CN117150977A - Well closing time optimization method based on flowback fluid characteristics of shale gas well - Google Patents
Well closing time optimization method based on flowback fluid characteristics of shale gas well Download PDFInfo
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- G06F30/20—Design optimisation, verification or simulation
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- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
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Abstract
The invention discloses a stuffy well time optimization method based on flowback liquid characteristics of a shale gas well, which belongs to the technical field of shale gas development, and comprises the steps of carrying out spontaneous imbibition experiments to obtain experimental related data of spontaneous imbibition and ion diffusion, and determining core scale critical time, namely dimensionless time; obtaining a crack width inversion model based on-site flowback fluid mineralization test data, and predicting the crack width and the crack volume of the shale gas well; substituting the flow back rate prediction model into the fracture volume and the dimensionless time to obtain the equivalent fracture spacing of the shale gas well, and determining the core matrix volume; substituting the equivalent crack spacing based on the characteristic length model to obtain the characteristic length; substituting the dimensionless time model and the characteristic length into the data related to the spontaneous imbibition experiment, and optimizing and determining the stuffy time of the shale gas well. The interaction mechanism of fracturing fluid imbibition and ion diffusion is considered, the accuracy of the well closing time of the shale gas well is improved, and the yield of the shale oil well is improved.
Description
Technical Field
The invention relates to the technical field of shale gas exploitation, in particular to a well closing time optimization method based on flowback characteristics of shale gas wells.
Background
Efficient development of shale gas reservoirs must rely on large-scale horizontal well multi-stage fracturing techniques. Numerous scholars research finds that when the well is closed for a period of time after large-scale hydraulic fracturing, the increase of the retention liquid is beneficial to promoting the imbibition process, and the oil and gas well yield can be greatly improved. According to the numerical simulation calculation results of Ghanbari et al and Sharma, the prolonged well closing time is used for improving the early productivity, but the overlong well closing time is unfavorable for improving the later productivity. Therefore, proper well closing time is important for fully playing the seepage and absorption replacement roles and improving the recovery ratio of unconventional oil and gas reservoirs.
Currently, the well-closure time determination methods can be broadly divided into the following three categories: the first empirical formula method constructs an empirical formula for calculating the time of a stuffy well according to the characteristics of a specific well or a specific reservoir, but the method has no general applicability; the second method is an analytical solution, normalized time is obtained by solving a piston displacement model in a capillary tube, and mine scale stuffy well time is obtained by combining a imbibition experimental result and a similar criterion, however, the method has the defects that how to select the parameters of a mine scale matrix unit, qing and the like are combined with a dimensionless time model proposed by Horn River shale core sample and Ma and the like, the time corresponding to the maximum imbibition displacement amount of the core scale is converted to the mine scale, the mine scale corresponding to the mine scale for 1 hour is calculated, and the basis for selecting the parameters of the mine scale matrix unit is not given; the third method is a numerical solution, the relation between productivity and well closing time is obtained by solving a non-piston displacement model, however, at present, the model does not consider the dynamic phase permeability change rule in the well closing process, and the productivity model is solved with larger error.
The special 'liquid-salt production' flowback dynamic curve of the shale gas well can provide rich information for evaluating the shape of the volumetric fracture network. Zolfaghari et al studied the relationship curve of flowback fluid salinity and accumulated liquid production, and proposed a method for inverting the slit width distribution based on the ion diffusion theory, but the model made more assumptions and did not consider the interaction mechanism of fracturing fluid imbibition and ion diffusion. Therefore, the time for the shale gas well to be closed is inaccurate, and the yield of the shale oil well is not high.
Therefore, how to provide a well-closing time optimization method, consider the interaction mechanism of fracturing fluid imbibition and ion diffusion, re-invert to obtain the characteristic length of the mine scale, and optimize the well-closing time of the shale gas well is a technical problem to be solved by the technicians in the field.
