Disclosure of Invention
In view of the above, the application provides a method, a device and electronic equipment for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir, which are beneficial to solving the problem of poor optimizing effect of the fracturing parameter optimizing method in the prior art.
In a first aspect, an embodiment of the present application provides a method for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir, including:
Determining a first data set according to acquired geological data of a single well of the gas field and production data, wherein the geological data comprise geological parameters, fracturing parameters and position parameters, and the production data comprise final recoverable reserve data EUR of the single well;
Determining main control parameters affecting the single well EUR from the geological parameters, the fracturing parameters and the position parameters by utilizing the first data set;
determining a second data set according to the main control parameters, wherein the second data set is used for establishing a regression model of the main control parameters and the single-well EUR;
dividing an optimization range and grid size of fracturing parameters to be optimized, and performing iterative optimization on the fracturing parameters to obtain optimal fracturing parameters of the regression model.
In one possible implementation, the determining, using the first data set, a master control parameter affecting the EUR from the geological parameter, the fracturing parameter, and the location parameter includes:
applying XGBoost algorithm to the first data set to calculate the influence degree of geological parameters, fracturing parameters and position parameters on the single-well EUR;
And determining main control parameters affecting the EUR of the single well according to a preset influence degree proportion.
In one possible implementation, determining a second data set from the master parameters includes rejecting parameter information other than the master parameters from the first data set, and retaining master parameters affecting single well EUR as the second data set.
In one possible implementation, the using the second data set for establishing a regression model of the master parameters and the single well EUR includes:
Dividing the second data set into a training set, a verification set and a test set according to a preset proportion;
carrying out maximum and minimum normalization processing on the training set data;
performing maximum and minimum normalization processing on the verification set data and the test set data by using the maximum value and the minimum value of the training set data;
And applying XGBoost algorithm to the normalized verification set data and test set data, and establishing a regression model of the main control parameters and the single-well EUR.
In one possible implementation, iteratively optimizing the fracturing parameters to obtain optimal fracturing parameters of the regression model includes:
Applying a Bayesian optimization algorithm, and starting iteration by taking the maximum value of the black box function as an optimization target;
Setting the maximum iteration times, and calculating the optimal fracturing parameter combination after the iteration is finished according to the maximum iteration times.
In one possible implementation, calculating the optimal fracturing parameter combination after the iteration is finished according to the maximum iteration number includes:
selecting random points, and calculating a single-well EUR value through a black box function;
Then constructing a substitution function of the black box function based on a Gaussian process method, and finding a high point of the substitution function;
Finally, adding the high points of the substitution function into the data set, and constructing the substitution function again to find the high points;
And outputting the fracturing parameter combination corresponding to the highest point of the final substitution function as the optimal fracturing parameter combination after the maximum iteration times are iterated.
In one possible implementation, the geological parameters include water saturation, porosity, brittleness index, average fracture pressure, fracturing parameters include fracturing construction displacement, average section length, number of sections, horizontal section length, propped dose per section, minimum pump down pressure, maximum pump down pressure, pumped nitrogen per section, average construction pressure, azimuth, location parameters include bottom hole longitude, latitude of a single well, and average depth TVD of a developed horizon, and production data includes a well's monthly gas production curve and rate of decline for obtaining a single well EUR.
In a second aspect, an embodiment of the present application provides a device for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir, including:
The parameter determining module is used for determining a first data set according to the acquired geological data of the single well of the gas field and the yield data, wherein the geological data comprise geological parameters, fracturing parameters and position parameters, and the yield data comprise final recoverable reserve data EUR of the single well;
The main control parameter determining module is used for determining main control parameters affecting the single well EUR from the geological parameters, the fracturing parameters and the position parameters by utilizing the first data set;
The model building module is used for determining a second data set according to the main control parameters, and the second data set is used for building a regression model of the main control parameters and the single well EUR;
And the fracturing parameter output module is used for dividing an optimization range and grid size of the fracturing parameters to be optimized, and carrying out iterative optimization on the fracturing parameters to obtain the optimal fracturing parameters of the regression model.
In a third aspect, an embodiment of the present application provides an electronic device, including:
A processor;
A memory;
and a computer program, wherein the computer program is stored in the memory, the computer program comprising instructions which, when executed by the processor, cause the electronic device to perform the method of any one of the possible implementations of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, where the computer readable storage medium includes a stored program, where when the program runs, the program controls a device in which the computer readable storage medium is located to execute the method according to any one of possible implementation manners of the first aspect.
