normalized rate curve in the Blasingame plot of the RTA software shows a monotonically decreasing straight line is used to determine the time when a typical gas well enters a quasi-steady state. It should be noted that other plots in the same or similar software (AG, NPI, etc.) can also be used, as long as the plot identification conditions are followed and the constraints in the identification below are applied.

[0202] Furthermore, the requirements for using charts to determine the time when a gas well enters a quasi-steady state are as follows: within the initial time range when the normalized rate curve on the Blasingame chart shows a monotonically decreasing straight line, select three or more data points to confirm its actual production time (five or more are required in cases of abnormally tight reservoirs or large fluctuations in gas well production). The earliest and latest actual production times of the three or more data points should be within 0.5 years. Then, the latest time should be selected as the time when the well enters a quasi-steady state.

[0203] Taking well A1-1, a typical Class II well selected from development unit A1 of the Sulige gas field block A, as an example, the well was put into production on April 10, 2012. A normalized production curve was generated using the Blasingame RTA software. Figure 1 Three data points were selected near the monotonically decreasing straight line. The horizontal axis dates from left to right were July 20, 2014, May 11, 2014, and September 14, 2014. The earliest time (May 11, 2014) and the latest time (September 14, 2014) among the three data points were within 0.5 years. Therefore, September 14, 2014, was selected as the time when the well entered the quasi-steady state. Subtracting the well's production date, the time required for the well to enter the quasi-steady state was determined to be 2.4 years.

[0204] c. Determine the time to reach the quasi-steady state.

[0205] Comparing the calculation results of steps a and b, if the time determined in step b is greater than the time determined in step a, and the difference is within 0.5 years for Class I wells, within 0.8 years for Class II wells, and within 1.3 years for Class III wells, then the time to reach the quasi-steady state determined in step b is the required time to reach the quasi-steady state for the gas well to be evaluated. If the time determined in step b is less than the time determined in step a, or the difference between steps a and b does not meet the gap requirement, then it is necessary to further verify the reliability and causes of each test result, and decide whether to use this result as an outlier for removal or correction.

[0206] It should be noted that the time difference is determined by the actual test data and production experience of the Sulige and Jingbian gas fields. Specifically, it is determined by substituting the tested limit dynamic parameters (effective permeability, viscosity under the original bottom conditions of the gas field, etc.), geological parameters (effective porosity, etc.), and well spacing into formula (2) and the time difference determined by the established RTA model according to step c. If the gas field where the gas well to be evaluated is located can establish a corresponding constraint relationship, it can use its own constraint relationship. If it cannot be established, the constraint relationship can be directly used under the condition of similar gas reservoir type.

[0207] Taking well A1-1, a typical Class II well selected from development unit A1 in block A of the Sulige gas field, as an example, the time for well A1-1 to enter the quasi-steady state determined by step a is 1.8 years, which is less than the time of 2.4 years determined by step b. The difference is 0.6 years (within the required 0.8 years), so the evaluation time of step b is considered reliable. Therefore, the time for well A1-1 to enter the quasi-steady state is 2.4 years.

[0208] Steps 2-3) Calculate the time for the development unit containing a typical gas well to enter the quasi-steady state.

[0209] Following step 2-2, the identification of the time when all typical gas wells in the same development unit enter the quasi-steady state is completed. Then, the average value of the time when typical gas wells of the same type enter the quasi-steady state is calculated. This average value is used to determine the time when gas wells of this type enter the quasi-steady state. The evaluation results are shown in Table 2.

[0210] Furthermore, if the number of typical wells screened in the development unit increases and the richness of production test data improves, the time for different types of typical gas wells in the development unit to enter the quasi-steady state should be updated according to the latest data in accordance with the above steps, and periodic "self-update" should be carried out. Among them, the evaluation period for low-permeability carbonate gas reservoirs is 2 years and the evaluation period for tight sandstone gas reservoirs is 1 year.

[0211] It should be noted that this step provides support in two main aspects. First, in situations where the evaluation time is tight, simply determining the well type (Class I, II, or III) allows for a rapid assessment of the well's time to reach a quasi-steady state, thus deciding whether to continue dynamic reserve evaluation (if the well's production time exceeds the quasi-steady state time, dynamic reserve evaluation can continue; if it hasn't reached a quasi-steady state, subsequent evaluation cannot proceed). Second, it provides relatively reliable dynamic (effective permeability K) and static (comprehensive compressibility coefficient under original formation pressure conditions, etc.) parameters within the same development unit, offering parameter support for wells lacking corresponding testing conditions.