Disclosure of Invention
Therefore, the invention provides a well closing time optimization method based on flowback fluid characteristics of a shale gas well, which aims to solve the problems of inaccurate well closing time of the shale gas well and low shale oil well yield caused by the fact that an interaction mechanism of fracturing fluid imbibition and ion diffusion is not considered in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions:
a well closing time optimization method based on flowback fluid characteristics of shale gas wells comprises the following steps:
step S1: carrying out spontaneous imbibition experiments to obtain experimental related data of spontaneous imbibition and ion diffusion, and determining critical time of a core scale, namely dimensionless time;
step S2: obtaining a crack width inversion model based on-site flowback fluid mineralization test data, and predicting the crack width and the crack volume of the shale gas well;
step S3: substituting the flow back rate prediction model into the fracture volume and the dimensionless time to obtain the equivalent fracture spacing of the shale gas well, and determining the core matrix volume;
step S4: substituting the equivalent crack spacing based on the characteristic length model to obtain the characteristic length;
step S5: substituting the dimensionless time model and the characteristic length into the data related to the spontaneous imbibition experiment, and optimizing and determining the stuffy time of the shale gas well.
Further, the fracture width inversion model in the step S2 is:
；
wherein f (W) f ) To characterize probability density function of crack morphology, W f For crack width, C f L is the salt concentration in the crack m N is the distance from the middle of the crack to the salt ion migration position caused by the concentration gradient in the matrix P,w For the recovery rate of the flowback fluid, D is the diffusion coefficient, C m For the salt concentration in the matrix, Δt is the time the fracture face is in contact with the fracturing fluid, dN P, w Representation pair N P, w Is a derivative of dC f Representation pair C f Is a derivative of (a).
Further, the flow back rate prediction model in the step S3 is as follows:
；
wherein V is imb To suck the volume of fracturing fluid into the rock matrix block, V inj T is the volume of fracturing fluid injected into the formation D For dimensionless time, n is the number of proppant lay-up layers in the fracture, r is the diameter of the proppant particles, a is the length of the matrix block, i.e., the equivalent fracture spacing, ϕ is the porosity, S wf For final water saturation, S wi Is the initial water saturation.
Further, the characteristic length model in the step S4 is:
；
wherein L is c For characteristic length, V b For the core matrix volume, A i Is the area of the imbibition contact surface in the ith direction, L Ai The value range of i is a positive integer which is more than or equal to 1 and less than or equal to n, and n is the number of the imbibition directions, so that the imbibition front edge is a distance from the opening surface to the closed boundary.
Further, the dimensionless time model in the step S5 is:
；
wherein t is D In order to be a dimensionless time,to specify a dimensionless time model at net pressure,to specify the dimensional time model of formation overburden pressure, L c Is the characteristic length, t is the dead time, k is the reservoir permeability, ϕ is the porosity, σ is the surface tension, and μw is the viscosity.
Further, the volume of the fracturing fluid injected into the stratum in the flow-back rate prediction model is the fracture volume.
Further, in the step S1, the spontaneous imbibition experimental test includes:
step S101: drying shale samples, taking out after cooling, measuring the quality of the dried samples, and testing the T of the dried samples 2 A spectrum;
step S102: placing the dried sample into a beaker filled with distilled water for spontaneous imbibition experiment, and repeatedly measuring the quality and T of the imbibition sample 2 Spectrum until there is no longer a significant change.
Further, in the step S1, the method for determining the critical time of the core scale specifically includes:
(1) Obtaining imbibition volume T in imbibition process 2 Spectrum and conductivity;
(2) According to the imbibition volume and T obtained in imbibition process 2 Spectrum and conductivity, drawing a permeation volume, a T2 spectrum area and a conductivity time-varying curve;
(3) By imbibition of volume, T 2 Spectral area and conductivityAnd (5) determining the critical time of the core scale along with the inflection point of the time change curve.
Further, in the early stage of imbibition in the step S1, the imbibition volume is positively correlated with the square root of time, and the ion diffusion rate is positively correlated with the square root of time in the whole imbibition process.
