CN114718556A - Method, device and equipment for acquiring artificial crack parameters - Google Patents
Method, device and equipment for acquiring artificial crack parameters Download PDFInfo
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Abstract
The application discloses a method, a device and equipment for acquiring artificial fracture parameters, wherein the method comprises the steps of acquiring a shale gas well model; obtaining initial probability distribution of artificial fracture parameters in a shale gas well model; obtaining a plurality of first sample parameters based on the initial probability distribution; obtaining a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and the reference fitting error; and acquiring target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model. The method can reduce the artificial workload in the process of acquiring the artificial crack parameters and improve the accuracy of the result of acquiring the artificial crack parameters.
Description
Technical Field
The embodiment of the application relates to the technical field of shale gas well exploitation, in particular to a method, a device and equipment for acquiring artificial fracture parameters.
Background
Shale gas belongs to artificial gas reservoirs, and artificial cracks are formed by large-scale hydraulic fracturing during exploitation. The production capacity of the shale gas well is closely related to the artificial fracture, and the core step of evaluating the production capacity of the shale gas well is to obtain the parameters of the artificial fracture.
In the related technology, a shale gas well model is established, then artificial fracture parameters are modified manually and continuously, the actual production indexes of the gas well such as gas production rate and liquid production amount are fitted by using the calculation result of the model, and if the calculation result is very close to the actual observation result, the artificial fracture parameters in the model are considered to accord with the real situation of the shale gas well, so that the artificial fracture parameters are obtained.
However, due to too many factors influencing the production capacity of the shale gas well, the related technology strongly depends on manual parameter adjustment, so that the fitting result has great uncertainty, and the accuracy of the obtained artificial fracture parameters is not high; in addition, because the number of samples is huge, the workload of manual parameter adjustment is huge, and therefore, the related technology is not suitable for practical situations and the efficiency is low.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for acquiring artificial crack parameters, which are used for reducing the artificial workload in the process of acquiring the artificial crack parameters and improving the accuracy of the result of acquiring the artificial crack parameters.
In a first aspect, an embodiment of the present application provides a method for obtaining an artificial fracture parameter, where the method includes: obtaining a shale gas well model; obtaining initial probability distribution of artificial fracture parameters in a shale gas well model; obtaining a plurality of first sample parameters based on the initial probability distribution; obtaining a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and the reference fitting error; and acquiring target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model.
In one possible implementation, obtaining the first sample parameter based on the initial probability distribution includes: random sampling is performed in the initial probability distribution by a Markov chain Monte Carlo method to obtain a plurality of first sample parameters.
In one possible implementation, obtaining a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters, and the reference fitting error includes: obtaining a plurality of first sample models based on the plurality of first sample parameters and the shale gas well model; obtaining a plurality of first fitting errors through a historical fitting error function based on the plurality of first sample models; establishing a first proxy model based on the first sample parameters and the first fitting errors; a plurality of second sample parameters are obtained based on the first proxy model and the reference fitting error.
In one possible implementation, building a first proxy model based on the plurality of first sample models and the plurality of first fitting errors includes: establishing a second proxy model through a K nearest neighbor classification (KNN) algorithm based on the first sample parameters and the first fitting errors; obtaining a plurality of third sample parameters by a Markov chain Monte Carlo method based on the second proxy model; and iterating the second proxy model based on the third sample parameters and the historical fitting error function to obtain the first proxy model.
In one possible implementation, iterating the second proxy model based on the plurality of third sample parameters and the historical fitting error function to obtain the first proxy model includes: and iterating the second proxy model based on the plurality of third sample parameters and the historical fitting error function, and taking the iterated second proxy model reaching a first condition as the first proxy model, wherein the first condition is that the difference value between the fitting error of the reference number of third sample parameters obtained through the historical fitting error function and the fitting error of the iterated second proxy model is smaller than a reference threshold value.
In one possible implementation, obtaining a plurality of second sample parameters based on the first surrogate model and the reference fitting error includes: and acquiring a plurality of sample parameters meeting the reference fitting error based on the first proxy model, and taking the plurality of sample parameters meeting the reference fitting error as a plurality of second sample parameters.
In one possible implementation, obtaining the target artificial fracture parameter based on the plurality of second sample parameters and the shale gas well model includes: obtaining a target probability distribution of the artificial fracture parameters based on the plurality of second sample parameters; and inverting the target artificial fracture parameters based on the target probability distribution and the shale gas well model to obtain the target artificial fracture parameters.
In a possible implementation manner, the artificial fracture parameter at least includes one of a length of the artificial fracture, a fracture height value of the artificial fracture, a water saturation of the artificial fracture, a width of the artificial fracture, and a conductivity of the artificial fracture.
