CN113868824B - Prediction method and system for shale gas pressure post-transformation seam network - Google Patents
Prediction method and system for shale gas pressure post-transformation seam network Download PDFInfo
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Abstract
The invention provides a prediction method for shale gas pressure post-reconstruction seam network, which comprises the following steps: collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting the gas production, the bottom hole pressure and the water yield of a multi-section fractured horizontal well as fitting data; constructing a level set function aiming at the representative points, and determining the level set function values of all the representative points in the reservoir according to microseism distribution; generating a plurality of groups of parameter sets comprising main fracture parameters and secondary fracture parameters by taking the fracturing fracture data as an average value and taking the average value of a preset proportion as a variance, and classifying the fracture types and the penetration types of all positions in a reservoir; and constructing an iterative history fitting algorithm based on a preset algorithm, and obtaining the distribution of the reformed fracture network after shale gas pressure and the distribution of the fracturing fluid after the fracturing fluid invades the stratum according to the converged parameters. The invention provides information such as space distribution, permeability and the like which can be used for automatically transforming the fracture network after the process of inversion according to the production data of the shale gas production well.
Description
Technical Field
The invention relates to the technical field of oil exploitation, in particular to a prediction method and a prediction system for improving a seam network after shale gas pressure.
Background
The shale gas development needs to be assisted by horizontal drilling and multi-stage fracturing technologies, and accurate recognition of fracturing modification parameters is the key to production prediction and design of yield increasing schemes such as repeated fracturing and infill wells. Accurate knowledge of the fracturing modification parameters is the basis for production prediction, and accurate knowledge of the fracture network distribution among the fracturing sections of the single well is the key for repeated fracturing design. In addition, accurate knowledge of the extent of fracture connectivity among multiple wells is a key to the design of infill wells. In addition, the accurate quantification of the uncertainty of the reconstruction of the seam network after the pressing is the key for risk assessment.
At present, the identification methods for spatial distribution, flow conductivity and the like of the reconstructed seam network after shale reservoir lamination mainly include the following three methods:
the first method comprises the following steps: and judging according to the microseism monitoring result, wherein the number and the position of the main cracks are estimated by artificially observing the microseism range. The method is simple to operate, but on one hand, the method is greatly influenced by subjectivity, and the estimation result is often greatly deviated. On the other hand, the influence of a large number of ineffective micro-earthquakes cannot be considered.
The second method comprises the following steps: the judgment is carried out through a fracturing simulation result, accurate crustal stress distribution parameters need to be provided for guaranteeing the simulation precision, but due to the existence of reservoir heterogeneity, accurate crustal stress data are difficult to obtain, and therefore the method is limited in field application.
The third method comprises the following steps: and performing reverse thrust according to the production dynamic data of the gas well so as to obtain the information of the natural main fracture. The method is a typical inverse problem solving process in mathematics. However, the existing reverse-thrust method does not consider the constraint effect of the microseism data on the fracture distribution to cause a large amount of prior information loss, and the inversion result has larger deviation.
The ensemble Kalman filtering method (ENKF) is one of the methods for processing inverse problems, and has been applied to reservoir physical property inversion and reservoir fracture prediction at present. However, the application of the existing collective Kalman filtering method in shale gas gap net inversion is limited, mainly because the method is a sequential assimilation method, and the defects of poor convergence and the like exist in the strong non-linear problem of improving the gap net distribution after the shale gas pressure is processed.
Therefore, the invention provides a prediction method and a prediction system for shale gas pressure post-transformation seam network.
Disclosure of Invention
In order to solve the above problems, the present invention provides a prediction method for shale gas pressure post-reformation seam crossing, which comprises the following steps:
the method comprises the following steps: collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting the gas yield, the bottom hole pressure and the water yield of the multi-section fractured horizontal well as fitting data;
step two: selecting uniformly-scattered representative points in a reservoir, constructing a level set function aiming at the representative points, and determining level set function values of all the representative points in the reservoir according to microseism distribution;
step three: taking the fracturing fracture data in the basic data as an average value, taking the average value of a preset proportion as a variance, generating a plurality of groups of parameter sets containing main fracture parameters and secondary fracture parameters, and classifying the fracture types and the permeability types of all positions in a reservoir;
step four: and constructing an iterative history fitting algorithm based on a preset algorithm by using the basic parameters and the fitting data, and obtaining the distribution of the shale gas pressure post-reconstruction seam network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum according to the converged parameters.
