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

Abstract

The application discloses a shale gas well flowback system optimization method based on distributed optical fiber data mining, and combines advanced distributed optical fiber (DTA / DAS) monitoring technology to "perceive" downhole temperature and "listen" to downhole sound; optical fiber monitoring results can quantitatively evaluate the fracturing reconstruction effect of a shale gas well and judge the gas production and liquid production profile distribution effect of the whole well section of the gas well. Through optical fiber data mining in the gas well flowback stage, the fracturing cluster efficiency and the artificial fracture conductivity are comprehensively considered to optimize the maximum flowback choke of the gas well, so that the gas well flowback system optimization work is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development, and particularly relates to a shale gas well flowback system optimization method based on distributed optical fiber data mining. BACKGROUND

[0002] After shale gas well fracturing, it usually goes through the stages of flowback and production. Under the existing technical conditions, how to design and optimize the system during the shale gas well flowback stage has direct guiding significance for maintaining the gas well productivity to the greatest extent, ensuring the gas well production effect, and supporting the preparation of gas reservoir development plan.

[0003] The traditional shale gas well flowback system mainly relies on setting a choke valve at the wellhead, and gradually adjusting the choke valve from small to large to obtain the gas well productivity. During the period of the 13th Five-Year Plan, shale gas wells usually use pressure relief to organize production. Shale gas operating companies generally pursue high test production and high initial cumulative gas production by using a large choke valve flowback system in the early stage. However, a large number of pilot test wells in shale gas blocks of PetroChina have shown that the system of using a large choke valve for rapid flowback can cause irreversible permanent damage to the artificial fracture conductivity, affecting the production effect of the gas well. With the progress of technology and the requirement of block benefit production, shale gas gradually moves towards fine development and management, and the optimization of the flowback stage 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 initial pressure relief flowback to current fine pressure control flowback. In particular, in deep high-pressure shale gas reservoirs, the traditional flowback system has become unsuitable, so the optimization of the flowback system according to the characteristics of the gas well under the geological engineering conditions is also the main reason for the continuous increase of the EUR of single well.

[0004] At present, a large number of shale gas wells show the characteristics of rapid production and pressure decline in the early stage of production, large downhole pressure difference, and continuous sand production during the production process. Fracture closure causes productivity to decline, and the traditional flowback system cannot meet the demand. It is urgent to carry out fine choke valve system regulation and optimization during the gas well flowback stage to maintain as much bottom hole pressure as possible, thereby delaying fracture closure, ensuring long-term stable flow of gas, and realizing the basic guarantee of shale gas storage and production increase. SUMMARY

[0005] The present application aims to provide a shale gas well flowback system optimization method based on distributed optical fiber data mining, which solves the problem of the lack of reasonable choke valve regulation method during the shale gas horizontal well opening flowback stage, and optimizes the flowback system to maintain the formation energy to the greatest extent, ensure the gas well production effect, and support the scientific preparation of the development plan.

[0006] First, the relevant keywords are explained:

[0007] EUR (The Estimated Ultimate Recovery): A shale gas well production effect evaluation index, generally refers to the cumulative shale gas production in 20 years in China, unit: 100 million cubic meters;

[0008] The present application is realized by the following scheme:

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

[0010] It comprises the following steps:

[0011] S1: According to the distributed optical fiber monitoring data, the gas and liquid production distribution of each cluster of each fractured section under different chokes of the shale gas well is obtained, and the production profile contribution cluster efficiency C is calculated eff ; The C eff values under different chokes are recorded, and the production profile cluster efficiency change curve graph under different chokes is drawn;

[0012] S2: According to the gas well seepage model, the shale formation and fracture comprehensive evaluation parameters are obtained by normalizing the pressure-time relationship curve The values under different chokes are recorded, and the artificial fracture conductivity change curve graph under different chokes is drawn;

[0013] S3: Combined with the production profile cluster efficiency change and artificial fracture conductivity change data under different chokes, the artificial fracture conductivity and production profile contribution cluster efficiency correlation curve under different chokes is drawn, and the choke corresponding to the intersection point of the curve is obtained as the maximum reasonable choke of the gas well.

