Optimal design method for vertical well fracturing sand addition to reduce bottom hole sand settling and improve perforation hole sand injection efficiency

Abstract

The embodiment of the present invention relates to a vertical well fracturing sand adding optimization design method for reducing bottom sand deposition and improving perforation hole sand injection efficiency, comprising step S100, obtaining formation parameters and completion parameters; step S200, based on the formation parameters and completion parameters, numerically simulating the migration and placement of proppant in the vertical wellbore to obtain the relationship between N groups of influencing factors and sand injection efficiency; step S300, analyzing the factors affecting the perforation hole sand injection efficiency based on the grey correlation method, sorting them according to the degree of correlation, and sequentially obtaining a preset number of influencing factors; step S400, based on the obtained preset number of influencing factors and using the control variable method, further numerically simulating the migration and placement of proppant in the vertical wellbore to obtain the optimal value of each factor.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of oil and gas field development, and in particular to a vertical well fracturing sand adding optimization design method for reducing bottom sand deposition and improving perforation hole sand feeding efficiency. Background Art

[0002] As conventional oil and gas are exhausted, low-permeability and unconventional oil and gas resources are becoming increasingly important. China is rich in unconventional oil and gas resources and has great development potential. Reservoir fracturing is an important core technology for achieving efficient exploitation of low-permeability and unconventional oil and gas. It uses a ground high-pressure pump group to pump fracturing fluid into the bottom of the well at a high displacement, and build up high pressure at the bottom of the well. When the pressure exceeds the ground stress near the well wall and the tensile strength of the formation rock, cracks will occur. Then, the sand-carrying fluid with proppant is pumped into the cracks through the perforated holes. After the fracturing fluid is returned, the cracks are closed on the proppant, thereby forming sand-filled cracks with high conductivity. The distribution pattern of the proppant in the crack will directly affect the conductivity of the crack and the production capacity of the oil and gas well.

[0003] However, the following problems exist in current vertical well fracturing: First, during the sand addition process, the proppant will settle to the bottom of the well under the action of gravity, eventually causing sand blockage; second, since the perforation holes are very small compared to the wellbore size, it is difficult for the proppant to pass through the holes into the cracks, resulting in low sand entry efficiency and the cracks cannot be effectively supported. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a vertical well fracturing sand adding optimization design method for reducing bottom hole sand settling and improving the sand injection efficiency of perforation holes, aiming to solve the problem that during the sand injection process, the proppant will settle at the bottom of the well under the action of gravity, causing sand blockage or the proppant has difficulty passing through the holes into the cracks, and the cracks cannot be effectively supported.

[0005] In order to solve the above technical problems, the embodiment of the present invention provides a vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving the sand feeding efficiency of perforation holes, comprising:

[0006] Step S100, obtaining formation parameters and completion parameters;

[0007] Step S200, based on the formation parameters and completion parameters, numerical simulation is performed on the migration and placement of proppant in the vertical wellbore to obtain the relationship between N groups of influencing factors and sand injection efficiency;

[0008] Step S300, analyzing factors affecting the sand feeding efficiency of the holes based on the grey correlation method, sorting them according to the degree of correlation, and obtaining a preset number of influencing factors in sequence;

[0009] Step S400, based on the obtained preset number of influencing factors and using the control variable method, further numerical simulation is performed on the migration and placement of the proppant in the vertical wellbore to obtain the optimal value of each factor.

[0010] Preferably, the step S200 includes:

[0011] The numerical values ​​of each influencing factor are arbitrarily set, and the migration and placement of the proppant in the vertical wellbore are numerically simulated using fluid simulation software. The sand injection efficiency of the corresponding perforation holes is calculated to obtain the relationship between N groups of influencing factors and the sand injection efficiency.

[0012] Preferably, in step S200, the calculation formula of the sand feeding efficiency R of the perforation hole is as follows:

[0013]

[0014]

[0015]

[0016] Among them, Q pf is the proppant mass flow rate in the perforated hole;

[0017] Q pw is the proppant mass flow rate in the vertical wellbore;

[0018] Q lf is the mass flow rate of sand-carrying fluid in the perforation hole;

[0019] Q lw The mass flow rate of sand-carrying fluid in the wellbore.

[0020] Preferably, the step S200 includes:

[0021] Based on the formation parameters and completion parameters, a vertical wellbore and perforation hole model is created;

[0022] Meshing the vertical wellbore and perforation hole model;

[0023] Based on the multiphase flow model in the fluid simulation software, the migration and placement of proppant in the vertical wellbore was numerically simulated to obtain the relationship between N groups of influencing factors and sand injection efficiency.

