Shale gas well flowback system optimization method based on distributed optical fiber data mining

Abstract

The invention discloses a shale gas well flowback system optimization method based on distributed optical fiber data mining, which combines with the current advanced distributed optical fiber (DTA / DAS) monitoring technology to'sense 'underground temperature and'listen' underground sound, and the optical fiber monitoring result can quantitatively evaluate the shale gas well fracturing modification effect, so that the shale gas well flowback system optimization method has the advantages that the shale gas well flowback system optimization method is high in practicability. And judging the gas production and liquid production profile distribution effect of the whole well section of the gas well. By mining optical fiber data in a gas well flowback stage, the gas well fracturing cluster efficiency and the artificial fracture flow conductivity are comprehensively considered, the maximum flowback oil nozzle of the gas well is optimized, and gas well flowback system optimization work is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and in particular to a shale gas well flowback system optimization method based on distributed optical fiber data mining. Background Art

[0002] After shale gas well fracturing is completed, it usually goes through two stages: flowback and production. Under existing technical conditions, how to design and optimize the system during the flowback stage of shale gas wells has direct guiding significance for maximizing the maintenance of gas well production capacity, ensuring gas well production effects, and supporting the formulation of gas reservoir development plans.

[0003] The traditional shale gas well flowback system mainly relies on setting a nozzle (throttle valve) at the wellhead, and obtaining the gas well production capacity by adjusting the nozzle step by step from small to large. During the "Thirteenth Five-Year Plan" period, shale gas wells usually adopt the pressure relief method to organize production. Shale gas operating companies generally pursue high test production and high initial cumulative gas production by adopting a large nozzle flowback system in the early stage. However, a large number of pilot test wells in the shale gas block of CNPC have shown that the use of a large nozzle rapid flowback system will cause irreversible permanent damage to the conductivity of artificial fractures and affect the production effect of gas wells. With the advancement of process technology and the requirements for block efficiency and production, shale gas has gradually moved towards refined development and management, and the optimization of the flowback system is also a key guarantee for the stable production of gas wells. In the past ten years, North American shale gas has gradually transitioned from the initial pressure relief flowback to the current fine pressure control flowback. Especially in deep high-pressure shale gas reservoirs, the traditional flowback system is no longer applicable. Therefore, the continuous optimization of the flowback system according to the characteristics of gas wells under geological and engineering conditions is also the main reason for the continuous increase in its single well EUR.

[0004] At present, a large number of shale gas wells show the characteristics of rapid decline in production and pressure in the initial stage of production, and large downhole pressure difference. In addition, the production process continues to produce sand and cracks close, resulting in a decrease in production capacity. The traditional flowback system can no longer meet the demand. It is urgent to carry out fine nozzle system regulation and optimization in the flowback stage of gas wells to maintain as much bottom hole pressure as possible, thereby delaying crack closure, ensuring long-term stable flow of gas, and achieving the basic guarantee for shale gas reserve increase and production. Summary of the invention

[0005] The purpose of the present invention is to provide a shale gas well flowback system optimization method based on distributed optical fiber data mining to address the above-mentioned shortcomings, thereby solving the problem of insufficient reasonable choke control methods in the existing shale gas horizontal well opening and flowback stage; this method is to reasonably control the choke in the flowback stage of shale gas wells, optimize the flowback system, maintain formation energy to the greatest extent, ensure the production effect of gas wells, and support the scientific preparation of development plans.

[0006] First of all, explain the relevant keywords:

[0007] EUR (The Estimated Ultimate Recovery): A shale gas well production effect evaluation indicator. In China, it generally refers to the cumulative shale gas production within 20 years, in units of 100 million cubic meters.

[0008] The present invention is achieved through the following scheme:

[0009] A shale gas well flowback system optimization method based on distributed optical fiber data mining includes the following steps:

[0010] It includes the following steps:

[0011] S1: Based on the distributed optical fiber monitoring data, the gas and liquid production distribution of each cluster in each fracturing section under different nozzles of the shale gas well is obtained, and the production profile contribution cluster efficiency C is calculated. eff ; Record C under different nozzles eff Numerical values, draw the curve of cluster efficiency change under different nozzles;

[0012] S2: Based on the gas well seepage model, the comprehensive evaluation parameters of shale formations and fractures are obtained by normalizing the pressure-time relationship curve. Record different oil nozzles Numerical values, draw the curve of the conductivity change of artificial fractures under different oil nozzles;

[0013] S3: Combining the data of production profile cluster efficiency change and artificial fracture conductivity change under different nozzles, by drawing the correlation curve between artificial fracture conductivity and production profile contribution cluster efficiency under different nozzles, the nozzle corresponding to the intersection of the curves is the maximum reasonable nozzle of the gas well.

