Automatic optimization method and system for raw material ratio of petroleum proppant

Abstract

The present invention discloses an automatic optimization method and system for the raw material ratio of petroleum proppant, which relates to the technical field of petroleum proppant production. The method and system collect geological information, historical ratio data and performance index data, train a cGAN model, obtain first geological information and first ratio data by using the cGAN model, train a performance index prediction model, predict the first performance index data by using the performance index prediction model, set a standard performance index, and if the first performance index data is less than the standard performance index, use an optimal ratio strategy to obtain the optimal ratio data; if the first performance index data is greater than or equal to the standard performance index, use the first ratio data as the optimal ratio data to produce proppant, obtain actual geological information, generate preliminary ratio data, obtain predicted performance index data, adjust the preliminary ratio data according to the optimal ratio strategy until the standard performance index is met, and improve the production efficiency and product quality of petroleum proppant.

Description

Technical Field

[0001] The present application relates to the technical field of petroleum proppant production, and in particular to a method and system for automatically optimizing the ratio of raw materials for petroleum proppant. Background Art

[0002] The use of proppants is crucial in the oil production process, helping to keep downhole fractures open and thereby increasing oil and gas production. The performance of proppants depends largely on the formulation of their raw materials, which include various components such as quartz sand, ceramsite, and resin coating. Traditional methods for formulating raw materials rely on empirical formulas and laboratory testing. This approach relies heavily on experience and trial and error, and may not be optimal, especially when faced with new geological conditions or extraction technologies. This is not only time-consuming and labor-intensive, but also difficult to achieve the optimal formulation, which affects the performance and cost-effectiveness of the proppant.

[0003] With the development of artificial intelligence technology, especially the advancement of deep learning models and optimization algorithms, modern proppant ratio methods increasingly use technical means to achieve automatic optimization of proppant raw material ratios, thereby improving proppant performance and reducing production costs.

[0004] In oilfield production, high-quality experimental data may be difficult to obtain, and modern proppant ratio methods rely heavily on the amount of available ratio data and performance indicators. Especially for new or complex geological conditions, insufficient and biased data may lead to insufficient model training and affect the accuracy of predictions. Summary of the Invention

[0005] The present application aims to solve at least one of the technical problems in the related art to a certain extent. To this end, one purpose of the present application is to propose a method and system for automatically optimizing the ratio of raw materials of petroleum proppants, thereby improving the production efficiency and product quality of petroleum proppants.

[0006] One aspect of the present application provides a method for automatically optimizing the ratio of petroleum proppant raw materials, comprising:

[0007] Step S100: collecting geological information of each oil field during historical oil field exploitation, historical proppant ratio data and performance index data corresponding to the geology;

[0008] The geological information refers to the geological information of the oil field, including but not limited to: stratigraphic information, pore structure information, and oil and gas characteristic data;

[0009] The ratio data refers to the ratio information of raw materials used to produce petroleum proppants, which affects the performance and quality of the proppants;

[0010] The performance index data refers to parameters used to measure the quality and effectiveness of proppants, reflecting the performance of proppants in oil and gas well fracturing operations;

[0011] The specific method for collecting the geological information of each oil field during historical oil field exploitation, the historical proportion data and performance index data of the petroleum proppant corresponding to the geology is as follows:

[0012] Step S110: using geological exploration equipment to collect geological information of each oil field during the historical oil field exploitation process;

[0013] Step S120: Access the database to obtain all proppant components of historically produced petroleum proppant to obtain historical ratio data , obtain the performance index data corresponding to each proppant ;

[0014] Among them, λ=1, 2, ..., m, and the sum of the proportions of all the ratio data is 1, that is, ;

[0015] Among them, δ=1, 2, ..., g, and the performance index data contains a total of g performance indicators ;

[0016] Step S200: using geological information and historical matching data to train a conditional generative adversarial network model, and using the conditional generative adversarial network model to obtain first geological information and first matching data;

[0017] The specific method of using geological information and historical matching data to train a conditional generative adversarial network model and using the conditional generative adversarial network model to obtain the first geological information and the first matching data is as follows:

[0018] Step S210: using existing geological information and historical matching data to train a conditional generative adversarial network model cGAN;

[0019] The specific method of using existing geological information and historical matching data to train the conditional generative adversarial network model cGAN is as follows:

[0020] Step S211: using geological information as feature data, using historical proppant ratio data corresponding to the geological information as label data, and encoding the feature data to obtain a conditional vector;

[0021] Step S212: Designing a generator structure for a conditional generative adversarial network model. The generator network structure consists of convolutional layers, each layer uses a ReLU activation function, and the last layer uses a sigmoid function as an activation function. The input of the generator is the condition vector C and the noise vector z, and the output is the historical proppant ratio data.