According to the method for obtaining the artificial fracture parameters, the initial probability distribution of the artificial fracture parameters is obtained, the first sample parameters are obtained based on the probability distribution, iteration and optimization are further carried out on the first sample parameters, the second sample parameters with higher representativeness are obtained, the artificial fracture parameters are obtained based on the second sample parameters, the accuracy of the finally obtained artificial fracture parameters is higher, and the problems that the existing artificial fracture parameters of the shale gas well cannot be accurately obtained and the artificial workload is large are effectively solved.
In a second aspect, an embodiment of the present application provides an apparatus for obtaining an artificial fracture parameter, where the apparatus includes: the first obtaining module is used for obtaining a shale gas well model; the second acquisition module is used for acquiring the initial probability distribution of the artificial fracture parameters in the shale gas well model; a third obtaining module, configured to obtain a plurality of first sample parameters based on the initial probability distribution; the fourth obtaining module is used for obtaining a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and the reference fitting error; and the fifth obtaining module is used for obtaining the target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model.
In a possible implementation manner, the third obtaining module is configured to perform random sampling in the initial probability distribution by using a markov chain monte carlo method to obtain a plurality of first sample parameters.
In one possible implementation manner, the fourth obtaining module is configured to obtain a plurality of first sample models based on the plurality of first sample parameters and the shale gas well model; obtaining a plurality of first fitting errors through a historical fitting error function based on the plurality of first sample models; establishing a first proxy model based on the first sample parameters and the first fitting errors; a plurality of second sample parameters are obtained based on the first proxy model and the reference fitting error.
In a possible implementation manner, the fourth obtaining module is configured to establish a second proxy model through a K-nearest neighbor classification KNN algorithm based on the plurality of first sample parameters and the plurality of first fitting errors; obtaining a plurality of third sample parameters by a Markov chain Monte Carlo method based on the second proxy model; and iterating the second agent model based on the third sample parameters and the historical fitting error function to obtain the first agent model.
In a possible implementation manner, the fourth obtaining module is configured to iterate the second proxy model based on the plurality of third sample parameters and the historical fitting error function, and use the iterated second proxy model that meets a first condition as the first proxy model, where the first condition is that a difference between a fitting error obtained by the reference number of third sample parameters through the historical fitting error function and a fitting error obtained by the iterated second proxy model is smaller than a reference threshold.
In a possible implementation manner, the fourth obtaining module is configured to obtain, based on the first proxy model, a plurality of sample parameters that satisfy a reference fitting error, and use the plurality of sample parameters that satisfy the reference fitting error as a plurality of second sample parameters.
In a possible implementation manner, the fifth obtaining module is configured to obtain a target probability distribution of the artificial fracture parameter based on a plurality of second sample parameters; and inverting the target artificial fracture parameters based on the target probability distribution and the shale gas well model to obtain the target artificial fracture parameters.
In one possible implementation manner, the target artificial fracture parameter includes at least one of a length of the artificial fracture, a fracture height value of the artificial fracture, a water saturation of the artificial fracture, a width of the artificial fracture, and a conductivity of the artificial fracture.
In a third aspect, an embodiment of the present application provides a computer device, where the computer device includes a processor and a memory, where the memory stores at least one instruction, and the at least one instruction, when executed by the processor, implements the method for acquiring artificial fracture parameters according to any one of the above first aspects.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where at least one instruction is stored in the computer-readable storage medium, and when executed, the at least one instruction implements the method for acquiring artificial fracture parameters according to any one of the above first aspects.
