Method for evaluating productivity of marine tight sandstone gas well based on reservoir reconstruction intensity

Abstract

This application discloses a method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity. According to this application, by introducing primary and secondary fracture classification, secondary fracture complexity, and reservoir stimulation intensity indices, and establishing the correlation between reservoir stimulation intensity and productivity, the method fully considers the impact of reservoir stimulation intensity parameters such as primary fracture volume, primary fracture density, and secondary fracture complexity on the productivity of closely spaced fractured long horizontal wells. It also meticulously analyzes the correlation between closely spaced fracture network parameters and productivity, forming a method for predicting gas well production under different fracturing operation modes. This provides a basis for optimizing fracturing parameters and improving fracturing effectiveness.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field development, and more specifically, relates to a method and electronic equipment for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity. Background Technology

[0002] The main technical means for developing marine tight sandstone gas is horizontal well segmented hydraulic fracturing. This technique uses hydraulic fracturing to maximize the fragmentation of tight sandstone, creating a network of fracture channels and improving the flow capacity of the tight sandstone gas reservoir. Factors affecting single-well productivity include reservoir conditions, horizontal well design, and fracturing engineering. Reservoir conditions include rock mineral composition, burial depth, thickness, porosity, permeability, reservoir pressure coefficient, geostress, and water saturation. Horizontal well design includes the azimuth, length, relative vertical position, and distance to adjacent wells of the horizontal segment. Fracturing engineering factors include the completion method of the horizontal segment, the number of segments and clusters, the amount of proppant added per meter, the type of fracturing fluid, the pump discharge rate and total pumped fluid volume, and the fracturing method. These factors comprise three main categories and 20 subcategories.

[0003] By evaluating the production of 1233 gas wells in a marine storm-prone coastal sedimentary thick tight sandstone gas reservoir, and applying machine learning and big data analysis techniques, the order of influence on production, in descending order, is as follows: horizontal section length, amount of proppant added per meter, production pressure differential per meter of fracture spacing (segment spacing, number of perforation clusters), reservoir porosity, horizontal section burial depth, water saturation, reservoir thickness, horizontal section spacing, and fracturing fluid type. Therefore, the key factors affecting production are horizontal section length, amount of proppant added per meter, and fracture spacing. Thus, the main factors affecting gas well productivity are horizontal well design and fracturing engineering factors. The reason for this is that, due to the sedimentary characteristics and reservoir formation features of marine tight sandstone, the gas reservoir conditions and fluid distribution are relatively uniform in a plane. Horizontal well development design can adopt a factory-customized model, exhibiting good consistency. The difference in single-well productivity mainly stems from the differences in fracturing construction design parameters. With the advancement of fracturing technology, the scale of reservoir fracturing has been continuously increasing. Factors such as the increase in the length of long horizontal sections, the decrease in fracture spacing, and the increase in the amount of sand added per meter have led to the continuous increase in single-well productivity and the gradual improvement in development results.

[0004] Currently, methods for evaluating the production capacity of marine tight sandstone gas wells include reservoir engineering analysis and numerical simulation. Reservoir engineering analysis classifies and evaluates wells based on the differences in individual well production indicators. There are three main methods: the average production rate during the initial production phase (IP30, IP90, etc.) over the first 30 or 90 days; the cumulative production over the well's lifecycle (EUR); and the horizontal section length normalization method (daily or cumulative production per kilometer of horizontal section). Reservoir engineering analysis solves the problem of classifying and evaluating individual wells, establishing production change patterns, but it does not consider the impact of fracturing parameters on production capacity. Numerical simulation methods are based on geological models, considering hydraulic fracture parameters to evaluate individual well production capacity, but their limitation lies in the significant uncertainty of fracture parameters.

