Method and equipment for evaluating contribution rate of layered yield of multi-layer commingled recovery gas well and storage medium

Abstract

The invention provides a multilayer commingled recovery gas well layering yield contribution rate evaluation method and device and a storage medium, and the method comprises the steps: determining the layering yield contribution rate of each reservoir section of a single well, and / or determining the layering yield contribution rate of each reservoir section of an adjacent well in the same block; wherein the step of determining the layered yield contribution rate of each reservoir section of the single well comprises the substeps of determining seepage characteristic parameters of each reservoir section of the single well to be evaluated; determining the layered yield contribution rate by using the seepage characteristic parameters of each reservoir section of the to-be-evaluated single well; the step of determining the layered yield contribution rate of each reservoir section of the adjacent wells in the same block comprises the steps that a relation model of seepage characteristic parameters of each reservoir section of a to-be-evaluated single well and layered gas production is established, and the correlation coefficient is larger than 0.9; and determining the layered gas production rate of the adjacent well through the relation model, and determining the layered yield contribution rate of the adjacent well through the seepage characteristic parameters of the adjacent well. According to the evaluation method for the contribution rate of the layered yield of the multi-layer commingled recovery gas well, the labor cost is effectively reduced, and convenience and rapidness are achieved.

Description

Technical Field

[0001] This invention relates to the field of natural gas technology development, specifically to a method for evaluating the contribution rate of multi-layered gas well production, a computer device, and a computer-readable storage medium storing a computer program. Background Technology

[0002] For gas reservoir development with multiple reservoirs, there are two development methods: single-layer production and multi-layer combined production. Currently, both domestically and internationally, the single-well multi-layer combined production method is mainly used in actual production. Its advantages are primarily reflected in higher gas production capacity per well, which reduces investment and development costs for the gas field and improves the overall profitability of the field. The production contribution rate of multi-layer combined production wells is one of the basic contents of production evaluation for combined production wells and a key link in the development process of multiple reservoir gas reservoirs, significantly impacting the efficient development of multiple reservoir gas wells and the exploitation of remaining reserves.

[0003] Currently, domestic and international research on various reservoir gas reservoirs mainly focuses on sandstone reservoirs, with limited research on the multi-layer synergistic production effects of fractured-porous carbonate gas reservoirs. Fractured-porous carbonate gas reservoirs are highly heterogeneous due to reservoir properties, with low matrix porosity and permeability. Gas well seepage is primarily facilitated and guided through peri-well fractures.

[0004] The methods for evaluating the production contribution rate of multi-layered syngas wells mainly involve field testing and theoretical research. Field testing obtains the gas production of each layer through multi-layer production profile testing (PLT testing), thereby determining the production contribution rate of each layer. However, the labor and equipment costs for testing are high, lacking economic efficiency. Theoretical evaluation of the production of multi-layered syngas wells is mainly based on static formation data and geological understanding, using methods such as formation coefficient partitioning, numerical simulation, and catastrophe theory to comprehensively determine the production contribution rate of different reservoir sections in syngas wells. The formation coefficient partitioning method uses conventional well logging interpretation parameters to extract the permeability and effective reservoir thickness of each reservoir section, calculates the formation coefficient, and then partitions the gas production capacity of each reservoir section using the formation coefficient. This method only considers the static physical properties and thickness of the reservoir for production partitioning, resulting in a single factor and relatively fixed results, with low accuracy for highly heterogeneous reservoirs. The catastrophe theory method establishes a mathematical model of seepage in multi-layered gas reservoirs and obtains the Laplace space dynamic response equation of multi-layered gas well production through Laplace transform. This method considers a wide range of factors and has good application results, but different models need to be established for different gas reservoirs in different blocks to divide production, resulting in limited model universality. Numerical simulation is more complex and is usually only usable by professionals specializing in oil and gas field numerical simulation. Its application is affected by the model and fitting process, and the results are subject to multiple solutions. Furthermore, this method is difficult to implement in the early stages of gas field development. The large amount of manpower, time, and workload required for numerical simulation also limits its application and promotion. Therefore, providing a method for rapidly evaluating the production contribution rate of multi-layered syndicated wells is of great significance.