[0022] The noise vector z is a vector obtained by random sampling and is used together with the conditional vector as the input of the generator to ensure data diversity;

[0023] Step S213: Design the discriminator structure of the conditional generative adversarial network model. The discriminator's network structure consists of convolutional layers, each layer uses the LeakyReLU activation function, and the last layer is a fully connected layer containing one neuron, using the sigmoid function as the activation function. The input of the discriminator is the real historical ratio data or the ratio data predicted by the generator, and the output of the discriminator is the probability that the input of the discriminator is the real historical ratio data;

[0024] Step S214: Taking the convergence of the loss function as the training goal, setting a loss function change threshold, and stopping training when the change of the loss function is less than the loss function change threshold;

[0025] The loss function includes the generator loss and the discriminator loss , generator loss The calculation formula is: , discriminator loss The calculation formula is: , where x is the real historical ratio data, z is the noise vector, C is the conditional vector, G is the generator, D is the discriminator, and m is the batch size;

[0026] The value of the loss function change threshold is set according to the actual production situation;

[0027] Step S220: acquiring new geological information collected during new oilfield development;

[0028] Step S230: using the trained conditional generative adversarial network model, taking the new geological information as input, and generating new proppant ratio data corresponding to the new geological information conditions;

[0029] The specific method of using the trained conditional generative adversarial network model and taking new geological information as input to generate new proppant ratio data corresponding to the new geological information conditions is as follows:

[0030] Step S231: Encode the new geological information and use the encoded new geological information as a condition vector;

[0031] Step S232: randomly generate a noise vector, and input the noise vector and the conditional vector as input to the generator of the trained conditional generative adversarial network model;

[0032] Step S233: The generator generates new matching data according to the input noise vector and condition vector and outputs it;

[0033] Step S240: Integrate historical geological information and historical matching data with new geological information and new matching data to obtain first geological information and first matching data;

[0034] Step S300: using geological information, historical mix ratio data and performance index data from historically mined oil fields to train a performance index prediction model;

[0035] Preferably, the performance indicator prediction model uses a neural network model as an initial model;

[0036] The specific method of using geological information, historical proportioning data and performance index data from historically mined oil fields to train a performance index prediction model is as follows:

[0037] Step S310: using geological information and historical proportioning data of historically mined oil fields as feature data, and using performance index data as feature labels corresponding to the feature data;

[0038] Step S320: Selecting a neural network model as an initial model, using geological information, historical proppant mix data corresponding to the geological information, and performance index data of the historical mix data under the geological information as a set of training samples;

[0039] Step S330: using the geological information of the historically mined oil fields and the historical proportioning data as input data for training the performance indicator prediction model, and using the performance indicator data as output data for training the performance indicator prediction model;

[0040] Step S340: accurately predicting the performance indicator data in each set of training samples is used as the prediction target, and converging the loss function Loss between the actual performance indicator data and the predicted performance indicator data is used as the training target of the performance indicator prediction model, thereby training to obtain the performance indicator prediction model;

[0041] The calculation formula of the loss function Loss is: ;

[0042] Where a is the number of training samples, i is a variable, i=1, 2, ..., a, represents the actual performance index data in the i-th training sample, Represents the performance index data predicted by the i-th training sample;

[0043] Step S400: using the first geological information and the first ratio data as input data, and using the performance index prediction model to predict and obtain first performance index data;

[0044] Step S500: setting a standard performance index. If the first performance index data is less than the standard performance index, adjusting the first ratio data using an optimal ratio strategy to obtain optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, using the first ratio data as the optimal ratio data to produce proppant.

[0045] The standard performance indicators are set according to the actual production needs and the experience of professionals;

[0046] The method for setting the standard performance index, if the first performance index data is less than the standard performance index, then adjusting the first ratio data by adopting the optimal ratio strategy to obtain the optimal ratio data, and if the first performance index data is greater than or equal to the standard performance index, then using the first ratio data as the optimal ratio data to produce the proppant is as follows:

[0047] Step S510: Define the objective function , used to evaluate the first performance indicator data of the first ratio data, Indicates the first ratio data;

[0048] Step S520: defining constraints, which include performance index constraints, ratio constraints, and sum constraints;

[0049] The expression of the performance indicator constraint is: , where T is the preset standard performance index;

[0050] The expression of the ratio constraint is: ,λ=1,2,...,m;

[0051] The expression of the sum constraint is: ;

[0052] Step S530: Calculate the difference ratio ΔQ between the first performance indicator data and the standard performance indicator. When the difference ratio ΔQ is less than or equal to a threshold value θ, determine the difference between the first performance indicator data and the standard performance indicator as a slight difference. When the difference ratio ΔQ is greater than the threshold value θ, determine the difference between the first performance indicator data and the standard performance indicator as a significant difference.

[0053] The difference ratio is a value that distinguishes the degree of difference between the first performance indicator data and the standard performance indicator, which is the ratio of the difference between the first performance indicator data and the standard performance indicator to the standard performance indicator. The calculation formula is: ;

[0054] The selection of the threshold value θ depends on the specific production and the need for adjustment of the ratio data;

[0055] Step S540: determining whether the gap between the first performance indicator data and the standard performance indicator is a small gap; if so, adjusting the first ratio data using the first adjustment strategy; if not, proceeding to step S550;

[0056] The first adjustment strategy is a linear adjustment strategy, and the expression is: ;

[0057] in, is the first ratio data after adjustment, is the adjustment coefficient, is the difference ratio of the first performance indicator;

[0058] The adjustment factor Used to control the adjustment range and set according to actual production needs;

[0059] The difference ratio of the first performance indicator Used to dynamically adjust the ratio data;

[0060] Step S550: When the gap between the first performance indicator data and the standard performance indicator is significant, the first ratio data is adjusted using the second adjustment strategy;

[0061] The second adjustment strategy is a nonlinear adjustment strategy, and the expression is: ;

[0062] Among them, α is the nonlinear adjustment coefficient, which is used to control the sensitivity of the adjustment and is set according to actual production needs;

[0063] Step S560: The adjusted first mix ratio data and the first geological information are predicted using a performance index prediction model. The predicted value is used to update the first performance index data. The new first performance index data is compared with the standard performance index. If the new first performance index data is less than the standard performance index, the process returns to step S540 to continue adjusting. If the new first performance index data is greater than or equal to the standard performance index, the corresponding first mix ratio data is used as the optimal mix ratio data for producing proppant.

