An interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs

Abstract

This invention belongs to the field of gas field development technology, specifically providing an interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs. The method includes: establishing pressure drop curves for vertical wells to evaluate dynamic reserves; dividing the dynamic reserves of gas wells into intervals; selecting typical gas wells within each interval; determining the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs; establishing a single-well analytical model to evaluate the dynamic evaluation indicators of typical gas wells; establishing interactive constraint relationships between indicators; and using these interactive constraint relationships, with dynamic reserve intervals as representatives, forming a chessboard quadrant dynamic constraint relationship between gas wells and dynamic evaluation indicators for each dynamic reserve interval. This invention solves the problems of low accuracy in existing empirical formulas, the strong ambiguity of modern evaluation methods, and low evaluation timeliness. It achieves dynamic interactive constraints on dynamic evaluation indicators, enabling rapid and accurate evaluation and application of production indicators for gas wells in this type of gas reservoir.

Description

Technical Field

[0001] This invention belongs to the field of gas field development technology, specifically relating to an interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs. Background Technology

[0002] The evaluation of dynamic indicators for gas wells is conducted throughout the entire process of gas reservoir development. They provide direct feedback on the dynamics and characteristics of gas well development, directly determining the optimization of gas well production systems, the formulation of gas field development policies, and impacting the evaluation of gas field development benefits. Currently, the main dynamic evaluation indicators for gas wells include the effective permeability reflecting reservoir seepage, dynamic reserves reflecting the well's production capacity, EUR (Economically Recoverable Reserves), stable production years, and stable production capacity within those years. These indicators are interconnected and form a systematic whole.

[0003] Traditional dynamic evaluation indicators for gas wells are primarily based on seepage mechanisms and material balance principles, combined with empirical formulas accumulated during gas field development. The advantages are simplicity, speed, ease of rapid field application, and suitability for the rapid, low-cost development of low-permeability carbonate gas reservoirs. However, this requires that the static reservoir parameters and dynamic effective permeability in the empirical formulas be known and accurate. When faced with this requirement, the common practice is to directly select reservoir parameters such as thickness, porosity, and gas saturation from well logging interpretations, while referencing well test interpretation results for effective permeability; if none are available, the empirical average value of the gas field or block is used. The problems with this approach are as follows: First, gas reservoir development often involves multiple layers of combined production, and calculating the logging interpretation parameters for these layers largely relies on individual experience. Second, the static parameters from logging interpretation are usually uncorrelated with the empirical average values ​​of dynamic parameters for the gas field or block; in other words, their values ​​are not systematically derived. Third, low-permeability carbonate gas reservoirs lack shut-in pressure recovery test data due to poor reservoir properties, long pressure recovery times, and profitability requirements. Furthermore, due to the strong heterogeneity of the reservoirs, the empirical average values ​​for the gas field or block differ significantly from those of individual wells. Therefore, when using traditional seepage theory and empirical formulas for low-permeability carbonate gas reservoirs, the unsystematic and irregular nature of static and dynamic parameters leads to large evaluation errors. Additionally, traditional methods, being based on deterministic analytical formulas, are difficult to apply when two or more parameters are difficult to determine, resulting in relatively simplistic relationships between evaluation indicators.

[0004] With continuous technological advancements, numerical methods combining oil and gas reservoir numerical simulation and modern computational mathematics have been proposed for multi-factor historical data fitting analysis and evaluation. Compared to traditional methods, modern methods have the advantage of establishing multi-dimensional reservoir models, considering more comprehensive factors, and determining multiple uncertain parameters through historical data fitting. However, they also have significant drawbacks: First, during the construction of numerical models and simulation of single-well production, it is still necessary to have reliable range constraints on multiple index parameters; otherwise, there may be multiple sets of different parameter combinations under the same fitting results, i.e., the strong ambiguity commonly found in numerical simulations. Second, the time required for constructing numerical models, conducting historical data fitting, and adjusting and optimizing is long, which is not conducive to rapid application in the field and affects the advancement and efficient production of low-permeability carbonate gas reservoirs. In particular, the evaluation of single-well indicators is difficult to be universal and timely.

[0005] Therefore, it can be seen that low-permeability carbonate gas reservoirs face the same problem when applying these two major methods to evaluate dynamic indicators: the indicators are unsystematic, uncorrelated, and uncertain. This leads to large calculation errors when using the former empirical method, and multiple solutions and poor timeliness when using the latter numerical approach. The reason for this difficulty is that even if gas wells have started production and people have some basic understanding of reservoir properties and gas well production, it is still impossible to guarantee that every mutually constraining and correlated calculation parameter can be accurately provided when applying the above two types of analysis methods, while also meeting the requirements of a large number of development wells, rapid development progress, and timeliness of indicator evaluation during the profitable development process. Summary of the Invention

[0006] The present invention provides an interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs. The purpose of this method is to overcome the problems of low accuracy of empirical formulas, strong ambiguity of modern evaluation methods, and low evaluation timeliness in the existing technology.

[0007] Therefore, this invention provides an interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs, comprising the following steps: S1. Select vertical wells in the gas field or block where the low-permeability carbonate gas reservoir is located, establish pressure drop curves for the vertical wells according to the constant volume material balance equation, and use the pressure drop curves to evaluate the dynamic reserves of the gas wells; the vertical wells are required to meet the requirements in terms of production time and pressure measurement data and to have no wellbore fluid accumulation. S2. Divide the dynamic reserves of gas wells into intervals; S3. Select typical gas wells that can represent the production characteristics of each dynamic reserve interval within the defined range. S4. Determine the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs in step S1. S5. Based on step S4, use the production instability analysis method to establish a single-well analytical model for the typical gas wells in step S3, and evaluate the dynamic evaluation index of the typical gas wells. S6. Establish interactive constraint relationships using the dynamic evaluation indicators of typical gas wells after evaluation; S7. Using the interactive constraint relationship from step S6, and taking the dynamic reserve interval as the representative, form a chessboard quadrant dynamic constraint relationship between gas wells and dynamic evaluation indicators for each dynamic reserve interval.