In the embodiment of the application, the main control parameters affecting the single well EUR are determined from the acquired geological data and yield data of the single well of the gas field, and then the regression model of the single well EUR established by utilizing the main control parameters is improved because the parameters establishing the regression model of the single well EUR are the most main parameters affecting the single well EUR. Therefore, when the fracturing parameters are used for optimizing the object in the follow-up process, other parameter information except the fracturing parameters is used as a model coefficient of the regression model, so that the fracturing parameter optimizing effect is improved finally.
Detailed Description
For a better understanding of the technical solution of the present application, the following detailed description of the embodiments of the present application refers to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application 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.
It should be understood that the term "and/or" as used herein is merely one way of describing an association of associated objects, meaning that there may be three relationships, e.g., a and/or b, which may represent: the first and second cases exist separately, and the first and second cases exist separately. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Referring to fig. 1, a flow chart of a method for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir provided by an embodiment of the present application, referring to fig. 1, the method for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir in an embodiment of the present application includes:
s101, determining a first data set according to acquired single well geological data of the gas field and production data.
In this embodiment, the acquired data includes two types, namely, geological data and production data. Further, the geological data includes geological parameters, fracturing parameters and location parameters, and the production data includes single well final recoverable volume data EUR.
In particular, the geologic parameters in this example include water saturation, porosity, brittleness index, and average fracture pressure. The fracturing parameters comprise fracturing construction displacement, average section length, section number, horizontal section length, supporting agent amount of each section, minimum pump stopping pressure, maximum pump stopping pressure, nitrogen amount pumped into each section, average construction pressure and azimuth angle. The location parameters include the well bottom longitude, latitude, and average depth TVD of the developed horizon for the individual well, and the production data includes the well's monthly gas production curve and rate of decline, and the estimated EUR for the individual well.
S102, determining main control parameters affecting the single well EUR from the geological parameters, the fracturing parameters and the position parameters by using the first data set.
And applying XGBoost algorithm in combination with the first data set, calculating the influence degree of geological parameters, fracturing parameters and position parameters on the single-well EUR, and selecting the variable with the influence degree of 50% as the EUR main control parameter. Referring to fig. 2, the finally determined main control parameters include water saturation, porosity, brittleness index and average fracture pressure, and the main control parameters include fracturing construction displacement, segment length, segment number, horizontal segment length and propping agent amount of each segment.
In the embodiment, XGBoost algorithm is a mature high-efficiency gradient lifting decision tree algorithm, and is improved on the basis of the original GBDT, so that the model effect is greatly improved. As a forward addition model, the core of the forward addition model is to integrate a plurality of weak learners into a strong learner by adopting an integrated idea, namely a Boosting idea. The method comprises the steps of commonly deciding by using a plurality of trees, and obtaining a final result by using the result of each tree as the difference between a target value and the predicted results of all the previous trees and accumulating all the results, thereby achieving the improvement of the whole model effect.
S103, determining a second data set according to the main control parameters, wherein the second data set is used for establishing a regression model of the main control parameters and the single-well EUR.
And only reserving EUR main control parameters for the second data set to form a new data set, and randomly dividing the new data set into a training set, a verification set and a test set according to the ratio of 6:2:2. Firstly, carrying out maximum and minimum normalization processing on training set data, and then carrying out the same processing on verification set data and test set data by utilizing the maximum value and the minimum value of the training set data.
Because the EUR main control parameters are determined to be the parameter data in the geological parameters and the fracturing parameters in S102, a XGBoost algorithm is applied to establish a regression model of the geological parameters, the fracturing parameters and the single-well EUR.
And S104, dividing an optimization range and grid size of fracturing parameters to be optimized, and performing iterative optimization on the fracturing parameters to obtain optimal fracturing parameters of the regression model.
In this embodiment, the range of the fracturing parameters to be optimized and the grid size are set, for example, the minimum value of the horizontal segment length is 1000, the maximum value is 3500, the grid size is 100, and the whole number is taken. The minimum value of the number of segments is 20, the maximum value is 60, the grid size is 5, and the integer is taken. The average injection rate has a minimum of 6, a maximum of 12, and a grid size of 1.
And (3) applying a Bayesian optimization algorithm, taking the maximum value of a black box function (XGBoost yield prediction model) as an optimization target, starting iteration to calculate an optimal fracturing parameter combination, and setting the iteration times to 40 times. The calculation process is as shown in fig. 3, firstly, random points are selected, and the function value (single well EUR value) is calculated through a black box function. And then constructing a substitution function of the black box function based on a Gaussian process method, and finding a high point of the substitution function. And finally, adding the high points of the substitution function into the data set, and constructing the substitution function again to find the high points. After 40 iterations, the fracturing parameters corresponding to the highest point of the final substitution function are combined and output.