In a fifth aspect, an embodiment of the present application provides a computer program (product), where the computer program (product) includes: computer program code which, when executed by a computer, causes the computer to perform the method of acquiring artificial fracture parameters in the above aspects.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for obtaining parameters of an artificial fracture according to an embodiment of the present disclosure;
FIG. 2 is a shale gas well model provided by an embodiment of the present application;
FIG. 3 is a shale gas well model provided by an embodiment of the present application;
FIG. 4 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 5 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 6 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 7 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 8 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 9 shows the screening results of a second sample model provided in the examples of the present application;
FIG. 10 is a visual inversion result provided by an embodiment of the present application;
FIG. 11 is a visual inversion result provided by an embodiment of the present application;
FIG. 12 is a visual inversion result provided by an embodiment of the present application;
FIG. 13 is a visual inversion result provided by an embodiment of the present application;
FIG. 14 is a visual inversion result provided by an embodiment of the present application;
FIG. 15 is a visual inversion result provided by an embodiment of the present application;
FIG. 16 is a visual inversion result provided by an embodiment of the present application;
FIG. 17 is a visual inversion result provided by an embodiment of the present application;
FIG. 18 is a visual inversion result provided by an embodiment of the present application;
FIG. 19 is a visual inversion result provided by an embodiment of the present application;
FIG. 20 is a visual inversion result provided by an embodiment of the present application;
FIG. 21 is a visual inversion result provided by an embodiment of the present application;
FIG. 22 is a visual inversion result provided by an embodiment of the present application;
fig. 23 is a visual inversion result provided in the embodiment of the present application;
FIG. 24 is a visual inversion result provided by an embodiment of the present application;
FIG. 25 is a visual inversion result provided by an embodiment of the present application;
FIG. 26 is a visual inversion result provided by an embodiment of the present application;
fig. 27 is a visual inversion result provided in the embodiment of the present application;
FIG. 28 is a visual inversion result provided by an embodiment of the present application;
FIG. 29 is a visual inversion result provided by an embodiment of the present application;
fig. 30 is a schematic view of an apparatus for obtaining parameters of an artificial fracture according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
Please refer to fig. 1, which shows a flowchart of a method for obtaining parameters of an artificial fracture according to an embodiment of the present application. The method provided by the embodiment of the application can comprise the following steps:
The shale gas well model refers to a shale gas multi-section fracturing horizontal well gas-liquid two-phase model, the model comprises two-phase fluid flow, and optionally, the two-phase fluid flow can be natural gas and formation water.
In a possible implementation manner, a shale gas well model is established according to the existing information of the shale gas well, and the shale gas well model is obtained. The existing information of the shale gas well comprises the information of the existing geology, gas reservoir, fluid, crack and well.
Illustratively, the shale gas well model is built according to the existing information of the shale gas well, and the method comprises the following steps of 1011-:
1011. and establishing an artificial fracture of the shale gas well.
In one possible implementation, an Embedded Discrete Fracture Mode (EDFM) technique is used to create an artificial fracture for a shale gas well.
1012. And based on the artificial fractures, establishing a shale gas well model according to the existing information of the shale gas well to obtain the shale gas well model.
In a possible implementation mode, a shale gas well gas-liquid two-phase numerical model is established according to the existing information of the shale gas well based on the artificial fractures, and the shale gas well gas-liquid two-phase numerical model is used as the shale gas well model.
In another possible implementation mode, based on the artificial fractures, a shale gas well gas-liquid two-phase simulation model is established according to the existing information of the shale gas well, and the shale gas well gas-liquid two-phase simulation model is used as the shale gas well model.
In order to enable the shale gas well model to be closer to the real situation of the shale gas well, in any one of the above implementation manners, a natural fracture can be added into the shale gas well model, and the natural fracture can be established by an embedded discrete fracture technology.
Referring to fig. 2, fig. 2 is a shale gas well model provided in an embodiment of the present application. As shown in fig. 2, in this embodiment, based on the artificial fracture, a shale gas well gas-liquid two-phase simulation model is established as a shale gas well model according to the existing information of the shale gas well. The simulation model comprises information such as the length of a shaft, the perforation position, the basic form of a fracture and the like, and does not comprise distribution information of natural fractures.
Optionally, the shale gas well model may further include a natural fracture, please refer to fig. 3, and fig. 3 is a shale gas well model provided in an embodiment of the present application. As shown in fig. 3, in this embodiment, based on the artificial fractures, a shale gas well gas-liquid two-phase simulation model is established as a shale gas well model according to the existing information of the shale gas well, where the shale gas well gas-liquid two-phase simulation model further includes distribution information of natural fractures. Optionally, the information on the distribution of natural fractures is obtained by stochastic simulation.
And 102, obtaining initial probability distribution of the artificial fracture parameters in the shale gas well model.
The shale gas well model comprises deterministic parameters and uncertain parameters, wherein the deterministic parameters comprise the length of a horizontal well, the number of perforation clusters, the length of a reservoir, the width of the reservoir, the thickness of the reservoir, the permeability of a matrix and the like; the uncertain parameters comprise the length of the artificial fracture, the height value of the artificial fracture, the water saturation of the artificial fracture, the width of the artificial fracture, the flow conductivity coefficient of the artificial fracture and the like, and the uncertain parameters are the artificial fracture parameters of the embodiment of the application.
In one possible implementation, obtaining an initial probability distribution of an artificial fracture parameter in a shale gas well model comprises: and primarily setting the deterministic parameters and the artificial fracture parameters of the shale gas well model, and acquiring the numerical values of the deterministic parameters and the initial probability distribution of the artificial fracture parameters.