[0008] Preferably, the intervals in step S2 are divided according to the following requirements: first, they should be able to reflect the production characteristics and benefit differences of various types of gas wells in the gas field or block; second, the proportion of gas wells in each interval should be balanced; and third, the number of intervals should not exceed 10.

[0009] Preferably, the required number of samples from typical gas wells in step S3 is as follows: When the total number of wells selected in a gas field or block is greater than 300, the number of typical gas wells is 10% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is 150 to 300, the number of typical gas wells is 15% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is less than 150, the number of typical gas wells is 20% of the total number of wells selected in the gas field or block.

[0010] Preferably, the typicality requirement for the typical gas well in step S3 is that it can reflect the production characteristics of the gas well within the dynamic reserve range, and the gas well production is continuous, normal, without interruption or intervention of gas well measures, and has complete field test results.

[0011] Preferably, step S4, which determines the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs, specifically includes the following steps: S4.1 Based on the typical gas wells selected in each dynamic reserve interval, select gas wells that meet the requirements for pressure recovery testing and have more than 3 gas production profile test data. S4.2. Evaluate the reservoir physical properties of the typical gas wells selected in step S4.1 using multiple methods; S4.3 Calculate the dynamic reserves of gas wells under different methods using the reservoir property parameters obtained in step S4.2. Then compare the dynamic reserves of gas wells under each method with the dynamic reserves evaluated by the pressure drop method. After comparison, select the method with the smallest error as the reservoir parameter evaluation method for this dynamic reserve range.

[0012] Preferably, in step S4.1, the gas production profile test data from three or more gas production profile tests are such that the test error of the three gas production profile test layers is within 10%.

[0013] Preferably, the error calculation method for the gas production profile test layer is as follows: first, the arithmetic mean of the production contribution rate of each layer in multiple tests is taken to obtain the average production contribution rate of the layer; then, the relative error between the production contribution rate of each layer and each test and the average contribution of the layer is calculated; and finally, the arithmetic mean of the relative error ranges calculated for each layer in multiple tests is taken as the gas production contribution test error of the layer in multiple tests.

[0014] Preferably, the multiple methods in step S4.2 are four methods.

[0015] Preferably, the software used in step S5 to establish the single-well analytical model employs the production instability analysis method.

[0016] Preferably, the following quality control requirements must be met when establishing the single-well analytical model in step S5: a. Fill in the reservoir parameters according to the evaluation method for reservoir parameters in the dynamic evaluation of low-permeability carbonate gas reservoirs determined in step S4. b. Selecting a radial flow model for vertical wells in low-permeability carbonate gas reservoirs; c. Set the epidermal coefficient S to zero; d. Do not select automatic fitting for dynamic reserves in the software; fill in the dynamic reserves evaluated according to the pressure drop curve. e. After the software parameters are filled in, perform historical curve fitting to obtain the comprehensive effective permeability k of a single well.

[0017] The beneficial effects of this invention are: The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs provided by this invention is an evaluation approach that uses dynamic interactive constraints on dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs. It has a wide range of applications and can address the current issues of unsystematic parameter selection, multiple interpretations of evaluation results, and rapid development progress in the evaluation of gas well indicators of low-permeability carbonate gas reservoirs. By utilizing conventional dynamic production data, it can achieve dynamic interactive constraints on production indicators, enabling quick and accurate evaluation and application of production indicators for gas wells in this type of gas reservoir. Attached Figure Description

[0018] The present invention will now be described in further detail with reference to the accompanying drawings.

[0019] Figure 1 This is a double logarithmic pressure recovery curve of well M in the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 2 This is a pressure drop curve of well M in the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 3It is a regression curve of dynamic reserves and interpreted comprehensive effective permeability of a typical gas well in Block A of Jingbian Gas Field, a low-permeability carbonate gas reservoir; Figure 4 This is a regression curve showing the correlation between dynamic reserves and EUR of a typical gas well in Block A of the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 5 This is a regression curve of dynamic reserves and stable production over 3 years for a typical gas well in Block A of the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 6 This is a regression curve of dynamic reserves and stable production over 5 years for a typical gas well in Block A of the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 7 This is a regression curve of dynamic reserves and 3-year stable production recovery of a typical gas well in Block A of the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 8 This is a regression curve of dynamic reserves and 5-year stable production recovery of a typical gas well in Block A of the Jingbian Gas Field, a low-permeability carbonate gas reservoir. Figure 9 This is a flowchart of the present invention. Detailed Implementation

[0020] like Figure 9 As shown, an interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs includes the following steps: S1. Select vertical wells in the gas field or block where the low-permeability carbonate gas reservoir is located, establish pressure drop curves for the vertical wells according to the constant volume material balance equation, and use the pressure drop curves to evaluate the dynamic reserves of the gas wells; the vertical wells are required to meet the requirements in terms of production time and pressure measurement data and to have no wellbore fluid accumulation. The vertical well requires a production time of ≥5 years and formation pressure measurement data from ≥3 actual formation pressure measurement points. Pressure drop curves are established for vertical wells based on the constant-volume material balance equation. Wells whose pressure drop curves do not deflect (i.e., there is no interference between wells, water production, or other factors) are selected for evaluation. The dynamic reserves of the gas wells are then assessed using these pressure drop curves. The dynamic reserves of the gas wells can be calculated by using the intercepts of the pressure drop curve p / Z and the Gp curve on the horizontal axis.