[0064] Step S600: obtaining current actual geological information, generating preliminary matching data, using the preliminary matching data and the actual geological information to obtain predicted performance index data, and adjusting the preliminary matching data according to the optimal matching strategy until the standard performance index is met;

[0065] The specific method of obtaining the current actual geological information, generating preliminary matching data, using the preliminary matching data and the actual geological information to obtain predicted performance index data, and adjusting the preliminary matching data according to the optimal matching strategy until the standard performance index is met is as follows:

[0066] Step S610: using geological exploration equipment to collect actual geological information of the currently exploited oil field, and using a conditional generative adversarial network model to generate preliminary matching data;

[0067] Step S620: Integrate the preliminary ratio data and the actual geological information, input them into the performance index prediction model as input data, and output the predicted performance index data;

[0068] Step S630: If the predicted performance index data does not meet the standard performance index, the preliminary matching data is adjusted according to the optimal matching strategy to obtain the optimal matching data that ultimately meets the standard performance index.

[0069] One aspect of the present application provides an automatic optimization system for petroleum proppant raw material ratios, comprising:

[0070] The data collection module is used to collect geological information of each oil field during historical oil field exploitation, historical proppant ratio data and performance index data corresponding to the geology;

[0071] A generative adversarial network module is used to train a conditional generative adversarial network model using geological information and historical matching data, and obtain first geological information and first matching data using the conditional generative adversarial network model;

[0072] The performance indicator model training module is used to train a performance indicator prediction model using geological information, historical mix ratio data and performance indicator data from historical oilfield production;

[0073] A performance indicator prediction module is used to use the first geological information and the first ratio data as input data and predict the first performance indicator data using the performance indicator prediction model;

[0074] An optimal ratio data acquisition module is used to set a standard performance index. If the first performance index data is less than the standard performance index, the first ratio data is adjusted using an optimal ratio strategy to obtain the optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, the first ratio data is used as the optimal ratio data for producing proppant.

[0075] The practical application module is used to obtain the current actual geological information, generate preliminary ratio data, use the preliminary ratio data and the actual geological information to obtain the predicted performance index data, and adjust the preliminary ratio data according to the optimal ratio strategy until the standard performance index is met.

[0076] One aspect of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for automatically optimizing the ratio of petroleum proppant raw materials are implemented.

[0077] One aspect of the present application provides a readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor to execute the steps of a method for automatically optimizing the ratio of petroleum proppant raw materials.

[0078] The automatic optimization method for petroleum proppant raw material ratio proposed in this application has the following advantages over the existing technology:

[0079] By training the conditional generative adversarial network model, new ratio data can be generated under new geological information conditions, providing more data samples for the subsequent training of performance indicator prediction models;

[0080] By collecting new oilfield geological information and integrating it with historical mix ratio data, new geological information and new mix ratio data are obtained, providing a basis for performance index prediction and optimization;

[0081] A neural network model is used as a performance indicator prediction model. Compared with traditional regression methods, the neural network model can better fit complex nonlinear relationships and improve the accuracy of prediction;

[0082] Different ratio adjustment strategies, including linear and nonlinear, are adopted, dynamically selecting the adjustment strategy based on actual conditions. This is more flexible and intelligent than traditional fixed adjustment methods. Furthermore, the concepts of difference ratio and threshold are introduced to dynamically determine the degree of performance deficiency, improving the accuracy and efficiency of ratio adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 A flow chart of the method for automatically optimizing the ratio of raw materials of petroleum proppants provided in this application;

[0084] Figure 2 Schematic diagram of the optimal ratio strategy for the automatic optimization method of petroleum proppant raw material ratio provided in this application;

[0085] Figure 3 A timing diagram of the automatic optimization method for the petroleum proppant raw material ratio provided in this application;

[0086] Figure 4 Functional module diagram of the automatic optimization system for petroleum proppant raw material ratio provided in this application;

[0087] Figure 5 A schematic diagram of the structure of an electronic device provided in this application;

[0088] Figure 6 This is a schematic diagram of the structure of a readable storage medium provided by this application. DETAILED DESCRIPTION

[0089] To better understand the present application, various aspects of the present application will be described in more detail with reference to the accompanying drawings. It should be understood that these detailed descriptions are merely descriptions of exemplary embodiments of the present application and are not intended to limit the scope of the present application in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0090] In the accompanying drawings, the size, dimensions, and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not drawn strictly to scale. As used herein, the terms "substantially," "approximately," and similar terms are used to indicate approximations, not degrees, and are intended to illustrate inherent deviations in measurements or calculations that would be recognized by a person of ordinary skill in the art. In addition, in this application, the order in which the steps are described does not necessarily represent the order in which these steps occur in actual operation, unless otherwise specified or inferred from the context.