According to one embodiment of the invention, the base parameters include: gas reservoir boundary data, the fracture data, fault data, matrix porosity permeability data, completion information, and production data.
According to an embodiment of the present invention, the step one comprises the following steps:
aiming at the gas production and the water production, respectively adopting corresponding methods to carry out averaging processing according to whether the well is closed in a preset time period, so as to obtain gas production daily data and water production daily data in the preset time period;
and regarding the bottom hole pressure, taking the bottom hole pressure of the last day in the preset time period as bottom hole pressure day data.
According to an embodiment of the present invention, the second step comprises the following steps:
dividing the reservoir into a plurality of cubes by taking each representative point as a cube center and taking the distance between the representative points as the cube side length;
counting the number of microseism events in each cube, and constructing a microseism number set based on the number of the microseisms of all the cubes;
and sequentially calculating to obtain the level set function values of all the representative points according to the number of the microseism events of the cube in which the representative points are positioned and the maximum value of the number of the microseisms in all the cubes.
According to an embodiment of the present invention, the second step comprises the following steps:
and calculating to obtain the level set function value of each position in the reservoir by an interpolation calculation method based on the level set function values of all representative points in the reservoir.
According to one embodiment of the present invention, the third step comprises the following steps:
and assigning values to all positions in the reservoir based on the level set function values of all the positions in the reservoir, wherein the assignment is used for representing the fracture type classification result and the permeability type classification result of the current position.
According to one embodiment of the invention, the primary fracture parameters comprise permeability, primary fracture porosity, half-length, azimuth, and the secondary fracture parameters comprise secondary fracture spatial distribution area, secondary fracture porosity, water saturation, and shape factor parameters.
According to one embodiment of the present invention, the step three comprises the following steps:
and according to the multiple sets of parameter sets, taking the distance between the current position and the shaft as an independent variable, and obtaining the water saturation of each position in the storage layer by a linear interpolation method.
According to one embodiment of the invention, the step four comprises the following steps:
establishing a plurality of double-hole double-infiltration examples which are in one-to-one correspondence with the plurality of sets of parameters, and setting a numerical simulation model based on the plurality of sets of parameters;
based on the fitting data, carrying out production numerical simulation through the numerical simulation model to obtain a plurality of simulation results corresponding to the plurality of calculation examples one by one;
calculating covariance matrixes between the simulation results and actual observed values, and constructing an iterative format of the iterative history fitting algorithm according to the covariance matrixes;
and performing a history fitting process based on the iterative format, and obtaining the distribution of the shale gas pressure post-reconstruction fracture network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum after convergence.
According to another aspect of the present invention, there is also provided a prediction system for shale gas pressure post-reformation seam network, the system comprising:
the device comprises a first module, a second module and a third module, wherein the first module is used for collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting the gas production rate, the bottom hole pressure and the water production rate of a multi-section fractured horizontal well as fitting data;
a second module for selecting representative points that are evenly spread within the reservoir, constructing a level set function for the representative points, and determining a level set function value for all representative points within the reservoir from the microseismic distribution;
the third module is used for generating a plurality of sets of parameter sets comprising main fracture parameters and secondary fracture parameters by taking the fracturing fracture data in the basic data as an average value and taking the average value of a preset proportion as a variance, and classifying the fracture types and the permeability types of all positions in a reservoir;
and the fourth module is used for constructing an iterative history fitting algorithm based on a preset algorithm by using the basic parameters and the fitting data, and obtaining the distribution of the shale gas pressure post-reconstruction seam network and the distribution of the fracturing fluid after invading the stratum according to the converged parameters.
The prediction method and the system for the shale gas pressure post-transformation gap network can automatically invert the information such as the spatial distribution, the permeability and the like of the shale gas pressure post-transformation gap network according to the production data of the shale gas production well. After the information is obtained, the productivity can be more accurately predicted, and the distribution of the residual gas can be determined, so that a foundation is laid for the adjustment of a subsequent development scheme. The method considers the prior constraint of the microseism, so that the inversion result is more accurate, and compared with other inversion methods based on inverse problem solving algorithms (including a collective Kalman filtering method and the like), the iterative history fitting algorithm constructed by the method is higher in fitting precision in reconstruction seam network inversion problems after strong nonlinear shale air pressure.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 shows a flow chart of a prediction method for shale gas pressure post-reformation of a fracture network according to an embodiment of the invention;
FIG. 2 shows a representative point profile for uniform dispersion within a reservoir, according to one embodiment of the invention;
FIG. 3 shows a microseismic event profile for a shale gas production well according to an embodiment of the present invention;
FIG. 4 shows a bottom hole pressure fit result graph according to an embodiment of the invention;
FIG. 5 shows a graph of water production fit results according to an embodiment of the present invention;
FIG. 6 shows a schematic distribution of shale gas pressure post-reformation seam network, according to an embodiment of the present invention; and
fig. 7 shows a block diagram of a prediction system for shale gas pressure post-reformation of a fracture network, according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings.