[0005] Chinese patent application number CN202110031394.7, entitled "A Method for Evaluating the Contribution Rate of Layered Production in Composite Sedimentary Gas Reservoirs," discloses a method for evaluating the contribution rate of layered production in composite sedimentary gas reservoirs. The method includes: S1 determining the main controlling factors of layered gas production contribution; S2 obtaining the layered gas production contribution rate; S3 obtaining the layered energy storage coefficient; S4 establishing a model relating the layered gas production contribution rate to the energy storage coefficient; and S5 determining the gas production of the marine low-permeability carbonate reservoir and the continental tight sandstone reservoir of the gas well to be evaluated. This patent's method for evaluating the contribution rate of layered production in composite sedimentary gas reservoirs of continental tight sandstone and marine low-permeability carbonate rocks solves the problem of production splitting in multi-layered syngas wells of different sedimentary systems in the Ordos Basin, where reservoir heterogeneity is strong, the number of wells is large, and gas production profile testing data is lacking. It provides a basis for the accurate evaluation of parameters such as layered production, dynamic reserves, recovery degree, and drainage range of tight sandstone and marine low-permeability carbonate reservoirs. However, this application differs from its research subject and methodology. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to solve one or more problems existing in the prior art. For example, one objective of the present invention is to address the problems of low efficiency, low accuracy, and high resource consumption in evaluating the production contribution rate of multi-layered syngas wells in fractured-porous carbonate gas reservoirs, and to propose a method for rapidly, efficiently, and accurately evaluating the production contribution rate of multi-layered syngas wells.

[0007] To achieve the above objectives, the present invention provides a method for evaluating the stratified production contribution rate of multi-layered syngas wells. The method may include: determining the stratified production contribution rate of each reservoir segment in a single well, and / or determining the stratified production contribution rate of each reservoir segment in adjacent wells within the same block; wherein determining the stratified production contribution rate of each reservoir segment in a single well includes: determining the seepage characteristic parameters of each reservoir segment in the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syngas well; and using the seepage characteristic parameters of each reservoir segment in the single well to determine the stratified production contribution rate of each reservoir segment in the single well to be evaluated. The determination of the stratified production contribution rate of each reservoir segment in adjacent wells within the same block includes: determining the seepage characteristic parameters of each reservoir segment in the single well to be evaluated through the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syndicated gas well; establishing a relationship model between the seepage characteristic parameters of each reservoir segment in the single well to be evaluated and the stratified gas production, wherein the correlation coefficient is greater than 0.9; determining the stratified gas production of each reservoir segment in adjacent wells within the same block through the relationship model, and determining the stratified production contribution rate of each reservoir segment in adjacent wells within the same block through the seepage characteristic parameters of each reservoir segment in adjacent wells within the same block.

[0008] According to one or more exemplary embodiments of one aspect of the present invention, the method may further include: determining subsequent construction measures by the interlayer range of the production contribution rate of each reservoir section in each well.

[0009] According to one or more exemplary embodiments of the present invention, the step of determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated through the wellbore structure, completion data, first logging data and second logging data of the multi-layer syngas well may include: determining the number of samples of each reservoir section of the single well to be evaluated through the wellbore structure and the completion data of the multi-layer syngas well.

[0010] According to one or more exemplary embodiments of one aspect of the present invention, the number of samples for each reservoir section of the single well to be evaluated can be determined by the following formula 1:

[0011] Formula 1:

[0012] Where N is the total number of reservoir samples in the single well to be evaluated, which is dimensionless; h1, h2, ..., h N S1 represents the thickness of each reservoir section in the single well to be evaluated, in meters; Δl represents the unit sampling length of the imaging logging instrument, in meters; S1, S2, ..., SN This represents the number of samples for each reservoir segment.

[0013] According to one or more exemplary embodiments of one aspect of the present invention, the step of determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated through the wellbore structure, completion data, first logging data and second logging data of the multi-layer syngas well may further include: determining the matrix permeability of the reservoir sampling unit well section through the first logging data; and determining the fracture length, fracture width and fracture apparent porosity of the reservoir sampling unit well section through the second logging data.

[0014] According to one or more exemplary embodiments of the present invention, the seepage characteristic parameters of each reservoir section of the single well to be evaluated can be determined by the following Equation 2:

[0015] Formula 2:

[0016] Among them, R1, R2...R N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 ;K mi The matrix permeability is expressed in μm. 2 ;F Li F represents the fracture length per well section used for reservoir sampling, in μm; Wi F represents the fracture width per well section sampled from the reservoir, in μm; φi The apparent porosity of fractures in a unit well section of the reservoir sampling unit, %.

