CN113283180A - K-means and SVR combination-based tight reservoir horizontal well fracturing productivity prediction method and application - Google Patents
K-means and SVR combination-based tight reservoir horizontal well fracturing productivity prediction method and application Download PDFInfo
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Abstract
The invention relates to a compact reservoir horizontal well fracturing capacity combined prediction method and application based on a Support Vector Regression (SVR) combination of k-means, and belongs to the technical field of oil and gas exploration and development. The method comprises obtaining a data set related to capacity impact; carrying out data cleaning and data preprocessing; calculating characteristic weight by a principal component analysis algorithm and carrying out characteristic weighting; finding out main control factors by a gray level correlation analysis algorithm; clustering geological factors and physical factors according to a K-means clustering analysis algorithm; building an SVR algorithm model according to the clustering result and the fracturing construction data, and finding out an optimal precision model for prediction; and performing fracturing construction suggestion guidance on the low-yield oil well by using the SVR model. By adopting the technical scheme, the invention can better solve the main factors of low yield of the oil well and huge economic loss caused by fracturing construction design and the like, and provides a new yield combined prediction mode for predicting the capacity of the compact oil.
Description
Technical Field
The invention relates to a capacity prediction method, in particular to a method for predicting the fracturing capacity of a tight reservoir horizontal well based on K-means and SVR combination and application thereof, and belongs to the technical field of oil and gas exploration and development.
Background
The tight oil reservoir is one of the main sources of the current unconventional oil and gas exploitation, and with the influence of the progress of drilling level and development technology in recent years, most oil fields adopt an exploitation mode of horizontal well matched with volume fracturing technology to replace the original exploitation method so as to achieve the important method of increasing production and improving recovery ratio. Under the mining mode, the main factors of long-time continuous high yield of the compact oil horizontal fracturing well are researched, the oil deposit productivity is accurately predicted, and the method has important guiding significance and economic benefit on efficient development of subsequent compact oil.
The existing methods related to reservoir productivity prediction are roughly divided into two categories: one is longitudinal prediction, mainly deducing a productivity formula based on the non-Darcy seepage; the other type is transverse prediction, and parameters are processed by using mathematical methods such as pattern recognition and the like, so that a capacity prediction model is established. The formula prediction based on mechanism analysis needs to be combined with actual production conditions, the accuracy and the rationality need to be further improved, and the prediction of the capacity of the dense oil by adopting a single machine learning algorithm cannot be solved: 1. the contribution degrees of different characteristic parameters are different; 2. the dense oil productivity has a plurality of influence parameters, and direct introduction may cause overfitting; 3. the geological parameters and the fluid physical parameters belong to static parameters, and the static parameters of the similar block wells are basically consistent. Therefore, from the data mining perspective, the combined model based on the cluster analysis algorithm and the regression algorithm is provided in combination with the actual situation of the site, and the problems can be solved. The main component analysis algorithm is used for performing characteristic weighting, the gray level correlation algorithm is used for analyzing the main control factors, the grid traversal algorithm is used for searching the optimal precision model to improve the model accuracy rate, and the method has important theoretical and practical significance, and can greatly improve the overall development economic benefit of the oil field by researching and optimizing the fracturing construction parameters of the low-yield oil well.
Disclosure of Invention
Aiming at the problems, the invention aims to avoid the great waste of human resources and financial resources caused by the uncertainty of fracturing construction parameters, thereby improving the construction quality and the overall economic benefit of a fracturing construction team of a compact oil field.
In order to achieve the purpose, the invention adopts the following technical scheme: the method for predicting the fracturing capacity of the tight oil reservoir horizontal well based on the combination of K-means and SVR comprises the following steps:
(1) and collecting tight oil reservoir geological data, fluid physical property data, fracturing construction data and post-fracturing energy production data.
(2) And carrying out data cleaning and data preprocessing on the acquired data.
(3) Weights are calculated and weighted for different types of data features.
(4) And finding out the main control factors of different types of data.
(5) And performing cluster analysis modeling on the data after the geological parameters and the fluid physical property parameters are processed.
(6) And (4) modeling the clustering result by a prediction algorithm in combination with the fracturing construction data, and searching for the optimal model parameter.
(7) And constructing an SVR algorithm model.
In the step 1), the collected geological data, fluid physical property data and fracturing construction data are used as model independent variables, and 'production accumulated in the year of production' in the pressed productivity data is selected as a dependent variable.
And 2) removing abnormal data by a mean square error method, and mapping the data to a [0,1] interval by maximum-minimum standardization.