[0021] Equation (1) Where p is the current formation pressure in MPa, and Z is the average gas deviation factor. i Z represents the original formation pressure, MPa. i G is the original gas deviation factor. p To accumulate gas production, 10 8 m 3 G represents dynamic reserves, 10 8 m 3 .

[0022] In practical applications, when low-permeability carbonate gas reservoirs are highly heterogeneous and large in scale (proven geological reserves greater than 100 billion cubic meters), it is recommended to select blocks or development units for low-permeability carbonate gas reservoirs.

[0023] S2. Divide the dynamic reserves of gas wells into intervals; Preferably, the intervals in step S2 are divided according to the following requirements: first, they should be able to reflect the production characteristics and benefit differences of various types of gas wells in the gas field or block; second, the proportion of gas wells in each interval should be balanced; and third, the number of intervals should not exceed 10.

[0024] Considering the geological, dynamic, and economic characteristics of Block A of the Jingbian Gas Field, and combining the range and average level of the evaluated single-well dynamic reserves, the dynamic reserves of the selected gas wells in the gas field or block are classified into intervals. First, the interval division should reflect the production characteristics (production characteristics are gas production indicators, including daily gas production, stable production period, etc.) and economic benefits (refer to the economic benefit indicators of each block / gas field) of Class I, II, and III gas wells in Block A of the Jingbian Gas Field as much as possible. Second, the proportion of gas wells in each interval should be relatively balanced. Third, the number of intervals should not be too few or too many, and the number of selected intervals should be between 8 and 10.

[0025] Taking Block A of the low-permeability carbonate gas reservoir in the large Jingbian gas field in China as an example, dynamic reserve intervals are divided. The average dynamic reserve of this block is 1.8 × 10⁻⁶. 8 m 3 Based on comprehensive indicators such as effective gas reservoir thickness, gas saturation (geological studies have shown that effective thickness and gas saturation are the main controlling geological factors for low-permeability carbonate gas reservoirs in this area), open flow rate of gas wells, differences in gas well production characteristics, and internal rate of return, the dynamic reserves of Class I gas wells are greater than or equal to 2.5 × 10⁻⁶. 8 m 3 The average internal rate of return is 81.2%, and the dynamic reserves of Class II gas wells range from 1.5 to 2.5 × 10⁻⁶. 8 m 3 The average internal rate of return is 55.1%, and the dynamic reserves of Class III gas wells are less than 1.5 × 10⁻⁶. 8 m 3 The average internal rate of return (IRR) is 25.3%. Therefore, 2.5 and 1.5 are key nodes for the Jingbian low-permeability carbonate gas reservoir (the IRR is at 25% and 55% respectively). Considering the number of intervals (8-10), the preliminary design for the Jingbian gas field's low-permeability carbonate gas reservoir is 0.5 × 10⁻⁶. 8 m 3This serves as the unit for dividing the reservoir. As shown in the first five columns of Table 1, each interval reflects the differences between different types of reservoirs, and the proportion of wells is relatively balanced. The number of divisions also meets the requirements, thus it is determined as the unit for dividing the dynamic reserves of the Jingbian Xiagu low-permeability carbonate gas reservoir.

[0026] Table 1. Statistical results of the dynamic reserve distribution of low-permeability carbonate gas reservoirs in Block A of Jingbian Gas Field.

[0027] S3. Select typical gas wells that can represent the production characteristics of each dynamic reserve interval within the defined range. Based on step S2, a certain number of typical gas wells that can represent the production characteristics of different intervals are selected. Considering the low reservoir permeability, long pressure recovery time, lack of pressure-related test data, and low-cost development requirements of low-permeability carbonate gas reservoirs, the sample size of the large-scale low-permeability carbonate Jingbian gas field (which has been in production for more than 20 years) in China is used as a reference. The number of qualified gas wells within the mid-stage of development (including the mid-stage of gas field development) is less than or equal to 400. Therefore, there is a constraint on the breadth of the samples, and the emphasis here is on typicality.

[0028] Preferably, the required number of samples from typical gas wells in step S3 is as follows: When the total number of wells selected in a gas field or block is greater than 300, the number of typical gas wells is 10% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is 150 to 300, the number of typical gas wells is 15% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is less than 150 (it is recommended that the sample number of total wells be greater than 100; otherwise, this embodiment can be compared with the physical properties), the number of typical gas wells is 20% of the total number of wells selected in the gas field or block.

[0029] Preferably, the typicality requirement for a typical gas well in step S3 is that it can reflect the production characteristics of the gas well within the dynamic reserve range (the production characteristics are the production indicators of gas production, including indicators such as daily gas production and stable production period), and the gas well production is continuous and normal, without interruption or intervention of gas well measures (including but not limited to production measures such as pressurization, sidetracking, layer checking and hole filling, bubble drainage, and plunger), and has complete field test results (including but not limited to pressure, fluid level, etc.).