[0017] According to one or more exemplary embodiments of the present invention, the stratified production contribution rate of each reservoir section of the single well to be evaluated can be determined by the following equation 3:

[0018] Formula 3:

[0019] Among them, η1, η2, ..., η N R1, R2, ..., R2 represent the flow dominance index of each reservoir section in the single well to be evaluated. The flow dominance index is numerically equal to the production contribution rate of each layer; N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 N represents the total number of samples in the reservoir section of the single well to be evaluated, which is dimensionless.

[0020] According to one or more exemplary embodiments of the present invention, the establishment of a model relating seepage characteristic parameters of each reservoir section of the single well to be evaluated to the stratified gas production may include: obtaining the stratified gas production through multi-layer gas production profile testing of the gas well, and establishing a model relating seepage characteristic parameters of each reservoir section of the single well to be evaluated to the stratified gas production.

[0021] According to one or more exemplary embodiments of one aspect of the present invention, the interlayer range of the production contribution rate of each reservoir section in each well can be determined by the following equation 4:

[0022] Equation 4: γ = η max / η min ;

[0023] Where, η max η min , representing the maximum and minimum values ​​of the flow dominance index for each well and each reservoir section, respectively, where %, the flow dominance index is numerically equal to the layered production contribution rate; γ is the interlayer range of the flow dominance index, dimensionless, and the flow dominance index is numerically equal to the layered production contribution rate.

[0024] Another aspect of the present invention provides a computer device, which may include: a processor; and a memory storing a computer program, which, when executed by the processor, implements the method for evaluating the stratified production contribution rate of multi-layered gas wells as described above.

[0025] Another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for evaluating the stratified production contribution rate of multi-layered gas wells as described above.

[0026] Compared with the prior art, the beneficial effects of the present invention include at least one of the following:

[0027] (1) The method for evaluating the contribution rate of multi-layer gas well production by layer provided by the present invention breaks through the limitations and singularity of the formation coefficient splitting method. It uses conventional logging interpretation parameters and high-resolution vertical fracture distribution data of multi-layer gas wells in fracture-pore carbonate gas reservoirs to establish a linear relationship with the results of multi-layer gas production profile test (PLT test) of gas wells, analyzes the permeability dominance index of each reservoir section, and determines the interlayer range of permeability dominance index of each reservoir section, so as to quickly, efficiently and accurately evaluate the contribution rate of multi-layer gas well production by layer.

[0028] (2) The multi-layered production contribution rate evaluation method of multi-layered gas wells provided by the present invention comprehensively considers the static and dynamic data of conventional logging and imaging logging, breaks through the complexity of mathematical model derivation of catastrophe theory method, and avoids the multiple solutions of numerical simulation method. Attached Figure Description

[0029] The above and other objects and / or features of the present invention will become clearer from the following description taken in conjunction with the accompanying drawings, in which:

[0030] Figure 1A flowchart illustrating the method for evaluating the contribution rate of multi-layered gas well production in an exemplary embodiment of the present invention is shown.

[0031] Figure 2 This paper shows a schematic diagram of the relationship between the seepage characteristic parameters of each reservoir section and the gas production of each layer in Well A in Example 1 of the present invention.

[0032] Figure 3 The diagram shows a model of the relationship between seepage characteristic parameters and gas production of each reservoir section in wells A and B in Example 1 of this invention. Detailed Implementation

[0033] The method, apparatus, and storage medium for evaluating the production contribution rate of multi-layered gas wells according to the present invention will be described in detail below with reference to the accompanying drawings and exemplary embodiments.

[0034] This invention utilizes conventional logging interpretation parameters and high-resolution longitudinal fracture distribution data from imaging logging in multi-layered syngas wells in fractured-porous carbonate gas reservoirs to extract matrix permeability, fracture length, fracture width, and apparent fracture porosity to create seepage characteristic parameters. A linear relationship is established with single-well multi-layer gas production profile testing (PLT testing). The seepage dominance index of each reservoir segment is analyzed, the inter-layer range of the seepage dominance index for each reservoir segment is determined, and the corresponding stratified production contribution rate is calculated. Simultaneously, the linear relationship and static logging data are used to quickly evaluate the stratified production of other multi-layered syngas wells in the same block. This comprehensive approach provides a rapid, efficient, and accurate method for evaluating the stratified production contribution rate of multi-layered syngas wells.