[0014] In S1, according to the optical fiber monitoring data mining and analysis method in the shale gas well flowback stage, the production capacity contribution evaluation index under different nozzles in the shale gas well flowback stage is established: the production profile contribution cluster efficiency C eff .

[0015] The specific steps are as follows:

[0016] S11: The downhole temperature and acoustic wave signals under different nozzle systems during the flowback phase are monitored by distributed optical fiber to obtain the gas and liquid production contribution of each cluster in each fracturing section under different nozzles of the gas well; the production profile distribution of each cluster in the whole well section is obtained by optical fiber monitoring interpretation under each nozzle system, and the cluster efficiency of production profile contribution is calculated;

[0017] S12: As the nozzle size increases step by step, the efficiency of the clusters contributed by the production profile increases gradually; a curve of the change of the production profile cluster efficiency under different nozzles is drawn.

[0018] In S2, the comprehensive evaluation parameters of shale formations and fractures are Establish a method for evaluating the conductivity of artificial fractures in shale gas wells, and realize the evaluation of the conductivity of artificial fractures under different oil nozzles;

[0019] According to the gas well seepage model, the comprehensive evaluation parameters of shale formations and fractures are obtained. This parameter includes both formation and hydraulic fracturing fracture characteristic parameters. Based on this, a quantitative evaluation method for artificial fracture conductivity is established, which enables the evaluation of the degree of damage to artificial fractures under different flowback systems and guides the oil nozzle control of gas wells during the flowback stage.

[0020] The specific steps are as follows:

[0021] S21: According to the flow characteristics of shale gas wells during the gas flowback stage, the unstable seepage stage with a slope of -0.5 is obtained by normalizing the double logarithmic curve of production and material balance time through pressure; The double logarithmic curve of

[0022] S22: Based on the slope of the normalized pressure characteristic curve of production under different nozzles during the gas well flowback stage, the comprehensive evaluation parameters of shale formations and fractures are obtained by relevant formulas

[0023] S23: Draw a curve of the change in conductivity of artificial fractures under different nozzles.

[0024] In S3, the specific steps are as follows:

[0025] S31: Plot the correlation curve between the conductivity of artificial fractures and the efficiency of production profile contribution clusters under different nozzles;

[0026] S32: Obtain the intersection of the curves, identify the corresponding nozzle as the maximum reasonable nozzle of the gas well, and guide the optimization of the gas well flowback system.

[0027] Production profile contribution cluster efficiency:

[0028] C eff =C con / C tol *100

[0029] Where: C eff represents the cluster efficiency of shale gas well production profile contribution; C con Indicates the number of clusters that contribute to gas or liquid production in shale gas wells; C tol Represents the total number of fracturing clusters in shale gas wells.

[0030] The relevant formulas in S22 include:

[0031]

[0032]

[0033] In the formula, m(p i ) and m(p wf ) is the pseudo pressure; q g is the gas well production; t is the production time; b is the vertical intercept of the double logarithmic curve of production normalized pressure and time; is the slope of the double logarithmic curve of production normalized pressure and time; φ is the formation porosity; μ is the fluid viscosity; c t is the comprehensive compression coefficient; A c is the contact area between matrix and fracture; T is the formation temperature.

[0034] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0035] 1. This solution innovatively uses distributed optical fiber monitoring data to obtain the gas and liquid production contribution cluster efficiency of gas wells under different nozzle systems, combined with the shale gas well volume fracturing model A comprehensive parameter calculation method was used to establish a scientific and reasonable maximum oil nozzle control system for the flowback stage of shale gas wells, which provided technical support for the optimization research of the flowback system, maintained the formation energy to the greatest extent, and ensured the production effect of the gas well.