[0212] Step 3) Determine the time when the gas well to be evaluated enters the pseudo-steady state.

[0213] Calculating the time for a gas well to reach a quasi-steady state involves two scenarios: First, if the well meets the typical well screening requirements, proceed directly to step 2-2 to determine whether it has reached a quasi-steady state or entered a quasi-steady state. Second, if the well's test data is incomplete or lacks corresponding dynamic and static parameters, use the dynamic and static parameters from the same development unit and type of gas well in step 2-3, and still follow step 2-2 to determine whether it has reached a quasi-steady state or entered a quasi-steady state. Regardless of the scenario, if the well's production time exceeds the quasi-steady state time, dynamic reserve evaluation can continue according to the following steps; otherwise, subsequent dynamic reserve evaluation cannot proceed.

[0214] Taking Well M in Development Unit A1 of Block A in the Sulige Gas Field as an example, the well was put into production on October 10, 2012, and is classified as a Class II well, belonging to the second situation mentioned above. Substituting the average dynamic and static parameters of Class II wells in Development Unit A1 in Table 2 into step 2-2, the theoretically calculated time to enter the quasi-steady state is 2.1 years, which is less than the 2.3 years fitted using the Blasingame chart, and the difference is 0.2 years (less than 0.8 years), which meets the calculation constraints. Therefore, the time for Well M to enter the quasi-steady state is 2.3 years. Currently, the production time is far longer than the quasi-steady state time, and subsequent dynamic reserve evaluation can be carried out.

[0215] It should be noted that most gas wells in low-permeability-tight gas reservoirs fall into the second category due to poor reservoir properties, long pressure recovery time, and the requirement for low-cost development.

[0216] Step 4) Determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated.

[0217] Depending on whether the gas well to be evaluated has deviation factor and gas viscosity test data, the calculation can be divided into the following two scenarios.

[0218] Scenario 1: The gas well to be evaluated has deviation factor (PVT) and gas viscosity test data. In this case, the deviation factor and gas viscosity test data are directly used, and the curves of their variation with bottom hole flowing pressure are plotted with the deviation factor and gas viscosity as the ordinates, respectively.

[0219] Taking a typical Class II well in development unit A1 of the Sulige gas field development block A, and well A1-1 with test data on deviation factors and gas viscosity changes as examples, we can obtain... Figure 2 Scatter points (measured data) Figure 3 The curve showing the variation of the scatter points (measured data).

[0220] Scenario 2: The gas well to be evaluated does not have deviation factor (PVT) or viscosity test data, but it is further divided into the following two scenarios based on whether gas component test data is available.

[0221] Scenario 1: The gas well to be evaluated has pre-production test data on formation pressure, gas layer temperature, and gas composition;

[0222] Scenario 2: The gas well to be evaluated has pre-production formation pressure and gas layer temperature test data, but does not have gas component test data.

[0223] If either of the above two scenarios applies, proceed to the next steps for further determination.

[0224] a. Optimize the deviation factor and gas viscosity calculation model for gas wells of the same type and within the same development unit.

[0225] Select typical gas wells of the same type and with test data of deviation factor and viscosity in the same development unit where the gas well to be evaluated is located (step 2-1). Using the Editors module in RTA software, input the basic parameters (original formation pressure, temperature, effective reservoir thickness, effective porosity, gas saturation, gas specific gravity, gas composition, critical temperature, and critical pressure), calculate the deviation factor and viscosity data under the commonly used calculation models, and then compare them with the measured data curves. The curve with the highest similarity is determined as the reasonable model for a single well. Then, the reasonable model for a single well that appears most frequently is determined as the reasonable model for the same development unit and the same type of gas well.

[0226] b. Calculate the deviation factor and gas viscosity change curve of the gas well to be evaluated.

[0227] b-1 For case ① in scenario 2, input the original formation pressure, temperature, reservoir properties (effective reservoir thickness, effective porosity, gas saturation), gas composition, and other test data of the well to be evaluated into the Editors module of the RTA software. Using the reasonable model selected in step a, directly calculate the deviation factor and viscosity change of the gas well to be evaluated, and plot the curves of its variation with bottom hole flowing pressure as the abscissa and deviation factor and gas viscosity as the ordinates.