Fig. 1 shows a flow chart of a prediction method for shale gas pressure post-reformation of a fracture network according to an embodiment of the invention.
As shown in fig. 1, in step S101, basic parameters required to be used in the shale gas numerical simulation process are collected, and the gas production rate, the bottom hole pressure, and the water production rate of the multi-stage fractured horizontal well are selected as fitting data.
In one embodiment, the base parameters include: gas reservoir boundary data, fracture data, fault data, matrix porosity permeability data, completion information, and production data.
In one embodiment, step S101 includes the following steps:
and S1011, aiming at the gas production rate and the water production, respectively adopting a corresponding method to carry out averaging processing according to whether the well is closed within a preset time period, so as to obtain daily data of the gas production rate and daily data of the water production rate within the preset time period.
And S1012, regarding the bottom hole pressure, taking the bottom hole pressure of the last day in a preset time period as bottom hole pressure day data.
Specifically, the fitting data includes all daily gas production data, daily water production data, and daily bottom hole pressure data within a preset time period.
Specifically, if the well is not related in the preset time period, the daily gas production and the daily water production in the preset time period are superposed, an average value is calculated, and the calculated average gas production and average water production are used as the daily data of gas production and the daily data of water production.
And if the well is shut in within a preset time period, namely the gas production and the water production are all zero during the well shut-in period, calculating the average gas production and the average water production except the well shut-in period as the daily data of the gas production and the daily data of the water production. Further, the preset time period may take a value of one month.
As shown in fig. 1, in step S102, representative points that are uniformly distributed in the reservoir (as shown in fig. 2) are selected, a level set function for the representative points is constructed, and level set function values for all the representative points in the reservoir are determined according to the microseismic distribution. Specifically, the level set function value represents the probability value of the reconstruction of the seam network after the current representative point is pressed.
In one embodiment, step S102 includes the following steps:
and S1021, dividing the reservoir into a plurality of cubes by taking each representative point as a cube center and taking the distance between the representative points as the cube side length.
S1022, counting the number of the microseismic events in each cube, and constructing a microseismic number set based on the microseismic numbers of all the cubes. Specifically, the set of microseismic numbers may be expressed as: { N 1 ，N 2 ，…，N n }。
And S1023, sequentially calculating to obtain level set function values of all representative points according to the number of the microseismic events of the cube where the representative points are located and the maximum value of the number of the microseismic events in all cubes.
Specifically, the level set function values of all the representative points can be calculated sequentially by the following formula:
wherein, y i Level set function value, N, representing the ith representative point i Indicates the number of microseismic events of the cube in which the ith representative point is positioned, max { N } 1 ，N 2 …, Nn represents the maximum number of micro-earthquakes in all cubes.
Further, step S102 further includes the steps of:
and calculating to obtain the level set function value of each position in the reservoir by an interpolation calculation method based on the level set function values of all representative points in the reservoir. Specifically, the interpolation calculation method may employ a kriging interpolation calculation method.
As shown in fig. 1, in step S103, with the fracture data in the basic data as an average value and the average value of a preset proportion as a variance, multiple sets of parameter sets including the primary fracture parameters and the secondary fracture parameters are generated, and the fracture types and the permeability types are classified for each position in the reservoir.
Specifically, the preset ratio may be 10%.
In one embodiment, step S103 includes the following steps:
and S1031, assigning values to the positions in the reservoir based on the level set function values of the positions in the reservoir, wherein the values are used for representing the fracture type classification result and the permeability type classification result of the current position.