[0091] It should also be understood that expressions such as "including", "comprising", "having", "containing" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present application, "may" is used to mean "one or more embodiments of the present application". And, the term "exemplary" is intended to refer to an example or illustration.

[0092] Unless otherwise defined, all terms used herein (including engineering and scientific terms) have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that, unless otherwise specified in this application, words defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0093] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0094] Example 1

[0095] like Figure 1 As shown, the method for automatically optimizing the ratio of petroleum proppant raw materials provided in this application includes:

[0096] Step S100: collecting geological information of each oil field during historical oil field exploitation, historical proppant ratio data and performance index data corresponding to the geology;

[0097] The geological information refers to the geological information of the oil field, including but not limited to: stratigraphic information, pore structure information, and oil and gas characteristic data;

[0098] The ratio data refers to the ratio information of raw materials used to produce petroleum proppants, which affects the performance and quality of the proppants;

[0099] The performance index data refers to parameters used to measure the quality and effectiveness of proppants, reflecting the performance of proppants in oil and gas well fracturing operations;

[0100] The specific method for collecting the geological information of each oil field during historical oil field exploitation, the historical proportion data and performance index data of the petroleum proppant corresponding to the geology is as follows:

[0101] Step S110: using geological exploration equipment to collect geological information of each oil field during the historical oil field exploitation process;

[0102] Step S120: Access the database to obtain all proppant components of historically produced petroleum proppant to obtain historical ratio data , obtain the performance index data corresponding to each proppant ;

[0103] Among them, λ=1, 2, ..., m, and the sum of the proportions of all the ratio data is 1, that is, ;

[0104] Among them, δ=1, 2, ..., g, and the performance index data contains a total of g performance indicators ;

[0105] The database is used to store the ratio data of petroleum proppants produced during the experiments and production processes in the historical period and the performance index data of the corresponding petroleum proppants;

[0106] This step builds a complete data set by collecting geological information, proportion data, and performance index data of historically mined oil fields, providing basic data for subsequent model training.

[0107] Step S200: using geological information and historical matching data to train a conditional generative adversarial network model, and using the conditional generative adversarial network model to obtain first geological information and first matching data;

[0108] The specific method of using geological information and historical matching data to train a conditional generative adversarial network model and using the conditional generative adversarial network model to obtain the first geological information and the first matching data is as follows:

[0109] Step S210: using existing geological information and historical matching data to train a conditional generative adversarial network model cGAN;

[0110] The specific method of using existing geological information and historical matching data to train the conditional generative adversarial network model cGAN is as follows:

[0111] Step S211: using geological information as feature data, using historical proppant ratio data corresponding to the geological information as label data, and encoding the feature data to obtain a conditional vector;

[0112] Step S212: Designing a generator structure for a conditional generative adversarial network model. The generator network structure consists of convolutional layers, each layer uses a ReLU activation function, and the last layer uses a sigmoid function as an activation function. The input of the generator is the condition vector C and the noise vector z, and the output is the historical proppant ratio data.

[0113] The noise vector z is a vector obtained by random sampling and is used together with the conditional vector as the input of the generator to ensure data diversity;

[0114] Step S213: Design the discriminator structure of the conditional generative adversarial network model. The discriminator's network structure consists of convolutional layers, each layer uses the LeakyReLU activation function, and the last layer is a fully connected layer containing one neuron, using the sigmoid function as the activation function. The input of the discriminator is the real historical ratio data or the ratio data predicted by the generator, and the output of the discriminator is the probability that the input of the discriminator is the real historical ratio data;

[0115] Step S214: Taking the convergence of the loss function as the training goal, setting a loss function change threshold, and stopping training when the change of the loss function is less than the loss function change threshold;

[0116] The loss function includes the generator loss and the discriminator loss , generator loss The calculation formula is: , discriminator loss The calculation formula is: , where x is the real historical ratio data, z is the noise vector, C is the conditional vector, G is the generator, D is the discriminator, and m is the batch size;

[0117] The batch size refers to the amount of data processed in one training cycle;

[0118] The value of the loss function change threshold is set according to the actual production situation;

[0119] Step S220: acquiring new geological information collected during new oilfield development;

[0120] Step S230: using the trained conditional generative adversarial network model, taking the new geological information as input, and generating new proppant ratio data corresponding to the new geological information conditions;

[0121] The specific method of using the trained conditional generative adversarial network model and taking new geological information as input to generate new proppant ratio data corresponding to the new geological information conditions is as follows:

[0122] Step S231: Encode the new geological information and use the encoded new geological information as a condition vector;

[0123] Step S232: randomly generate a noise vector, and input the noise vector and the conditional vector as input to the generator of the trained conditional generative adversarial network model;

[0124] Step S233: The generator generates new matching data according to the input noise vector and condition vector and outputs it;

[0125] Step S240: Integrate historical geological information and historical matching data with new geological information and new matching data to obtain first geological information and first matching data;

[0126] This step generates new ratio data under new geological information conditions by training the adversarial network model, providing more possibilities for proppant ratio and more data samples for the training of subsequent performance indicator prediction models.