[0228] b-2 For case 2, case ②, it is necessary to first use the average value of the gas composition of typical wells of the same development unit and the same type as the average value of the well to be evaluated, and then proceed according to step b-1.

[0229] It should be noted that steps a and b can be performed using other software with similar RTA functions, following the same approach to achieve the same calculation purpose.

[0230] Furthermore, if the number of typical wells screened in the development unit increases and the production test data is abundant, the model optimization of deviation factors and gas viscosity for different types of gas wells in the development unit should be updated according to the above steps based on the latest data, and periodic "self-update" should be carried out. Among them, the evaluation cycle for low-permeability carbonate gas reservoirs is 2 years and the evaluation cycle for tight sandstone gas reservoirs is 1 year.

[0231] Taking the gas well M to be evaluated in development unit A1 of the Sulige gas field development block A as an example, this well belongs to case 2①. According to step a, firstly, typical wells A1-1 to A1-5 with test data on deviation factor and viscosity in the same development unit are identified. Taking the deviation factor calculation of well A1-1 as an example, the deviation factor under commonly used calculation models (BWR, AGA8 Detail, and Carbon Dioxide models) is calculated using RTA software and compared with the measured data curve. Figure 2 The curve calculated by the BWR model showed the highest similarity to the measured data, thus it was determined to be the reasonable model for well A1-1. Wells A1-2 to A1-5 were then calculated using the same procedure, and the BWR model appeared four times, the most frequent occurrence (Table 3). Therefore, the deviation factor BWR model was determined to be the preferred model for development unit A1. Similarly, the viscosity of the gas well A1-1 was calculated using the same comparative method. Figure 3 The Carr et al. gas viscosity model was selected as the preferred model for well A1-1. Wells A1-2 to A1-5 were then calculated using the same procedure. The Carr et al. model appeared three times, the most frequent occurrence (Table 3). Therefore, the Carr et al. gas viscosity model was determined as the preferred model for development unit A1. Following step b-1, the formation pressure P before production of well M was... i (28.5MPa), air layer temperature T i (110℃) and gas composition (gas specific gravity G is 0.6, excluding CO2, H2S and N2) are input into the Editors module of the RTA software. The BWR model is selected for the deviation factor, and the Carr. et al. model is selected for the gas viscosity. This yields the deviation factor and gas viscosity changes under different formation pressures in well M. Then, with the bottomhole flowing pressure as the abscissa, the deviation factor ( Figure 4-1 ), gas viscosity ( Figure 5-1 The curve of its variation with bottom hole flowing pressure is plotted using the vertical axis.

[0232] Table 3. Optimal Calculation Model for Development Unit A1, Deviation Factor, and Gas Viscosity

[0233]

[0234]

[0235] Step 5) Calculate the simulated pressure of the gas well to be evaluated.

[0236] Based on the deviation factor of the development unit where the gas well to be evaluated is located and the shape of the gas viscosity curve calculated in step 4), different functions are selected for fitting, and R is selected. 2The function with the higher value is the deviation factor and the function corresponding to the gas viscosity curve. Substituting it into the pseudo-pressure calculation formula (Equation (3)) will allow us to calculate the pseudo-pressure change characteristics of the gas well to be evaluated.

[0237]

[0238] In the formula, m(p) is the pseudo-pressure, psi; p is the formation pressure, psi; z(p) is the deviation factor under varying formation pressure, decimal; μ(p) is the gas viscosity under varying formation pressure, decimal.

[0239] It should be noted that:

[0240] First, the commonly used deviation factors (such as BWR, AGA8 Detail, and Carbon Dioxide models) and viscosity calculation models have relatively complex calculation formulas. Directly substituting them into formula (3) would make on-site engineering calculations difficult. Therefore, a relatively simple function is selected for fitting instead of directly using the preferred model in step a of step 4). This is not only to simplify the calculation of formula (3) and improve the efficiency of formula (3), but also for convenient and quick application on-site.