Specifically, each position of the reservoir is assigned with 1 or 0, if the current position is assigned with 1, the reservoir is regarded as a secondary developed fracture, the permeability of the fracture grid is assigned as the permeability of the fracture, and if not, the reservoir is assigned as the permeability of the matrix;
further, the assignment can indicate the probability of developing cracks at the current position, so that the crack type classification result and the penetration type classification result of the current position are determined. If the value of the current position is 0, the current position is a non-developed crack, and the permeability is the matrix permeability; if the current position is assigned a value greater than 0, for example 0.6, a random number uniformly distributed according to 0 to 1 is generated, if the random number is less than 0.6, the current position is a non-developing fracture and the permeability is the matrix permeability, otherwise the current position is a developing fracture and the permeability value is the fracture permeability.
And S1032, according to the multiple sets of parameter sets, taking the distance between the current position and the shaft as an independent variable, and obtaining the water saturation of each position in the storage layer through a linear interpolation method.
In one embodiment, the primary fracture parameters include permeability, primary fracture porosity, half-length, azimuth angle, and the secondary fracture parameters include secondary fracture spatial distribution area, secondary fracture porosity, water saturation, and shape factor parameters.
As shown in fig. 1, in step S104, an iterative history fitting algorithm is constructed based on a preset algorithm by using the basic parameters and the fitting data, and the distribution of the shale gas pressure post-reformation fracture network and the distribution of the fracturing fluid after invasion into the formation are obtained according to the converged parameters.
Specifically, the preset algorithm may employ the Levenberg-Marquardt method (Levenberg-Marquardt). Further, the post-press reconstruction seam network spatial distribution and attributes corresponding to the production data back-push can be obtained after convergence.
In one embodiment, step S104 includes the following steps:
s1041, establishing a plurality of double-hole double-infiltration examples which are in one-to-one correspondence with the plurality of groups of parameter sets, and setting a numerical simulation model based on the plurality of groups of parameter sets.
S1042, based on the fitting data, carrying out production numerical simulation through a numerical simulation model to obtain a plurality of simulation results corresponding to a plurality of calculation examples one by one. Specifically, the gas production rate, the water production rate and the bottom hole pressure can be simulated in sequence to obtain a simulation result.
S1043, calculating covariance matrixes between the simulation results and the actual observed values, and constructing an iteration format of an iteration history fitting algorithm according to the covariance matrixes.
And S1044, performing a history fitting process based on the iteration format, and obtaining the distribution of the shale gas pressure post-reconstruction fracture network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum after convergence.
Specifically, the iterative format may be represented by:
wherein l represents an iterationIndexes; k represents a time index; j represents an achievement indicator; β represents an iteration step scaling parameter; g k,j,l Represent
In that
Taking the value of (A); m is pr,k,j Represents the prior estimation of the jth model parameter (i.e. the jth assimilation data D) obs,k-1 Later model parameter estimation);
a covariance matrix representing prior model parameters;
a covariance matrix representing model parameters and observed data; g represents a numerical simulation prediction model.
In summary, the method overcomes the technologies of a complex fracture network spatial distribution representation method after shale gas pressure, a multi-well multi-parameter history fitting method, an effective utilization microseism data constraint method, fracture network parameter uncertainty depiction and the like. The method can automatically predict rock air pressure according to production dynamic data and microseism monitoring data and then transform the spatial distribution and the flow conductivity of the seam network.
In one embodiment, taking a shale gas well A as an example, the well is 2000m long. The reservoir temperature was 83 degrees celsius and the virgin formation pressure was 38.2 MPa. The Lane pressure is 6MPa and the Lane volume is 3m 3 /t。
And performing microseism monitoring on the shale gas well A, and detecting 28 sections of microseism signals (shown in figure 3) in total, wherein the microseism signals cover the whole well section on the plane. The microseismic showed the longest response crack half length of 284m, with an average corresponding half length of 134 m.
The bottom hole pressure and the water yield are fitted to the shale gas well A by adopting the prediction method for the shale gas pressure post-reconstruction seam network, which is provided by the application, to obtain the fitting accuracy of more than 85% (as shown in fig. 4 and 5).
FIG. 4 shows a bottom hole pressure fit result graph in accordance with an embodiment of the invention. FIG. 4 is a graph with the abscissa representing time in days; the ordinate represents the bottom hole pressure in Bar.
As shown in fig. 4, the triangular marking curve in the graph represents a real logging bottom pressure curve, and the black solid curve represents a simulation prediction fitting result curve, it can be seen that the fitting accuracy of the two curves is greater than 85%, which indicates that the prediction method for shale gas pressure post-reconstruction seam network provided by the application has high accuracy and can be practically applied.