The invention has the following advantages:
(1) According to the invention, through an indoor imbibition displacement test, the interaction mechanism of imbibition and ion diffusion of the fracturing fluid is considered, the accuracy of the well closing time of the shale gas well is improved, and the yield of the shale oil well is improved.
(2) According to the method, the mineralization degree of the flowback fluid, the ion type of the flowback fluid, the flowback rate and other parameters are processed and analyzed, the width of the scale cracks and the equivalent crack spacing of the mine are obtained through inversion, and the dimensionless time model is combined to establish the shale gas well stuffy time optimization method, so that the experimental process is simple, and the operation is easy.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the ambit of the technical disclosure.
FIG. 1 is a flow chart of a method for optimizing the time of a dead well provided by the invention;
FIG. 2 is a flowchart showing step S1 in the method for optimizing the time of a dead well according to the present invention;
FIG. 3 is a spontaneous imbibition test T 2 A spectrum test result graph;
FIG. 4 is a graph of signal distribution frequencies corresponding to different relaxation times;
FIG. 5 shows the imbibition volume, T 2 Spectral area and conductivity versus time curve;
FIG. 6 is a graph of cumulative gas/cumulative water versus days of production for the LuA and Wei B wells;
FIG. 7 is a flowback volume/flowback rate data versus days of flowback plot for the LuA well and the Wei well;
FIG. 8 is a graph of flowback fluid mineralization data versus days of flowback for both the LuA and Wei B wells;
FIG. 9 is a graph of fracture volume distribution for different widths of LuA and Wei wells;
FIG. 10 is a schematic illustration of shale fracture distribution provided by the invention;
FIG. 11 is a schematic illustration of a fracture-matrix provided by the present invention;
FIG. 12 is a graph of probability distribution of matrix block lengths for different widths of LuA and Wei wells.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problems in the prior art, a method for optimizing the stuffy well time based on flowback characteristics of a shale gas well is provided, as shown in fig. 1, and comprises the following steps:
step S1: carrying out spontaneous imbibition experiments to obtain experimental related data of spontaneous imbibition and ion diffusion, and determining critical time of a core scale, namely dimensionless time;
step S2: obtaining a crack width inversion model based on-site flowback fluid mineralization test data, and predicting the crack width and the crack volume of the shale gas well;
step S3: substituting the flow back rate prediction model into the fracture volume and the dimensionless time to obtain the equivalent fracture spacing of the shale gas well, and determining the core matrix volume;
step S4: substituting the equivalent crack spacing based on the characteristic length model to obtain the characteristic length;
step S5: substituting the dimensionless time model and the characteristic length into the data related to the spontaneous imbibition experiment, and optimizing and determining the stuffy time of the shale gas well.
According to the invention, through an indoor imbibition displacement test, the interaction mechanism of imbibition and ion diffusion of the fracturing fluid is considered, the accuracy of the well closing time of the shale gas well is improved, and the yield of the shale oil well is improved. The method is characterized in that the mineralization degree of the flowback fluid, the ion type of the flowback fluid, the flowback rate and other parameters are processed and analyzed, the width of the scale cracks and the equivalent crack spacing of the mine are obtained through inversion, and the dimensionless time model is combined to establish the shale gas well stuffy time optimization method, so that the experimental process is simple, and the operation is easy.