[0005] From the perspective of seepage mechanics, the flow mechanism of marine tight sandstone gas wells includes a bilinear flow of gas from the matrix to fractures and from fractures to the wellbore in the initial stage of production, followed by a linear flow stage from the matrix to fractures. Radial or elliptical flow is almost nonexistent. Because marine formations are relatively homogeneous with minimal differences in reservoir conditions, well production dynamics are primarily related to artificial fracture stimulation. The initial production capacity of a single well is determined by the linear density of fractures along the length of the horizontal section and in the vertical direction of the horizontal section. In fracturing engineering, a smaller fracture spacing is more conducive to increasing initial production. The stable production period or early decline rate of a single well is related to the distribution of secondary fractures near the main fracture. A greater number of secondary fractures and higher interconnectivity between fractures result in a higher degree of matrix fragmentation, more complete utilization of reserves within the matrix, a better foundation for stable production, and a smaller decline rate. This is reflected in fracturing engineering by the amount of proppant added per meter and the size of the fracture spacing. The life cycle and cumulative gas production of a single well are determined by the effective utilized reservoir volume (SRV) and the maximum pressure drop of the reservoir. It is evident that the length of the horizontal section, the amount of sand added per meter, and the spacing between fractures have a significant effect on the productivity enhancement of a single well, and are the primary factors for evaluating the productivity of marine tight sandstone gas.

[0006] Current methods for evaluating single-well productivity are based on gas reservoir engineering analysis and only consider dynamic indicators of a single well. Their evaluation accuracy is low and is not conducive to the optimization of subsequent pressure construction parameters. Summary of the Invention

[0007] In view of this, the embodiments of this application provide a method and electronic equipment for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, so as to form a means of evaluating the productivity of marine tight sandstone gas wells and solve the problem of productivity evaluation of tight sandstone gas wells.

[0008] In a first aspect, embodiments of this application provide a method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, including:

[0009] Based on the fracture morphology, fractures formed in marine tight sandstone by segmented perforation and clustered fracturing in horizontal sections of horizontal wells are classified into primary fractures and secondary fractures.

[0010] Based on the sand volume V per meter 砂 Volume V of a single main fracture 主缝 The secondary fracture complexity α is determined by the linear density n of the primary fracture and is used to reflect the development and distribution characteristics of secondary fractures near the primary fracture.

[0011] The reservoir stimulation intensity β is determined based on the secondary fracture complexity α. The reservoir stimulation intensity β is used to reflect the degree of stimulation of the reservoir per meter along the horizontal section.

[0012] Establish a correspondence between production capacity prediction parameters and production capacity data, wherein the production capacity prediction parameters include secondary fracture complexity α and reservoir stimulation intensity β;

[0013] Based on the established classification criteria, multiple fracturing construction schemes are divided into multiple categories. For each category, a correspondence is established between the fracturing construction schemes under that category and the production capacity prediction parameters, thereby further obtaining the correspondence between the fracturing construction schemes under that category and the production capacity data.

[0014] The category corresponding to the fracturing operation scheme to be predicted is determined according to the set criteria, and the production capacity data of the fracturing operation scheme to be predicted is predicted based on the correspondence between the fracturing operation scheme and the production capacity data under the category.

[0015] In some implementation schemes, determining the secondary crack complexity α specifically includes:

[0016] The volume V of the main crack per meter is determined by the following formula. 缝 :

[0017] V 缝 =V 主缝 *n;

[0018] The complexity α of the secondary crack is determined according to the following formula:

[0019]

[0020] In some implementation schemes, determining the reservoir stimulation intensity β specifically includes:

[0021] The reservoir stimulation intensity β is determined according to the following formula:

[0022] β = α*n.

[0023] In some implementations, the set standard is the reservoir stimulation intensity β.

[0024] In some implementation schemes, the capacity prediction parameters also include the main fracture support volume and the main fracture linear density, and the capacity data includes: initial production, cumulative production during the stable production period, decline rate, life cycle, and cumulative production within the life cycle.

[0025] In some implementation schemes, the correspondence between capacity forecasting parameters and capacity data includes some or all of the following:

[0026] The relationship between the main fracture support volume and the initial production of the gas well;

[0027] The relationship between the linear density of the main fracture and the initial production of the gas well;

[0028] The relationship between the complexity α of secondary cracks and the cumulative output during the stable production period;

[0029] The relationship between the complexity α of secondary cracks and the decay rate;

[0030] The relationship between reservoir stimulation intensity β and lifetime;

[0031] The relationship between reservoir stimulation intensity β and cumulative production over its lifetime.