[0035] Exemplary Example 1

[0036] This exemplary embodiment provides a method for evaluating the stratified production contribution rate of multi-layered syngas wells. The method may include: determining the stratified production contribution rate of each reservoir section in a single well.

[0037] Determining the stratified production contribution rate of each reservoir segment in a single well includes: determining the seepage characteristic parameters of each reservoir segment in the single well to be evaluated based on the wellbore structure, completion data, first logging data, and second logging data of the multi-layer syndicated gas well; and determining the stratified production contribution rate of each reservoir segment in the single well to be evaluated based on the seepage characteristic parameters of each reservoir segment. Here, the inter-layer range of the stratified production contribution rate of each reservoir segment in each well can be used to determine subsequent construction measures.

[0038] In this exemplary embodiment, the step of determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syngas well may include: determining the sample size of each reservoir section of the single well to be evaluated using the wellbore structure and completion data of the multi-layered syngas well. The sample size of each reservoir section of the single well to be evaluated can be determined by the following formula 1:

[0039] Formula 1:

[0040] Where N is the total number of reservoir section samples in the single well to be evaluated; h1, h2, ..., h N S1 represents the thickness of each reservoir section in the single well to be evaluated, in meters; Δl represents the unit sampling length of the imaging logging instrument, in meters; S1, S2, ..., S N This represents the number of samples for each reservoir segment.

[0041] In this exemplary embodiment, determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layer syngas well may further include: determining the matrix permeability of the reservoir sampling unit well section using the first logging data of the multi-layer syngas well, and determining the fracture length, fracture width, and fracture apparent porosity of the reservoir sampling unit well section using the second logging data of the multi-layer syngas well. Here, the first logging data may be conventional logging data, and the second logging data may be imaging logging data.

[0042] In this exemplary embodiment, the seepage characteristic parameters of each reservoir section of the single well to be evaluated can be determined by the following Equation 2:

[0043] Formula 2:

[0044] Among them, R1, R2...R N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 ;K mi The matrix permeability is expressed in μm. 2 ;F Li F represents the fracture length per well section used for reservoir sampling, in μm; Wi F represents the fracture width per well section sampled from the reservoir, in μm; φi The apparent porosity of fractures in a unit well section of the reservoir sampling unit, %.

[0045] In this exemplary embodiment, the production contribution rate of each reservoir section in the single well to be evaluated can be determined by the following equation 3:

[0046] Formula 3:

[0047] Among them, η1, η2, ..., η N R1, R2, ..., R2 represent the flow dominance index of each reservoir section in the single well to be evaluated. The flow dominance index is numerically equal to the production contribution rate of each layer; N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 N represents the total number of samples in the reservoir section of the single well to be evaluated, which is dimensionless.

[0048] Here, the seepage dominance index of each reservoir segment in the single well to be evaluated reflects the stratified production contribution rate of each reservoir segment, and the two are equal in value.

[0049] In this exemplary embodiment, the interlayer range of the production contribution rate of each reservoir segment in the single well to be evaluated can be determined by the following equation 4:

[0050] Equation 4: γ = η max / η min ;

[0051] Where, η max η min , representing the maximum and minimum values ​​of the seepage dominance index for each reservoir section of the single well to be evaluated, respectively, where %, the seepage dominance index is numerically equal to the layered production contribution rate; γ is the interlayer range of the seepage dominance index, dimensionless, and the seepage dominance index is numerically equal to the layered production contribution rate.

[0052] Here, η max η min Numerically, γ is also equal to the maximum and minimum values ​​of the production contribution rate of each reservoir segment in the single well to be evaluated, and γ is also equal to the inter-layer range of the production contribution rate of each layer.

[0053] Exemplary Example 2

[0054] Figure 1 A flowchart illustrating the method for evaluating the production contribution rate of multi-layered syngas wells in an exemplary embodiment of the present invention is shown.

[0055] This exemplary embodiment provides a method for evaluating the contribution rate of production from different layers in a multi-layered syngas well.

[0056] The method may include: determining the stratified production contribution rate of each reservoir segment in a single well, and / or determining the stratified production contribution rate of each reservoir segment in adjacent wells within the same block.

[0057] Determining the stratified production contribution rate of each reservoir section in a single well may include: determining the seepage characteristic parameters of each reservoir section in the single well to be evaluated through the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syngas well; and determining the stratified production contribution rate of each reservoir section in the single well to be evaluated using the seepage characteristic parameters of each reservoir section in the single well to be evaluated.