[0241] Secondly, the variation curves of the deviation factor and gas viscosity are selected using R... 2 The purpose of using high-value functions is to restore the optimal model performance as much as possible while simplifying it. Furthermore, if polynomial fitting is used, the higher the number of terms, the better the fit. Therefore, when computational resources permit, it is desirable to use R-squared values ​​that are high enough to achieve the best fit. 2 The function with the highest value is substituted into the calculation (usually with no more than 4 terms). This is because in current gas reservoir engineering calculations, people usually choose the simpler binomial form. However, under high pressure conditions, R... 2 The fitted function with high values ​​initially deviates from the binomial fitted function, R0 2 A higher fitting function value is closer to the optimal model, which helps improve evaluation accuracy at high compressibility rates. Furthermore, for a faster and simpler calculation process, a binomial regression relationship can be used. If the high-pressure properties of the gas reservoir in the same development unit are unclear, or if there is sufficient test evidence indicating large variations in high-pressure properties or deep burial of the gas layer, it is required to select R0. 2 The function with the highest value.

[0242] Taking well M in development unit A1 of block A of the Sulige gas field as an example, based on the deviation factor and gas viscosity pattern obtained in step 3), polynomial and exponential functions were used for fitting regression. The regression results are shown in Table 4. The deviation factor and gas viscosity both showed the highest degree of agreement with the trinomial function (R0). 2 The maximum value has been reached (1). Selecting it as the deviation factor for well M and the gas viscosity regression model, and substituting it into equation (3), the pseudo-pressure calculation results for well M can be obtained, such as... Figure 6As shown. Furthermore, under high pressure conditions, R 2 The results of a high-value fitting function initially deviating from the binomial fitting function can be seen in... Figure 4-1 and 4-2 , Figure 5-1 and Figure 5-2 The comparison clearly shows that when the bottom hole pressure is greater than 27 MPa, the binomial fitting function begins to deviate from the optimal model. This indicates that in medium and high pressure production environments, choosing binomial fitting will reduce the accuracy of the evaluation to some extent.

[0243] Table 4. Fitting results of M-well deviation factor and gas viscosity function

[0244]

[0245] Step 6) Calculate the superimposed production of the gas well to be evaluated under different production times.

[0246] Using the concept of superimposed time (a process that divides a continuous production curve into multiple small time intervals and treats the production as a constant in each close segment, only compressing or extending the time), the superimposed time definition formula (4) is obtained. Referring to the definition of superimposed time, the superimposed production definition formula (5) is further derived, that is, keeping the length of time constant, only using a single equilibrium production to express the fluctuating production change. Using methods such as VBA in Excel or MATLAB programming, the production data of the gas well to be evaluated can be substituted into formula (5) to calculate the superimposed production of the gas well under different production times. Furthermore, from the connotation of the formula, that is, the production curve of continuous production is required, it can be seen that the production time does not include the shut-in time, that is, the pressure and production data of the day when the daily gas production is 0 need to be deleted. Furthermore, in order to use the imperial seepage formula in the subsequent steps, the daily gas production unit needs to be converted to mscf / d.

[0247]

[0248] In the formula, t 叠加 For the superposition time, d; q 叠加 For the superimposed output, mscf / d; q i Let be the daily gas production on day i, 10 4 m 3 / d;t n Let d be the production day n.

[0249] It should be noted that the advantage of superimposed production functions is that they can handle situations where gas well production fluctuates continuously and significantly, especially suitable for low-permeability to tight gas reservoirs with large production fluctuations. However, this type of function has a large error when applied to depleted gas reservoirs with high compressibility (variation of deviation factor). Therefore, when applying this method, it is required that the deviation factor fitting be as close as possible to the optimal model, which is also the requirement in step 5) for R... 2 The key reason for substituting functions with higher values ​​instead of directly using binomials.

[0250] Taking well M in development unit A1 of block A of the Sulige gas field as an example, after deleting the data with a daily gas production of 0 (as shown in Table 5), the daily gas production (based on the domestic 10 4 m 3 By converting / d to imperial units mscf / d and substituting the production time into equation (5), the superimposed output under different production times can be obtained. The calculation results are shown in the superimposed output column in Table 5.

[0251] Table 5 Production history of Well M and calculation data of bottom hole flowing pressure and superimposed production.

[0252]

[0253]

[0254] Step 7) Calculate the slope of the superimposed normalized pressure versus production time curve of the gas well to be evaluated.