[0032] In some implementation plans, establishing the relationship between capacity forecasting parameters and capacity data specifically includes:

[0033] Grey relational analysis, entropy method and / or hierarchical analysis are used to analyze the relationship between various capacity forecasting parameters and capacity data, and to obtain the influence weight of different capacity forecasting parameters on capacity data.

[0034] Based on the aforementioned influence weights, select the main controlling factors affecting capacity data from the capacity forecasting parameters;

[0035] Establish a relationship curve between capacity data and corresponding key control factors to serve as the relationship between capacity forecasting parameters and capacity data.

[0036] In some implementations, the method further includes:

[0037] Before establishing the relationship between production capacity prediction parameters and production capacity data, the production capacity data is normalized to obtain production capacity data for each 100-meter gas well, so as to establish the relationship between production capacity prediction parameters and normalized production capacity data.

[0038] In some implementations, the method further includes:

[0039] If the predicted production capacity data of the fracturing operation plan does not meet the requirements, the predicted fracturing operation plan is adjusted, and then the production capacity data of the adjusted fracturing operation plan is predicted until the predicted production capacity data meets the requirements.

[0040] Secondly, embodiments of this application also provide an electronic device, which includes:

[0041] Memory, which stores executable instructions;

[0042] A processor that executes the executable instructions in the memory to implement the method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity.

[0043] According to this application, by introducing the classification of primary and secondary fractures, the complexity of secondary fractures, and the reservoir stimulation intensity index, and establishing the correlation between reservoir stimulation intensity and production capacity, the impact of reservoir stimulation intensity parameters such as primary fracture volume, primary fracture density, and secondary fracture complexity on the production capacity of closely spaced fractured long horizontal wells is fully considered. The correlation between closely spaced fracture network parameters and production capacity is analyzed in detail, and a method for predicting gas well production under different fracturing construction modes is formed, providing a basis for optimizing the design of fracturing stimulation parameters and improving the fracturing stimulation effect.

[0044] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0045] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.

[0046] Figure 1 A flowchart is shown of a method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, according to an embodiment of this application.

[0047] Figure 2 A schematic diagram of the primary and secondary crack distribution according to an exemplary embodiment of the present invention is shown.

[0048] Figure 3 A schematic diagram showing the relationship between the main fracture support volume and the initial production per 100 meters of the gas well, according to an exemplary embodiment of the present invention, is provided.

[0049] Figure 4 A schematic diagram showing the relationship between the density of the main fracture and the initial production per 100 meters of gas well, according to an exemplary embodiment of the present invention, is presented.

[0050] Figure 5 A schematic diagram showing the relationship between the complexity of secondary cracks and the cumulative output over 100 days of stable production, according to an exemplary embodiment of the present invention, is presented.

[0051] Figure 6 A schematic diagram showing the relationship between reservoir stimulation intensity and cumulative production over 100 days, according to an exemplary embodiment of the present invention, is provided. Detailed Implementation

[0052] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0053] Example 1

[0054] Figure 1 A flowchart illustrating a method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, according to an embodiment of this application, is shown. As shown, the method includes steps S1 to S6.

[0055] Step S1: Based on the fracture morphology, the fractures formed in marine tight sandstone by segmented perforation and clustered fracturing in the horizontal section of a horizontal well are classified into primary fractures and secondary fractures.

[0056] Primary and secondary fractures can be clearly identified by fracture morphology. By identifying primary and secondary fractures, the differences in the fracture network system formed by segmented perforation and clustered fracturing in the horizontal section of a horizontal well within marine tight sandstone can be reflected, resulting in differences in the production capacity of a single well in marine tight sandstone gas.

[0057] The main fracture is a propped fracture generated by the initiation pressure and expanded by proppant addition during hydraulic fracturing. It has the highest conductivity and can be considered a high-conductivity main fracture. The number of main fractures within a segment can be considered to be determined by the number of perforation clusters; the more perforation clusters, the more main fractures, and the greater the main fracture linear density.

[0058] Secondary fractures refer to propped fractures that occur alongside and are formed by the addition of proppant during the extension of the main fracture. The formation of secondary fractures can be attributed to the microscopic heterogeneity of the reservoir and the agitation effect during fracturing. The stronger the reservoir's microscopic heterogeneity, the stronger the agitation effect during fracturing, and the greater the amount of proppant added per meter, the easier it is for secondary fractures to form.