[0030] S4. Determine the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs in step S1. In low-permeability carbonate gas reservoirs, the main gas-producing layers are usually quite prominent (i.e., layers contributing more than 70% to gas production in production profile tests). However, considering economic benefits, multi-layer combined production is still chosen to increase single-well production and the utilization of reserves across multiple layers. This leads to reservoir parameter calculations often being based on individual interpretations, regardless of whether traditional or modern methods are used, resulting in a lack of standardized operations, unsystematic evaluation, and large calculation errors. To address this issue, a method for calculating reservoir parameter constraints in dynamic evaluation is proposed: Preferably, step S4, which determines the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs, specifically includes the following steps: S4.1 Based on the typical gas wells selected in each dynamic reserve interval, select gas wells that meet the requirements for pressure recovery testing and have more than 3 gas production profile test data. The requirements for pressure recovery testing are as follows: the pressure recovery test is successful, the double logarithmic pressure recovery is relatively smooth and even, there is no rapid up-and-down fluctuation similar to the "hump" effect in the transition period between the late stage of the reservoir section and the front of the radial flow section, and the curve fitting is good. It is preferred to have gas wells with more than 2 pressure recovery tests and similar reservoir parameter interpretations, that is, the two pressure recovery interpretation curves have a high degree of overlap. The gas production profile test data of more than 3 times means that the test error of the small layer of the gas production profile test is within 10%.

[0031] Preferably, the error calculation method for the gas production profile test layer is as follows: first, the arithmetic mean of the production contribution rate of each layer in multiple tests is taken to obtain the average production contribution rate of the layer; then, the relative error between the production contribution rate of each layer and each test and the average contribution of the layer is calculated; and finally, the arithmetic mean of the relative error ranges calculated for each layer in multiple tests is taken as the gas production contribution test error of the layer in multiple tests.

[0032] S4.2. Evaluate the reservoir physical properties of the typical gas wells selected in step S4.1 using multiple methods; Taking well M in the low-permeability carbonate gas reservoir of Jingbian Gas Field as an example, the production layers of this well are h8 and mw1. 2 mw1 3 mw2 2 There are a total of 4 sub-layers (well logging interpretation results are shown in Table 2), among which h8 can be further divided into 5 sections based on sand body development, and mw1 2 Divided into 3 segments, mw1 3 mw2 2 One segment each. The well pressure recovery test curve is as follows: Figure 1As shown, this is a typical radial flow (reservoir flow characteristic) model for a low-permeability carbonate gas well. The overall curve is smooth and even, without "humps" or abrupt changes, meeting the requirements. Simultaneously, this well has three gas production profile test data (see Table 3 for details). The three production profile data show that the test errors for each sub-layer are 7.8%, 4.1%, 1.7%, and 7.0%, respectively, all less than 10%, meeting the requirements for gas production profile testing. Based on this, the average contribution of each sub-layer in the three production profile tests is calculated to be 4.3%, 12.9%, 76.1%, and 6.7%, respectively, showing mw1 3 This is the main producing layer. In addition, four static pressure tests were conducted on Well M, and the gas well pressure drop curve is shown below. Figure 2 As shown (the black dots in the curve represent measured and calculated data), the curve shows no deflection, and the evaluated dynamic reserves of the gas well are 3.83 × 10⁻⁶. 8 m 3 ; Table 2. Well logging interpretation results of production layers in Well M of Jingbian Gas Field, a low-permeability carbonate gas reservoir.

[0033] Table 3. Historical Gas Production Profile Data of Well M in Jingbian Gas Field, Low-Permeability Carbonate Gas Reservoir

[0034] S4.3 Calculate the dynamic reserves of gas wells under different methods using the reservoir property parameters obtained in step S4.2. Then compare the dynamic reserves of gas wells under each method with the dynamic reserves evaluated by the pressure drop method. After comparison, select the method with the smallest error as the reservoir parameter evaluation method for this dynamic reserve range.

[0035] The various methods are as follows: Method 1: Calculated based on the average physical property level of the low-permeability carbonate gas reservoir in the block where the gas well is located.

[0036] Taking Well M as an example, the average physical properties of the low-permeability carbonate gas reservoir in the block where Well M is located are directly selected for calculation. The average effective thickness of the ancient gas reservoir below all gas wells in this area is 5.58m, the porosity is 6.5%, and the gas saturation is 72%. That is, the reservoir parameters of Well M are an average effective thickness of 5.58m, a porosity of 6.5%, and a gas saturation of 72%.

[0037] Method 2: The porosity of a gas well is calculated by weighting and summing the effective thickness of the well to account for differences in gas content. and gas saturation Sg The effective thickness is then calculated based on the differences in gas content of each sub-layer and then weighted averaged.

[0038] Specifically, the effective thickness h of Method 2 is calculated as shown in Equation (2).

[0039] Effective thickness h of gas well = (Small layer 1) -段1气层厚度 100%+ small layer 1 -段2含气层厚度 50%+……)+(small layer 2) -段1气层厚度 100%+ small layer 2 -段2气层厚度 100% + ... + ( ...) + ... (2) That is, the effective thickness h of well M = (h 8-段1含气层厚度 50%+ h 8-段2气层厚度 100%+ h 8-段3气层厚度 100%+……)+(mw1) 2 -段1气层厚度 100%+ mw1 2 -段2气层厚度 100%+……)+…… =1.2 0.5+2.3+0.7+2.5+1.2+1.4+2.7+1.4+4.4=19.4m.

[0040] Then, porosity The calculation method for gas saturation Sg is shown in equation (3). The calculation method for gas saturation Sg is related to porosity. The algorithm is consistent.