[0127] Step S300: using geological information, historical mix ratio data and performance index data from historically mined oil fields to train a performance index prediction model;

[0128] The performance index prediction model is a deep learning model that predicts the performance index data of the petroleum proppant produced under the ratio data based on the geological information of the oil field and the ratio data of the petroleum proppant corresponding to the geology;

[0129] Preferably, the performance indicator prediction model uses a neural network model as an initial model;

[0130] The specific method of using geological information, historical proportioning data and performance index data from historically mined oil fields to train a performance index prediction model is as follows:

[0131] Step S310: using geological information and historical proportioning data of historically mined oil fields as feature data, and using performance index data as feature labels corresponding to the feature data;

[0132] Step S320: Selecting a neural network model as an initial model, using geological information, historical proppant mix data corresponding to the geological information, and performance index data of the historical mix data under the geological information as a set of training samples;

[0133] Step S330: using the geological information of the historically mined oil fields and the historical proportioning data as input data for training the performance indicator prediction model, and using the performance indicator data as output data for training the performance indicator prediction model;

[0134] Step S340: accurately predicting the performance indicator data in each set of training samples is used as the prediction target, and converging the loss function Loss between the actual performance indicator data and the predicted performance indicator data is used as the training target of the performance indicator prediction model, thereby training to obtain the performance indicator prediction model;

[0135] The calculation formula of the loss function Loss is: ;

[0136] Where a is the number of training samples, i is a variable, i=1, 2, ..., a, represents the actual performance index data in the i-th training sample, Represents the performance index data predicted by the i-th training sample;

[0137] This step uses historical data to train a performance indicator prediction model. It can predict the performance indicators of proppant application based on the ratio data and geological information, providing a basis for optimizing the ratio. A neural network model is used as the performance indicator prediction model. Compared with traditional regression methods, the neural network model can better fit complex nonlinear relationships and improve the accuracy of prediction.

[0138] Step S400: using the first geological information and the first ratio data as input data, and using the performance index prediction model to predict and obtain first performance index data;

[0139] The specific method of using the first geological information and the first ratio data as input data and using the performance index prediction model to predict and obtain the first performance index data is as follows:

[0140] Standardizing the first geological information and the first ratio data, loading the trained performance indicator prediction model, inputting the standardized first geological information and the first ratio information as input data into the performance indicator prediction model, and the performance indicator prediction model outputting the corresponding first performance indicator data;

[0141] Step S500: setting a standard performance index. If the first performance index data is less than the standard performance index, adjusting the first ratio data using an optimal ratio strategy to obtain optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, using the first ratio data as the optimal ratio data to produce proppant.

[0142] The optimal matching strategy includes a first adjustment strategy and a second adjustment strategy;

[0143] The standard performance indicators are set according to the actual production needs and the experience of professionals;

[0144] Figure 2 Schematic diagram of the optimal ratio strategy for the automatic optimization method of petroleum proppant raw material ratio provided in this application;

[0145] The method for setting the standard performance index, if the first performance index data is less than the standard performance index, then adjusting the first ratio data by adopting the optimal ratio strategy to obtain the optimal ratio data, and if the first performance index data is greater than or equal to the standard performance index, then using the first ratio data as the optimal ratio data to produce the proppant is as follows:

[0146] Step S510: Define the objective function , used to evaluate the first performance indicator data of the first ratio data, Indicates the first ratio data;

[0147] The optimization goal of the objective function is to maximize the value of the objective function;

[0148] Step S520: defining constraints, which include performance index constraints, ratio constraints, and sum constraints;

[0149] The expression of the performance indicator constraint is: , where T is the preset standard performance index;

[0150] The expression of the ratio constraint is: ,λ=1,2,...,m;

[0151] The expression of the sum constraint is: ;

[0152] Step S530: Calculate the difference ratio ΔQ between the first performance indicator data and the standard performance indicator. When the difference ratio ΔQ is less than or equal to a threshold value θ, determine the difference between the first performance indicator data and the standard performance indicator as a slight difference. When the difference ratio ΔQ is greater than the threshold value θ, determine the difference between the first performance indicator data and the standard performance indicator as a significant difference.

[0153] The difference ratio is a value that distinguishes the degree of difference between the first performance indicator data and the standard performance indicator, which is the ratio of the difference between the first performance indicator data and the standard performance indicator to the standard performance indicator. The calculation formula is: ;

[0154] The selection of the threshold value θ depends on the specific production and the need for adjustment of the ratio data;

[0155] Assume that, through expert opinions and historical data analysis, it is determined that when the first performance indicator data is less than or equal to 5% of the standard performance indicator, it is considered a slight gap, and when the first performance indicator data exceeds 5% of the standard performance indicator, it is considered a significant gap. Therefore, the threshold θ is set to 0.05;

[0156] Step S540: determining whether the gap between the first performance indicator data and the standard performance indicator is a small gap; if so, adjusting the first ratio data using the first adjustment strategy; if not, proceeding to step S550;

[0157] The first adjustment strategy is a linear adjustment strategy, and the expression is: ;

[0158] in, is the first ratio data after adjustment, is the adjustment coefficient, is the difference ratio of the first performance indicator;

[0159] The adjustment factor Used to control the adjustment range and set according to actual production needs;