[0059] A single main fracture is large in scale and has a certainty, but the number of fractures is limited and the contact surface with the matrix is ​​singular; while secondary fractures are distributed in a network, have randomness, and the contact surface between the secondary fracture network and the matrix is ​​large, connecting a large amount of reserves.

[0060] Initial production capacity of gas wells mainly comes from the main fracture system, while subsequent stable production or delayed decline requires the supply of in-matrix reserves through communication with the secondary fracture system.

[0061] Step S2, based on the sand volume V per meter 砂 Volume V of a single main fracture 主缝 The secondary crack complexity α is determined by the linear density n of the primary crack. The secondary crack complexity α is used to reflect the development and distribution characteristics of secondary cracks near the primary crack.

[0062] The volume of sand added per meter, V, can be obtained or calculated using any method deemed appropriate by a person skilled in the art.砂 Volume V of a single main fracture 主缝 The linear density of the main crack, n, is not limited in this application.

[0063] In some possible implementations, specifically, the volume V per meter of the main crack can be determined according to the following formula. 缝 :

[0064] V 缝 =V 主缝 *n;

[0065] The complexity α of the secondary crack is determined according to the following formula:

[0066]

[0067] This application introduces a secondary fracture complexity α to reflect the development and distribution characteristics of secondary fractures near the main fracture. To directly characterize the scale V of the secondary fracture network system... 次缝 The internal connectivity is almost impossible. Through in-depth research, the inventors believe that, as shown above, by using fracturing construction parameters and the volume V of a single main fracture, it is possible to achieve this. 主缝 Indirect characterization can be used, which can be effective in this scheme. As mentioned above, the volume V of a single main crack can be used as a starting point. 主缝 The volume V per meter of main fracture is calculated using the linear density n of the main fracture. 缝 And define α = V 次缝 / V 缝 =(V 砂 -V 缝 ) / V 缝 =V 砂 / V 缝 -1, to indirectly characterize the scale V of the secondary crack network system. 次缝 And internal connectivity relationships.

[0068] Step S3: Determine the reservoir stimulation intensity β based on the secondary fracture complexity α. The reservoir stimulation intensity β is used to reflect the degree of stimulation of the reservoir per meter along the horizontal section.

[0069] In some possible implementations, the reservoir stimulation intensity β can be determined according to the following formula:

[0070] β = α*n.

[0071] After in-depth research, the inventors characterized the reservoir stimulation intensity β by multiplying the fracture linear density and the complexity of secondary fractures, which can effectively reflect the fracture development per meter of reservoir. The greater the reservoir stimulation intensity β, the more developed the fractures and the higher the degree of rock fragmentation per meter of reservoir, and the more uniform the utilization of reserves within the matrix, which is more conducive to improving single-well productivity.

[0072] Step S4: Establish the correspondence between production capacity prediction parameters and production capacity data. The production capacity prediction parameters include secondary fracture complexity α and reservoir stimulation intensity β.

[0073] In some examples, the capacity prediction parameters also include the main fracture support volume, the main fracture linear density, etc., and the capacity data includes: initial production, cumulative production during the stable production period, decline rate, life cycle, and cumulative production within the life cycle, etc.

[0074] The above-mentioned capacity forecast parameters and capacity data may be obtained or calculated in any manner that is deemed appropriate by those skilled in the art, and this application does not limit such methods.

[0075] In some examples, before establishing the relationship between production capacity prediction parameters and production capacity data, the production capacity data can be normalized to obtain the production capacity data for each 100 meters of gas well, in order to establish the relationship between the production capacity prediction parameters and the normalized production capacity data.

[0076] In some implementations, establishing the relationship between capacity forecasting parameters and capacity data specifically includes:

[0077] Grey relational analysis, entropy method and / or analytic hierarchy process are used to analyze the relationship between various capacity forecasting parameters and capacity data, and to obtain the influence weight of different capacity forecasting parameters on capacity data.

[0078] Based on the aforementioned influence weights, select the main controlling factors affecting capacity data from the capacity forecasting parameters;

[0079] Establish a relationship curve between capacity data and corresponding key control factors to serve as the relationship between capacity forecasting parameters and capacity data.