Where the deterministic parameter values are fixed model parameter values, they may be set as empirical values. For the initial setting of the artificial fracture parameters, a prior distribution can be given to the artificial fracture parameters to obtain the initial probability distribution of the artificial fracture parameters.
Prior distribution refers to estimating a probability distribution for a target sample based on other relevant parameters prior to testing or sampling. Optionally, in this embodiment, a random, uniform estimated probability distribution is assigned to the artificial fracture parameters as the distribution of the initial sampling.
Optionally, if the shale gas well model further includes a natural fracture, the natural fracture parameter is a deterministic parameter in the shale gas well model, and a set of fixed parameter combinations may be used in the embodiment of the present application to initially set the natural fracture parameter.
In one possible implementation manner, the initial probability distribution of the artificial fracture is sampled, and a plurality of sampled artificial fracture parameters are used as first sample parameters.
Alternatively, the plurality of first sample parameters may be obtained by performing random sampling in the initial probability distribution by a markov chain monte carlo method. The Markov chain Monte Carlo method is a method for simulating and sampling by a computer under the Bayes theory framework.
Alternatively, the plurality of first sample parameters may be obtained by sampling in the initial probability distribution by a latin hypercube sampling method in an orthogonal experiment. The Latin hypercube sampling method can ensure that the first sample parameters are uniformly distributed in a high-dimensional space, thereby ensuring the unbiased property of sampling. Optionally, the number of the first sample parameters may be appropriately adjusted according to the number of the artificial fracture parameters, which is not limited in the embodiment of the present invention.
And 104, acquiring a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and the reference fitting error.
Due to inaccuracy of the first sample parameter, the result error is large when the first sample parameter is directly adopted to obtain the artificial fracture parameter, and therefore the shale gas well model and the reference fitting error are combined to obtain the second sample parameter which is more representative and accurate on the basis of the obtained first sample parameter.
In one possible implementation manner, the obtaining of the plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and the reference fitting error comprises the following steps.
1041, obtaining a plurality of first sample models based on the plurality of first sample parameters and the shale gas well model.
In one possible implementation, obtaining a plurality of first sample models based on the plurality of first sample parameters and the shale gas well model includes: and inputting the plurality of first sample parameters into the shale gas well model to obtain a plurality of shale gas well models corresponding to the plurality of first sample parameters, and taking the plurality of shale gas well models corresponding to the plurality of first sample parameters as the first sample models. And the first sample parameters correspond to the first sample models one to one.
For example: and taking the first sample parameter as an input parameter of an oil reservoir simulator, and combining an embedded discrete fracture preprocessor according to the shale gas well model to obtain a first sample model.
1042, obtaining a plurality of first fitting errors by a historical fitting error function based on the plurality of first sample models.
The historical fit error function is used to evaluate the accuracy of the sample model. Optionally, the history fit error function is defined according to the following formula:
where i is the sequence of actual data points, j is the sequence of history fit objective functions, n is the number of actual points, m is the number of history fit objective functions, xij,modelSimulation results of the representative model, i.e. the first sample model, xij,historyIs actual production data, NFjIs a normalized value defined as the maximum difference, w, between the simulation result and the actual production dataijRepresenting the weight of the history fit data.
And calculating a first fitting error corresponding to each first sample model through a historical fitting error function based on the plurality of first sample models. As can be seen from the foregoing, the first sample parameters correspond to the first sample models one-to-one, and therefore, the first sample parameters also correspond to the first fitting errors one-to-one.
1043, building a first proxy model based on the first plurality of sample parameters and the first plurality of fitting errors.
In one possible implementation, the building of the first proxy model based on the plurality of first sample parameters and the plurality of first fitting errors includes the following steps.
10431, building a second proxy model by a K-nearest neighbor classification, KNN, algorithm based on the first plurality of sample parameters and the first plurality of fitting errors.
Optionally, a second surrogate model characterizing the first fitting error and the first sample parameter is established using K-nearest neighbor (KNN) training data based on the first sample parameters and the first fitting errors. And the first sample parameter is input as an independent variable based on the second proxy model, and the first fitting error is output as a dependent variable of the second proxy model, so that the second proxy model is generated by training data.
Further alternatively, the basic concept of the K-nearest neighbor classification algorithm is shown as the following formula:
θiis a combination of uncertainty parameters for the k nearest observation points, i.e. k first sample parameters, i denotes each first sample parameter, y (θ)i) Is the target variable value of the k nearest observation points, i.e. the first fitting error corresponding to the first sample parameter of the k nearest observation points, theta0Is a combination of uncertainty parameters, i.e. sample parameters as arguments, y (theta)0) Is the target variable value to be predicted, i.e. the fitting error as a dependent variable.