[0024] Preferably, the step S300 includes:

[0025] Step S310, obtaining raw data of factors affecting sand feeding efficiency of perforation holes;

[0026] Step S320, determine the reference sequence x o (k), compare sequence x i(k), where x o (k) is the sand feeding efficiency of the hole;

[0027] Step S330, using the averaging method to perform dimensionless processing on the reference sequence and the comparison sequence data;

[0028] Step S340, calculating the absolute value of the difference between the processed comparison sequence and the corresponding elements of the reference sequence;

[0029] Step S350, determining the maximum value among the absolute values and minimum value Calculate the grey correlation coefficient of each sequence;

[0030] Step S360, calculating the correlation degree according to the grey correlation coefficient of each sequence;

[0031] Step S370, sorting according to the degree of association, and obtaining a preset number of main influencing factors in sequence;

[0032] Among them, i is the parameter number;

[0033] k is the data sequence number;

[0034] x o (t) is the mean dimension-normalized value of the reference sequence;

[0035] x s (t) is the mean dimension-normalized value of the comparison sequence.

[0036] Preferably, the calculation formula for calculating the grey relational coefficient of each sequence in step S350 is as follows:

[0037]

[0038] Among them, ρ is the resolution coefficient, and its value range is 0 to 1.

[0039] Preferably, the step S360 includes:

[0040] According to the grey correlation coefficient of each sequence, the average value of the correlation coefficient of each comparison sequence is calculated as the correlation degree. The calculation formula is as follows:

[0041]

[0042] Preferably, the comparison sequence x i (k) Includes: fracturing fluid displacement, sand ratio, proppant mass, proppant particle size, fracturing fluid viscosity, and fracturing fluid quality.

[0043] In the model creation stage, we can adjust the wellbore size, shape, perforation position, hole size, perforation method, perforation density, perforation depth and other factors as needed, which has unique advantages in model diversity. In the simulation calculation stage, we can arbitrarily set the construction parameters (such as fracturing fluid viscosity, proppant particle size, fracturing fluid displacement, sand ratio, proppant quality, etc.) in order to obtain a large number of construction method combinations, so as to obtain the optimal hydraulic fracturing design. The entire numerical simulation process is carried out on the computer, without the need to build any external physical model, saving material costs; the existing proppant migration and sedimentation simulation device requires the collaboration of multiple people, while the numerical simulation is completed by the computer, saving labor costs. In addition, numerical simulation collects data comprehensively and quickly, and various dynamic data can be obtained after the simulation is completed.

[0044] There are many factors that affect the sand feeding efficiency in vertical well fracturing, and the relationships between the factors are very complex. It is impossible to clearly define the role of each factor, resulting in a lack of objective, sufficient, and comprehensive information in the fracturing design process, which results in the design of invalid or even counterproductive solutions. Clarifying the primary and secondary relationships of the factors affecting the sand feeding efficiency and prescribing the right remedies is a key step in the optimization design method of vertical well fracturing and sand addition. To this end, after the proppant laying and migration simulation calculation, the gray correlation analysis method can be used to analyze the influence of various factors on the sand feeding efficiency during the fracturing process, determine the primary and secondary relationships between the factors, and find out the influence of each factor on the sand feeding efficiency. Further design optimization can be performed for the factors with greater influence, which can provide important basis and guidance for later fracturing construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0046] Figure 1 It is a schematic diagram of the wellbore and perforation hole model;

[0047] Figure 2 for Figure 1 Schematic diagram after meshing;

[0048] Figure 3 This is a comparison chart of proppant migration and placement before and after optimization.

[0049] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0052] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] The present invention provides a vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency, comprising:

[0054] Step S100, obtaining formation parameters and completion parameters;

[0055] In this embodiment, the formation parameters and completion parameters include the type of fracturing fluid, the diameter of the perforation hole, the length of the perforation hole, the diameter of the fracturing string, the length of the wellbore, and the distance between the hole and the bottom of the well.

[0056] Specifically, in this embodiment, the formation parameters and completion parameters may be values ​​as shown in Table 1, or may be other collected values, which are not specifically limited here.