[0058] Determining the stratified production contribution rate of each reservoir segment in adjacent wells within the same block may include: determining the seepage characteristic parameters of each reservoir segment in the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syndicated gas well; establishing a relationship model between the seepage characteristic parameters of each reservoir segment in the single well to be evaluated and the stratified gas production, wherein the correlation coefficient is greater than 0.9; determining the stratified gas production of each reservoir segment in adjacent wells within the same block using the relationship model; and determining the stratified production contribution rate of each reservoir segment in adjacent wells within the same block using the seepage characteristic parameters of each reservoir segment in adjacent wells within the same block.

[0059] Specifically, such as Figure 1 As shown, the evaluation method for the contribution rate of multi-layer gas production wells mainly includes the following steps: Extracting the matrix permeability of the reservoir sampling unit well section from conventional logging data in the static logging data; extracting the fracture length, fracture width, and fracture apparent porosity of the reservoir sampling unit well section from imaging logging data in the static logging data. Here, matrix permeability, fracture length, fracture width, and fracture apparent porosity can be uniformly referred to as the seepage characteristic parameters of each reservoir section in the single well to be evaluated; obtaining the test dynamic data, i.e., the test results of multi-layer gas production profile testing; establishing a linear relationship model between the seepage characteristic parameters of each reservoir section in the single well to be evaluated and the test results of multi-layer gas production profile testing; calculating the correlation coefficient, where a correlation coefficient greater than 0.9 indicates "Yes," meaning that the seepage characteristic parameters of each reservoir section in the single well to be evaluated correlate with the multi-layer gas production profile test results. The test results of the gas production profile test are linearly correlated. A seepage dominance index for each reservoir segment is established using seepage characteristic parameters. This parameter reflects the stratified production contribution rate of each reservoir segment. The inter-layer range of the stratified production contribution rate is calculated, and this inter-layer range is numerically equal to the inter-layer range of the seepage characteristic parameters. Based on the inter-layer range, subsequent measures for modification and reserve exploitation of the single well to be evaluated are determined. Simultaneously, a linear relationship model is established between the established seepage characteristic parameters of each reservoir segment of the single well to be evaluated and the test results of the multi-layer gas production profile test. The stratified production contribution rate of adjacent wells in the same block is determined using the seepage characteristic parameters of adjacent wells. The inter-layer range of the stratified production contribution rate of adjacent wells is then calculated, and this inter-layer range is numerically equal to the inter-layer range of the seepage characteristic parameters. Based on the inter-layer range, subsequent measures for modification and reserve exploitation of adjacent wells are determined.

[0060] In this exemplary embodiment, the step of determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layered syngas well may include: determining the sample size of each reservoir section of the single well to be evaluated using the wellbore structure and completion data of the multi-layered syngas well. The sample size of each reservoir section of the single well to be evaluated can be determined by the following formula 1:

[0061] Formula 1:

[0062] Where N is the total number of reservoir section samples in the single well to be evaluated; h1, h2, ..., h N S1 represents the thickness of each reservoir section in the single well to be evaluated, in meters; Δl represents the unit sampling length of the imaging logging instrument, in meters; S1, S2, ..., S N This represents the number of samples for each reservoir segment.

[0063] In this exemplary embodiment, determining the seepage characteristic parameters of each reservoir section of the single well to be evaluated using the wellbore structure, completion data, first logging data, and second logging data of the multi-layer syngas well may further include: determining the matrix permeability of the reservoir sampling unit well section using the first logging data of the multi-layer syngas well, and determining the fracture length, fracture width, and fracture apparent porosity of the reservoir sampling unit well section using the second logging data of the multi-layer syngas well. Here, the first logging data may be conventional logging data, and the second logging data may be imaging logging data.

[0064] In this exemplary embodiment, the seepage characteristic parameters of each reservoir section of the single well to be evaluated can be determined by the following Equation 2:

[0065] Formula 2:

[0066] Among them, R1, R2...R N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 ;K mi The matrix permeability is expressed in μm. 2 ;F Li F represents the fracture length per well section used for reservoir sampling, in μm; Wi F represents the fracture width per well section sampled from the reservoir, in μm; φi The apparent porosity of fractures in a unit well section of the reservoir sampling unit, %.