[0041] Porosity = ((Small layer 1) -段1含气层厚度 50% Small layer 1 -段1孔隙度 +Small layer 1 -段2气层厚度 100% Small layer 1 -段2孔隙度 +Small layer 1 -段3气层厚度 100% Small layer 1 -段3孔隙度 +……)+(Small layer 2) -段1气层厚度 100% Small layer 2 -段1孔隙度 +Small layer 2 -段2气层厚度 100% Small layer 2 -段2孔隙度 +Small layer 2 -段3气层厚度 100% Small layer 2 -段3孔隙度 +……)) / (effective thickness h of gas well calculated by method 2)(3) That is, the porosity of well M. = (h) 8-段1含气层厚度 50% h 8-段1孔隙度 + h 8-段1气层厚度 100% h 8-段2孔隙度 + h 8-段3气层厚度 100% h 8-段3孔隙度 +……) / (effective thickness h of well M calculated by method 2) = (h) 8-段1含气层厚度 50% h 8-段1孔隙度 + h 8-段1气层厚度 100% h 8-段2孔隙度 + h 8-段3气层厚度 100% h 8-段3孔隙度 +……) / ( h 8-段1含气层厚度 50%+ h 8-段2气层厚度 100%+ h 8-段3气层厚度 100%+……) =(10.4 0.6 + 10.36 2.3+9.28 0.7 + 10.21 2.5+……) / 19.4 =130.76 / 19.4 =6.74%.

[0042] gas saturation Sg With porosity The algorithms are consistent.

[0043] According to the above algorithm, the gas saturation of M can be determined. Sg = 1434.36 / 19.4 = 73.94%.

[0044] Method 3: The physical properties of the main producing layer of the gas well are directly selected.

[0045] Taking well M in the low-permeability carbonate gas reservoir of Jingbian Gas Field as an example, the main producing layer mw1 of well M is directly selected.3 Physical properties, namely, the effective thickness of well M is 4.4m, porosity The gas saturation was 5.24%. Sg It is 79.52%.

[0046] Method 4: The calculation method for the effective thickness of a gas well is as follows: the effective thickness is first calculated by weighting the gas-bearing properties of each sub-layer, and then the effective thickness of each sub-layer after considering the gas-bearing properties is weighted and averaged according to the contribution ratio of the gas production profile test (Equation 4).

[0047] Porosity of gas wells The calculation method for gas saturation Sg is as follows: First, calculate the weighted porosity of each sub-layer segment. And the gas saturation Sg, which is the effective thickness weighted average calculated first according to the differences in gas-bearing layers in each sub-layer, and then the porosity of the same sub-layer. The gas well porosity is obtained by weighting the effective thickness of the gas saturation Sg according to the differences in gas content across segments, and finally by weighting the average of the effective thicknesses according to the contribution ratio of each gas production profile test. and gas saturation Sg .

[0048] Among them, the effective thickness of method 4 h The calculation method is shown in equation (4).

[0049] Effective thickness of gas well h= (Effective thickness of layer 1 considering gas content differences) Gas production contribution ratio of layer 1 + effective thickness of layer 2 considering gas content differences The gas production contribution ratio of layer 2 + the effective thickness of layer 3 considering gas content differences. Gas production contribution ratio of layer 3 + ...) / 100% =( (small layer 1) -段1含气层厚度 50%+ small layer 1 -段2气层厚度 100%+ small layer 1 -段3气层厚度 100%+……) Gas production contribution ratio of layer 1 + (layer 2) -段1气层厚度 100%+ small layer 2 -段2气层厚度 100%+) Gas production contribution ratio of layer 1 + ...) / 100% (4) That is, the effective thickness h of well M = ( (h 8-段1含气层厚度 50%+ h 8-段2气层厚度 100%+ h 8-段3气层厚 100%+……) h8 gas production contribution ratio + (mw1) 2 -段1含气层厚度 50%+ mw1 2 -段2气层厚度 100%) mw1 2 Gas production contribution ratio + ...) / 100% =(1.2 (0.5+2.3+0.7+2.5+1.2) 0.043 + (1.4 + 2.7 + 1.4) 0.129 + 4.4 0.761+2.2 0.067 =4.52m.

[0050] Porosity As shown in equation (5), the calculation method for gas saturation Sg is related to porosity. The algorithm is consistent.

[0051] Gas well porosity = ((Small layer 1) -段1含气层厚度 50% Small layer 1 -段1孔隙度 +Small layer 1 -段2气层厚度 100% Small layer 1 -段2孔隙度 +Small layer 1 -段3气层厚度 100% Small layer 1 -段2孔隙度 +……) / (Effective thickness of layer 1 is calculated considering gas content differences) Gas production contribution ratio of layer 1 + (layer 2) -段1含气层厚度 50% Small layer 2 -段1孔隙度 +Small layer 2 -段2气层厚度 100% Small layer 2 -段2孔隙度 +Small layer 2 -段3气层厚度 100% Small layer 2 -段2孔隙度 +……) / (Effective thickness of layer 2 is calculated considering gas content differences) Gas production contribution ratio of layer 2 + ...) / 100% =((small layer 1) -段1含气层厚度 50% Small layer 1 -段1孔隙度 +Small layer 1 -段2气层厚度 100% Small layer 1 -段2孔隙度 +Small layer 1 -段3气层厚度 100% Small layer 1 -段2孔隙度 +……) / (small layer 1) -段1含气层厚度 50%+ small layer 1 -段2气层厚度 100%+……) Gas production contribution ratio of layer 1 + (layer 2) -段1含气层厚度 50% Small layer 2 -段1孔隙度 +Small layer 2 -段2气层厚度 100% Small layer 2 -段2孔隙度 +Small layer 2 -段3气层厚度 100% Small layer 2 -段2孔隙度 +……) / (small layer 2) -段1含气层厚度 50%+ small layer 2 -段2气层厚度 100%+……) Gas production contribution ratio of layer 2 + ...) / 100% (5) That is, the porosity of well M. = (h) 8-段1含气层厚度 50% h 8-段1孔隙度 + h 8-段2气层厚度 100% h 8-段2孔隙度 +……) / (h8 calculates effective thickness considering gas content differences) h8 gas production contribution ratio + (mw1) 2 -段1气层厚度 100% h 8-段1孔隙度 + mw1 2 -段2气层厚度 100% mw1 2 8-段2孔隙度+……) / ( mw1 2 (Effective thickness calculated considering gas content differences) mw1 2 Gas production contribution ratio + ...) / 100% =((h) 8-段1含气层厚度 50% h 8-段1孔隙度 + h 8-段2气层厚度 100% h 8-段2孔隙度 +……) / ( h 8-段1含气层厚度 50%+h 8-段1气层厚度 100%+……) h8 gas production contribution ratio + (mw1) 2 -段1气层厚度 100% h 8-段1孔隙度 + mw1 2 -段2气层厚度 100% mw1 2 8-段2孔隙度 +……) / ( mw1 2 (Effective thickness calculated considering gas content differences) mw1 2 Gas production contribution ratio + ...) / 100% =((10.4 1.2 / 2 + 10.36 2.3 + ...) / (1.2 / 2 + 2.3 + ...) 0.043+(5.58) 1.4+3.21 2.7 + ...) / (1.4 + 2.7 + ...) 0.129 + ...) / 100% =5.28% According to the above algorithm, the gas saturation of well M is Sg=78.64%.