The KNN algorithm can effectively ensure that the obtained target variable value is more representative in high-dimensional space distribution. Meanwhile, unlike the polynomial algorithm, the KNN algorithm does not have the problem of overfitting. In addition, the KNN algorithm can ensure high efficiency of calculation, and the calculation time can be increased by 5-20 times compared with other algorithms.
10432, obtaining a plurality of third sample parameters by a markov chain monte carlo method based on the second proxy model.
Based on the second proxy model, a number of sample parameters may be regenerated using the Markov chain Monte Carlo method, with the sample parameters as third sample parameters.
10433, iterating the second surrogate model based on the plurality of third sample parameters and the historical fitting error function to obtain the first surrogate model.
In one possible implementation, iterating the second proxy model based on the plurality of third sample parameters and the historical fitting error function to obtain the first proxy model includes: and iterating the second proxy model based on the third sample parameters and the historical fitting error function, and taking the iterated second proxy model reaching the first condition as the first proxy model. The first condition is that the difference value between the fitting error of the reference number of third sample parameters obtained through the historical fitting error function and the fitting error obtained through the iterated second proxy model is smaller than the reference threshold value.
Wherein iterating the second proxy model based on the plurality of third sample parameters and the historical fitting error function comprises: inputting the plurality of third sample parameters into the second proxy model to obtain fitting errors of the third sample parameters obtained through the second proxy model, and taking the fitting errors of the third sample parameters obtained through the second proxy model as second fitting errors; selecting a third sample parameter with the minimum second fitting error, and acquiring a third sample model based on the shale gas well model; repeating the steps 1042 and 10431 based on the third sample model to obtain an iterated second proxy model, if the iterated second model does not satisfy the first condition, continuing to obtain new third sample parameters based on the iterated second model according to the step 10432 until the iterated second proxy model satisfies the first condition, and not obtaining new third sample parameters, and using the iterated second proxy model as the first proxy model. The first proxy model thus obtained is sufficiently accurate to describe the relationship between the fitting error and the sample parameters.
It should be noted that the fitting error of the third sample parameter in the first condition, which is obtained by the historical fitting error function, and the fitting error of the third sample parameter, which is obtained by the iterated second proxy model, are obtained by the following method.
Inputting the plurality of third sample parameters into a shale gas well model to obtain a plurality of shale gas well models corresponding to the plurality of third sample parameters, and inputting the plurality of shale gas well models corresponding to the plurality of third sample parameters into a historical fitting error function to obtain a fitting error of the third sample parameters obtained through the historical fitting error function; and inputting the plurality of third sample parameters into the second proxy model to obtain the fitting error of the third sample parameters obtained by the second proxy model.
1044, a plurality of second sample parameters are obtained based on the first surrogate model and the reference fitting error.
In one possible implementation, obtaining a plurality of second sample parameters based on the first surrogate model and the reference fitting error includes: and acquiring a plurality of sample parameters meeting the reference fitting error based on the first proxy model, and taking the plurality of sample parameters meeting the reference fitting error as a plurality of second sample parameters.
Optionally, a plurality of second sample models satisfying the reference fitting error are obtained based on the first proxy model, and sample parameters corresponding to the plurality of second sample models satisfying the reference fitting error are used as the plurality of second sample parameters.
And screening the second sample model from the shale gas well model corresponding to the third sample parameter generated last in the step 1043. The second fitting error is the fitting error calculated by the second proxy model of the third sample parameter generated last in the step 1043. The reference fitting error is a specific fitting error threshold, and optionally, the reference fitting error may be set according to engineering experience, for example: the reference fitting error may be obtained based on the fitting effect of a representative simulation curve (bottom hole flowing pressure curve, water production curve).
And after the second sample model is screened out, counting the artificial crack parameters corresponding to the second sample model, and taking the artificial crack parameters corresponding to the second sample model as the second sample parameters.
Optionally, the screened second sample model may or may not include a natural fracture distribution.
Referring to fig. 4-9, fig. 4-9 show the screening results of a second sample model provided in the embodiments of the present application. Fig. 4, 6, and 8 show results obtained without considering the natural fracture, and fig. 5, 7, and 9 show results obtained with considering the fixed set of natural fracture parameters.
And 105, acquiring target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model.
In one possible implementation manner, the target artificial fracture parameter is obtained based on the plurality of second sample parameters and the shale gas well model, and the method comprises the following steps.
1051. And obtaining a target probability distribution of the artificial fracture parameters based on the plurality of second sample parameters.
And counting the plurality of second sample parameters to obtain the target probability distribution of the artificial fracture parameters.
1052. And inverting the target artificial fracture parameters based on the target probability distribution and the shale gas well model to obtain the target artificial fracture parameters.