[0067] In this exemplary embodiment, the production contribution rate of each reservoir section in the single well to be evaluated can be determined by the following equation 3:

[0068] Formula 3:

[0069] Among them, η1, η2, ..., η N R1, R2, ..., R2 represent the flow dominance index of each reservoir section in the single well to be evaluated. The flow dominance index is numerically equal to the production contribution rate of each layer; N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 N represents the total number of samples in the reservoir section of the single well to be evaluated, which is dimensionless.

[0070] Here, the seepage dominance index of each reservoir segment in the single well to be evaluated reflects the stratified production contribution rate of each reservoir segment, and the two are equal in value.

[0071] In this exemplary embodiment, establishing a model relating seepage characteristic parameters of each reservoir section of the single well to be evaluated to the gas production of each layer may include: obtaining the gas production of each layer through multi-layer gas production profile testing of the gas well, and establishing a model relating seepage characteristic parameters of each reservoir section of the single well to be evaluated to the gas production of each layer. Here, the linear regression correlation coefficient R of the relationship model is... 2If the value is greater than 0.9, then the multi-layered production contribution rate evaluation method of this exemplary embodiment can be promoted and used in wells within the same block.

[0072] In this exemplary embodiment, the interlayer range of the production contribution rate of each reservoir section in each well can be determined by the following equation 4:

[0073] Equation 4: γ = η max / η min ;

[0074] Where, η max η min , representing the maximum and minimum values ​​of the flow dominance index in each well reservoir section, respectively, where %, the flow dominance index is numerically equal to the layer production contribution rate; γ is the interlayer range of the flow dominance index, dimensionless, and the flow dominance index is numerically equal to the layer production contribution rate.

[0075] Here, η max η min Numerically, γ is also equal to the maximum and minimum values ​​of the production contribution rate of each reservoir segment in the single well to be evaluated, and γ is also equal to the inter-layer range of the production contribution rate of each layer.

[0076] Exemplary Example 3

[0077] This exemplary embodiment provides a computer device, which may include: a processor; and a memory storing a computer program that, when executed by the processor, implements the method for evaluating the stratified production contribution rate of multi-layered gas wells as described in Exemplary Embodiment 1 and / or Exemplary Embodiment 2.

[0078] Exemplary Example 4

[0079] This exemplary embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for evaluating the stratified production contribution rate of multi-layered gas wells as described in Exemplary Embodiment 1 and / or Exemplary Embodiment 2.

[0080] To better understand the above exemplary embodiments 1-2, further detailed explanations will be provided below with reference to specific examples.

[0081] Example 1

[0082] In this example, the method for evaluating the production contribution rate of multi-layered syngas wells as described in Exemplary Example 1 is applied to Well A of a fractured-pore carbonate gas reservoir, specifically including:

[0083] First, based on the wellbore structure and completion data of Well A, a total sample size of 6 data segments was established, with reservoir thickness ranging from 2.0 to 11.4 m. Using the imaging logging instrument's unit well section of 0.125 m, the sample size for each reservoir segment calculated using Equation 1 above ranged from 16 to 91. The statistical results of the logging static data for different reservoir segments of Well A are shown in Table 1.

[0084] Table 1. Statistical results of logging static data for different reservoir sections in Well A.

[0085] reservoir section Well depth(m) Layer thickness (m) Number of samples I 3186.4-3191.3 4.9 39 II 3207.8-3212.4 4.6 37 III 3236.0-3240.0 4.0 32 IV 3242.7-3244.7 2.0 16 V 3250.1-3253.7 3.6 29 VI 3315.9-3327.3 11.4 91

[0086] Secondly, based on static data from conventional logging and imaging logging, parameters such as matrix permeability, fracture length, fracture width, and fracture apparent porosity are extracted. The seepage characteristic parameters of each reservoir section of Well A are calculated using Equation 2 above, and a relationship model is established between the seepage characteristic parameters of each reservoir section of Well A and the gas production profile test results, i.e., the gas production of each layer. Figure 2 This diagram illustrates the relationship between seepage characteristic parameters and stratified gas production in each reservoir section of Well A in Example 1 of this invention. Figure 2 As shown, the linear regression correlation coefficient R 2 If the value is 0.941, which is greater than 0.9, then the seepage characteristic parameters and gas production of each reservoir section of the well satisfy a positive linear relationship, indicating that the method is applicable to the well to be evaluated and can be extended to adjacent wells in the same block.

[0087] The production contribution rate of each reservoir section in Well A was calculated using Equation 3 above. The seepage characteristic parameters, production contribution rate calculation results, and gas production profile test results of different reservoir sections in Well A are shown in Table 2.