And 3) performing characteristic weighting on factors of geology, fluid physical property and fracturing construction by a principal component analysis algorithm, and replacing original data with weighted data.
And 4) performing relevance sorting on all the characteristic values through a gray level relevance algorithm, so as to screen the characteristic values mainly influencing output in factors of geology, fluid physical property and fracturing construction.
And 5) selecting the characteristics belonging to the geological parameters and the fluid physical property parameters from the reconstructed data volume of the characteristic values selected in the step 4) as a data volume of a k-means model to model. A grid search algorithm is used to find the optimal number of clusters k.
And 6) combining the classification characteristics and the fracturing construction parameters obtained in the step 5) with a reconstruction data body, and selecting the accumulated oil production in the current year of production as an output value of an algorithm to construct an SVR prediction model. A grid search algorithm is used to find the optimal penalty parameter c and the kernel function parameter g.
And 7), setting parameters of the SVR according to the optimal value searched in the step 6), and inputting data into the SVR model to obtain a model with optimal prediction accuracy.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. according to the method, abnormal data are removed through a mean square error method, the accuracy of the data is improved, different characteristics are made to be comparable through removing dimensions in a standardized mode, and the influence of characteristic numerical value levels caused by the dimensions on analysis results is eliminated. 2. Different feature weights are calculated through a principal component analysis algorithm, and the problem of hard clustering of the k-means algorithm is solved. 3. And (3) finding out the most representative characteristic influencing the capacity change by adopting a gray level correlation analysis algorithm, and improving the accuracy of model prediction. 4. The fracturing construction parameters of the low-yield wells of the similar blocks can be optimized and guided.
Drawings
FIG. 1 is a flow chart of a fracturing productivity prediction algorithm for a tight reservoir horizontal well;
FIG. 2 is a schematic view of SVR;
FIG. 3 is a schematic diagram of a combination model.
Detailed Description
In order to more clearly illustrate the technical solution of the present invention, the following detailed description of the present invention is provided with reference to the accompanying drawings and examples.
The method for predicting the fracturing capacity of the tight oil reservoir horizontal well based on the combination of K-means and SVR and the application flow chart are shown in figure 1, and the method is specifically realized through the following steps:
1. geological data, fluid physical property data, fracturing construction data and post-fracturing energy production data of a plurality of blocks of the tight oil reservoir are collected. In particular, the method can be used for obtaining the geological: permeability, porosity, natural gamma, effective thickness; fluid physical Properties: formation crude oil viscosity, formation crude oil density, content saturation; fracturing construction: the proppant usage, the fracturing fluid type, the number of stages, the average sand ratio and the like are used as independent variables required by the algorithm, and the yield after pressing is as follows: and (4) taking the accumulated oil production in the current year of production as a dependent variable required by the algorithm.
2. And carrying out statistics and distribution query on each characteristic data, and screening abnormal data by a mean square error method. Because the oil well data have different dimensions and magnitude levels, if the screened data are directly used for modeling training, the algorithm is greatly influenced by the higher median value of the data, and the characteristic of lower numerical mean value is ignored. Therefore, min-max normalization is performed on the screened data, and each eigenvalue result is mapped to [0,1], and the specific conversion function is as follows:
wherein x, min and max are sample data of the characteristic value, the minimum value in the sample data of the characteristic value and the maximum value in the sample data of the characteristic value respectively.
3. And (3) performing principal component analysis on the data in the step (2), and performing feature weighting on all feature value calculation weights to construct a new data body.
4. And (3) carrying out gray level correlation algorithm analysis on the weighted data in the step (3), carrying out correlation calculation on all characteristic values (subsequences) and accumulated oil production (parent sequence) in the current year of production to obtain correlation degree sorting, and respectively finding out the first 3 characteristic values with the maximum correlation between geological and fluid physical properties and fracturing construction data and oil production as the input of a subsequent model. The calculation method of the correlation coefficient of each index of the subsequence and the parent sequence is as follows:
rho is a resolution coefficient, rho is more than 0 and less than 1, and the smaller rho is, the larger the difference between the correlation coefficients is, and the stronger the distinguishing capability is. Usually ρ is 0.5. x is the number of0(k)，xi(k) Respectively representing the kth number of the mother sequence and the kth number of the ith characteristic value of the subsequence. Zetai(k)The correlation coefficient of the kth value representing the ith feature with the kth value of the mother sequence. And calculating the relevance through the relevance coefficient, and then carrying out final sorting. Wherein the degree of association is calculated as follows:
wherein r isiAnd n is the sample number, and is the relevance size of the ith feature and the mother sequence. Finally, for all the advances riAnd (3) obtaining the relevancy ranking by row ranking, wherein the finally selected geological main control factors are as follows: permeability, porosity, effective thickness; the main factors of the physical properties of the fluid are as follows: formation crude oil viscosity, formation crude oil density, oil saturation; the fracturing construction main control factors are as follows: proppant amount, fracturing fluid type, number of stages.