[0036] 2. The present invention breaks through the limitation that a reasonable choke system has not yet been formed in the current shale gas well flowback stage. By combining optical fiber monitoring data mining with gas reservoir engineering methods, it provides new ideas and new ways for optimizing the flowback system, which can be extended and applied to the choke control in the flowback stage of shale gas wells in different blocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is the curve diagram of the change of production profile contribution cluster efficiency under different oil nozzles;

[0038] Figure 2 This is the flow pattern judgment diagram of shale gas well flowback stage;

[0039] Figure 3 For different oil nozzles Change curve chart;

[0040] Figure 4 This is the curve diagram of the conductivity change of artificial fractures under different oil nozzles;

[0041] Figure 5 This is the relationship diagram between the conductivity of artificial fractures and the efficiency of production profile contribution clusters under different oil nozzles;

[0042] Figure 6 It is a comprehensive diagram of the percentage of gas and water production in each cluster of gas wells at 7mm nozzle system;

[0043] Figure 7 It is a comprehensive diagram of the percentage of gas and water production in each cluster of gas wells at the 8mm nozzle system;

[0044] Figure 8 The curve diagram of the cluster efficiency change of gas well production profile contribution under different nozzle systems;

[0045] Fig. 9 This is a curve diagram of the conductivity change of artificial fractures in gas wells under different nozzles;

[0046] Fig.10 This is the relationship diagram between the conductivity of artificial fractures and the efficiency of production profile contribution clusters in gas wells under different nozzles;

[0047] Fig.11 Schematic diagram of the overall process of this method. DETAILED DESCRIPTION

[0048] All features disclosed in this specification, or steps in all methods or processes disclosed, except mutually exclusive features and / or steps, can be combined in any manner.

[0049] Any feature disclosed in this specification (including any additional claims and abstract), unless otherwise stated, may be replaced by other equivalent or alternative features having similar purposes. That is, unless otherwise stated, each feature is only an example of a series of equivalent or similar features.

[0050] In the description of the present invention, it is necessary to understand that the directions or positional relationships indicated by the terms "up", "down", "left", "right", etc. are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0051] In addition, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features.

[0052] Example 1

[0053] The present invention provides a technical solution:

[0054] The optimization method of shale gas well flowback system based on distributed optical fiber data mining has the following steps:

[0055] S1: Based on the distributed optical fiber (DTS / DAS) monitoring data, the gas and liquid production distribution of each cluster in each fracturing section of the shale gas well under different oil nozzles is obtained, and the production profile contribution cluster efficiency C is calculated eff (The number of clusters that contribute to gas or liquid production (C con ) than the total number of fracturing clusters (Ctol )); record C under different nozzles eff The numerical value is used to draw the curve of cluster efficiency change under different nozzles.

[0056] S2: According to the gas well seepage model, the nozzle is continuously enlarged during the gas well flowback period, and the production pressure difference increases. By normalizing the pressure-time relationship curve, the comprehensive evaluation parameters of shale formations and fractures are obtained. Its value reflects the change of artificial fracture conductivity; record the Numerical values, draw the curve of the conductivity change of artificial fractures under different oil nozzles;

[0057] S3: Combining the data of production profile cluster efficiency change and artificial fracture conductivity change under different nozzles, by drawing the correlation curve between artificial fracture conductivity and production profile contribution cluster efficiency under different nozzles, the nozzle corresponding to the intersection of the curves is the maximum reasonable nozzle of the gas well.

[0058] In S1, according to the optical fiber monitoring data mining and analysis method in the shale gas well flowback stage, the production capacity contribution evaluation index under different nozzles in the shale gas well flowback stage is established: the production profile contribution cluster efficiency (C eff ).

[0059] The specific steps are as follows:

[0060] S11: The downhole temperature and acoustic wave signals under different nozzle systems during the flowback phase are monitored by distributed optical fiber (DTS / DAS), and the gas and liquid production contribution of each cluster in each fracturing section under different nozzles of the gas well is obtained. The production profile contribution has a positive correlation with the fracturing effect and the physical properties of the production layer, and can reflect the production capacity contribution of the entire well section of the gas well; the production profile distribution of each cluster in the entire well section obtained by optical fiber monitoring interpretation under each nozzle system is calculated, and the production profile contribution cluster efficiency (the number of clusters that contribute to gas or liquid production (C con ) than the total number of fracturing clusters (C tol ));

[0061] C eff =C con / C tol *100 (1)

[0062] Where: C eff It represents the cluster efficiency of shale gas well production profile contribution, in "%";

[0063] C con It indicates the number of clusters that contribute to gas or liquid production in shale gas wells, which is a number;

[0064] C tol It represents the total number of fracturing clusters in shale gas wells, which is a number.

