CN111797575B - A method for optimizing process parameters of deep shale fracturing - Google Patents

Abstract

The invention discloses a method for optimizing process parameters of deep shale fracturing, comprising the following steps: collecting basic parameters of deep shale in multiple different blocks; adopting a fuzzy clustering method to divide the deep shale into reservoirs; Use software to simulate the shape of hydraulic fractures, and determine the hydraulic fracturing target of each type of reservoir after division according to the simulation results; Fracturing results optimize construction parameters. The invention can divide the deep shale of multiple different blocks into clustering reservoirs, and then optimize the fracturing process parameters for each type of reservoir, greatly reducing the workload of fracturing process parameter optimization and saving costs.

Description

A method for optimizing process parameters of deep shale fracturing

technical field

The invention relates to the technical field of fracturing reformation, in particular to a method for optimizing process parameters of deep shale fracturing.

Background technique

my country is very rich in shale gas resources. According to the latest research results of the Oil and Gas Center of the Ministry of Land and Resources in 2012, the recoverable resources of shale gas in my country are 25 trillion cubic meters. Only the Cambrian Qiongzhusi Formation and the Silurian Longma in Sichuan Province The shale gas resources in the Xi Formation can be compared with the total conventional natural gas resources in the Sichuan Basin, and the development and utilization potential is huge. In order to reduce the external dependence of my country's natural gas resources, change my country's current coal-based energy consumption structure, and develop green and environmentally friendly shale gas resources, it has important strategic significance for the long-term rapid development of the national economy and national energy security.

The accumulation mechanism and occurrence mechanism of shale gas are fundamentally different from those of conventional oil and gas. Due to its compactness, shale is not only a hydrocarbon-generating parent rock, but also a reservoir, which is in-situ saturation and early accumulation. The matrix permeability of shale reservoirs is extremely low, especially shale gas reservoirs, which are Nadaxi grade. In order to realize the economic development of shale gas, large-scale hydraulic fracturing is required to crush the reservoir as much as possible to form a complex fracture network, thereby effectively increasing the seepage area of shale gas and reducing the seepage resistance.

Due to the heterogeneity of the formation rock in plane and longitudinal direction, the mineral composition, mechanical properties, natural fractures and in-situ stress state of the reservoir rocks in each block are quite different. There are also certain differences in the ability to form complex network of seams.

SUMMARY OF THE INVENTION

In view of the above problems, the present invention aims to provide a method for optimizing the process parameters of deep shale fracturing. According to the characteristics of shale reservoirs in different blocks, the clustering method is used to divide the reservoirs, and the numerical simulation method is used to realize "effective fracturing". The goal and idea of maximizing the volume and maximizing the complexity of the fractures in the reformed volume” refined the design of the construction parameters, and formed a set of fracturing parameter optimization methods for different shale reservoir characteristics.

The technical scheme of the present invention is as follows:

A method for optimizing process parameters of deep shale fracturing, comprising the following steps: collecting basic parameters of deep shale in multiple different blocks; using fuzzy clustering method to divide the deep shale into reservoirs; using software to simulate hydraulic pressure Fracture shape, according to the simulation results to determine the hydraulic fracturing target of each type of reservoir; according to the hydraulic fracturing target, use the numerical simulation method to simulate the hydraulic fracturing results of different construction parameters, and optimize according to the hydraulic fracturing results construction parameters.

Preferably, the basic parameters include brittle minerals, Poisson's ratio, elastic modulus, tensile strength, shear strength, fracture toughness, natural fractures, mechanical brittleness index, maximum horizontal principal stress, minimum horizontal principal stress, and horizontal in-situ stress difference, vertical stress difference.

Preferably, the fuzzy clustering method adopts a systematic clustering method, which specifically includes the following steps:

Setting the data matrix of the basic parameters, standardizing the data in the matrix, and compressing the data to the [0,1] interval;

Establish a fuzzy similarity matrix and calculate the similarity of each block;

According to the calculation result of the similarity degree, each block is clustered.

Preferably, the method for establishing the data matrix is as follows: set the universe of discourse U={x ₁ , _{x 2} , .

x _i ={x _i1 ,x _i2 ,...,x _im }(i=1,2,...,n) (1)

Then, the data matrix is obtained as:

where x _nm represents the raw data of the mth index of the nth classification object.