[0057] Table 1 Example parameter table

[0058] project Value (or type) Fracturing fluid type Slippery Water Perforation hole diameter 12mm Perforation hole length 50mm Fracturing string diameter 100mm Wellbore length 5m Distance from hole to bottom of well 2m

[0059] Step S200, based on the formation parameters and completion parameters, numerical simulation is performed on the migration and placement of proppant in the vertical wellbore to obtain the relationship between N groups of influencing factors and sand injection efficiency;

[0060] Specifically, the step S200 includes:

[0061] Step S210: creating a vertical wellbore and perforation hole model based on the formation parameters and completion parameters;

[0062] In this embodiment, based on the formation parameters and completion parameters, a vertical wellbore and perforation hole model is created using fluid simulation software (in this embodiment, the fluid simulation software is CFD software (such as Fluent)), such as Figure 1 ;

[0063] Step S220: Meshing the vertical wellbore and perforation hole model;

[0064] The grid division of the vertical wellbore and the perforation hole model in step S220 is shown in FIG. Figure 2 .

[0065] Step S230: Based on the multiphase flow model in the fluid simulation software, numerical simulation is performed on the migration and placement of the proppant in the vertical wellbore to obtain the relationship between N groups of influencing factors and the sand injection efficiency.

[0066] In this embodiment, the multiphase flow model in the fluid simulation software may be a multiphase flow model in Fluent, and no specific limitation is made here.

[0067] Specifically, based on the multiphase flow model in the fluid simulation software, the values ​​of each influencing factor are arbitrarily set, and the migration and placement of the proppant in the vertical wellbore are numerically simulated using the fluid simulation software (Fluent). The sand injection efficiency of the corresponding perforation holes is calculated to obtain the original relationship data between N groups of influencing factors and the sand injection efficiency, as shown in Table 3.

[0068] Among them, the influencing factors include fracturing fluid displacement, sand ratio, proppant quality, proppant particle size, fracturing fluid viscosity, fracturing fluid quality, etc.

[0069] The calculation formula of the sand feeding efficiency R of the perforation hole is as follows:

[0070]

[0071]

[0072]

[0073] Among them, Q pf is the proppant mass flow rate in the perforated hole;

[0074] Q pw is the proppant mass flow rate in the vertical wellbore;

[0075] Q lf is the mass flow rate of sand-carrying fluid in the perforation hole;

[0076] Q lw The mass flow rate of sand-carrying fluid in the wellbore.

[0077] Step S300, analyzing factors affecting the sand feeding efficiency of the holes based on the grey correlation method, sorting them according to the degree of correlation, and obtaining a preset number of influencing factors in sequence;

[0078] There are many factors that affect the sand feeding efficiency of the hole (such as fracturing fluid displacement, sand ratio, proppant quality, proppant particle size, fracturing fluid viscosity, fracturing fluid quality, etc.). The influence of each factor on the sand feeding efficiency of the hole is very complex, and it is difficult to distinguish the primary and secondary factors among the factors, and it is impossible to optimize the main factors. Therefore, a method is needed to determine the degree of influence of each factor. Grey correlation analysis is such a method. The grey correlation analysis method can be used to analyze the degree of influence of each factor on the sand feeding efficiency of the hole during the fracturing process, determine the primary and secondary relationship between each factor, and find out the degree of influence of each factor on the sand feeding efficiency of the hole. Step S400, based on the obtained preset number of influencing factors and using the control variable method, further numerical simulation is performed on the migration and laying of the proppant in the vertical wellbore to obtain the optimal value of each factor.

[0079] Specifically, the step S300 includes:

[0080] Step S310, obtaining raw data of factors affecting sand feeding efficiency of perforation holes;

[0081] In this embodiment, the original data is the original relationship data of N groups of influencing factors and sand feeding efficiency obtained by arbitrarily setting the values ​​of each influencing factor in step S200, using fluid simulation software (Fluent) to perform numerical simulation on the migration and placement of proppant in the vertical wellbore, and calculating the sand feeding efficiency of the corresponding perforation holes. In this embodiment, the original data is shown in Table 3.

[0082] Step S320, determine the reference sequence x o (k), compare sequence x i (k), where x o (k) is the sand feeding efficiency of the hole;

[0083] Specifically, the comparison sequence x i (k) Includes: fracturing fluid displacement, sand ratio, proppant mass, proppant particle size, fracturing fluid viscosity, and fracturing fluid mass, see Table 2.