[0088] Table 2 shows the calculation results of seepage characteristic parameters, stratified production contribution rate, and gas production profile test results for different reservoir sections in Well A.

[0089]

[0090]

[0091] As shown in Table 2, the contribution rate of each reservoir section of Well A to gas production ranges from 2.23% to 38.43%, with section III and section V being the main gas-producing sections of Well A.

[0092] Using Equation 4 above, the interlayer range of the production contribution rate of each reservoir section in Well A is 17.23. The gas supply capacity of layer I and layer II is limited, and measures to improve the gas supply and tap the remaining reserves can be considered in the later stage.

[0093] Meanwhile, Well B, adjacent to Well A, also extracted parameters such as matrix permeability, fracture length, fracture width, and fracture apparent porosity based on static data from conventional logging and imaging logging. Using Equation 2 above, the seepage characteristic parameters of each reservoir section in Well B were calculated. In the absence of multi-layer gas production profile testing (PLT testing) in Well B, the parameters were... Figure 2 The relationship model shown allows for rapid evaluation of the gas production capacity of each reservoir in Well B by simply determining the seepage characteristic parameters of each reservoir section. The calculation results of the seepage characteristic parameters and stratified production contribution rates for each reservoir section of Well B are shown in Table 3.

[0094] Table 3 shows the calculation results of seepage characteristic parameters and stratified production contribution rate for each reservoir section of Well B.

[0095] reservoir section <![CDATA[Seepage characteristic parameter (μm 2 )]]> Stratified output contribution rate (%) I 1.51 7.11 II 0.59 3.57 III 6.00 24.41 IV 2.33 10.27 V 11.10 44.06 VI 2.41 10.58

[0096] As shown in Table 3, the production contribution rate of layer V is 44.06%, indicating that layer V is the main gas-producing layer of well B. The production contribution rate of layer II is 3.57%, indicating that layer II has limited gas supply capacity. Figure 3 This diagram illustrates the relationship between seepage characteristic parameters and stratified gas production in each reservoir section of Well A and Well B in Example 1 of this invention. The seepage characteristic parameters of each reservoir section in Well B in Table 3 above are mapped one-to-one to... Figure 2 The results obtained from the relationship model of well A shown are as follows: Figure 3 As shown, the gas production corresponding to the seepage characteristic parameters of reservoir sections I, II, III, IV, V and VI of well B are 39,000 m³ / day, 20,000 m³ / day, 135,000 m³ / day, 57,000 m³ / day, 243,000 m³ / day and 58,000 m³ / day, respectively. The gas production of section V is 243,000 m³ / day, and section V is the main gas-producing section of well B. The gas production of section II is 20,000 m³ / day, and the gas supply capacity of section II is limited.

[0097] Using Equation 4 above, the interlayer range of the production contribution rate of layer V and layer II is calculated to be 12.34, which means that measures to improve the situation and tap the potential of the remaining reserves can be considered in the later stage.

[0098] In summary, the advantages proposed by this invention include at least the following:

[0099] (1) The multi-layer gas well production contribution rate evaluation method provided by the present invention effectively reduces labor costs and improves timeliness and convenience.

[0100] (2) The method for evaluating the production contribution rate of multi-layer gas wells provided by the present invention is applicable to the rapid evaluation of the production contribution rate of different reservoir sections in multi-layer gas wells, which improves the formation energy utilization rate and comprehensive development effect of different layers in multi-layer gas wells, and has good applicability and promotion prospects for practical field application.

[0101] Although the method, apparatus and storage medium for evaluating the production contribution rate of multi-layered gas wells of the present invention have been described above in conjunction with exemplary embodiments, those skilled in the art should understand that various modifications and changes can be made to the exemplary embodiments of the present invention without departing from the spirit and scope defined by the claims.

Claims

1. A method for evaluating the contribution rate of each layer in a multilayer commingled gas well, characterized in that, The method comprises: determining the contribution rate of each reservoir section of a single well and / or determining the contribution rate of each reservoir section of a neighboring well in the same block; wherein The determination of the contribution rate of each reservoir section of a single well comprises: determining the percolation characteristic parameters of each reservoir section of the single well to be evaluated by the well structure, completion data, first logging data and second logging data of the multi-layer commingled gas well; and determining the contribution rate of each reservoir section of the single well to be evaluated by using the percolation characteristic parameters of each reservoir section of the single well to be evaluated. The determination of the contribution rate of each reservoir section of a neighboring well in the same block comprises: determining the percolation characteristic parameters of each reservoir section of the single well to be evaluated by the well structure, completion data, first logging data and second logging data of the multi-layer commingled gas well; establishing a relationship model of the percolation characteristic parameters and the contribution rate of each reservoir section of the single well to be evaluated, wherein the correlation coefficient is greater than 0.9; determining the contribution rate of each reservoir section of the neighboring well in the same block by the relationship model and the percolation characteristic parameters of each reservoir section of the neighboring well in the same block.