As shown in fig. 4, the comparison between the optimized fracturing parameters of the old well of the gas field part and the actual fracturing parameters of the operator is shown. The number of the optimized fracturing stages is 50 and is far higher than that of the current design, and technical iterations also prove that the optimized fracturing parameters accord with the current situation of the current close cutting fracturing technology. As shown in fig. 4 (b), compared with the actual single well EUR, the optimized single well EUR is 8% higher, and the fracturing parameters optimized by the method can provide theoretical assistance for the production increase of the gas field, thus having good field application prospect.
Based on the same XGBoost model and data, a grid search method was applied to optimize the fracturing parameters, and the results are shown in table 1. Taking 4000 parameter changes as an example, the grid searching method needs to be searched 4000 times, and the parameter optimizing method based on Bayesian optimization only needs to be calculated 40 times, so that the result matching degree with the grid searching method of 94% can be achieved. The result proves that the method is excellent in optimizing accuracy and efficiency.
Table 1 Bayesian optimization and grid search results comparison
Corresponding to the method for optimizing the fracturing parameters of the new well of the tight sandstone gas reservoir provided by the embodiment, the application also provides an embodiment of the device for optimizing the fracturing parameters of the new well of the tight sandstone gas reservoir.
Referring to fig. 5, the apparatus 20 for optimizing fracturing parameters of a new well of a tight sandstone gas reservoir according to an embodiment of the present application includes:
A parameter determination module 201, configured to determine a first data set according to acquired geological data of a single well of the gas field, and yield data, where the geological data includes geological parameters, fracturing parameters and position parameters, and the yield data includes final recoverable reserve data EUR of the single well;
A master parameter determination module 202 configured to determine master parameters affecting the single well EUR from the geological parameters, the fracturing parameters, and the location parameters using the first data set;
the model building module 203 is configured to determine a second data set according to the master control parameter, where the second data set is used to build a regression model of the master control parameter and the single well EUR;
the fracturing parameter output module 204 is configured to divide an optimization range and a grid size of a fracturing parameter to be optimized, and perform iterative optimization on the fracturing parameter to obtain an optimal fracturing parameter of the regression model.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the apparatus described above, which is not described herein again.
Corresponding to the embodiment, the embodiment of the application also provides electronic equipment.
Referring to fig. 6, a schematic structural diagram of an electronic device according to an embodiment of the present application is provided. As shown in fig. 6, the electronic device 300 may include a processor 301, a memory 302, and a communication unit 303. The components may communicate via one or more buses, and it will be appreciated by those skilled in the art that the electronic device structure shown in the drawings is not limiting of the embodiments of the application, as it may be a bus-like structure, a star-like structure, or include more or fewer components than shown, or may be a combination of certain components or a different arrangement of components.
Wherein the communication unit 303 is configured to establish a communication channel, so that the electronic device may communicate with other devices.
The processor 301, which is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, performs various functions of the electronic device and/or processes data by running or executing software programs and/or modules stored in the memory 302, and invoking data stored in the memory. The processor may be comprised of integrated circuits (INTEGRATED CIRCUIT, ICs), such as a single packaged IC, or may be comprised of packaged ICs that connect multiple identical or different functions. For example, the processor 301 may include only a central processing unit (central processing unit, CPU). In the embodiment of the application, the CPU can be a single operation core or can comprise multiple operation cores.
Memory 302 for storing instructions for execution by processor 301, memory 302 may be implemented by any type of volatile or nonvolatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
The execution of the instructions in memory 302, when executed by processor 301, enables electronic device 300 to perform some or all of the steps of the method embodiments described above.
Corresponding to the above embodiment, the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium may store a program, where when the program runs, the device where the computer readable storage medium is located may be controlled to execute some or all of the steps in the above method embodiment. In particular, the computer readable storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (random access memory, RAM), or the like.
Corresponding to the above embodiments, the present application also provides a computer program product comprising executable instructions which, when executed on a computer, cause the computer to perform some or all of the steps of the above method embodiments.
In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relation of association objects, and indicates that there may be three kinds of relations, for example, a and/or B, and may indicate that a alone exists, a and B together, and B alone exists. Wherein A, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of the following" and the like means any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c may represent a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
Those of ordinary skill in the art will appreciate that the various elements and algorithm steps described in the embodiments disclosed herein can be implemented as a combination of electronic hardware, computer software, and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In several embodiments provided by the present application, any of the functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory RAM), a magnetic disk, or an optical disk.
The foregoing is merely exemplary embodiments of the present application, and any person skilled in the art may easily conceive of changes or substitutions within the technical scope of the present application, which should be covered by the present application. The protection scope of the present application shall be subject to the protection scope of the claims.