[0052] The evaluation results of the four methods are shown in Table 4.

[0053] The reservoir parameters calculated by the above different methods were substituted into the IHS Harmony software (a commercial software for production instability analysis) to evaluate the dynamic reserves of gas wells under different reservoir parameter calculation methods. Then, the dynamic reserves of gas wells under this method were compared with the dynamic reserves evaluated by the pressure drop method. After comparison, the method with the smallest error was selected as the reservoir evaluation method for this dynamic reserve range.

[0054] It should be noted that commercial software for yield instability analysis is not limited to IHS Harmony, but the same software must be used in all evaluation processes involving dynamics.

[0055] In this step, the software operation process requires the following quality control measures to achieve systematicity and normalization, thereby reducing systematic errors and improving the uniqueness and accuracy of data processing and evaluation parameters. The seepage model requires the selection of a Radical model, with a skin coefficient... S Unified reference selection of gas well pressure recovery test interpretation parameters, effective permeability k The interpretation parameters of the pressure recovery test for selected gas wells are standardized, and then the production data of these wells are used to conduct historical fitting to evaluate the dynamic reserves of the gas wells. The method that calculates the dynamic reserves of the gas wells most closely matches the dynamic reserves evaluated by the pressure drop method is identified as the optimal method. Finally, using the above process, all gas wells in the selected gas field or block are subjected to this analysis. The method with the lowest average error and the highest proportion of gas wells is considered the suitable reservoir parameter evaluation method for that block / gas field.

[0056] Taking well M in the low-permeability carbonate gas reservoir of Jingbian Gas Field as an example, when using IHS Harmony software, the seepage model requires the selection of the Radical model and the skin coefficient. S Select parameter -6 from the well pressure recovery test interpretation of well M, effective permeability. k The 1.5896 mD interpreted from the pressure recovery test well was selected, and then the historical production curve (production data is shown in Table 5) was used to conduct a fitting evaluation of the dynamic reserves. The evaluation results calculated using different reservoir parameters are shown in the first and second rows from the bottom of Table 4. The results show that method 4 is the closest, followed by method 3.

[0057] It should be noted that the application example of the Jingbian Gas Field, a large-scale low-permeability carbonate gas reservoir in China, shows that the gas production profile test data is generally limited (the gas production profile data coverage rate is 25.6%). When the gas production profile test data is not available, method 3 can be simplified and selected, that is, the reservoir properties of the main producing layer can be used for corresponding calculations.

[0058] Table 4. Comparison of Dynamic Reserves Evaluation Results for Well M in Jingbian Gas Field with Different Stratigraphic Properties in Low-Permeability Carbonate Gas Reservoirs

[0059] S5. Based on step S4, use the production instability analysis method to establish a single-well analytical model for the typical gas wells in step S3, and evaluate the dynamic evaluation index of the typical gas wells (see Table 6). Preferably, the software used in step S5 to establish the single-well analytical model employs the production instability analysis method.

[0060] Using IHS Harmony software (a commercial software for production instability analysis), a single-well analytical model was established for each typical gas well. The establishment of the single-well analytical model was required to meet the following quality control requirements: a. Fill in the reservoir parameters according to the evaluation method for reservoir parameters in the dynamic evaluation of low-permeability carbonate gas reservoirs determined in step S4; including effective thickness. h Porosity Gas saturation Sg ; b. Selecting a radial flow model for vertical wells in low-permeability carbonate gas reservoirs; c. Set all epidermal coefficients S to zero; that is, adopt the normalization approach to set the epidermal coefficient S to zero. S The impact of the system on effective penetration rate k The comprehensive effective penetration rate is obtained from the value parameters, thereby reducing the ambiguity of multiple parameters; d. In the software, do not select automatic fitting for OGIP (Dynamic Reserves); fill in the dynamic reserves based on the pressure drop curve evaluation. e. After the software parameters are filled in, perform historical curve fitting to obtain the comprehensive effective permeability k of a single well.