[0065] S12: As the nozzle increases step by step, the efficiency of the cluster produced by the production profile gradually increases; the curve of the efficiency of the cluster produced by the production profile under different nozzles is drawn, such as Figure 1 shown.

[0066] In S2, the comprehensive evaluation parameters of shale formations and fractures are Establish a method for evaluating the conductivity of artificial fractures in shale gas wells, and realize the evaluation of the conductivity of artificial fractures under different oil nozzles;

[0067] During the flowback period, the nozzle of a shale gas well continues to increase. During the process of nozzle increase, the production pressure difference increases, resulting in continuous closure of fractures and a decrease in gas well production capacity. Therefore, combined with the gas reservoir seepage theory and considering that the natural fractures in the shale reservoir are relatively developed, the comprehensive evaluation parameters of shale formations and fractures are obtained according to the gas well seepage model. This parameter includes both formation and hydraulic fracturing fracture characteristic parameters, reflecting the conductivity of artificial fractures in shale gas wells. Based on this, a quantitative evaluation method for the conductivity of artificial fractures is established, which realizes the evaluation of the damage degree of artificial fractures under different flowback systems and can guide the oil nozzle regulation of gas wells during the flowback stage.

[0068] The specific steps are as follows:

[0069] S21: According to the flow characteristics of shale gas wells during the gas flowback stage, the unstable seepage stage with a slope of -0.5 is obtained by pressure-normalizing the double logarithmic curve of production and material balance time, such as Figure 2 As shown; the production normalization pressure and The double logarithmic curve of

[0070] S22: Based on the slope of the normalized pressure characteristic curve of production under different nozzles during the gas well flowback stage, the comprehensive evaluation parameters of shale formations and fractures can be obtained by formulas (2) and (3): like Figure 3 As shown;

[0071]

[0072]

[0073] In formulas (2) and (3), m(p i ) and m(p wf ) is the pseudo pressure, dimensionless; q g is the gas well production, m 3 ; t is the production time, d; b is the vertical intercept of the double logarithmic curve of normalized pressure and time; is the slope of the double logarithmic curve of production normalized pressure and time; φ is the formation porosity, a decimal; μ is the fluid viscosity, cP; c t is the comprehensive compression coefficient, psi-1 ; A c is the contact area between matrix and crack, m 2 ; T is the formation temperature, K.

[0074] S23: Draw the curve of the conductivity change of artificial fractures under different nozzles, such as Figure 4 shown.

[0075] In S3, the influence of the production profile contribution cluster efficiency and the change of artificial fracture conductivity on the production effect of shale gas wells is comprehensively considered, wherein the production profile contribution cluster efficiency is positively correlated with the choke system, and the artificial fracture conductivity is negatively correlated with the choke system, and the maximum reasonable choke of the shale gas well is selected; the specific steps are as follows:

[0076] S31: Plot the correlation curve between the conductivity of artificial fractures and the efficiency of production profile contribution clusters under different nozzles;

[0077] S32: Obtain the intersection of the curves, identify the corresponding nozzle as the maximum reasonable nozzle of the gas well, and guide the optimization of the gas well flowback system.

[0078] Example 2

[0079] This embodiment uses the method of the present invention to carry out the work of the shale gas well flowback system and guide the maximum reasonable oil nozzle control in the shale gas well flowback stage.

[0080] The length of the gas well fracturing section in this example is 2424 meters. The gas and liquid production profile distribution and proportion of each cluster in each fracturing section under the two nozzle systems of 7mm and 8mm are monitored by distributed optical fiber. Figure 6 and Figure 7 As shown;

[0081] (1) Calculate the production profile contribution cluster efficiency C under two nozzle systems eff , draw the curve of the efficiency change of the production cluster under different nozzles, such as Figure 8 shown.

[0082] (2) Obtain comprehensive evaluation parameters of shale formations and fractures by normalizing the pressure-time relationship curve of the well Record different oil nozzles The value is plotted to draw the curve of the conductivity of artificial fractures under different nozzles, such as Fig. 9 shown.