The target artificial fracture parameters at least comprise one of the length of the artificial fracture, the height value of the artificial fracture, the water saturation of the artificial fracture, the conductivity of the artificial fracture and the width of the artificial fracture.
The inversion of the artificial fracture parameters refers to the fact that parameter distribution rules such as the geometric morphology of the artificial fracture and the diversion coefficient are obtained by applying an established shale gas well model and utilizing a computer program through numerical simulation calculation and historical production data fitting.
The production history fitting refers to calculating main dynamic indexes in the shale gas development process by using recorded shale gas reservoir static parameters, comparing the calculated result with the observed main dynamic indexes of the gas well, such as wellhead pressure, yield and the like, modifying the gas reservoir static parameters if the two indexes are different, and calculating again by using the modified static parameters and comparing until the calculated result is equivalent to the actually measured dynamic parameters. In the embodiment of the application, the shale gas well artificial fracture parameters to be inverted are equivalent to the static parameters to be modified in the method.
Optionally, based on the target probability distribution and the shale gas well model, the target artificial fracture parameters are gradually inverted by using a statistical method, which may include any one or more of the following methods, and the embodiment of the present application is not limited.
The method comprises the steps of inverting the length of an artificial fracture based on target probability distribution and a shale gas well model. The parameter depends on geological factors and construction effects, and the empirical value of the length of the artificial fracture is generally in the range of 80-120 meters.
And secondly, inverting the seam height value of the artificial fracture based on the target probability distribution and the shale gas well model. The parameter depends on the thickness of a specific shale gas reservoir, the fluctuation range of the seam height value of the artificial fracture is large, the numerical value can reach below 10 meters at the lowest, and the maximum reaches about the height of the gas reservoir.
And thirdly, inverting the water saturation of the artificial fracture of the shale gas well based on the target probability distribution and the shale gas well model. Because shale gas often encounters the phenomenon of reverse drainage of fracturing water in the early development stage, the water saturation of the artificial fracture generally fluctuates in an interval of 0.6-0.7%. However, different reservoir conditions may also result in different artificial fracture water saturations (which can sometimes fall below 0.5%).
And fourthly, inverting the width of the artificial fracture of the shale gas well based on the target probability distribution and the shale gas well model. The width equivalent value of the artificial fracture is generally 0.2-0.8 m, and the parameter has close relation with the yield of water.
And fifthly, inverting the diversion coefficient of the artificial fracture of the shale gas well based on the target probability distribution and the shale gas well model. The value of this parameter depends on the effectiveness of the fracturing operation, with wide fluctuations (10-8 mm to sagm).
Optionally, after inverting the target artificial fracture parameter based on the target probability distribution and the shale gas well model, the method further includes: and generating a visual inversion result based on the inverted artificial fracture parameters. Optionally, the inverted artificial fracture parameters may include one or more of the length of the inverted artificial fracture, the fracture height value of the inverted artificial fracture of the shale gas well, the water saturation of the inverted artificial fracture of the shale gas well, the width of the inverted artificial fracture, and the conductivity coefficient of the inverted artificial fracture of the shale gas well.
In a possible implementation mode, a combined type histogram and a probability distribution map are drawn based on the length of the inverted artificial fracture, the height value of the inverted artificial fracture, the water saturation of the inverted artificial fracture, the width of the inverted artificial fracture and the diversion coefficient of the inverted artificial fracture. Optionally, the visualization may or may not include the natural fracture parameters.
Please refer to fig. 10-19, which illustrate a visual inversion result provided by the embodiments of the present application. In fig. 10-19, a combined histogram and probability distribution plot are used to represent the inversion results, where the bar represents the frequency of occurrence of the artificial fracture parameters for this range, and the curve represents the probability distribution curve fitted from the frequency bar. Fig. 10, 12, 14, 16, 18 represent inversion results without regard to the natural fracture, and fig. 11, 13, 15, 17, 19 represent inversion results with regard to a fixed set of natural fracture parameters.
In another possible implementation manner, an accumulated probability distribution map is generated based on the length of the inverted artificial shale gas well fracture, the seam height value of the inverted artificial shale gas well fracture, the water saturation of the inverted artificial shale gas well fracture, the width of the inverted artificial shale gas well fracture and the diversion coefficient of the inverted artificial shale gas well fracture. Optionally, the visualization may or may not include the natural fracture parameters.
The cumulative probability distribution map refers to a visualization distribution map with probabilities of P10, P50, and P90. The values of P10 and P90 are helpful in understanding the actual range of a certain artificial fracture parameter that is representative, while the value of P50 may determine the average representative value of a certain artificial fracture parameter. The cumulative probability distribution map facilitates better ranging of the inversion results.