[0061] After establishing an analytical model for the production instability analysis of typical gas wells according to the above quality control requirements, the comprehensive effective permeability of typical gas wells was interpreted. k The forecasts for EUR (economically recoverable reserves), production capacity under different stable production periods, and cumulative gas production at the end of the stable production period are included. The main dynamic evaluation indicators evaluated in this step include the explained comprehensive effective permeability. k The results of EUR, production capacity under different production stabilization periods, and extraction degree at the end of different production stabilization periods are shown in Table 6.

[0062] Table 5. Production data (partial) of Well M in Jingbian Gas Field, a low-permeability carbonate gas reservoir.

[0063] Table 6 Evaluation results of dynamic indicators of typical gas wells in Well A area of ​​Jingbian Gas Field under different dynamic reserve ranges

[0064] S6. Establish interactive constraint relationships using the dynamic evaluation indicators of typical gas wells after evaluation; Using the evaluation results from step S5, the following interactive constraint relationships of dynamic evaluation indicators for gas wells in low-permeability carbonate gas reservoirs in blocks / gas fields can be determined: ① Determine the relationship between the dynamic reserves of typical gas wells and the interpreted comprehensive effective permeability (including skin effect), that is, establish a regression correlation relationship using the dynamic reserves of typical gas wells and the interpreted comprehensive effective permeability ( Figure 3); ② Determine the relationship between dynamic reserves of typical gas wells and EUR, and establish a regression correlation relationship by using dynamic reserves of typical gas wells and EUR / dynamic reserves ( Figure 4 ); ③ Establish the relationship between dynamic reserves and stable production capacity under different stable production times, that is, use the regression correlation between dynamic reserves of typical gas wells and stable production allocation under different stable production times (based on Figure 5 , Figure 6 For example, representing the dynamic reserves and production allocation relationship for 3 years and 5 years of stable production respectively); ④ Establish the relationship between dynamic reserves and gas well recovery rate under different stable production periods, that is, use the regression correlation between the dynamic reserves of typical gas wells and the stable production allocation under different stable production periods (1 year to 10 years of stable production) (using Figure 7 , Figure 8 For example, these represent the dynamic reserves and production allocation relationship for 3 years and 5 years of stable production, respectively.

[0065] It should be noted that stable production and production allocation refer to a calendar year of 365 days, without taking into account the well opening rate of this type of gas reservoir.

[0066] S7. Using the interactive constraint relationship from step S6, and taking the dynamic reserve interval as the representative, form a chessboard quadrant dynamic constraint relationship between gas wells and dynamic evaluation indicators for each dynamic reserve interval.

[0067] Using the dynamic reserves, EUR, interpreted comprehensive effective permeability k, and the correlation of reasonable production allocation at different stable production times established in step S6, and taking the dynamic reserve interval of a block / gas field as a representative, a chessboard quadrant dynamic constraint relationship table of gas wells with different dynamic reserves in low-permeability carbonate gas reservoirs (representing the geological, production characteristics, and benefit value of different types of gas wells) and dynamic evaluation indicators can be formed. After knowing any one of the indicators of a gas well, interpolation can be used to quickly understand other dynamic evaluation indicators of gas wells in this type of gas reservoir and obtain the constraint relationship of other production indicators.

[0068] Based on the actual application needs of low-permeability carbonate gas reservoirs, the following two chessboard quadrant dynamic evaluation index dynamic constraint relationship tables can be formed: (1) Form a rapid evaluation table of the dynamic reserves of low-permeability carbonate gas reservoirs / gas wells and the corresponding EUR (taking Table 7 as an example for typical gas wells in Block A of Jingbian Gas Field). The values ​​in this table are derived from step 6). Figure 4The average value of EUR / dynamic reserves in different dynamic reserve intervals is the ratio of EUR / dynamic reserves in that interval; (2) Dynamic interaction constraint relationship table of gas field / gas well production indicators in low-permeability carbonate gas reservoirs (Typical gas wells in Block A of Jingbian Gas Field are exemplified in Table 8). Thus, the following uses can be achieved conveniently and quickly: ① Knowing the dynamic reserves of a well in this type of gas reservoir, the EUR in the dynamic reserve interval can be quickly evaluated; ② Knowing the dynamic reserves of a well in this type of gas reservoir, the comprehensive effective permeability k of the interpretation and the reasonable production allocation under different stable production times can be quickly interpolated; ③ Knowing the stable production time and its allocation, the dynamic reserves and the comprehensive effective permeability k of this type of gas well can be quickly interpolated; ④ When using conventional gas reservoir engineering calculations or using IHS Harmony software or other commercial software to carry out index evaluation, the reservoir parameter calculation method can be obtained, and the dynamic reserves and the interpreted comprehensive effective permeability can be constrained. k The relevant relationship.

[0069] Table 7 Dynamic Reserve Range and EUR Rapid Evaluation Table for Block A of Jingbian Gas Field

[0070] Table 8. Interaction Constraints of Dynamic Indicators for Typical Gas Wells in Block A of Jingbian Gas Field

[0071] The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs of the present invention provides a systematic approach based on multi-factor division of gas well intervals in low-permeability carbonate gas reservoirs, reasonable screening of typical gas wells in intervals, optimization of reservoir static parameter evaluation methods, and normalization of gas reservoir dynamic evaluation indicators. This approach improves the problems of unsystematic parameter selection, multiple solutions, and poor evaluation timeliness in the current evaluation of dynamic evaluation indicators for gas wells in low-permeability carbonate gas reservoirs, and enhances the initiative of dynamic evaluation indicators in guiding the development and adjustment of such gas fields.