As shown in fig. 2, the specific procedure of the spontaneous imbibition experiment in step S1 is as follows:
(1) Drying the sample before the experiment, adopting a 105 ℃ closed oven to dry for 48 hours, taking out after cooling, and measuring the mass of the dried sample;
(2) The dried sample T was tested by using a MiniMR-VTP low-field nuclear magnetic resonance analyzer (magnetic field strength 0.5T, magnet temperature 32 ℃, echo time 300 μm, interval time 3000ms, echo number 8000) from Namez analytical instruments, inc., suzhou 2 A spectrum;
(3) Washing the beaker and the electrode of the conductivity meter by using distilled water, adding 200ml of distilled water into the washed beaker, testing the conductivity of the distilled water, and if the conductivity is higher than 2 mu s/cm, washing the beaker again until the test value meets the requirement;
(4) And placing the dried sample into a beaker filled with distilled water for spontaneous imbibition experiments, and sealing the mouth of the beaker by using a preservative film to reduce water evaporation and experimental errors. After a certain period of time, the sample is taken out, and the surface liquid is wiped. Using a mertler analytical balance (model ME204E, precision 0.0001g, measuring range 220 g) of the weight. Testing sample T using nuclear magnetic resonance analyzer 2 Spectrum, simultaneously using a conductivity meter to measure the conductivity of the liquid in the beaker, recording experimental data, putting the sample back into the beaker, and continuing to perform spontaneous imbibition experiments;
(5) Taking out the sample after a period of time, repeating the step (5) until the mass and T of the sample are tested for several times 2 No significant changes in the spectrum occur.
In step S1, the method for determining the critical time of the core scale specifically includes:
(1) Obtaining imbibition volume T in imbibition process 2 Spectrum and conductivity;
(2) According to the imbibition volume and T obtained in imbibition process 2 Spectrum and conductivity, drawing a permeation volume, a T2 spectrum area and a conductivity time-varying curve;
(3) By imbibition of volume, T 2 And determining the critical time of the core scale by the inflection points of the curve of the spectral area and the conductivity change with time.
In the early stage of imbibition in step S1, the imbibition volume is positively correlated with the square root of time, and the ion diffusion rate is positively correlated with the square root of time in the whole imbibition process.
In the step S2, a crack width inversion model is established, and the method specifically comprises the following steps:
(1) Assuming that the gas reservoir is homogeneous and infinitely large and isotropic, the fluid is a newtonian fluid, the flow satisfies darcy's law, the transport of salt in the fluid is from the matrix to the fracture, and the mineralization of salt in the reservoir is much greater than the mineralization of the fracturing fluid. Fick's law of diffusion is used to represent the flow equation of salt ions from the matrix to the fracture:
（1）
wherein J is i Is ion diffusion flux; d is a diffusion coefficient; a is that f,i Is the interfacial area between the matrix and the fracture; c (C) m Is the salt concentration within the matrix; c (C) f,i Is the salt concentration in the fracture; l (L) m Is the middle to base of the crackDistance of salt ion transport sites caused by the intra-mass concentration gradient.
(2) During fracturing and imbibition, if the entire source of salt ions is a reservoir matrix and its internal salinity is much higher than the salinity in the fracture, the salinity inside the matrix is essentially unchanged during ion diffusion, the difference between the two being equal to the salt concentration within the matrix, as follows:
（2）
（3）
formula (1) is rewritten as:
（4）
wherein J is i Is ion diffusion flux; d is a diffusion coefficient; a is that f,i Is the interfacial area between the matrix and the fracture; c (C) m Is the salt concentration within the matrix; l (L) m The distance from the middle of the crack to the salt ion transport position caused by the concentration gradient in the matrix.
(3) Assuming that the slit is a cuboid with a width W f, i The volume of the crack is:
（5）
wherein V is f,i For crack volume, A f,i W is the interface area between the matrix and the crack f,i Is the width of the crack;
deformed by formula (4), multiplied by Deltat/V f,i Average salt concentration in the cracks was obtained:
（6）
wherein,the delta t is the time of contact between the fracture surface and the fracturing fluid;
neglecting the average gas saturation in the fractures during flowback of the fracturing fluid lost in the reservoir:
（7）
wherein,to average gas saturation in cracks, Q w For cumulative return discharge of flow-back fluid, S g,ini For initial gas saturation, V f,i Is the fracture volume;
the initial gas saturation is small and formula (7) is rewritten as:
（8）
average water saturation in fractureThe method comprises the following steps:
（9）
introducing an artificial fracture width calculation function f (w), and distributing the water saturation in the artificial fracture as follows:
（10）
integrating according to a probability density function:
（11）
wherein W is f, max For maximum crack width, W f,min Is the minimum crack width;
according to the integral formula, the probability density function is written as:
（12）
combining formula (6), can be obtained:
（13）
（14）
from formula (14):
（15）
substituting the formula (15) into the probability density function (12) to obtain:
（16）
(4) Defining normalized flowback fluid recovery N P,w The method comprises the following steps:
（17）
then
（18）
（19）
The probability density function characterizing the morphology of the fracture is:
（20）
wherein f (W) f ) To characterize probability density function of crack morphology, W f For crack width, C f L is the salt concentration in the crack m N is the distance from the middle of the crack to the salt ion migration position caused by the concentration gradient in the matrix P,w For the recovery rate of the flowback fluid, D is the diffusion coefficient, C m For the salt concentration in the matrix, Δt is the time the fracture face is in contact with the fracturing fluid, dN P,w Representation pair N P,w Is a derivative of dC f Representation pair C f Is a derivative of (a).