2. The method according to claim 1, wherein, The method further comprises: determining the subsequent construction measures by the interlayer range of the contribution rate of each reservoir section of each well.

3. The method according to claim 1, wherein, The step of determining the percolation characteristic parameters of each reservoir section of the single well to be evaluated by the well structure, completion data, first logging data and second logging data of the multi-layer commingled gas well comprises: determining the sample number of each reservoir section of the single well to be evaluated by the well structure and the completion data of the multi-layer commingled gas well.

4. The method according to claim 3, characterized in that, The sample number of each reservoir section of the single well to be evaluated is determined by the following formula 1: Formula 1: Wherein, N is the total number of single well reservoir section samples to be evaluated, dimensionless; h1, h2, …, hN are the thicknesses of the reservoir sections of the single well to be evaluated, m; Δ1 is the unit sampling length of the imaging logging instrument, m; S1, S2, …, SN are the sample numbers of the reservoir sections. N Wherein, N is the total number of single well reservoir section samples to be evaluated, dimensionless; h1, h2, …, hN are the thicknesses of the reservoir sections of the single well to be evaluated, m; Δ1 is the unit sampling length of the imaging logging instrument, m; S1, S2, …, SN are the sample numbers of the reservoir sections. N Wherein 5. The method according to claim 3, wherein, The determination of the percolation characteristic parameters of each reservoir section of the single well to be evaluated by the well structure, completion data, first logging data and second logging data of the multi-layer commingled gas well further comprises: determining the matrix permeability of a reservoir sampling unit well section by the first logging data; and determining the fracture length, fracture width and fracture apparent porosity of the reservoir sampling unit well section by the second logging data.

6. The method according to claim 5, wherein, The percolation characteristic parameters of each reservoir section of the single well to be evaluated are determined by the following formula 2: Formula 2: Among them, R1, R2...R N Here are the seepage characteristic parameters for each reservoir section, in μm. 2 ;K mi The matrix permeability is expressed in μm. 2 ;F Li F represents the fracture length per well section used for reservoir sampling, in μm; Wi F represents the fracture width per well section sampled from the reservoir, in μm; φi The apparent porosity of fractures in a unit well section of the reservoir sampling unit, %.

7. The method according to claim 1, wherein, The contribution rate of each reservoir section of the single well to be evaluated is determined by the following formula 3: Formula 3: wherein η1, η2, ···ηN N is the seepage advantage index of each reservoir section of the single well to be evaluated, %; the seepage advantage index is equal to the stratified production contribution rate in value; R1, R2, ···R N is the seepage characteristic parameter of each reservoir section of the single well to be evaluated, μm 2 ; N is the total number of reservoir section samples of the single well to be evaluated, dimensionless.

8. The method according to claim 1, wherein, The establishment of the relationship model of the percolation characteristic parameters and the contribution rate of each reservoir section of the single well to be evaluated comprises: obtaining the contribution rate of each reservoir section by multi-layer gas production profile testing of the gas well, and establishing the relationship model of the percolation characteristic parameters and the contribution rate of each reservoir section of the single well to be evaluated.

9. The method according to claim 2, wherein, The interlayer range of the contribution rate of each reservoir section of each well is determined by the following formula 4: Formula 4: γ = η max / η min ; wherein η max , η min are, respectively, the maximum and minimum values of the seepage dominance index for each well and each reservoir section, in %, the seepage dominance index being numerically equal to the contribution of the layer to the production; γ is the interlayer range of the seepage dominance index, dimensionless, the seepage dominance index being numerically equal to the contribution of the layer to the production.

10. A computer device, comprising: The computer device comprises: a processor; and a memory storing a computer program, when the computer program is executed by the processor, the multi-layer commingled gas well contribution rate evaluation method according to any one of claims 1-9 is realized.

11. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by the processor, realizes the multi-layer commingled gas well contribution rate evaluation method according to any one of claims 1-9.

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