[0014] In the S1, according to the optical fiber monitoring data mining analysis method in the shale gas well flowback stage, the production capacity contribution evaluation index under different chokes 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: Through the distributed optical fiber monitoring of the downhole temperature and acoustic signal under different choke systems in the flowback stage, the gas and liquid production contribution of each cluster of each fractured section under different chokes of the gas well is obtained; the production profile distribution of each cluster in the whole well section under each level of choke system is obtained through the optical fiber monitoring interpretation, and the production profile contribution cluster efficiency is calculated;

[0017] S12: With the gradual increase of the choke, the production profile contribution cluster efficiency gradually increases; the production profile cluster efficiency change curve graph under different chokes is drawn.

[0018] In the S2, according to the shale formation and fracture comprehensive evaluation parameters ​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 nozzles;

[0019] Based on the gas well seepage model, the comprehensive evaluation parameters of shale formation and fractures are obtained. This parameter includes both formation and hydraulic fracturing fracture characteristic parameters; based on this, a quantitative evaluation method for the conductivity of artificial fractures was established, realizing the evaluation of the degree of damage to artificial fractures under different flowback regimes, and guiding the control of gas well nozzles during the flowback stage.

[0020] The specific steps are as follows:

[0021] S21: Based on the flow characteristics of the shale gas well flowback stage, an unsteady flow stage with a slope of -0.5 is obtained through a double logarithmic curve of pressure-normalized production versus mass balance time; the production-normalized pressure and mass balance time data from this stage are then used to determine the flow characteristics of the flow. From the double logarithmic curve, we can obtain the slope of the straight line segment.

[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 formation and fractures are obtained using relevant formulas.

[0023] S23: Plot the curves showing the changes in the flow-guiding capacity of artificial cracks under different nozzle types.

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

[0025] S31: Plot the correlation curves between the flow conduction capacity of artificial fractures and the efficiency of the generation and sprue contribution clusters under different nozzle conditions;

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

[0027] Production contribution cluster efficiency:

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

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

[0030] The relevant formulas in S22 include:

[0031]

[0032]

[0033] In the formula, m(p) i ) and m(p wf ) represents the simulated pressure; q g t represents the gas well production rate; b represents the production time; and b represents the ordinate of the logarithmic curve of normalized pressure versus time. φ represents the slope of the logarithmic curve of pressure versus time for normalized production; μ represents formation porosity; c represents fluid viscosity; t The overall compression factor; A c The contact area between the matrix and the fracture is T; the formation temperature is T.

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

[0035] 1. This innovative approach utilizes distributed fiber optic monitoring data to acquire the gas and fluid production contribution cluster efficiency of gas wells under different nozzle regimes, combined with the volumetric fracturing model of shale gas wells. Based on the comprehensive parameter calculation method, a scientific and reasonable maximum nozzle control system for the flowback stage of shale gas wells was established, providing technical support for the optimization research of the flowback system, maximizing the maintenance of formation energy, and ensuring the production effect of gas wells.

[0036] 2. This invention overcomes the limitation that a reasonable nozzle system has not yet been formed in the flowback stage of shale gas wells. By combining fiber optic monitoring data mining and reservoir engineering methods, it provides new ideas and approaches for optimizing the flowback system and can be extended to nozzle control in the flowback stage of shale gas wells in different blocks. Attached Figure Description

[0037] Figure 1 Curves showing the variation of cluster efficiency contribution to production profile under different nozzles;

[0038] Figure 2 This is a flow regime determination diagram for the flowback stage of shale gas wells.