In field applications, value C of salt concentration in the matrix m Can be obtained according to the on-site flow-back fluid mineralization test data, D can be used for determining a value according to the ion type of the tested flow-back fluid, L m According to 10W f The flowback rate is calculated and determined according to the injection volume of the fracturing fluid and the volume of the fracturing fluid collected after flowback. During calculation, according to flowback data and salinity change, W is calculated f Corresponding f (W) f ) Thereafter, each salinity stage is assigned to f (W) f ) The sum indicates the distribution of the fracture volume fraction over the wide range of the slit. Accordingly, fracture volume fraction distribution diagrams of different fracture widths can be obtained, and the well weighted average fracture width is obtained according to the fracture volume fractions of the different fracture widths.
In step S3, a flow back rate prediction model is established, and the method specifically comprises the following steps:
the flowback rate prediction model based on the fracture-matrix three-dimensional imbibition theory simplifies the "broken up" reservoir after large-scale volumetric fracturing into a cube with a fracture, as shown in fig. 11, and lays proppant in the rock fracture.
(1) According to the principle of conservation of mass, the volume of fracturing fluid injected into a stratum is equal to the volume of an artificial crack, m matrix blocks are formed through fracturing operation, and the lengths of the matrix blocks are equal to the equivalent crack spacing:
（21）
wherein m is the number of matrix blocks, V inj For the volume of fracturing fluid injected into the formation, a is the length of the matrix block, r is the diameter of the proppant particles, and n is the number of proppant placement layers in the fracture;
(2) Volume of fracturing fluid V imbibed into rock matrix block imb Volume of fracturing fluid V with injected formation inj Ratio of:
（22）
wherein L is D =2x/a is a dimensionless length scale, x is the length of penetration of the fracturing fluid into the matrix, S wf Is the final water saturation, S wi Is the initial water saturation, ϕ is the porosity;
(3) The calculation formula of the flow back rate is obtained through deduction:
（23）
wherein t is D Is dimensionless time;
(4) And inverting to obtain the equivalent crack spacing based on the flow-back rate calculation formula.
The feature length model in step S4 is:
（24）
wherein L is c Is of special interestLength of sign, V b For the core matrix volume, A i Is the area of the imbibition contact surface in the ith direction, L Ai The value range of i is a positive integer which is more than or equal to 1 and less than or equal to n, and n is the number of the imbibition directions, so that the imbibition front edge is a distance from the opening surface to the closed boundary.
The dimensionless time model in step S5 is:
（25）
wherein t is D In order to be a dimensionless time,to specify a dimensionless time model at net pressure,to specify the dimensional time model of formation overburden pressure, L c Is the characteristic length, t is the dead time, k is the reservoir permeability, ϕ is the porosity, σ is the surface tension, and μw is the viscosity.