Please refer to fig. 20-29, which illustrate a visual inversion result provided by the embodiment of the present application. In fig. 20-29, the inversion results are represented using cumulative probability distribution plots, where fig. 20, 22, 24, 26, 28 represent cumulative probability distributions without consideration of natural fracture parameters, and fig. 21, 23, 25, 27, 29 represent cumulative probability distributions with consideration of a fixed set of natural fracture parameters.
The method for acquiring the artificial fracture parameters effectively solves the problems that the artificial fracture parameters of the shale gas well cannot be accurately acquired and the artificial workload is large at present, and through establishing a shale gas well model, setting a prior probability distribution interval of the artificial fracture parameters, and adopting a Markov chain-Monte Carlo algorithm to carry out iterative optimization on the artificial fracture parameters of the shale gas horizontal well, the most representative artificial fracture numerical value and a numerical model which is as close to a real situation as possible are acquired, so that the production dynamic prediction of the shale gas horizontal well is more approximate to actual production, and the uncertainty and the artificial workload brought by a large number of non-representative samples are reduced. The posterior probability distribution of the parameters, namely the target probability distribution, is obtained by counting all optimized artificial fracture parameters, the distribution trend of the geometric forms (length, width, fracture height values and the like) and the flow conductivity coefficients of the artificial fractures obtained by the determination method is an infinite approximation to the underground real condition, researchers and decision makers can have brand-new quantitative knowledge on the underground fracture network, and the method has very important significance on evaluation of the fracturing effect of the deep shale gas well, final recoverable reserve prediction and reasonable well spacing optimization.
Based on the same technical concept, please refer to fig. 30, which shows a schematic diagram of an apparatus for acquiring an artificial fracture parameter provided in an embodiment of the present application, where the apparatus includes, but is not limited to, the following modules 701-705:
the first obtaining module 701 is used for obtaining a shale gas well model.
A second obtaining module 702, configured to obtain an initial probability distribution of an artificial fracture parameter in a shale gas well model.
A third obtaining module 703 is configured to obtain a plurality of first sample parameters based on the initial probability distribution.
In a possible implementation manner, the third obtaining module 703 is configured to perform random sampling in the initial probability distribution by using a markov chain monte carlo method to obtain a plurality of first sample parameters.
A fourth obtaining module 704, configured to obtain a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters, and the reference fitting error.
In a possible implementation manner, the fourth obtaining module 704 is configured to obtain a plurality of first sample models based on a plurality of first sample parameters and the shale gas well model; obtaining a plurality of first fitting errors through a historical fitting error function based on the plurality of first sample models; establishing a first proxy model based on the first sample parameters and the first fitting errors; a plurality of second sample parameters are obtained based on the first proxy model and the reference fitting error.
Optionally, the fourth obtaining module 704 establishes a second proxy model through a K-nearest neighbor classification KNN algorithm based on the plurality of first sample parameters and the plurality of first fitting errors; obtaining a plurality of third sample parameters by a Markov chain Monte Carlo method based on the second proxy model; and iterating the second proxy model based on the third sample parameters and the historical fitting error function to obtain the first proxy model.
Optionally, the fourth obtaining module 704 is configured to iterate the second surrogate model based on the plurality of third sample parameters and the historical fitting error function, and use the iterated second surrogate model that reaches a first condition as the first surrogate model, where the first condition is that a difference between a fitting error of the reference number of third sample parameters obtained through the historical fitting error function and a fitting error of the iterated second surrogate model is smaller than a reference threshold.
Optionally, the fourth obtaining module 704 is configured to obtain a plurality of sample parameters satisfying the reference fitting error based on the first proxy model, and use the plurality of sample parameters satisfying the reference fitting error as the plurality of second sample parameters.
And a fifth obtaining module 705, configured to obtain a target artificial fracture parameter based on the plurality of second sample parameters and the shale gas well model.
In a possible implementation manner, the fifth obtaining module 705 is configured to obtain a target probability distribution of the artificial fracture parameter based on a plurality of second sample parameters; and inverting the target artificial fracture parameters based on the target probability distribution and the shale gas well model to obtain the target artificial fracture parameters.
The target artificial fracture parameters at least comprise one of the length of the artificial fracture, the height value of the artificial fracture, the water saturation of the artificial fracture, the width of the artificial fracture and the flow conductivity coefficient of the artificial fracture.
It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above. In addition, the apparatus and method embodiments provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments for details, which are not described herein again.
In an exemplary embodiment, a computer device is also provided that includes a processor and a memory having at least one instruction stored therein. The at least one instruction is configured to be executed by one or more processors to implement any of the methods of acquiring artificial fracture parameters described above.