[0039] Figure 3 For different nozzles Change curve graph;

[0040] Figure 4 A graph showing the variation of the flow-guiding capacity of artificial cracks under different nozzle types;

[0041] Figure 5 A graph showing the relationship between the flow conduction capacity of artificial fractures and the efficiency of cluster contribution to blasting under different nozzle types;

[0042] Figure 6 A composite chart showing the percentage of gas and water production in each cluster of gas wells under a 7mm nozzle system;

[0043] Figure 7 A composite chart showing the percentage of gas and water production in each cluster of gas wells under an 8mm nozzle system;

[0044] Figure 8 A graph showing the variation of cluster efficiency contribution to gas well production profile under different nozzle systems;

[0045] Figure 9 This is a graph showing the variation of the conductivity of artificial fractures in a gas well under different nozzle conditions.

[0046] Figure 10 A graph showing the relationship between the conductivity of artificial fractures and the efficiency of production profile contribution clusters in gas wells under different nozzle conditions;

[0047] Figure 11 This is a flowchart illustrating the entire process of this method. Detailed Implementation

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

[0049] Any feature disclosed in this specification (including any appended claims and abstract) may be replaced by other equivalent or similar features, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.

[0050] In the description of this invention, it should be understood that the terms "upper", "lower", "left", "right", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

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

[0052] Example 1

[0053] This invention provides a technical solution:

[0054] The optimization method for shale gas well flowback regime based on distributed optical fiber data mining includes the following steps:

[0055] S1: Based on the gas and fluid production distribution of each cluster in each fracturing section under different nozzles in shale gas wells obtained from distributed optical fiber (DTS / DAS) monitoring data, the cluster efficiency C contributing to the production profile is calculated. eff (Number of clusters contributing to gas or liquid production (C)) con ) compared to the total number of fracturing clusters (C)tol Record C under different nozzles eff Numerical values ​​were used to plot the efficiency variation curves of different nozzle types for different production clusters.

[0056] S2: Based on the gas well seepage model, the nozzle size continuously increases during the gas well flowback period, leading to an increase in production pressure differential. By normalizing the pressure-time relationship curve, comprehensive evaluation parameters for the shale formation and fractures are obtained. Its value reflects the change in the conductivity of the artificial fracture; records are kept for different nozzles. Numerical values ​​were used to plot the variation curves of the artificial fracture conductivity under different nozzles.

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

[0058] In S1, based on the fiber optic monitoring data mining and analysis method for the shale gas well flowback stage, an evaluation index for the production capacity contribution under different nozzles during 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: By monitoring downhole temperature and acoustic signals under different nozzle regimes during the flowback stage using distributed optical fiber (DTS / DAS), the gas and fluid production contributions of each cluster in each fracturing section of the gas well under different nozzle regimes are obtained. The production profile contribution is positively correlated with the fracturing effect and formation properties, and can reflect the overall production capacity contribution of the gas well. The production profile distribution of each cluster in the entire well section obtained by optical fiber monitoring under each nozzle regime is interpreted, and the production profile contribution cluster efficiency (number of clusters contributing to gas or fluid production (C)) is calculated. con ) compared to the total number of fracturing clusters (C) tol ));

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

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

[0063] C con This indicates the number of clusters that contribute to gas or liquid production in shale gas wells; it is a number.

[0064] C tol This represents the total number of fracturing clusters in a shale gas well; it is a number.

[0065] S12: As the nozzle size increases, the efficiency of the formation and profiling clusters gradually increases; curves showing the change in formation and profiling cluster efficiency under different nozzle sizes are plotted, as shown below. Figure 1 As shown.

[0066] In S2, based on the comprehensive evaluation parameters of shale formations and fractures... 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 nozzles;

[0067] During the flowback period of shale gas wells, the nozzle size continuously increases. This increase in nozzle size leads to a rise in production pressure differential, causing continuous fracture closure and a decrease in well productivity. Therefore, based on gas reservoir seepage theory and considering the well-developed natural fractures in shale reservoirs, comprehensive evaluation parameters for the shale formation and fractures are determined using a 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 was established, enabling the evaluation of the degree of damage to artificial fractures under different flowback regimes, which can guide the control of gas well nozzles during the flowback stage.