[0160] The difference ratio of the first performance indicator Used to dynamically adjust the ratio data;

[0161] Step S550: When the gap between the first performance indicator data and the standard performance indicator is significant, the first ratio data is adjusted using the second adjustment strategy;

[0162] The second adjustment strategy is a nonlinear adjustment strategy, and the expression is: ;

[0163] Among them, α is the nonlinear adjustment coefficient, which is used to control the sensitivity of the adjustment and is set according to actual production needs;

[0164] Step S560: The adjusted first mix ratio data and the first geological information are predicted using a performance index prediction model. The predicted value is used to update the first performance index data. The new first performance index data is compared with the standard performance index. If the new first performance index data is less than the standard performance index, the process returns to step S540 to continue adjusting. If the new first performance index data is greater than or equal to the standard performance index, the corresponding first mix ratio data is used as the optimal mix ratio data for producing proppant.

[0165] When the first performance index data is greater than or equal to the standard performance index, the first ratio data corresponding to the first performance index data at this time meets the standard performance index required for the petroleum proppant, and therefore the first ratio data corresponding to the first performance index data is used as the optimal ratio data for producing the petroleum proppant;

[0166] This step uses different ratio adjustment strategies based on the comparison of predicted performance indicators with standard performance indicators to ensure that the proppant ratio meets the required performance level. Two different adjustment strategies are proposed, including linear adjustment and nonlinear adjustment. The adjustment strategy is dynamically selected based on actual conditions, which is more flexible and intelligent than the traditional fixed adjustment method. At the same time, the concepts of difference ratio and threshold are introduced to dynamically determine the degree of performance indicator deficiency, improving the accuracy and efficiency of ratio adjustment.

[0167] Step S600: obtaining current actual geological information, generating preliminary matching data, using the preliminary matching data and the actual geological information to obtain predicted performance index data, and adjusting the preliminary matching data according to the optimal matching strategy until the standard performance index is met;

[0168] The specific method of obtaining the current actual geological information, generating preliminary matching data, using the preliminary matching data and the actual geological information to obtain predicted performance index data, and adjusting the preliminary matching data according to the optimal matching strategy until the standard performance index is met is as follows:

[0169] Step S610: using geological exploration equipment to collect actual geological information of the currently exploited oil field, and using a conditional generative adversarial network model to generate preliminary matching data;

[0170] Step S620: Integrate the preliminary ratio data and the actual geological information, input them into the performance index prediction model as input data, and output the predicted performance index data;

[0171] Step S630: If the predicted performance index data does not meet the standard performance index, the preliminary matching data is adjusted according to the optimal matching strategy to obtain the optimal matching data that ultimately meets the standard performance index.

[0172] Example 2

[0173] like Figure 3 , which is a timing diagram of the method for automatically optimizing the ratio of petroleum proppant raw materials provided in this application, including:

[0174] First, data obtained from historical oilfield production is collected, including geological information, historical mix data, and performance index data;

[0175] The collected geological information and historical ratio data are used as training samples to train the conditional generative adversarial network model;

[0176] Acquire new geological information during new oilfield development, use the trained conditional generative adversarial network model, take the new geological information as input, generate new matching data, integrate historical data and new data, and obtain first geological information and first matching data;

[0177] The collected geological information, historical ratio data and performance index data are used as training data to train the performance index prediction model;

[0178] Taking the first geological information and the first ratio data as input, inputting them into the performance index prediction model to obtain first performance index data;

[0179] Comparing the first performance indicator data with the standard performance indicator, formulating an optimal ratio strategy to adjust the first ratio data to obtain the optimal ratio data;

[0180] Collect real-time geological information data and use the conditional generative adversarial network model to generate preliminary matching data;

[0181] The performance index prediction model is used to predict the performance index data with preliminary ratio data and real-time geological information as input data. The preliminary ratio data is adjusted according to the optimal ratio strategy until the performance index data meets the standard performance index. The final ratio data is used as the optimal ratio data to produce oil proppants.

[0182] Example 3

[0183] like Figure 4 As shown, the petroleum proppant raw material ratio automatic optimization system provided in this application includes:

[0184] The data collection module is used to collect geological information of each oil field during historical oil field exploitation, historical proppant ratio data and performance index data corresponding to the geology;

[0185] A generative adversarial network module is used to train a conditional generative adversarial network model using geological information and historical matching data, and obtain first geological information and first matching data using the conditional generative adversarial network model;

[0186] The performance indicator model training module is used to train a performance indicator prediction model using geological information, historical mix ratio data and performance indicator data from historical oilfield production;

[0187] A performance indicator prediction module is used to use the first geological information and the first ratio data as input data and predict the first performance indicator data using the performance indicator prediction model;

[0188] An optimal ratio data acquisition module is used to set a standard performance index. If the first performance index data is less than the standard performance index, the first ratio data is adjusted using an optimal ratio strategy to obtain the optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, the first ratio data is used as the optimal ratio data for producing proppant.

[0189] The practical application module is used to obtain the current actual geological information, generate preliminary ratio data, use the preliminary ratio data and the actual geological information to obtain the predicted performance index data, and adjust the preliminary ratio data according to the optimal ratio strategy until the standard performance index is met.