[0080] For example, numerical simulation can be used to establish the relationship curve between production capacity data and corresponding main control factors, neural networks can be used to establish the curve between production capacity data and corresponding main control factors, or multiple factors can be analyzed comprehensively.

[0081] In some examples, the correspondence between capacity forecasting parameters and capacity data includes some or all of the following:

[0082] The relationship between the main fracture support volume and the initial production of the gas well;

[0083] The relationship between the linear density of the main fracture and the initial production of the gas well;

[0084] The relationship between the complexity α of secondary cracks and the cumulative output during the stable production period;

[0085] The relationship between the complexity α of secondary cracks and the decay rate;

[0086] The relationship between reservoir stimulation intensity β and lifetime;

[0087] The relationship between reservoir stimulation intensity β and cumulative production over its lifetime.

[0088] As mentioned above, all of the above production capacity data can be normalized production capacity data.

[0089] Step S5: According to the set classification criteria, the multiple fracturing construction schemes are divided into multiple categories, and for each category, the correspondence between the fracturing construction schemes under that category and the production capacity prediction parameters is established, thereby further obtaining the correspondence between the fracturing construction schemes under that category and the production capacity data.

[0090] Therefore, it is possible to establish a refined pattern of gas well production variation under different fracturing operation modes, that is, to obtain a refined direct correspondence between fracturing operation parameters and production data.

[0091] In some implementations, reservoir stimulation intensity β can be selected as a classification criterion to classify multiple fracturing operation schemes.

[0092] Step S6: Determine the category corresponding to the fracturing construction scheme to be predicted according to the set standard, and predict the production capacity data of the fracturing construction scheme to be predicted based on the correspondence between the fracturing construction scheme and the production capacity data under the category.

[0093] Therefore, the corresponding production capacity relationship can be predicted directly based on the fracturing construction plan.

[0094] In some embodiments, the method further includes:

[0095] If the predicted production capacity data of the fracturing operation plan does not meet the requirements, the predicted fracturing operation plan is adjusted, and then the production capacity data of the adjusted fracturing operation plan is predicted until the predicted production capacity data meets the requirements, thereby optimizing the fracturing operation plan.

[0096] Those skilled in the art can set the requirements for measuring production capacity data as needed.

[0097] In the above embodiments, by introducing the classification of primary and secondary fractures, the complexity of secondary fractures, and the reservoir stimulation intensity index, and establishing the correlation between reservoir stimulation intensity and production capacity, the influence of reservoir stimulation intensity parameters such as primary fracture volume, primary fracture density, and secondary fracture complexity on the production capacity of closely spaced fractured long horizontal wells is fully considered. The correlation between closely spaced fracture network parameters and production capacity is analyzed in detail, forming a method for predicting gas well production under different fracturing construction modes. This provides a basis for optimizing the design of fracturing stimulation parameters and improves the fracturing stimulation effect.

[0098] In application, for example, if the classification standard is reservoir stimulation intensity β, then for each fracturing operation scheme to be predicted, the reservoir stimulation intensity β can be calculated first based on its fracturing operation parameters, then the category corresponding to the fracturing operation scheme to be predicted can be found based on its reservoir stimulation intensity β, and the production capacity data of the fracturing operation scheme to be predicted can be predicted in a refined manner based on the correspondence between the fracturing operation scheme and the production capacity data established under the category.

[0099] If the production capacity data is unsatisfactory, the fracturing operation plan to be predicted is adjusted, and its reservoir stimulation intensity β is recalculated (if the reservoir stimulation intensity β is changed). Based on the updated reservoir stimulation intensity β, the category corresponding to the adjusted plan is found, and the production capacity data is predicted again until the production capacity data obtained based on the adjusted plan is satisfactory. Then, the plan is regarded as the preferred plan, or the plan with the best prediction result can be found from multiple predictions as the preferred plan, which is considered to be able to guide the fracturing operation.

[0100] Example 2

[0101] According to an exemplary embodiment of this application, a production capacity prediction is carried out for a fractured well in a marine tight sandstone gas field block in Canada, as detailed below.