[0068] The specific steps are as follows:

[0069] S21: Based on the flow characteristics of the flowback stage of shale gas wells, an unsteady flow stage with a slope of -0.5 is obtained through a double logarithmic curve of pressure-normalized production versus material balance time. Figure 2 As shown; the pressure of production normalization through this stage of data and From the double logarithmic curve, we can obtain the slope of the straight line segment.

[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 formation and fractures can be obtained from formulas (2) and (3). like Figure 3 As shown;

[0071]

[0072]

[0073] In formulas (2) and (3), m(p) i ) and m(p wf ) represents the pseudo-pressure, dimensionless; q g For gas well production, m 3 ; t is the production time, d; b is the ordinate of the logarithmic curve of output normalization pressure versus time; φ represents the slope of the normalized pressure-time double logarithmic curve for production; φ represents formation porosity, decimal; μ represents fluid viscosity, cP; c t The overall compression ratio, psi-1 A c The contact area between the matrix and the crack is m. 2 T represents the formation temperature, in K.

[0074] S23: Plot the curves showing the variation of the flow conductivity of the artificial fracture under different nozzle conditions, such as... Figure 4 As shown.

[0075] In step S3, the impact of changes in the efficiency of the formation contribution cluster and the conductivity of artificial fractures on the production effect of shale gas wells is comprehensively considered. The efficiency of the formation contribution cluster is positively correlated with the nozzle system, while the conductivity of artificial fractures is negatively correlated with the nozzle system. The optimal maximum reasonable nozzle for shale gas wells is then selected. The specific steps are as follows:

[0076] S31: Plot the correlation curves between the flow conduction capacity of artificial fractures and the efficiency of the generation and sprue contribution clusters under different nozzle conditions;

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

[0078] Example 2

[0079] This embodiment utilizes the method of the present invention to carry out the shale gas well flowback system operation, and guides the maximum reasonable nozzle control during the shale gas well flowback stage.

[0080] In this example, the length of the fracturing section in the gas well is 2424 meters. Distributed optical fiber was used to monitor the gas and fluid production profiles and proportions of each cluster in each fracturing section under both 7mm and 8mm nozzle conditions. Figure 6 and Figure 7 As shown;

[0081] (1) Calculate the production and profile contribution cluster efficiency C under the two nozzle systems. eff Plot the production efficiency curves for different nozzle types, such as... Figure 8 As shown.

[0082] (2) By using the well-regulated pressure-time relationship curve, the comprehensive evaluation parameters of shale formation and fractures are obtained. Record the effects of different nozzles Numerical values ​​were used to plot the variation curves of the artificial fracture conductivity under different nozzle types, such as... Figure 9 As shown.