[0190] Example 4

[0191] Figure 5 This is a schematic diagram of the electronic device structure provided by an embodiment of the present application. Figure 5 According to another aspect of the present application, an electronic device is provided. The electronic device may include one or more processors and one or more memories. The memories may store computer-readable code that, when executed by the one or more processors, may execute the above-described method for automatically optimizing the petroleum proppant raw material ratio.

[0192] The method or system according to the embodiment of the present application can also be used Figure 5 The electronic device architecture shown in FIG. Figure 5As shown, the electronic device may include a bus, one or more CPUs, a read-only memory (ROM), a random access memory (RAM), a communication port connected to a network, input / output components, a hard disk, etc. A storage device in the electronic device, such as a ROM or a hard disk, may store the method for automatically optimizing the petroleum proppant raw material ratio provided in the present application. The method for automatically optimizing the raw material ratio of petroleum proppant may, for example, include: collecting geological information of each oil field during historical oil field exploitation, historical ratio data and performance index data of petroleum proppant corresponding to the geology; using geological information and historical ratio data to train a conditional generative adversarial network model, and using the conditional generative adversarial network model to obtain first geological information and first ratio data; using geological information, historical ratio data and performance index data in historical oil field exploitation to train a performance index prediction model; using the first geological information and first ratio data as input data, and using the performance index prediction model to predict the first performance index data; setting a standard performance index, if the first performance index data is less than the standard performance index, then using the optimal ratio strategy to adjust the first ratio data to obtain the optimal ratio data, if the first performance index data is greater than or equal to the standard performance index, then using the first ratio data as the optimal ratio data to produce proppant; obtaining the current actual geological information, generating preliminary ratio data, using the preliminary ratio data and the actual geological information to obtain predicted performance index data, and adjusting the preliminary ratio data according to the optimal ratio strategy until the standard performance index is met. Furthermore, the electronic device may also include a user interface. Of course, Figure 5 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different devices. Figure 5 One or more components of an electronic device are shown.

[0193] Example 5

[0194] Figure 6 This is a schematic diagram of the structure of a readable storage medium provided by an embodiment of the present application. Figure 6 The figure shows a computer-readable storage medium according to one embodiment of the present application. The computer-readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by a processor, the method for automatically optimizing the petroleum proppant raw material ratio according to the embodiment of the present application described with reference to the above figures can be executed. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0195] In addition, according to the embodiment of the present application, the process described with reference to the flowchart above can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided in the present application, such as: collecting geological information of each oil field during the historical exploitation of the oil field, historical proportioning data and performance index data of the petroleum proppant corresponding to the geology; using the geological information and historical proportioning data to train a conditional generative adversarial network model, and using the conditional generative adversarial network model to obtain first geological information and first proportioning data; using the geological information, historical proportioning data and performance index data in the historical exploitation of the oil field to train a performance index prediction model; The computer program comprises a method for producing a proppant by using a central processing unit (CPU) and a computer program comprising: a computer program comprising: a computer program for producing a proppant; ...

[0196] The methods, apparatus, and devices of the present application may be implemented in many ways. For example, the methods, apparatus, and devices of the present application may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of steps for the method is for illustration only, and the steps of the method of the present application are not limited to the order specifically described above unless otherwise specified. In addition, in some embodiments, the present application may also be implemented as programs recorded in a recording medium, which include machine-readable instructions for implementing the methods according to the present application. Therefore, the present application also covers recording media that store programs for executing the methods according to the present application.