Preferably, the data standardization is realized by translation and range transformation, and the method is as follows:

In the formula: 0≤x″ _ik ≤1, and the influence of dimension is eliminated.

Preferably, the similarity degree is calculated by the similarity coefficient method or the distance method, and the similarity coefficient method includes the angle cosine method, the maximum and minimum method, the arithmetic mean minimum method, the geometric mean minimum method, the quantity product method, the correlation coefficient method, Exponential similarity coefficient method, the distance method includes direct distance method, Hamming distance method, Euclidean distance method, Chebyshev distance method, reciprocal distance method, and exponential distance method.

Preferably, a clustering method based on a fuzzy equivalent matrix or a direct clustering method is used for clustering, and a threshold value needs to be determined during clustering, and the threshold value is determined by F statistic.

Preferably, the construction parameters include the number of clusters, displacement, and liquid intensity.

Compared with the prior art, the present invention has the following advantages:

In the present invention, the fuzzy clustering method is used to firstly divide the deep shale in different blocks into clusters and reservoirs, and then the numerical simulation method is used for each type of reservoir to optimize the fracturing process parameters, which greatly reduces the number of fracturing. Process parameter optimization workload and cost savings.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a schematic diagram of the result of clustering division of reservoirs according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the results of numerical simulation of the number of clusters in the embodiment of the present invention, and the change of the slit width with the number of clusters;

FIG. 3 is a schematic diagram of the result of numerical simulation of the number of clusters according to the embodiment of the present invention;

FIG. 4 is a schematic diagram of the results of the numerical simulation of the number of clusters in the embodiment of the present invention, and the transformation volume changes with the number of clusters;

Fig. 5 is a schematic diagram of the results of the numerical simulation of displacement according to the embodiment of the present invention;

FIG. 6 is a schematic diagram of the results of the numerical simulation of displacement in accordance with the embodiment of the present invention;

FIG. 7 is a schematic diagram of the results of the numerical simulation of displacement according to the embodiment of the present invention;

FIG. 8 is a schematic diagram of the results of numerical simulation of the liquid intensity variation of the crack width with the liquid intensity according to the embodiment of the present invention;

FIG. 9 is a schematic diagram of the result of numerical simulation of the liquid strength of the embodiment of the present invention, and the change of the half-slit length with the liquid strength;

Fig. 10 is a schematic diagram showing the result of numerical simulation of the strength of the liquid used in the modification volume of the liquid according to the change of the strength of the liquid used in the embodiment of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments in the present application and the technical features in the embodiments may be combined with each other under the condition of no conflict. Unless otherwise defined, technical or scientific terms used in the present disclosure shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "comprising" or "comprising" and similar words in the present disclosure means that the elements or items appearing before the word encompass the elements or items listed after the word and their equivalents, but do not exclude other elements or items.

A method for optimizing process parameters of deep shale fracturing, comprising the following steps:

S1: Collect basic parameters of deep shale in multiple different blocks, the basic parameters include brittle minerals, Poisson's ratio, elastic modulus, tensile strength, shear strength, fracture toughness, natural fractures, mechanical brittleness index, Maximum horizontal principal stress, minimum horizontal principal stress, horizontal in-situ stress difference, and vertical in-situ stress difference.

S2: The deep shale is divided by a fuzzy clustering method, and the fuzzy clustering method adopts a systematic clustering method, which specifically includes the following steps:

S21: Set the data matrix of the basic parameters, standardize the data in the matrix, and compress the data into the [0,1] interval, specifically:

Suppose the universe of discourse U = {x ₁ , x ₂ ,..., x _n } is the object to be classified, and each object has m indicators to represent its characteristics, namely

x _i ={x _i1 ,x _i2 ,...,x _im }(i=1,2,...,n) (1)

Therefore, the data matrix is obtained as:

where x _nm represents the raw data of the mth index of the nth classification object.

Data normalization is performed using any of the following methods:

1) Translation, range transformation

In the formula: 0≤x″ _ik ≤1, and the influence of dimension is eliminated.

2) Translation, standard deviation transformation

Data standardization is carried out directly by formulas (4)-(6). After transformation, the mean value of each variable is 0, the standard deviation is 1, and the influence of dimension is eliminated. However, the obtained _x'ik is not necessarily on the interval [0,1].