5. And (4) establishing a k-means cluster analysis model for the geological and fluid physical property data selected in the step (4).
6. And (4) constructing a new data body by combining the clustering characteristic value obtained in the step (5) and the fracturing construction type data obtained in the step (4), as shown in a schematic diagram of fig. 3. The method comprises the steps of dividing a training set and a test set for modified samples, carrying out SVR algorithm modeling, traversing kernel function parameters g and penalty parameters c in the SVR algorithm through a grid traversal search algorithm, using mean square error MSE of a predicted value and a true value as performance indexes of a model, setting SVR final model parameters by finding out parameters set by the minimum mean square error, wherein the model is more optimal when the mean square error is smaller. The mean square error MSE calculation mode is as follows:
wherein
Is the predicted value of the ith sample, yiIs the actual value of the ith sample, and n is the number of samples.
The above examples are only for illustrating the present invention, and the implementation steps of the methods and the like can be changed, and all equivalent changes and modifications based on the technical scheme of the present invention should not be excluded from the protection scope of the present invention.
Claims (7)
1. The method for predicting the fracturing capacity of the tight oil reservoir horizontal well based on the combination of K-means and SVR comprises the following steps:
1) collecting sample data influencing productivity, wherein the sample data comprises permeability, porosity, formation crude oil viscosity, formation crude oil density, proppant consumption, fracturing fluid type and number of stages;
2) data cleaning is carried out on the collected data, repeated data are removed, and a data body is constructed by singular data;
3) carrying out dimensionless preprocessing on the data volume;
4) calculating the feature weight by adopting a principal component analysis algorithm to perform feature weighting;
5) extracting main control factors by adopting a gray level correlation analysis algorithm;
6) carrying out cluster analysis modeling on the geological parameters and the fluid physical property parameters;
7) and establishing a prediction model based on support vector regression.
2. The method for predicting the fracturing productivity of the tight reservoir horizontal well based on the combination of K-means and SVR as claimed in claim 1 and the application thereof, wherein said step 2) is to perform data screening on the original sample, to perform statistics on the mean and variance of each characteristic value, and to select the value range as [ mean-variance, mean + variance ].
3. The method and the application for predicting the fracturing capacity of the tight reservoir horizontal well based on the combination of K-means and SVR as claimed in claim 1, wherein the step 3) is to perform maximum-minimum standardization preprocessing on the screened data, so that the data are distributed in the interval [0,1 ].
4. The method and application for predicting the fracturing capacity of the tight reservoir horizontal well based on the combination of K-means and SVR as claimed in claim 1, wherein step 4) adopts a principal component analysis algorithm to calculate the feature weight of the normalized data volume, and weights the calculated feature to reconstruct the data volume.
5. The method and application for predicting the fracturing capacity of the tight reservoir horizontal well based on the combination of K-means and SVR as claimed in claim 1, wherein said step 5) adopts a gray level correlation analysis algorithm to calculate and sequence the correlation degree of the normalized data volume, and the final selected geological major factors are: permeability, porosity, effective thickness; the main factors of the physical properties of the fluid are as follows: formation crude oil viscosity, formation crude oil density, oil saturation; the fracturing construction main control factors are as follows: the amount of the propping agent, the amount of the fracturing fluid, the type of the fracturing fluid and the number of stages are used as independent variables, and the cumulative oil production in the current year of production is used as a dependent variable.
6. The method and application for predicting the fracturing productivity of the tight reservoir horizontal well based on the combination of K-means and SVR as claimed in claim 1, wherein the step 6) performs K-means cluster modeling on the geological parameters and the fluid physical parameters.
7. The method for predicting the fracturing productivity of the tight oil reservoir horizontal well based on the combination of the K-means and the SVR and the application thereof as claimed in claim 1 or claim 6, wherein the step 7) is to establish a new data body for algorithm training of the SVR (kernel, C, gamma) by combining the clustered category features and the fracturing parameters, wherein the kernel is a kernel type, C is a penalty coefficient, and gamma is a kernel coefficient, the algorithm traversal parameters are searched through grid traversal, the mean square error MSE is used as a model evaluation standard to determine the optimal parameters, and the parameters are kernel RBF, C is 0.13, and g is 0.035.
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