[0084] Table 2 Grey correlation analysis parameters

[0085] factor unit Code Fracturing fluid discharge <![CDATA[m 3 / min]]> <![CDATA[X1]]> Sand ratio -- <![CDATA[X2]]> Proppant quality <![CDATA[Kg / m 3 ]]> <![CDATA[X3]]> Proppant particle size mm <![CDATA[X4]]> Fracturing fluid viscosity mPa·s <![CDATA[X5]]> Fracturing fluid quality <![CDATA[Kg / m 3 ]]> <![CDATA[X6]]> Sand injection efficiency of perforation holes -- <![CDATA[X0]]>

[0086] Table 3 Factors affecting sand feeding efficiency of perforation holes

[0087] Serial number <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> <![CDATA[X0]]> 1 0.5 0.1 2000 0.2 1 1000 0.3 2 2 0.15 3200 0.5 5 1200 0.45 3 1.2 0.2 2200 0.8 3 1100 0.25 4 4.5 0.14 3500 0.3 6 1200 0.56 5 3.2 0.16 2500 0.7 9 1250 0.6 6 5 0.22 2700 0.6 7 1150 0.35 7 2.5 0.17 3000 0.4 7 1050 0.4 8 3 0.13 2050 0.45 8 1250 0.5 9 4 0.16 2450 0.55 4 1050 0.35 10 1.5 0.15 2950 0.65 2 1200 0.33

[0088] Step S330, using the averaging method to perform dimensionless processing on the reference sequence and the comparison sequence data;

[0089] In this embodiment, when the original data is the data in Table 3, the data after the dimensionless processing in step S330 is shown in Table 4.

[0090] Table 4 Dimensionless data processing results

[0091]

[0092]

[0093] Step S340, calculating the absolute value of the difference between the processed comparison sequence and the corresponding elements of the reference sequence;

[0094] Specifically, when the original data is the data in Table 3, the absolute values ​​of the differences between the corresponding elements of the comparison sequence and the reference sequence after processing in step S340 are shown in Table 5.

[0095] Table 5 Calculation results of the absolute value of the difference between the corresponding elements of the comparison sequence and the reference sequence after processing

[0096] Serial number <![CDATA[X1]]> <![CDATA[X2]]> <![CDATA[X3]]> <![CDATA[X4]]> <![CDATA[X5]]> <![CDATA[X6]]> 1 0.551 0.101 0.020 0.345 0.541 0.140 2 0.370 0.151 0.105 0.129 0.139 0.052 3 0.173 0.655 0.217 0.942 0.034 0.349 4 0.273 0.483 0.051 0.787 0.215 0.321 5 0.299 0.454 0.525 0.108 0.264 0.375 6 0.969 0.537 0.161 0.309 0.490 0.149 7 0.066 0.098 0.152 0.201 0.368 0.061 8 0.128 0.400 0.450 0.349 0.316 0.131 9 0.604 0.157 0.067 0.212 0.087 0.061 10 0.259 0.143 0.304 0.455 0.422 0.241

[0097] Step S350, determining the maximum value among the absolute values and minimum value Calculate the grey correlation coefficient of each sequence;

[0098] Specifically, when the original data is the data in Table 3, step S350 determines the maximum value from the absolute value results of the difference is 0.02 and the minimum It is 0.969.

[0099] Specifically, the calculation formula for calculating the grey relational coefficient of each sequence in step S350 is as follows:

[0100]

[0101] In this embodiment, when the original data is the data in Table 3, the grey correlation coefficients of each sequence in step S350 are shown in Table 6.

[0102] Wherein, ρ is a resolution coefficient, and its value range is 0 to 1. In this embodiment, ρ is 0.5.

[0103] Table 6 Correlation coefficient calculation results

[0104]

[0105]

[0106] Step S360, calculating the correlation degree according to the grey correlation coefficient of each sequence;

[0107] The step S360 includes:

[0108] According to the grey correlation coefficient of each sequence, the average value of the correlation coefficient of each comparison sequence is calculated as the correlation degree. The calculation formula is as follows:

[0109]

[0110] Among them, i is the parameter number;

[0111] k is the data sequence number;

[0112] x o (t) is the mean dimension-normalized value of the reference sequence;

[0113] x s (t) is the mean dimension-normalized value of the comparison sequence.

[0114] Step S370, sorting according to the degree of association, and obtaining a preset number of main influencing factors in sequence;

[0115] In this embodiment, when the original data is the data in Table 3, the ranking table of factors affecting the sand injection efficiency of the perforation holes in step S370 is shown in Table 7.

[0116] Table 7 Ranking of factors affecting sand injection efficiency of perforation holes

[0117] factor Fracturing fluid discharge Sand ratio Proppant quality Proppant particle size Fracturing fluid viscosity Fracturing fluid quality Relevance 0.638 0.668 0.768 0.627 0.685 0.772 Sorting 5 4 2 6 3 1

[0118] Arrange the correlation degrees in order of size to form a correlation sequence, which reflects the "primary and secondary" relationship of each comparison series to the reference series.