In an exemplary embodiment, there is also provided a storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor to cause a computer to implement any one of the above-mentioned methods for acquiring artificial fracture parameters.
Alternatively, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc-read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, a computer program or a computer program product is further provided, in which at least one computer instruction is stored, and the at least one computer instruction is loaded and executed by a processor, so as to enable a computer to implement any one of the above-mentioned methods for acquiring artificial fracture parameters. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the module is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. Further, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may also be an electrical, mechanical or other form of connection.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
It should also be understood that, in the embodiments of the present application, the size of the serial number of each process does not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
The term "at least one" in this application means one or more, and the term "plurality" in this application means two or more, for example, a plurality of data means two or more data.
It is to be understood that the terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The above description is only exemplary of the present application and should not be taken as limiting the present application, and any modifications, equivalents, improvements and the like that are made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. A method for acquiring artificial fracture parameters is characterized by comprising the following steps:
obtaining a shale gas well model;
acquiring initial probability distribution of artificial fracture parameters in the shale gas well model;
obtaining a plurality of first sample parameters based on the initial probability distribution;
obtaining a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters and a reference fitting error;
and acquiring target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model.
2. The method of claim 1, wherein obtaining a plurality of first sample parameters based on the initial probability distribution comprises:
and randomly sampling by a Markov chain Monte Carlo method in the initial probability distribution to obtain the plurality of first sample parameters.
3. The method of claim 1, wherein the obtaining a second plurality of sample parameters based on the shale-gas well model, the first plurality of sample parameters, and a reference fitting error comprises:
obtaining a plurality of first sample models based on the plurality of first sample parameters and the shale gas well model;
obtaining a plurality of first fitting errors through a historical fitting error function based on the plurality of first sample models;
building a first proxy model based on the plurality of first sample parameters and the plurality of first fitting errors;
obtaining the plurality of second sample parameters based on the first surrogate model and the reference fitting error.
4. The method of claim 3, wherein building a first proxy model based on the first plurality of sample parameters and the first plurality of fitting errors comprises:
establishing a second proxy model by a K-nearest neighbor classification (KNN) algorithm based on the first sample parameters and the first fitting errors;
obtaining a plurality of third sample parameters by a Markov chain Monte Carlo method based on the second proxy model;
and iterating the second proxy model based on the third sample parameters and the historical fitting error function to obtain the first proxy model.
5. The method of claim 4, wherein iterating the second proxy model based on the third plurality of sample parameters and the historical fitting error function to obtain the first proxy model comprises:
and iterating the second proxy model based on the plurality of third sample parameters and the historical fitting error function, and taking the iterated second proxy model reaching a first condition as the first proxy model, wherein the first condition is that the difference value between the fitting error of the reference number of third sample parameters obtained through the historical fitting error function and the fitting error of the iterated second proxy model is smaller than a reference threshold value.
6. The method of claim 3, wherein obtaining the plurality of second sample parameters based on the first surrogate model and the reference fitting error comprises:
obtaining a plurality of sample parameters meeting the reference fitting error based on the first proxy model, and taking the plurality of sample parameters meeting the reference fitting error as the plurality of second sample parameters.
7. The method of claim 1, wherein obtaining target artificial fracture parameters based on the plurality of second sample parameters and the shale-gas well model comprises:
obtaining a target probability distribution of the artificial fracture parameters based on the plurality of second sample parameters;
and inverting the target artificial fracture parameters based on the target probability distribution and the shale gas well model to obtain the target artificial fracture parameters.
8. The method of any of claims 1-7, wherein the target artificial fracture parameters comprise at least one of a length of the artificial fracture, a fracture height value of the artificial fracture, a water saturation of the artificial fracture, a width of the artificial fracture, and a conductivity of the artificial fracture.
9. An apparatus for obtaining artificial fracture parameters, the apparatus comprising:
the first acquisition module is used for acquiring a shale gas well model;
the second obtaining module is used for obtaining the initial probability distribution of the artificial fracture parameters in the shale gas well model;
a third obtaining module, configured to obtain a plurality of first sample parameters based on the initial probability distribution;
a fourth obtaining module, configured to obtain a plurality of second sample parameters based on the shale gas well model, the plurality of first sample parameters, and a reference fitting error;
and the fifth acquisition module is used for acquiring target artificial fracture parameters based on the plurality of second sample parameters and the shale gas well model.
10. A computer device comprising a processor and a memory, the memory having stored therein at least one instruction which, when executed by the processor, implements the method of acquiring artificial fracture parameters of any of claims 1 to 8.
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