FIG. 5 shows a graph of the water production fit results according to one embodiment of the present invention. FIG. 5 is a graph with time on the abscissa in days; the ordinate represents the water production rate in m 3 /d。
As shown in fig. 5, a curve 1 is a real observed water yield curve, and a curve 2 is a simulated prediction fitting result curve, and it can be seen that the fitting accuracy of the two curves is greater than 85%, which indicates that the prediction method for shale gas pressure post-reformation seam network provided by the application has high accuracy and can be practically applied.
Fig. 6 shows a schematic distribution diagram of shale gas pressure post-reforming seam network according to an embodiment of the invention. The abscissa and ordinate of fig. 6 are used to indicate the relative position within the reservoir in m.
As shown in fig. 6, the inversion result of the spatial distribution of the fracture network of the fracturing reformation of the shale gas well a is shown, and the darker color represents that the probability of the secondary fracture network is higher. The results of the inversion of the physical parameters of the remaining gap nets are shown in Table 1 below. Therefore, the method achieves good effects on the prediction problem of the fracture-modified fracture network of the example.
TABLE 1 fracture network parameter inversion results for fracture reformation
| Parameter(s) | Mean value | Standard deviation of | 95% confidence interval |
| Permeability of logarithmic main crack | 7.94 | 0.256 | 7.428-8.452 |
| Porosity of main crack | 0.0756 | 0.0104 | 0.0548-0.0964 |
| Half-length of main crack | 57.85 | 3.81 | 50.23-65.47 |
| Permeability of crack | 0.0878 | 0.0285 | 0.0308-0.1448 |
| Porosity of crack | 0.0107 | 0.000837 | 0.009-0.0124 |
| Form factor | 0.0603 | 0.0108 | 0.0387-0.0819 |
Experiments show that the iteration history fitting algorithm constructed based on the Levenberg-Marquardt algorithm can be used for automatic prediction of the reformed seam network after shale gas pressure based on microseism prior constraint is considered. According to the method, the distribution of the transformed fracture network after fracturing and the distribution of the fracturing fluid after invading the stratum can be quickly and accurately inverted according to the production data of the shale gas production well, so that a foundation is laid for the adjustment of a subsequent development scheme.
Fig. 7 shows a block diagram of a prediction system for shale gas pressure post-reformation of seam network according to an embodiment of the invention.
As shown in fig. 7, the prediction system 700 includes a first module 701, a second module 702, a third module 703, and a fourth module 704.
The first module 701 is used for collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting gas production, bottom hole pressure and water production of the multi-section fractured horizontal well as fitting data.
The second module 702 is used to select uniformly dispersed representative points within the reservoir, construct a level set function for the representative points, and determine the level set function values for all representative points within the reservoir from the microseismic distributions.
The third module 703 is configured to generate a plurality of sets of parameter sets including a main fracture parameter and a secondary fracture parameter by using the fracturing fracture data in the basic data as an average value and using an average value of a preset proportion as a variance, and classify fracture types and permeability types at various positions in the reservoir.
The fourth module 704 is configured to construct an iterative history fitting algorithm based on a preset algorithm by using the basic parameters and the fitting data, and obtain distribution of the shale gas pressure post-reconstruction fracture network and distribution of the fracturing fluid after invading the formation according to the converged parameters.
In conclusion, the prediction method and the prediction system for the shale gas pressure post-transformation gap net can automatically invert the information such as the space distribution, the permeability and the like of the post-transformation gap net according to the production data of the shale gas production well. After the information is obtained, the productivity can be more accurately predicted, and the residual gas distribution is determined, so that a foundation is laid for the adjustment of a subsequent development scheme. The method considers the prior constraint of the microseism, so that the inversion result is more accurate, and compared with other inversion methods based on inverse problem solving algorithms (including a collective Kalman filtering method and the like), the iterative history fitting algorithm constructed by the method is higher in fitting precision in reconstruction seam network inversion problems after strong nonlinear shale air pressure.
It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.
Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrase "one embodiment" or "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A prediction method for shale gas pressure post-reconstruction seam network is characterized by comprising the following steps:
the method comprises the following steps: collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting the gas production, the bottom hole pressure and the water yield of a multi-section fractured horizontal well as fitting data;
step two: selecting uniformly-scattered representative points in a reservoir, constructing a level set function aiming at the representative points, and determining level set function values of all the representative points in the reservoir according to microseism distribution;
step three: taking the fracturing fracture data in the basic data as an average value, taking the average value of a preset proportion as a variance, generating a plurality of groups of parameter sets containing main fracture parameters and secondary fracture parameters, and classifying the fracture types and the permeability types of all positions in a reservoir;
step four: and constructing an iterative history fitting algorithm based on a preset algorithm by using the basic parameters and the fitting data, and obtaining the distribution of the shale gas pressure post-reconstruction seam network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum according to the converged parameters.
2. The method of claim 1, wherein the base parameters comprise: gas reservoir boundary data, the fracture data, fault data, matrix porosity permeability data, completion information, and production data.
3. The method of claim 1, wherein step one comprises the steps of:
respectively carrying out averaging processing by adopting corresponding methods according to whether the well is closed within a preset time period aiming at the gas production rate and the water production quantity to obtain gas production rate daily data and water production quantity daily data within the preset time period;
and regarding the bottom hole pressure, taking the bottom hole pressure of the last day in the preset time period as bottom hole pressure day data.
4. The method of claim 1, wherein the step two comprises the steps of:
dividing the reservoir into a plurality of cubes by taking each representative point as a cube center and taking the distance between the representative points as the cube side length;
counting the number of microseism events in each cube, and constructing a microseism number set based on the number of the microseisms of all the cubes;
and sequentially calculating the level set function values of all the representative points according to the number of the microseism events of the cube where the representative points are located and the maximum value of the number of the microseisms in all the cubes.
5. The method of claim 1, wherein the step two comprises the steps of:
and calculating the level set function value of each position in the reservoir by an interpolation calculation method based on the level set function values of all the representative points in the reservoir.
6. The method of claim 5, wherein the third step comprises the steps of:
and assigning values to each position in the reservoir based on the level set function values of each position in the reservoir, wherein the assignments are used for representing the fracture type classification result and the permeability type classification result of the current position.
7. The method of claim 1, wherein the primary fracture parameters comprise permeability, primary fracture porosity, half-length, azimuth angle, and the secondary fracture parameters comprise secondary fracture spatial distribution area, secondary fracture porosity, water saturation, and shape factor parameters.
8. The method of claim 7, wherein the third step comprises the steps of:
and according to the multiple sets of parameter sets, taking the distance between the current position and the shaft as an independent variable, and obtaining the water saturation of each position in the storage layer by a linear interpolation method.
9. The method of claim 1, wherein the fourth step comprises the steps of:
establishing a plurality of double-hole double-infiltration examples which are in one-to-one correspondence with the plurality of sets of parameters, and setting a numerical simulation model based on the plurality of sets of parameters;
performing production numerical simulation through the numerical simulation model based on the fitting data to obtain a plurality of simulation results corresponding to the plurality of calculation examples one by one;
calculating covariance matrixes between the simulation results and the actual observed values, and constructing an iteration format of the iteration history fitting algorithm according to the covariance matrixes;
and performing a history fitting process based on the iterative format, and obtaining the distribution of the shale gas pressure post-reconstruction fracture network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum after convergence.
10. A predictive system for post-pneumatic shale transformation of fracture networks, the system comprising:
the first module is used for collecting basic parameters required to be used in the shale gas numerical simulation process, and selecting the gas production rate, the bottom hole pressure and the water yield of the multi-section fractured horizontal well as fitting data;
a second module for selecting representative points that are evenly spread within the reservoir, constructing a level set function for the representative points, and determining a level set function value for all representative points within the reservoir from the microseismic distribution;
the third module is used for generating a plurality of groups of parameter sets comprising main fracture parameters and secondary fracture parameters by taking the fracturing fracture data in the basic data as an average value and taking the average value of a preset proportion as a variance, and classifying the fracture types and the penetration types of all positions in a reservoir;
and the fourth module is used for constructing an iterative history fitting algorithm based on a preset algorithm by using the basic parameters and the fitting data, and obtaining the distribution of the shale gas pressure post-reconstruction seam network and the distribution of the fracturing fluid after the fracturing fluid invades the stratum according to the converged parameters.
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| CN109472037A (en) * | 2017-09-08 | 2019-03-15 | 中国石油化工股份有限公司 | Shale gas reservoir man-made fracture parameter preferred method and system |
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