Core samples of spontaneous imbibition experiments are respectively taken from deep shale Lu A well (vertical depth 3986.12-3989.18 m) in Luzhou area and deep shale Wei B well (vertical depth 2705.18-2710.13 m) in Weifar area, and the layers are a 11-layer and a 12-layer of Loongxi group dragon. The rock mineral mainly comprises quartz (33.7-63.0 wt%), feldspar (2.8-8.4 wt%), calcite (2.3-9.1 wt%), dolomite (4.3-10.8 wt%), pyrite (4.2-9.1 wt%), and clay mineral (20.9-42.1 wt%). Clay minerals are mainly illite (relative content 63.0-77.2 wt%), illite/montmorillonite (relative content 18.5-30.5 wt%) and chlorite (relative content 5.0-11.5 wt%).
As a result of the basic physical property parameter test, as shown in Table 1, the porosity measured by the static capacity method (helium as the test medium) was 6.21-7.15%, and the permeability measured by the pulse attenuation method (nitrogen as the test medium) was 0.010-0.019mD.
TABLE 1 shale sample physical Properties parameters
The liquid sample is high-purity distilled water, and the density at room temperature is 1.0g/cm 3 The viscosity was 1.0 mPa.s.
Developing a spontaneous imbibition-ion diffusion synchronous experiment, and nuclear magnetic resonance T in imbibition process 2 The spectrum, as shown in FIG. 3, shows, T 2 The spectra are bimodal and correspond to relaxation times of 0.1-10ms and 10-100ms, respectively. In the imbibition process, the core T of A1 and A2 2 The shift in the spectrum peaks to the right indicates an increase in the pore size in the matrix, which may be related to the water swelling of clay minerals and salt dissolution. B1 and B2 core T 2 The spectral shape was essentially unchanged, indicating that no significant change in the pore structure inside the core occurred. In addition, T is measured at different times 2 The spectral results show that the signal amplitude of the dried sample is minimal, and that the signal amplitude rises with time, and then remains substantially unchanged, indicating that the moisture content in the core remains substantially unchanged, reaching saturation.
Comparing the signal distribution frequency of different relaxation times before and after the percolation experiment, as shown in fig. 4, it can be seen that the hydrogen proton signal in the dry state is very weak, mainly from clay water or minerals containing crystal water. After imbibition, the hydrogen proton signal is mainly distributed in T 2 In the range of 0.1-100ms, the duty ratio is more than 98.5%. Wherein the frequency distribution of two cores of the Wei B well is basically consistent, T 2 The ratio of the core within the range of 0.1-1ms to the core is about 65%, which is obviously higher than that of the core of the filter A well, so that the sucked water is mainly distributed in the nano micropores. After two core imbibition experiments of the LuA well, the distribution characteristics of hydrogen proton signals are different, and water inhaled by the core A1 is mainly distributed in nanometer mesopores (T 2 Between 1 and 10ms, the ratio is about 70 percent), and the water absorbed by the core A1 is mainly distributed in nano micropores and nano mesopores (T) 2 Between 0.1 and 1 and 10ms respectively, with a ratio of 48.3% and 43.1%, respectively).
FIG. 5 shows the water absorption volume (calculated by mass measurement by weighing method) and the nuclear magnetic resonance T during the experiment 2 Spectral area (T measured at different moments) 2 Spectral area and Dry sample T 2 Spectral area difference) And the square root of conductivity over time. It can be seen that the imbibition volume and T 2 The change trend of the spectrum area is basically consistent, which shows that the difference between the two test methods is smaller. In the early stage of imbibition, the water absorption volume of the core is in a proportional relation with the square root of time, and then, an inflection point appears on a curve (corresponding time at the inflection point in the spontaneous imbibition experiment of the cores A1-B2 is 22h,29h,3h and 4h respectively), the curve is nearly parallel to a time axis, and the core has no obvious water absorption characteristic, which is similar to T 2 The spectrum test results are consistent. The conductivity is also proportional to the square root of time, but this linear dependence continues throughout the imbibition process.
In field applications, value C of salt concentration in the matrix m Can be obtained according to the on-site flow-back fluid mineralization test data, D can be used for determining a value according to the ion type of the tested flow-back fluid, L m According to 10W f The flowback rate is calculated and determined according to the injection volume of the fracturing fluid and the volume of the fracturing fluid collected after flowback.