3) Logarithmic transformation

x′″ _ik = lg x _ik (7)

Taking the logarithm narrows the order of magnitude between the variables.

S22: establish a fuzzy similarity matrix, and calculate the similarity degree of each block, specifically: the similarity degree of x _i and x _j r _ij =R( _xi , x _j ), and its value is calculated by the following method:

(1) Similarity coefficient method

1) Angle cosine method

2) Maximum and minimum method

3) Arithmetic average minimum method

4) Geometric mean minimum method

5) Quantity product method

6) Correlation coefficient method

7) Exponential similarity coefficient method

(2) Distance method

1) Direct distance method

r _ij =1-cd(x _i ,x _j ) (17)

where c is an appropriately selected parameter such that 0≤r _ij ≤1, and d(x _i ,x _j ) represents the distance between them, which can be calculated by the following method:

Hamming distance

Euclidean distance

Chebyshev distance

2) Reciprocal distance method

where M is an appropriately selected parameter such that 0≤r _ij ≤1.

3) Exponential distance method

r _ij =exp[-d(x _i ,x _j )] (22)

S23: According to the calculation result of the similarity degree, use the fuzzy equivalent matrix-based clustering method or the direct clustering method to cluster each block, and a threshold value needs to be determined during clustering, and the threshold value is determined by the F statistic Sure.

(1) Clustering method based on fuzzy equivalent matrix

1) transitive closure method

According to the fuzzy matrix R obtained by calibration, it should be transformed into a fuzzy equivalent matrix R ^* . Use the quadratic method to find the transitive closure of R, that is, t(R)=R ^* . Then let λ change from large to small, and a dynamic clustering graph can be formed.

2) Boolean matrix method

矩阵R²＝R^*R≤R。The theoretical basis of the Boolean matrix method is the following theorem: Let R be a subset of U, a similar Boolean matrix on R, then R is transitive (when it is an equivalent Boolean matrix)

The matrix R ² =R ^* R≤R.

The specific steps of the Boolean matrix method are as follows:

为等价矩阵。因此，由

可得λ水平上的分类。Find the λ-truncated matrix R _λ of the fuzzy similarity matrix, if R _λ is determined to be equivalent according to the theorem, then the classification of U at the λ level can be obtained from R _λ ; if R _λ is determined to be unequal, then R _λ is in There is a special sub-matrix of the above form in a certain arrangement, at this time, it is only necessary to change the 0 of the special sub-matrix to 1 until the sub-matrix of the above form is no longer generated. so obtained

is an equivalent matrix. Therefore, by

Classification at the λ level is available.

(2) Direct clustering method

The so-called direct clustering method means that after establishing the fuzzy similarity matrix, instead of seeking the transitive closure t(R), nor using the Boolean matrix method, the clustering graph is obtained directly from the fuzzy similarity matrix. The steps are as follows:

1) Take λ ₁ =1 (maximum value), make a similarity class [ _xi ] _R for each x _i , and

[x _i ] _R = {x _j |r _ij =1} (23)

That is, x _i and x _j satisfying r _ij =1 are placed in one class to form a similar class. The difference between similar classes and equivalence classes is that different similar classes may have common elements, which can appear

At this time, as long as the similar classes with common elements are merged, the equivalent classification at the level of λ ₁ =1 can be obtained.

2) Take λ ₂ as the second largest value, and directly find the element pair (x _i , x _j ) (ie r _ij =λ ₂ ) with a similarity of λ ₂ from R, which will correspond to the equivalent of λ ₁ =1 In the classification, the class where x _i is located is merged with the class where x _j is located, and after combining all these cases, the equivalent classification corresponding to λ ₂ is obtained.

3) Take λ ₃ as the third largest value, directly find the element pair (x _i , x _j ) (ie r _ij =λ ₃ ) with a similarity of λ ₃ from R, and classify the equivalent corresponding to λ ₂ The class where x _i is located is merged with the class where x _j is located. After combining all these cases, the equivalent classification corresponding to λ ₃ is obtained.

4) And so on, until the merging to U becomes a class.