[0102] Figure 2 A schematic diagram of the primary and secondary fracture distribution according to an exemplary embodiment of the present invention is shown. The volume V of a single primary fracture can be simulated. 主缝 =48m 3 / fracture, linear density of main fracture n = 0.03 fractures / m, volume of main fracture per meter:

[0103] V 缝 =V 主缝 *n=1.44m 3 / m;

[0104] And obtain V 砂 =2.302m 3 / m, calculate:

[0105] Secondary crack complexity: α = V 次缝 / V 缝 =(V 砂 -V 缝 ) / V 缝 =V 砂 / V 缝 -1 = 0.5986;

[0106] Reservoir stimulation intensity: β=α×n=0.01796.

[0107] The system can normalize the production capacity of a single well, statistically analyze the main production indicators per 100 meters, and plot graphs showing the relationship between the main fracture support volume and the initial production per 100 meters, the main fracture linear density and the initial production per 100 meters, the secondary fracture complexity and the cumulative production during the stable production period, and the reservoir stimulation intensity and cumulative production, thereby determining the optimal fracture parameters. After optimization, the well achieved an initial production of 1.76 × 10⁻⁶ meters. 4 m 3 The cumulative output over 100 days of production is 111.78 × 10⁻⁶. 4 m 3 .in, Figure 3 This diagram illustrates the relationship between the main fracture support volume and the initial production per 100 meters of the gas well in this exemplary embodiment. Figure 4 This is a schematic diagram showing the relationship between the main fracture density and the initial production per 100 meters of gas well in this exemplary embodiment. Figure 5 This exemplary embodiment shows a schematic diagram of the relationship between the complexity of secondary cracks and the cumulative output over 100 days of stable production. Figure 6 A schematic diagram showing the relationship between reservoir stimulation intensity and cumulative production over 100 days in this exemplary embodiment is presented.

[0108] The well is 1700m long. The final optimized fracturing construction scheme obtained in this exemplary embodiment is as follows: 13 fracturing stages are designed, with 3-5 clusters in each stage. The fracturing design and construction parameters are shown in Table 1.

[0109] Table 1 Fracturing Design and Construction Parameters

[0110]

[0111] Example 3

[0112] The electronic device according to embodiments of this application includes a memory and a processor.

[0113] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0114] The processor may be a central processing unit (CPU) or other form of processing unit with data processing and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this application, the processor is used to run the computer-readable instructions stored in the memory to perform the above-described method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity.

[0115] Those skilled in the art should understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this application.

[0116] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0117] Example 4

[0118] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity.

[0119] A computer-readable storage medium according to embodiments of this application stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of this application are performed.

[0120] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0121] For further details and advantages of this embodiment, please refer to the description above.

[0122] Example 5

[0123] This embodiment provides a device for evaluating the production capacity of marine tight sandstone gas wells based on reservoir stimulation intensity, including a primary and secondary fracture division unit, a secondary fracture complexity calculation unit, a reservoir stimulation intensity calculation unit, a first production capacity prediction unit, and a classified fracturing production capacity prediction unit.

[0124] The primary and secondary fracture division unit is used to classify fractures formed in marine tight sandstone by segmented perforation and clustered fracturing in horizontal sections of horizontal wells into primary fractures, secondary fractures, and a second production capacity prediction unit, based on fracture morphology.

[0125] The secondary crack complexity calculation unit is used to calculate the sand volume V per meter. 砂 Volume V of a single main fracture 主缝 The secondary crack complexity α is determined by the linear density n of the primary crack. The secondary crack complexity α is used to reflect the development and distribution characteristics of secondary cracks near the primary crack.

[0126] The reservoir stimulation intensity calculation unit is used to determine the reservoir stimulation intensity β based on the secondary fracture complexity α. The reservoir stimulation intensity β is used to reflect the degree of stimulation of the reservoir per meter along the horizontal section.

[0127] The first production capacity prediction unit is used to establish the correspondence between production capacity prediction parameters and production capacity data. The production capacity prediction parameters include secondary fracture complexity α and reservoir stimulation intensity β.

[0128] The classification fracturing capacity prediction unit is used to classify multiple fracturing construction schemes into multiple categories according to the set classification criteria, and for each category, establish the correspondence between the fracturing construction schemes under that category and the capacity prediction parameters, thereby further obtaining the correspondence between the fracturing construction schemes and capacity data under that category.