The gas production water and the like of the filter A well and the well B well obtained by field test are subjected to statistical arrangement, and flowback data, the mineralization degree of flowback fluid and the ion type are subjected to test analysis, so that as shown in fig. 6-8, the flowback rate of the two wells is lower and is lower than 20%. The salinity test results of the flowback fluid are shown in fig. 8, and the salinity of the flowback fluid is gradually increased along with the time, wherein sodium ions and chloride ions are main mineralization sources, and the content of other ions is low.
Based on flowback data of the LuA well and the Wei B well, the salt concentration value C in the matrix m 20000ppm and 35000ppm are respectively taken, the main types of salt ions are chloride ions and sodium ions, and the diffusion coefficients are 1.484×10 -9 m 2 S, obtaining the average salt concentration C in the crack f,i And crack width W f,i The relation is:
according to the flowback data and the salinity change, calculating to obtain W f Corresponding f (W) f ) Thereafter, each salinity stage is assigned to f (W) f ) Summing, representing the distribution of fracture volume fractions over the seam width, can result in fracture volume fraction distribution maps for different seam widths for LuA and WeiB wells, as shown in FIG. 9.
And obtaining weighted average seam widths of the LuA well and the Wei B well which are respectively 1.73mm and 1.30mm according to the volume fractions of the cracks with different seam widths.
Comparing the calculation results of the LuA well and the Wei well, the salt yield of the Wei well is higher, the calculated crack width is mainly distributed at 1-2mm, the crack width of the Lu B well is mainly distributed at 2-2.5mm, the Wei well has larger crack area, and the crack complexity after fracturing is higher as shown in figure 10.
Parameters such as dimensionless time measured by indoor experiments of core samples of the LuA well and the Wei B well, flowback rate measured by a mine field scale, calculation results of a slit width distribution theoretical model and the like are carried into calculation, and probability distribution of matrix block length a (namely equivalent slit spacing) is obtained, and the result is shown in figure 12. The result shows that the matrix block weighted average length of the LuA well is 0.20m, and the characteristic length L C 0.10m; matrix block weighted average length of Wei well is 0.32m, characteristic length L C 0.16m.
Based on the flow-back rate prediction model, the results in the spontaneous imbibition laboratory, the flow-back fluid mineralization degree test result and the crack width inversion result are combined. And substituting the equivalent crack spacing based on the characteristic length model to obtain the characteristic length. Substituting the dimensionless time model and the characteristic length into the data related to the spontaneous imbibition experiment, and optimizing and determining the stuffy time of the shale gas well.
The results of the spontaneous imbibition experiment of the core of the LuA well A1 and the calculation result of the characteristic length show that the critical time of the core scale is 22 hours, the characteristic length is 0.10m, and the well closing time of the mine scale is 12.5 days. The core spontaneous imbibition experimental result and the characteristic length calculation result of the Wei B well B1 show that the critical time of the core scale is 3.0 hours, the characteristic length is 0.16m, and the well closing time of the mine scale is 16.7 days.
It can be seen that the rate of imbibition of the deep shale samples is slower than the mid-deep samples, and that the time for imbibition to reach the stabilization stage is longer, but the impact of the mine scale matrix block size on the time to kill is greater. It may be associated with the development of natural fractures in deep shale gas reservoirs, which may result in "more broken" reservoirs after imbibition of fracturing fluid into the formation, and ultimately, shorter well-packing times in deep shale gas reservoirs.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (9)
1. The well closing time optimization method based on the flowback fluid characteristics of the shale gas well is characterized by comprising the following steps of:
step S1: carrying out spontaneous imbibition experiments to obtain experimental related data of spontaneous imbibition and ion diffusion, and determining critical time of a core scale, namely dimensionless time;
step S2: obtaining a crack width inversion model based on-site flowback fluid mineralization test data, and predicting the crack width and the crack volume of the shale gas well;
step S3: substituting the flow back rate prediction model into the fracture volume and the dimensionless time to obtain the equivalent fracture spacing of the shale gas well, and determining the core matrix volume;
step S4: substituting the equivalent crack spacing based on the characteristic length model to obtain the characteristic length;
step S5: substituting the dimensionless time model and the characteristic length into the data related to the spontaneous imbibition experiment, and optimizing and determining the stuffy time of the shale gas well.