[0119] Step S400, based on the obtained preset number of influencing factors and using the control variable method, further numerical simulation is performed on the migration and placement of the proppant in the vertical wellbore to obtain the optimal value of each factor.

[0120] The main influencing factors selected in step S370 are further simulated and optimized using CFD software (such as Fluent), and the simulation steps are the same as S210 to S230. It is worth noting that the difference from the simulation calculation in step S200 is that the values ​​of the influencing factors in this step are not set arbitrarily, but the control variable method is used to optimize the best value of each factor.

[0121] In this embodiment, it is assumed that we can only optimize three parameters this time, so we take the top three factors, namely, fracturing fluid quality, proppant quality, and fracturing fluid viscosity, and use Fluent software to further simulate and optimize using the control variable method to obtain the optimization scheme as shown in Table 8. The comparison diagram of proppant migration and paving before and after optimization is shown in Table 8. Figure 3 .

[0122] Table 8 Optimization scheme

[0123] factor unit Numeric Fracturing fluid quality <![CDATA[Kg / m 3 ]]> 1350 Proppant quality <![CDATA[Kg / m 3 ]]> 2650 Fracturing fluid viscosity mPa·s 5

[0124] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. All equivalent structural changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency, characterized in that: include: Step S100, obtaining formation parameters and completion parameters; Step S200, based on the formation parameters and completion parameters, numerical simulation is performed on the migration and placement of proppant in the vertical wellbore to obtain the relationship between N groups of influencing factors and sand injection efficiency; Step S300, analyzing factors affecting the sand feeding efficiency of the holes based on the grey correlation method, sorting them according to the degree of correlation, and obtaining a preset number of influencing factors in sequence; Step S400, based on the acquired preset number of influencing factors and using the control variable method, further numerical simulation is performed on the migration and placement of the proppant in the vertical wellbore to obtain the optimal value of each factor; Wherein, the step S200 includes: The values ​​of each influencing factor are arbitrarily set, and the migration and placement of the proppant in the vertical wellbore are numerically simulated using fluid simulation software to calculate the sand feeding efficiency of the corresponding perforation holes to obtain the relationship between N groups of influencing factors and the sand feeding efficiency; In step S200, the calculation formula of the sand feeding efficiency R of the perforation hole is as follows: Among them, Q pf is the proppant mass flow rate in the perforated hole; Q pw is the proppant mass flow rate in the vertical wellbore; Q lf is the mass flow rate of sand-carrying fluid in the perforation hole; Q lw The mass flow rate of sand-carrying fluid in the wellbore; The step S300 includes: Step S310, obtaining raw data of factors affecting sand feeding efficiency of perforation holes; Step S320, determine the reference sequence x o (k), compare sequence x i (k), where x o (k) is the sand feeding efficiency of the hole; Step S330, using the averaging method to perform dimensionless processing on the reference sequence and the comparison sequence data; Step S340, calculating the absolute value of the difference between the processed comparison sequence and the corresponding elements of the reference sequence; Step S350, determining the maximum value among the absolute values and minimum value Calculate the grey correlation coefficient of each sequence; Step S360, calculating the correlation degree according to the grey correlation coefficient of each sequence; Step S370, sorting according to the degree of association, and sequentially obtaining a preset number of main influencing factors; Among them, i is the parameter number; k is the data sequence number; x o (t) is the mean dimension-normalized value of the reference sequence; x s (t) is the mean dimension-normalized value of the comparison sequence.

2. The vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency as claimed in claim 1, characterized in that: The step S200 includes: Based on the formation parameters and completion parameters, a vertical wellbore and perforation hole model is created; Meshing the vertical wellbore and perforation hole model; Based on the multiphase flow model in the fluid simulation software, the migration and placement of proppant in the vertical wellbore was numerically simulated to obtain the relationship between N groups of influencing factors and sand injection efficiency.

3. The vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency as claimed in claim 1, characterized in that: The calculation formula for calculating the grey correlation coefficient of each sequence in step S350 is as follows: Among them, ρ is the resolution coefficient, and its value range is 0 to 1.

4. The vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency as claimed in claim 3, characterized in that: The step S360 includes: According to the grey correlation coefficient of each sequence, the average value of the correlation coefficient of each comparison sequence is calculated as the correlation degree. The calculation formula is as follows:

5. The vertical well fracturing sand adding optimization design method for reducing bottom sand settling and improving perforation hole sand feeding efficiency as claimed in claim 3, characterized in that: The comparison sequence x i (k) Includes: fracturing fluid displacement, sand ratio, proppant mass, proppant particle size, fracturing fluid viscosity, and fracturing fluid quality.

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