(3) Determination of the threshold value

In fuzzy clustering analysis, for each different λ∈[0,1], different classifications can be obtained. Many practical problems need to select a certain threshold λ to determine a specific classification of the sample, which raises the question of how to determine the threshold . There are generally two methods:

1) According to the actual needs, in the dynamic clustering diagram, adjust the value of λ to get the appropriate classification, without the need to accurately estimate how many classes the samples should be divided into in advance. Of course, the threshold λ can also be determined by experts with rich experience combined with professional knowledge, so as to obtain an equivalent classification at the λ level.

2) Use F statistic to determine the best value: let the universe of discourse U={x ₁ , x ₂ ,..., x _n } be the sample space (the total number of samples is n), and each sample x _i has m features, That is, formula (1), so the original data matrix is obtained, as shown in the following table:

Table 1 Index relationship table

称为总体样本的中心向量。in,

is called the center vector of the population sample.

第j类的聚类中心为向量

其中

为第k个特征的平均值，即Let the number of classifications corresponding to the λ value be r, the number of samples of the jth class be n _j , and the samples of the jth class are recorded as:

The cluster center of the jth class is a vector

in

is the average of the k-th feature, that is

as F statistic

与

间的距离，

为第j类中第i个样本x^(j)与其中心

间的距离。称为F统计量，它是遵从自由度为r-1，n-r的分布。它的分子表征类与类之间的距离，分母表征类内样本间的距离。因此，F值越大，说明类与类之间的距离越大；类与类间的差异越大，分类就越好。for

and

distance between,

is the i-th sample x ^(j) in the j-th class and its center

distance between. Called the F statistic, it follows a distribution with degrees of freedom r-1, nr. Its numerator represents the distance between classes and the denominator represents the distance between samples within a class. Therefore, the larger the F value, the greater the distance between classes; the greater the difference between classes, the better the classification.

S3: Use software to simulate the shape of hydraulic fractures, and determine the hydraulic fracturing targets for each type of reservoir after division according to the simulation results.

S4: According to the hydraulic fracturing target, numerical simulation method is used to simulate hydraulic fracturing results of different construction parameters, and construction parameters are optimized according to the hydraulic fracturing results, and the construction parameters include cluster number, displacement, and liquid strength.

Example 1

S1: Collect basic parameters of deep shale in multiple different blocks, as shown in Table 2:

Table 2 Basic parameters of reservoir

S2: Use fuzzy clustering method to divide reservoirs from block 1 to block 5. Blocks 1 to 5 are classified objects, and each object is determined by 11 factor indicators, so the original data matrix can be obtained. Among the 11 factor indicators, different data have different dimensions. In order to make data with different dimensions can be compared, the data are also projected to the interval (0,1). Calculate the degree of similarity between 11 parameters of different blocks, regard the factor parameters of each block as a point in the space, and use some measure, such as Euclidean distance, to measure the distance between points, and the distance is relatively high. Points that are close are classified into one class, and points that are farther away should belong to the same class.

The clustering result is shown in Figure 1. It can be seen from Figure 1 that in the clustering connection tree diagram, the degree of similarity between segments is represented by relative distances (0-25). The smaller the distance, the higher the similarity between the segments; the larger the distance, the greater the difference. The relative distance between the fracturing geological parameters of block 1 and block 2 is between 0 and 1, and the two blocks are close to each other, which is classified as the first category; the relative distance of the fracturing geological parameters of block 3 and block 5 is Between 15 and 20, the two blocks are close, and are classified as the second category; the relative distance of the fracturing geological parameters of block 4 is between 20 and 25, which is quite different from the other four blocks, and is classified as the third kind.

S3: According to the geological characteristics of each block, use the software to simulate the hydraulic fracture shape, and determine the hydraulic fracturing target of each type of reservoir after division according to the simulation results. Among them, block 1 and block 2 take complex fracture network as the hydraulic fracturing target, block 3 and block 5 take the main fracture + branch fracture as the hydraulic fracturing target, and block 4 take the main fracture as the hydraulic fracturing target.

S4: According to the hydraulic fracturing target, use a numerical simulation method to simulate hydraulic fracturing results of different construction parameters, and optimize the construction parameters according to the hydraulic fracturing results.