[0255] Step 7-1) Calculate the superimposed output normalization pseudo-pressure under different production times.

[0256] Superimposed production normalization pseudo-pressure refers to a mathematical processing method that uses superimposed production to process pseudo-pressure. That is, the superimposed production at different production times is used as the denominator, and the difference between the pseudo-pressure under the original formation pressure and the pseudo-pressure at the bottom hole flowing pressure under different production times is used as the numerator. The result is the result of dividing the two at different production times.

[0257] Step 7-2) Calculate the slope of the superimposed production normalized pseudo-pressure versus production time curve.

[0258] Plot the corresponding curve with the normalized pseudo-pressure of the superimposed production of the gas well to be evaluated as the vertical axis and the production time as the horizontal axis. Take the slope of the obvious straight line segment to obtain the result. The slope of the straight line segment of the curve is expressed as shown in Equation (6). Furthermore, in order to use the imperial seepage formula in the subsequent steps, the daily gas production unit is required to be converted to mscf / d and the pressure data is converted to psi.

[0259]

[0260] In the formula, The slope of the straight line segment (psi)2 / (mscf*cp); q 叠加 For the cumulative output, mscf / d; The simulated pressure difference is expressed in psi.

[0261] Taking well M in development unit A1 of block A in the Sulige gas field as an example, the production time, daily gas production, pseudo-pressure, and calculated superimposed production data are shown in Table 5. The superimposed production at different production times is used as the denominator, and the difference between the pseudo-pressure at the original formation pressure and the pseudo-pressure at the bottom hole flowing pressure at different production times is used as the numerator (m(p) in Table 5). i )-m(p wf This allows us to obtain the normalized pseudo-pressure of superimposed output under different production times (Table 5 [m(p)]). i )-m(p wf Then, with production time on the horizontal axis and the normalized pseudo-pressure of the M-well superimposed production rate on the vertical axis, the curve of the M-well superimposed production rate normalized pseudo-pressure versus production time can be plotted, as shown below. Figure 7 As shown in the light gray overlay yield curve, the slope of the calculated straight line is 2044.2 psi. 2 / (mscf*cp).

[0262] Furthermore, from Figure 7 The main advantages of using superimposed production functions can be seen from this.

[0263] Without using the superposition function, the curve of production normalization pressure versus production time is multi-segmented ("without superposition" in the figure). The relatively flat segment in the middle also has several small fluctuations, making it difficult to determine a definite slope, which will cause a large calculation error.

[0264] Using a superimposed production function instead of a superimposed time function has two advantages. First, when superimposed time is used as the horizontal axis, the "superimposed time" curve in the figure shows that the actual production data is stretched on the time axis (the curve processed by superimposed time is significantly longer than the actual production time). This means that the production data no longer follows the original order, and the time axis loses its physical meaning. However, after using the superimposed production function (the "superimposed production" curve in the figure), the length of the curve processed by superimposed production is consistent with the actual production time. This means that the production data is no longer "misplaced" in time, and the true presentation of production data plays a positive role in the determination of the gas well flow state. The time to enter the quasi-steady state can be directly read from the beginning of the monotonically decreasing straight line (the timing of the quasi-steady state is particularly crucial for the stability and reliability of the evaluation of low-permeability-tight gas reservoir indicators, and the subsequent changes in the flow state are of great significance for deepening the understanding of the gas well seepage mechanism and reliably determining the timing of measures). Secondly, when using superimposed time, the actual production data is stretched on the time axis, resulting in scattering of the data in the middle and later stages, especially in cases where the production time is short (early stage of quasi-steady state). This leads to multiple solutions in the determination of the slope (e.g., Figure 7 The latter part of the "overlapping time" curve shows two different slopes, which affects the accuracy of the evaluation.

[0265] Step 8) Calculate the dynamic reserves of the gas well to be evaluated.

[0266] By substituting the parameters of the gas well to be evaluated into equation (7), the dynamic reserves of the gas well can be calculated. Furthermore, if test results are available for the deviation factor, gas viscosity, and comprehensive compressibility coefficient under the original conditions, these parameters can be directly applied.

[0267] If it is not present, it can be divided into two main situations.