[0083] (3) According to the above (1) and (2), the cluster efficiency of different nozzle systems and the conductivity of artificial fractures are obtained. The value is plotted, and the trend lines of the conductivity and cluster efficiency of the artificial fractures under different nozzles are plotted. The optimal and maximum reasonable nozzle for the well is 6.5 mm to 7 mm. Fig.10The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Shale gas well flowback system optimization method based on distributed optical fiber data mining, Features: It includes the following steps: S1: Based on the distributed optical fiber monitoring data, the gas and liquid production distribution of each cluster in each fracturing section under different nozzles of the shale gas well is obtained, and the production profile contribution cluster efficiency C is calculated. eff ; Record C under different nozzles eff Numerical values, draw the curve of cluster efficiency change under different nozzles; S2: Based on the gas well seepage model, the comprehensive evaluation parameters of shale formations and fractures are obtained by normalizing the pressure-time relationship curve. Record different oil nozzles Numerical values, draw the curve of the conductivity change of artificial fractures under different oil nozzles; S3: Combining the data of production profile cluster efficiency change and artificial fracture conductivity change under different nozzles, by drawing the correlation curve between artificial fracture conductivity and production profile contribution cluster efficiency under different nozzles, the nozzle corresponding to the intersection of the curves is the maximum reasonable nozzle of the gas well.

2. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 1, Features: In S1, according to the optical fiber monitoring data mining and analysis method in the shale gas well flowback stage, the production capacity contribution evaluation index under different nozzles in the shale gas well flowback stage is established: the production profile contribution cluster efficiency C eff .

3. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 2, Features: The specific steps are as follows: S11: The downhole temperature and acoustic wave signals under different nozzle systems during the flowback phase are monitored by distributed optical fiber to obtain the gas and liquid production contribution of each cluster in each fracturing stage under different nozzles of the gas well; The production profile distribution of each cluster in the whole well section obtained by optical fiber monitoring interpretation under each level of nozzle system is used to calculate the cluster efficiency of production profile contribution; S12: As the nozzle size increases step by step, the efficiency of the clusters contributed by the production profile increases gradually; a curve of the change of the production profile cluster efficiency under different nozzles is drawn.

4. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 2, Features: In S2, the comprehensive evaluation parameters of shale formations and fractures are Establish a method for evaluating the conductivity of artificial fractures in shale gas wells, and realize the evaluation of the conductivity of artificial fractures under different oil nozzles; According to the gas well seepage model, the comprehensive evaluation parameters of shale formations and fractures are obtained. This parameter includes both formation and hydraulic fracturing fracture characteristic parameters. Based on this, a quantitative evaluation method for artificial fracture conductivity is established, which enables the evaluation of the degree of damage to artificial fractures under different flowback systems and guides the oil nozzle control of gas wells during the flowback stage.

5. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 4, Features: The specific steps are as follows: S21: According to the flow characteristics of shale gas wells during the gas flowback stage, the unstable seepage stage with a slope of -0.5 is obtained by pressure-normalizing the double logarithmic curve of production and material balance time; Through this stage, the data output regularization pressure and The double logarithmic curve of S22: Based on the slope of the normalized pressure characteristic curve of production under different nozzles during the gas well flowback stage, the comprehensive evaluation parameters of shale formations and fractures are obtained by relevant formulas S23: Draw a curve of the change in conductivity of artificial fractures under different nozzles.

6. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 4, Features: In S3, the specific steps are as follows: S31: Plot the correlation curve between the conductivity of artificial fractures and the efficiency of production profile contribution clusters under different nozzles; S32: Obtain the intersection of the curves, identify the corresponding nozzle as the maximum reasonable nozzle of the gas well, and guide the optimization of the gas well flowback system.

7. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 3, Features: The production profile contribution cluster efficiency is: C eff =C con / C tol *100 Where: C eff represents the cluster efficiency of shale gas well production profile contribution; C con Indicates the number of clusters that contribute to gas or liquid production in shale gas wells; C tol Represents the total number of fracturing clusters in shale gas wells.

8. The method for optimizing the shale gas well flowback system based on distributed optical fiber data mining according to claim 4, Features: The relevant formulas in S22 include: In the formula, m(p i ) and m(p wf ) is the pseudo pressure; q g is the gas well production; t is the production time, d; b is the vertical intercept of the double logarithmic curve of production normalized pressure and time; is the slope of the double logarithmic curve of production normalized pressure and time; φ is the formation porosity; μ is the fluid viscosity; c t is the comprehensive compression coefficient; A c is the contact area between matrix and fracture; T is the formation temperature.

Images