[0072] The advantages of this invention are: first, it has a wide range of applications and is suitable for the characteristics of strong heterogeneity in low-permeability carbonate gas reservoirs; second, it eliminates the need for shut-in testing and other testing costs, thus meeting the low-cost development needs of low-permeability carbonate gas reservoirs; and third, the chessboard quadrant dynamic evaluation index dynamic constraint relationship table is convenient, fast, and timely for evaluating the indicators of gas wells already in production.

[0073] The above examples are merely illustrative of the present invention and do not constitute a limitation on the scope of protection of the present invention. All designs that are the same as or similar to the present invention are within the scope of protection of the present invention.

Claims

1. A method for interactive constraint of dynamic evaluation indicators for gas wells in low-permeability carbonate gas reservoirs, characterized in that: Includes the following steps: S1. Select vertical wells in the gas field or block where the low-permeability carbonate gas reservoir is located. Establish pressure drop curves for the vertical wells based on the constant volume mass balance equation. Use the pressure drop curves to evaluate the dynamic reserves of the gas wells. The vertical wells are required to meet the requirements for both production time and pressure measurement data and to have no wellbore fluid accumulation. The production time of the vertical wells is required to be ≥5 years since they were put into production, and the pressure measurement data is required to be formation pressure measurement data from ≥3 formation pressure measurement points. S2. Divide the dynamic reserves of gas wells into intervals; the intervals shall be divided according to the following requirements: First, they shall be able to reflect the production characteristics and benefit differences of various types of gas wells in the gas field or block; second, the proportion of gas wells in each interval shall be balanced; and third, the number of intervals shall not exceed 10. S3. Select typical gas wells that can represent the production characteristics of each dynamic reserve interval within the defined range. The typicality requirement for the typical gas wells is that they can reflect the production characteristics of gas wells within the dynamic reserve interval, and that the gas wells produce continuously and normally without interruption or intervention of gas well measures, and have complete field test results. S4. Determine the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs in step S1. Step S4, which determines the evaluation method for reservoir parameters in the dynamic evaluation of gas wells in low-permeability carbonate gas reservoirs, specifically includes the following steps: S4.1 Based on the typical gas wells selected in each dynamic reserve interval, select gas wells that meet the requirements for pressure recovery testing and have more than 3 gas production profile test data. S4.

2. Evaluate the reservoir physical properties of the typical gas wells selected in step S4.1 using multiple methods; The various methods include: Method 1: Calculate the thickness based on the average of the block; Method 2: Calculate the thickness by weighting the entire gas layer and the gas-bearing layer by half, and weight other parameters with this thickness; Method 3: Select the physical property parameters of the main producing layer; Method 4: Weight the gas layer thickness according to the contribution ratio of the gas production profile test, and weight other parameters with this thickness. S4.3 Calculate the dynamic reserves of gas wells under different methods using the reservoir physical parameters obtained in step S4.

2. Then compare the dynamic reserves of gas wells under each method with the dynamic reserves evaluated by the pressure drop method. After comparison, select the method with the smallest error as the reservoir parameter evaluation method for this dynamic reserve range. S5. Based on step S4, use the production instability analysis method to establish a single-well analytical model for the typical gas wells in step S3, and evaluate the dynamic evaluation index of the typical gas wells. When establishing the single-well analytical model in step S5, the following quality control requirements must be met: a. Fill in the reservoir parameters according to the evaluation method for reservoir parameters in the dynamic evaluation of low-permeability carbonate gas reservoirs determined in step S4. b. Selecting a radial flow model for vertical wells in low-permeability carbonate gas reservoirs; c. Set the epidermal coefficient S to zero; d. Do not select automatic fitting for dynamic reserves in the software; fill in the dynamic reserves evaluated according to the pressure drop curve. e. After the software parameters are filled in, perform historical curve fitting and obtain the comprehensive effective permeability k of a single well. The dynamic evaluation indicators for the typical gas wells include the explained comprehensive effective permeability k, EUR, production capacity under different production stabilization times, and recovery rate at the end of different production stabilization periods. S6. Establish interactive constraint relationships between indicators by using dynamic evaluation indicators of typical gas wells being evaluated. S7. Using the interactive constraint relationship from step S6, and taking the dynamic reserve interval as the representative, form a chessboard quadrant dynamic constraint relationship between gas wells and dynamic evaluation indicators for each dynamic reserve interval.

2. The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs as described in claim 1, characterized in that: The required number of samples from typical gas wells in step S3 is as follows: When the total number of wells selected in a gas field or block is greater than 300, the number of typical gas wells is 10% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is 150 to 300, the number of typical gas wells is 15% of the total number of wells selected in the gas field or block; When the total number of wells selected in a gas field or block is less than 150, the number of typical gas wells is 20% of the total number of wells selected in the gas field or block.

3. The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs as described in claim 1, characterized in that: In step S4.1, the gas production profile test data from three or more gas production profile tests are provided, and the test error of the three gas production profile test layers is within 10%.

4. The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs as described in claim 3, characterized in that: The error calculation method for the gas production profile test layer is as follows: First, the average production contribution rate of the layer is obtained by taking the arithmetic mean of the production contribution rate of each layer in multiple tests. Then, the relative error between the production contribution rate of each layer and each test and the average contribution of the layer is calculated. Finally, the arithmetic mean of the relative error ranges calculated for each layer in multiple tests is the gas production contribution test error of the layer in multiple tests.

5. The interactive constraint method for dynamic evaluation indicators of gas wells in low-permeability carbonate gas reservoirs as described in claim 1, characterized in that: In step S5, the software used for establishing the single-well analytical model employs the production instability analysis method.

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