[0268] First, if basic data such as original formation pressure, temperature, reservoir properties (effective reservoir thickness, effective porosity, gas saturation), and gas composition are available, but deviation factors, gas viscosity, and comprehensive compressibility coefficient are not available, then according to the reasonable model of deviation factors and gas viscosity selected in step 4 for the selected development unit and similar gas wells, the basic data such as original formation pressure, temperature, and reservoir properties (effective reservoir thickness, effective porosity, gas saturation) of the well to be evaluated are input into the Editors module of the RTA software to calculate the original condition deviation factor, gas viscosity, and comprehensive compressibility coefficient of the gas well to be evaluated.

[0269] Second, if the well possesses the original formation pressure and temperature but lacks reservoir properties (effective reservoir thickness, effective porosity, gas saturation), gas composition, deviation factor, gas viscosity, and overall compressibility coefficient, then the dynamic and static parameters of the same type as the development unit determined in step 2 are first used as the parameters of the gas well to be evaluated. Then, according to the reasonable model of deviation factor and gas viscosity selected for the same type of gas well in the development unit in step 4, the basic data such as the original formation pressure, temperature, and reservoir properties (effective reservoir thickness, effective porosity, gas saturation) of the gas well to be evaluated are input into the Editors module of the RTA software. The original condition deviation factor, gas viscosity, and overall compressibility coefficient of the gas well to be evaluated are then calculated.

[0270]

[0271] In this formula, p i Original formation pressure, psi; S g Z represents the gas saturation level, a decimal. i The deviation factor under the original formation pressure conditions, a decimal; (μC) t ) i The gas viscosity μ and the overall compressibility C under the original formation pressure conditions t The product of psi -1 .

[0272] Taking well M in development unit A1 of the Sulige gas field development block A as an example, the original formation pressure of this well is known to be 4057.97 psi, gas saturation is 0.55, and it contains no CO2, H2S, or N2. The gas specific gravity is 0.6. Step 4 optimizes the deviation factor and viscosity model. The reservoir properties are referenced from the static parameters of Class II wells in development unit A1. The deviation factor under the original formation pressure condition is 0.969, and the comprehensive compressibility coefficient under the original formation pressure condition is 0.000139 psi. -1 The slope of the superimposed output normalized simulated pressure and production time is 2044.2 psi. 2 / (mscf*cp) gives the reserves of well M as 772005.46mscf, which is equivalent to 2186.32×10 4 m 3 .

[0273] The principle of this invention is to provide a dynamic reserve calculation method for low-permeability tight gas reservoirs based on development unit division, high-pressure property calculation model and regression optimization, and superimposed production function processing. The aim is to improve the current dynamic reserve method for low-permeability tight gas reservoirs, which has limited application or poor evaluation quality, enhance the evaluation quality of dynamic reserves of such gas reservoirs, and further improve the reliability of this indicator in guiding gas field development adjustments. The advantages of this invention are: First, it eliminates the need for shut-in testing and other testing costs, meeting the low-cost development needs of low-permeability carbonate gas reservoirs and tight sandstone gas reservoirs; second, this evaluation method mainly relies on small-scale reservoir homogenization, selection of reliable models, and the establishment of new data processing ideas to achieve data quality control, and by applying relatively mature commercial software, it achieves a near-optimized model and data form, making it relatively simple to operate and highly applicable in the field; third, this method can form a connection between static indicators (reservoir properties, fluids, etc.) and dynamic indicators (dynamic reserves, daily gas production, etc.) based on development units, enabling rapid indicator evaluation of new wells in the same development unit in the early stages, which is particularly suitable for the rapid advancement of production and construction work in low-permeability carbonate and tight sandstone reservoirs; fourth, it proposes a "self-renewal" requirement based on development unit understanding, realizing the periodic evaluation requirements from single wells to development units to gas fields, which has important positive significance for deepening the understanding of gas fields.

[0274] Its key technological advancement lies in proposing the use of superimposed production data to calculate the dynamic reserves of low-permeability to tight gas wells. The advantages are: First, unlike production data processed with superimposed time, which is misaligned and cannot be compared with actual production and pressure at real time, making it difficult to identify the flow regime of gas wells (especially at the quasi-steady-state time point), production data processed with superimposed production data conforms to the real timeline, making flow regime identification (especially at the quasi-steady-state time point) clearer and more intuitive. Second, unlike the scattering and stretching of production data at the middle and end of the superimposed time processing, the slope normalization is higher, reducing the ambiguity among different evaluators and significantly improving the overall evaluation quality (application examples in Jingbian Gas Field and Sulige Gas Field show that using the same process, but comparing the accuracy of evaluating the dynamic reserves of gas wells using superimposed time with the pressure drop method, the accuracy is 71.2%, while using superimposed production data can increase it to 88.3%).