Among them, the numerical simulation results of the number of clusters in Block 1 and Block 2 are shown in Figure 2-4. It can be seen from Figure 2-4 that with the increase of the number of clusters, the total reformed volume of hydraulic fractures shows an increasing trend, but the size of the fractures increases. The width and length are reduced; when the number of clusters is greater than 4 clusters, the transformation volume tends to decrease, and the recommended number of clusters is 3-4 clusters. Using the same method to obtain the recommended number of clusters for block 3 and block 5 is 4-6 clusters, and the recommended number of clusters for block 4 is 6-8 clusters.

Among them, block 1 and block 2 are based on the number of clusters of 4, and the numerical simulation results of displacement are shown in Figure 5-7. Taking into account the prevention of casing changes and the pursuit of transformation volume, the recommended displacement under 4 clusters is 12- 14m ³ /min. Using the same method to obtain the recommended displacement of block 3 and block 5 is 14-16m ³ /min, and the recommended displacement of block 4 is more than 16m ³ /min.

Among them, block 1 and block 2 are based on the number of clusters of 4 and the displacement of 14m ³ /min, and the numerical simulation results of liquid strength are shown in Figure 8-10. When the number is 4 and the displacement is 14m ³ /m, the recommended liquid strength is 24-26m ³ /m. The recommended liquid strength of block 3 and block 5 obtained by the same method is 24-28 m ³ /m, and the recommended liquid strength of block 4 is more than 30 m ³ /m.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Technical personnel, within the scope of the technical solution of the present invention, can make some changes or modifications to equivalent examples of equivalent changes by using the technical content disclosed above, but any content that does not depart from the technical solution of the present invention, according to the present invention. The technical essence of the invention Any simple modifications, equivalent changes and modifications made to the above embodiments still fall within the scope of the technical solutions of the present invention.

Claims (5)

1. a deep shale fracturing process parameter optimization method, is characterized in that, comprises the following steps: Collect basic parameters of deep shale from multiple different blocks, including brittle minerals, Poisson's ratio, elastic modulus, tensile strength, shear strength, fracture toughness, natural fractures, mechanical brittleness index, maximum level Principal stress, minimum horizontal principal stress, horizontal in-situ stress difference and vertical in-situ stress difference; The deep shale is divided by a fuzzy clustering method; the fuzzy clustering method adopts a systematic clustering method, which specifically includes the following steps: Set the data matrix of the basic parameters, standardize the data in the matrix, and compress the data to the [0,1] interval; the method for establishing the data matrix is as follows: set the universe of discourse U={x ₁ , x ₂ ,...,x _n } is the object to be classified, and each object has m indicators to represent its characteristics, namely: x _i ={x _i1 ,x _i2 ,...,x _im } and i=1,2,...,n (1) Then, the data matrix is obtained as:

where x _nm represents the raw data of the mth index of the nth classification object; Establish a fuzzy similarity matrix to calculate the similarity of each block; According to the calculation result of the similarity degree, cluster each block; Use software to simulate the shape of hydraulic fractures, and determine the hydraulic fracturing targets for each type of reservoir after division according to the simulation results; According to the hydraulic fracturing target, a numerical simulation method is used to simulate the hydraulic fracturing results of different construction parameters, and the construction parameters are optimized according to the hydraulic fracturing results. 2. deep shale fracturing process parameter optimization method according to claim 1, is characterized in that, described data standardization adopts translation, range transformation method to realize, and this method is specifically as follows:

In the formula: 0≤x″ _ik ≤1, and the influence of dimension is eliminated. 3. The deep shale fracturing process parameter optimization method according to claim 1, wherein the similarity degree is calculated by a similarity coefficient method or a distance method, and the similarity coefficient method includes an angle cosine method, a maximum and a minimum method, arithmetic mean minimum method, geometric mean minimum method, quantity product method, correlation coefficient method, exponential similarity coefficient method, the distance methods include direct distance method, Hamming distance method, Euclidean distance method, Chebyshev distance method method, reciprocal distance method, exponential distance method. 4. The deep shale fracturing process parameter optimization method according to claim 1, characterized in that, clustering is performed based on a fuzzy equivalent matrix clustering method or a direct clustering method, and a threshold value needs to be determined during clustering, and the The threshold is determined by the F statistic. 5 . The method for optimizing process parameters of deep shale fracturing according to claim 1 , wherein the construction parameters include the number of clusters, the displacement and the strength of the liquid used. 6 .

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