2. The method for optimizing the time for stuffy well based on flowback characteristics of shale gas well according to claim 1, wherein the fracture width inversion model in the step S2 is:
；
wherein f (W) f ) To characterize probability density function of crack morphology, W f For crack width, C f L is the salt concentration in the crack m N is the distance from the middle of the crack to the salt ion migration position caused by the concentration gradient in the matrix P,w For the recovery rate of the flowback fluid, D is the diffusion coefficient, C m For the salt concentration in the matrix, Δt is the time the fracture face is in contact with the fracturing fluid, dN P,w Representation pair N P, w Is a derivative of dC f Representation pair C f Is a derivative of (a).
3. The method for optimizing the time for stuffy well based on flowback characteristics of shale gas well according to claim 1, wherein the flowback rate prediction model in the step S3 is as follows:
；
wherein V is imb To suck the volume of fracturing fluid into the rock matrix block, V inj T is the volume of fracturing fluid injected into the formation D For dimensionless time, n is the number of proppant lay-up layers in the fracture, r is the diameter of the proppant particles, a is the length of the matrix block, i.e., the equivalent fracture spacing, ϕ is the porosity, S wf For final water saturation, S wi Is the initial water saturation.
4. The method for optimizing the stuffy well time based on flowback fluid characteristics of a shale gas well according to claim 1, wherein the characteristic length model in the step S4 is as follows:
；
wherein L is c Is the characteristic length，V b For the core matrix volume, A i Is the area of the imbibition contact surface in the ith direction, L Ai The value range of i is a positive integer which is more than or equal to 1 and less than or equal to n, and n is the number of the imbibition directions, so that the imbibition front edge is a distance from the opening surface to the closed boundary.
5. The method for optimizing the time to stuffy well based on flowback characteristics of shale gas well according to claim 1, wherein the dimensionless time model in step S5 is:
；
wherein t is D In order to be a dimensionless time,to specify a dimensionless time model at net pressure, +.>To specify the dimensional time model of formation overburden pressure, L c Is the characteristic length, t is the dead time, k is the reservoir permeability, ϕ is the porosity, σ is the surface tension, and μw is the viscosity.
6. The method for optimizing the time to stuffy well based on flowback characteristics of a shale gas well of claim 3, wherein the volume of fracturing fluid injected into the formation in the flowback rate prediction model is the fracture volume.
7. The method for optimizing the time for a stuffy well based on flowback fluid characteristics of a shale gas well according to claim 1, wherein in the step S1, the spontaneous imbibition experimental test comprises:
step S101: drying shale samples, taking out after cooling, measuring the quality of the dried samples, and testing the T of the dried samples 2 A spectrum;
step S102: placing the dried sample into a beaker filled with distilled water for spontaneous imbibition experiment, and repeatingMeasuring mass and T of imbibition sample 2 Spectrum until there is no longer a significant change.
8. The method for optimizing the time for stuffy well based on flowback fluid characteristics of shale gas well according to claim 1, wherein in the step S1, the method for determining the critical time of the core scale is specifically as follows:
(1) Obtaining imbibition volume T in imbibition process 2 Spectrum and conductivity;
(2) According to the imbibition volume and T obtained in imbibition process 2 Spectrum and conductivity, drawing a permeation volume, a T2 spectrum area and a conductivity time-varying curve;
(3) By imbibition of volume, T 2 And determining the critical time of the core scale by the inflection points of the curve of the spectral area and the conductivity change with time.
9. The method for optimizing the time for a stuffiness based on flowback fluid characteristics of a shale gas well according to claim 7, wherein the early stage of imbibition in the step S1 is positively correlated with the square root of time, and the ion diffusion rate is positively correlated with the square root of time in the whole imbibition process.
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