[0275] Evaluation or testing methods not described in detail in this embodiment are well-known or commonly used testing methods in the industry, and will not be described in detail here.

[0276] In one embodiment of the present invention, a computer device is provided, see [link to previous document]. Figure 10The computer device includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment can be used to operate a method for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs.

[0277] This invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for determining the dynamic reserves of a gas well in a low-permeability-tight gas reservoir in the above embodiments.

[0278] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0279] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0280] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0281] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0282] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs, characterized in that, Includes the following steps: Determine the time when the gas well enters the quasi-steady state, and screen out gas wells with a production time longer than the quasi-steady state time as gas wells to be evaluated for subsequent dynamic reserves assessment; Determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated; The simulated pressure of the gas well to be evaluated is calculated based on the deviation factor and the gas viscosity change curve. Calculate the cumulative production of the gas well under different production times; The slope of the normalized pseudo-pressure versus production time curve is calculated based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times. The dynamic reserves of the gas well to be evaluated are calculated by superimposing the slope of the production-normalized pseudo-pressure versus production-time curve and other parameters.

2. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The step of determining the time it takes for a gas well to enter a quasi-steady state and selecting gas wells with a production time greater than the quasi-steady state time as gas wells to be evaluated for subsequent dynamic reserves assessment specifically includes: S101, based on the geological conditions and dynamic characteristics of different gas fields, the gas fields where the gas wells to be evaluated are located are divided into development units. S102, the gas wells in the development unit are divided into Class I, Class II, and Class III wells; and typical Class I, II, and III wells with long production time, no wellbore fluid accumulation during production, and high certainty of dynamic and static parameters are selected to calculate the time for typical wells in the same development unit to enter the quasi-steady state; the specific calculation method is as follows: a. The time for a typical well to reach quasi-steady state is calculated using the seepage formula. The specific formula is as follows: In the formula, r is the radius of influence; K is the effective permeability of the reservoir; t is the time for the gas well to enter the pseudo-steady state; μ is the gas viscosity under the original formation pressure. C represents the effective porosity of the reservoir. t This is the overall compressibility coefficient under the original formation pressure; The average time for typical gas wells of the same type to enter the pseudo-steady state is taken to obtain the reference time for this type of gas well to enter the pseudo-steady state in the same development unit. b. Calculate the time for a gas well to enter a pseudo-steady state by fitting typical charts; c. Compare the calculation results of step a and step b. If the time determined by step b is greater than the time determined by step a, and the time difference between the calculation results of step a and step b for each type of typical well meets the preset requirements, then the time to enter the pseudo-steady state determined by step b is the time to enter the pseudo-steady state for the gas well to be evaluated. If the time determined by step b is less than the time determined by step a, or the time difference between steps a and b does not meet the preset requirements, then further check the test results and remove or correct the results. S103, calculate the average value of the time when typical gas wells of the same type in the same development unit enter the pseudo-steady state, and use this average value as the time when gas wells of this type enter the pseudo-steady state; S104. If the production time of the gas well to be evaluated is greater than the quasi-steady-state time, then subsequent dynamic reserves evaluation will be carried out; otherwise, subsequent dynamic reserves evaluation will not be carried out.

3. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The steps for determining the deviation factor and gas viscosity variation curve of the gas well to be evaluated specifically include: S201. If the gas well to be evaluated has test data for deviation factor and gas viscosity, then plot the curves of its variation with bottom hole flowing pressure as the abscissa and deviation factor and gas viscosity as the ordinate, respectively. S202, If the gas well to be evaluated does not have deviation factor and gas viscosity test data, then: By comparing the test data of typical gas wells of the same type with deviation factors and viscosity in the same development unit where the gas well to be evaluated is located, a reasonable model is determined based on the comparison between the measured data and the model calculation data. If the gas well to be evaluated has pre-production formation pressure, reservoir temperature, and gas composition test data, then the deviation factor and viscosity variation of the gas well to be evaluated are directly calculated using the aforementioned reasonable model. The curves showing the variation of the deviation factor and gas viscosity with bottom hole flowing pressure are plotted with the bottom hole flowing pressure as the abscissa and the deviation factor and gas viscosity as the ordinates, respectively. If the gas well to be evaluated has pre-production formation pressure and reservoir temperature test data but lacks gas composition test data, then the average gas composition of typical wells of the same type in the same development unit is first used as the average value of the gas composition of the gas well to be evaluated. Then, the deviation factor and viscosity variation of the gas well to be evaluated are calculated using the aforementioned reasonable model. The curves showing the variation of the deviation factor and gas viscosity with bottom hole flowing pressure are plotted with the bottom hole flowing pressure as the abscissa and the deviation factor and gas viscosity as the ordinates, respectively.

4. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The step of calculating the pseudo-pressure of the gas well to be evaluated based on the deviation factor and the gas viscosity change curve specifically includes: Based on the deviation factor and the shape of the gas viscosity change curve, different functions were selected for fitting, including polynomial and exponential fitting functions, with Ri being one of them. 2 The function with the highest value corresponds to the deviation factor and the gas viscosity curve. Substituting these values ​​into the pseudo-pressure calculation formula, the pseudo-pressure variation characteristics of the gas well to be evaluated are calculated. The pseudo-pressure calculation formula is as follows: In the formula, m(p) is the pseudo-pressure; p is the formation pressure; z(p) is the deviation factor under varying formation pressure; and μ(p) is the gas viscosity under varying formation pressure.

5. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The steps for calculating the superimposed production of the gas well under different production times specifically include: The cumulative production of the gas well under different production times is calculated using the production data of the gas well to be evaluated. The specific calculation formula is as follows: In the formula, q 叠加 To superimpose output q i t represents the daily gas production on day i; n This refers to the nth day of production.

6. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The step of calculating the slope of the normalized pseudo-pressure versus production time curve based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times specifically includes: Using the superimposed production at different production times as the denominator and the difference between the pseudo-pressure under the original formation pressure and the pseudo-pressure at the bottom hole flowing pressure under different production times as the numerator, the two are divided at different production times to obtain the normalized pseudo-pressure of the superimposed production at different production times. Plot the corresponding curve with the normalized pseudo-pressure of the superimposed production of the gas well to be evaluated as the vertical axis and the production time as the horizontal axis, and regress its slope by taking the obvious straight line segment; the specific formula for calculating the slope is as follows: In the formula, q is the slope of the straight line segment; 叠加 To accumulate output; This is a simulated pressure difference.

7. The method for determining the dynamic reserves of a gas well in a low-permeability tight gas reservoir according to claim 1, characterized in that, The step of calculating the dynamic reserves of the gas well to be evaluated by superimposing the slope of the production-normalized pseudo-pressure versus production-time curve and other parameters specifically includes: The dynamic reserves of the gas well are calculated based on the deviation factor, gas viscosity, and overall compressibility coefficient. The specific calculation formula is as follows: In the formula, p i S represents the original formation pressure. g Z represents the gas saturation level. i The deviation factor under the original formation pressure conditions; (μC) t ) i The gas viscosity μ and the overall compressibility C under the original formation pressure conditions t product.

8. A system for determining the dynamic reserves of gas wells in low-permeability tight gas reservoirs, characterized in that, include: The pseudo-steady-state time calculation module is used to determine the time when a gas well enters the pseudo-steady-state state and to screen out gas wells with a production time greater than the pseudo-steady-state time as gas wells to be evaluated for subsequent dynamic reserves evaluation. The deviation factor and gas viscosity variation curve determination module is used to determine the deviation factor and gas viscosity variation curve of the gas well to be evaluated. The pseudo-pressure calculation module is used to calculate the pseudo-pressure of the gas well to be evaluated based on the deviation factor and the gas viscosity change curve. The superimposed production calculation module is used to calculate the superimposed production of the gas well under different production times. The curve slope calculation module is used to calculate the slope of the superimposed production normalized pseudo-pressure versus production time curve based on the pseudo-pressure of the evaluated gas well and the superimposed production at different production times. The dynamic reserves evaluation module is used to calculate the dynamic reserves of the gas well to be evaluated by superimposing the slope of the production-normalized pseudo-pressure and production-time curves and other parameters.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.

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