[0083] (3) Based on the different nozzle systems obtained in (1) and (2) above, the production profile contribution efficiency and the ability of artificial fractures to conduct water are determined. Values ​​were plotted to show the trends of artificial fracture conductivity and cluster efficiency under different nozzle sizes in the well. The optimal and maximum reasonable nozzle size for the well was determined to be 6.5mm to 7mm. Figure 10As shown. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the flowback regime of shale gas wells based on distributed optical fiber data mining, characterized by: It includes the following steps: S1: Based on the gas and fluid production distribution of each cluster in each fracturing section under different nozzles in shale gas wells obtained from distributed optical fiber monitoring data, the cluster efficiency C contributing to the production profile is calculated. eff Record C under different nozzles eff Numerical values ​​were used to plot the efficiency variation curves of the production profile contribution clusters under different nozzles; The efficiency of the secundum contribution cluster is: C eff =C con / C tol *100 Where: C eff Indicates the cluster efficiency of shale gas well production profile contribution; C con This indicates the number of clusters in shale gas wells that contribute to gas or liquid production; C tol This indicates the total number of fracturing clusters in a shale gas well; S2: Based on the gas well seepage model, the comprehensive evaluation parameters of shale formation and fractures are obtained through the normalized pressure-time relationship curve. Record the effects of different nozzles Numerical values ​​were used to plot the variation curves of the artificial fracture conductivity under different nozzles. The specific steps are as follows: S21: Based on the flow characteristics of the shale gas well flowback stage, an unsteady flow stage with a slope of -0.5 is obtained through a double logarithmic curve of pressure-normalized production versus mass balance time; the production-normalized pressure and mass balance time data from this stage are then used to determine the flow characteristics of the flow. From the double logarithmic curve, we can obtain the slope of the straight line segment. ; 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 formation and fractures are obtained using relevant formulas. ; The relevant formulas in S22 include: ； In the formula, and To simulate pressure; For gas well production; 'b' represents the production time; 'b' represents the ordinate of the logarithmic curve of output normalization pressure versus time. The slope of the logarithmic curve of production normalization pressure versus time; Formation porosity; For fluid viscosity; This is the overall compression coefficient; The contact area between the matrix and the fracture; T is the formation temperature; S23: Plot the curves showing the variation of the flow-guiding capacity of artificial cracks under different nozzle types; S3: Combining the data on the changes in the efficiency of the production and profile contribution clusters and the changes in the conductivity of artificial fractures under different nozzles, the correlation curves between the conductivity of artificial fractures and the efficiency of the production and profile contribution clusters under different nozzles are plotted, and the nozzle corresponding to the intersection of the curves is the maximum reasonable nozzle for the gas well.

2. The method for optimizing shale gas well flowback regimes based on distributed optical fiber data mining as described in claim 1, characterized in that: In S1, based on the fiber optic monitoring data mining and analysis method for the shale gas well flowback stage, an evaluation index for the production capacity contribution under different nozzles during the shale gas well flowback stage is established: the production profile contribution cluster efficiency C. eff .

3. The method for optimizing shale gas well flowback regimes based on distributed optical fiber data mining as described in claim 2, characterized in that: The specific steps are as follows: S11: By monitoring downhole temperature and acoustic signals under different nozzle regimes during the flowback stage using distributed optical fiber, the gas and fluid production contributions of each cluster in each fracturing section under different nozzle regimes in the gas well can be obtained. The distribution of each cluster of occurrence profiles in the entire well section was obtained by fiber optic monitoring and interpretation under each level of nozzle system, and the cluster efficiency contributing to the occurrence profile was calculated. S12: As the nozzle size increases, the efficiency of the production and profiling clusters gradually increases; curves showing the change in production and profiling cluster efficiency under different nozzle sizes are plotted.

4. The method for optimizing shale gas well flowback regimes based on distributed optical fiber data mining as described in claim 2, characterized in that: In S2, based on the comprehensive evaluation parameters of shale formations and fractures... 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 nozzles; Based on the gas well seepage model, the comprehensive evaluation parameters of shale formation and fractures are obtained. This parameter includes both formation and hydraulic fracturing fracture characteristic parameters; based on this, a quantitative evaluation method for the conductivity of artificial fractures was established, realizing the evaluation of the degree of damage to artificial fractures under different flowback regimes, and guiding the control of gas well nozzles during the flowback stage.

5. The method for optimizing shale gas well flowback regimes based on distributed optical fiber data mining as described in claim 4, characterized in that: In step S3, the specific steps are as follows: S31: Plot the correlation curves between the flow conduction capacity of artificial fractures and the efficiency of the generation and sprue contribution clusters under different nozzle conditions; S32: Obtain the intersection of the curves, identify the corresponding nozzle as the maximum reasonable nozzle for the gas well, and guide the optimization of the gas well backflow system.

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