[0129] The second capacity prediction unit is used to determine the category corresponding to the fracturing construction scheme to be predicted according to the set standard, and predict the capacity data of the fracturing construction scheme to be predicted according to the correspondence between the fracturing construction scheme and the capacity data under the category.

[0130] For further details and advantages of this embodiment, please refer to the description above.

[0131] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, characterized in that, include: Based on the fracture morphology, fractures formed in marine tight sandstone by segmented perforation and clustered fracturing in horizontal sections of horizontal wells are classified into primary fractures and secondary fractures. Based on the volume of sand added per meter Volume of a single main crack and main crack linear density Determine the complexity of secondary cracks Secondary crack complexity Used to reflect the development and distribution characteristics of secondary fractures near the main fracture; Based on the complexity of secondary cracks Determine the intensity of reservoir stimulation reservoir stimulation intensity Used to reflect the degree of reservoir modification per meter along the horizontal section; Establish a correspondence between capacity forecasting parameters and capacity data, wherein the capacity forecasting parameters include secondary crack complexity. and reservoir stimulation intensity ,in, Determine the complexity of secondary cracks Specifically, it includes: The volume of each meter of the main crack is determined according to the following formula. : ； The complexity of secondary cracks is determined according to the following formula. : ， Determine the intensity of reservoir stimulation Specifically, it includes: The reservoir stimulation intensity is determined according to the following formula. : ;as well as The correspondence between capacity forecasting parameters and capacity data includes some or all of the following: secondary crack complexity. Relationship with cumulative output during the stable production period; complexity of secondary cracks Relationship with decline rate; reservoir stimulation intensity Relationship with life cycle; reservoir stimulation intensity The relationship between cumulative output over the product lifecycle; Based on the established classification criteria, multiple fracturing construction schemes are divided into multiple categories. For each category, a correspondence is established between the fracturing construction schemes under that category and the production capacity prediction parameters, thereby further obtaining the correspondence between the fracturing construction schemes under that category and the production capacity data. The category corresponding to the fracturing construction scheme to be predicted is determined according to the set criteria, and the production capacity data of the fracturing construction scheme to be predicted is predicted based on the correspondence between the fracturing construction scheme and the production capacity data under the category.

2. The method according to claim 1, characterized in that, The set standard is reservoir stimulation intensity. .

3. The method according to claim 1, characterized in that, The capacity prediction parameters also include the main fracture support volume and the main fracture linear density. The capacity data includes: initial production, cumulative production during the stable production period, decline rate, life cycle, and cumulative production within the life cycle.

4. The method according to claim 3, characterized in that, The correspondence between capacity forecasting parameters and capacity data also includes some or all of the following: The relationship between the main fracture support volume and the initial production of the gas well; The relationship between the linear density of the main fracture and the initial production of the gas well.

5. The method according to claim 1, characterized in that, Establishing the relationship between capacity forecasting parameters and capacity data specifically includes: Grey relational analysis, entropy method and / or hierarchical analysis are used to analyze the relationship between various capacity forecasting parameters and capacity data, and to obtain the influence weight of different capacity forecasting parameters on capacity data. Based on the aforementioned influence weights, select the main controlling factors affecting capacity data from the capacity forecasting parameters; Establish a relationship curve between capacity data and corresponding key control factors to serve as the relationship between capacity forecasting parameters and capacity data.

6. The method according to claim 1, characterized in that, The method further includes: Before establishing the relationship between production capacity prediction parameters and production capacity data, the production capacity data is normalized to obtain production capacity data for each 100-meter gas well, so as to establish the relationship between production capacity prediction parameters and normalized production capacity data.

7. The method according to claim 1, characterized in that, The method further includes: If the predicted production capacity data of the fracturing operation plan does not meet the requirements, the predicted fracturing operation plan is adjusted, and then the production capacity data of the adjusted fracturing operation plan is predicted until the predicted production capacity data meets the requirements.

8. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the method for evaluating the productivity of marine tight sandstone gas wells based on reservoir stimulation intensity, as described in any one of claims 1-7.

Images