[0197] In addition, the parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0198] The above-described specific embodiments further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is merely a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for automatically optimizing the ratio of raw materials of petroleum proppants, characterized in that: include: Collect geological information of each oil field during historical oil field exploitation, historical proportion data and performance index data of petroleum proppants corresponding to the geology; Using geological information and historical matching data to train a conditional generative adversarial network model, and using the conditional generative adversarial network model to obtain first geological information and first matching data; The performance index prediction model is obtained by training the geological information, historical proportion data and performance index data of the historically exploited oil fields; The first geological information and the first ratio data are used as input data, and the first performance indicator data is predicted by using the performance indicator prediction model; A standard performance index is set. If the first performance index data is less than the standard performance index, the first ratio data is adjusted by using an optimal ratio strategy to obtain the optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, the first ratio data is used as the optimal ratio data to produce the proppant. The optimal matching strategy includes a first adjustment strategy and a second adjustment strategy; Obtain current actual geological information, generate preliminary ratio data, use the preliminary ratio data and actual geological information to obtain predicted performance index data, and adjust the preliminary ratio data according to the optimal ratio strategy until the standard performance index is met; The method of setting the standard performance index, if the first performance index data is less than the standard performance index, then adjusting the first ratio data by using the optimal ratio strategy to obtain the optimal ratio data, and if the first performance index data is greater than or equal to the standard performance index, then using the first ratio data as the optimal ratio data to produce the proppant is as follows: Step S510: Define the objective function , used to evaluate the first performance indicator data of the first ratio data, Indicates the first ratio data; Step S520: defining constraint conditions, wherein the constraint conditions include performance index constraint, ratio constraint and sum constraint; Step S530: Calculate the difference ratio ΔQ between the first performance indicator data and the standard performance indicator. When the difference ratio ΔQ is less than or equal to a threshold value θ, determine the difference between the first performance indicator data and the standard performance indicator as a small difference. When the difference ratio ΔQ is greater than a threshold value θ, the gap between the first performance indicator data and the standard performance indicator is determined to be a significant gap; Step S540: determining whether the gap between the first performance indicator data and the standard performance indicator is a small gap, if so, adjusting the first ratio data using the first adjustment strategy, if not, proceeding to step S550; Step S550: When the gap between the first performance indicator data and the standard performance indicator is significant, the first ratio data is adjusted using the second adjustment strategy; Step S560: The adjusted first ratio data and the first geological information are predicted using a performance index prediction model, and the predicted value is used to update the first performance index data, and the new first performance index data is compared with the standard performance index. If the new first performance index data is less than the standard performance index, the process returns to step S540 to continue adjusting, and if the new first performance index data is greater than or equal to the standard performance index, the corresponding first ratio data is used as the optimal ratio data to produce proppant; The specific method of using geological information and historical matching data to train a conditional generative adversarial network model and using the conditional generative adversarial network model to obtain the first geological information and the first matching data is: The conditional generative adversarial network model cGAN is trained using existing geological information and historical matching data; Access to new geological information collected during new oil field development; Using the trained conditional generative adversarial network model, the new geological information is used as input to generate new proportion data of petroleum proppants corresponding to the new geological information conditions; Integrate the historical geological information and historical matching data with the new geological information and new matching data to obtain first geological information and first matching data; The expression of the performance indicator constraint is: , where T is the preset standard performance index; The expression of the ratio constraint is: ,λ=1,2,...,m; The expression of the sum constraint is: ; The first adjustment strategy is a linear adjustment strategy, and the expression is: ; in, is the adjusted first ratio data, is the adjustment factor, is the difference ratio of the first performance indicator; The second adjustment strategy is a nonlinear adjustment strategy, and the expression is: ; Among them, α is the nonlinear adjustment coefficient.

2. The method for automatically optimizing the ratio of petroleum proppant raw materials according to claim 1, characterized in that: The specific method of collecting the geological information of each oil field during the historical exploitation of the oil field, the historical proportion data and performance index data of the petroleum proppant corresponding to the geology is as follows: During the historical oil field exploitation process, geological exploration equipment was used to collect geological information on each oil field; Access the database to obtain all proppant components of historically produced petroleum proppants and obtain historical ratio data , obtain the performance index data corresponding to each proppant ; Among them, λ=1, 2, ..., m, and the sum of the proportions of all the ratio data is 1, that is, ; Among them, δ=1, 2, ..., g, and the performance index data contains a total of g performance indicators .

3. The method for automatically optimizing the ratio of petroleum proppant raw materials according to claim 2, characterized in that: The specific method of using the existing geological information and historical matching data to train the conditional generative adversarial network model cGAN is: Taking geological information as feature data, taking historical proppant ratio data corresponding to the geological information as label data, and encoding the feature data to obtain a conditional vector; Design the generator structure of the conditional generative adversarial network model. The network structure of the generator consists of convolutional layers. Each layer uses the ReLU activation function. The last layer uses the sigmoid function as the activation function. The input of the generator is the conditional vector C and the noise vector z, and the output is the historical ratio data of the proppant. Design the discriminator structure of the conditional generative adversarial network model. The network structure of the discriminator consists of convolutional layers. Each layer uses the LeakyReLU activation function. The last layer is a fully connected layer containing one neuron. The sigmoid function is used as the activation function. The input of the discriminator is the real historical ratio data or the ratio data predicted by the generator. The output of the discriminator is the probability that the input of the discriminator is the real historical ratio data. The training goal is to achieve convergence of the loss function, and a loss function change threshold is set. When the change of the loss function is less than the loss function change threshold, the training is stopped. The loss function includes the generator loss and the discriminator loss , the generator loss The calculation formula is: , the discriminator loss The calculation formula is: , where x is the real historical ratio data, z is the noise vector, C is the conditional vector, G is the generator, D is the discriminator, and m is the batch size.

4. The method for automatically optimizing the ratio of petroleum proppant raw materials according to claim 3, characterized in that: The specific method of using geological information, historical proportioning data and performance index data in historically produced oil fields to train a performance index prediction model is as follows: The geological information and historical proportion data of the historically exploited oil fields are used as feature data, and the performance index data are used as feature labels of the corresponding feature data; Selecting a neural network model as an initial model, taking geological information, historical proportion data of petroleum proppants corresponding to the geological information, and performance index data of the historical proportion data under the geological information as a set of training samples; Using geological information and historical proportion data of historically exploited oil fields as input data for training a performance indicator prediction model, and using the performance indicator data as output data for training the performance indicator prediction model; The prediction target is to accurately predict the performance indicator data in each group of training samples, and the training target of the performance indicator prediction model is to converge the loss function Loss between the real performance indicator data and the predicted performance indicator data, and the performance indicator prediction model is obtained by training; The calculation formula of the loss function Loss is: ; Where a is the number of training samples, i is a variable, i=1, 2, ..., a, represents the actual performance index data in the i-th training sample, Represents the performance indicator data predicted by the i-th training sample.

5. A system for automatically optimizing the ratio of raw materials of petroleum proppants, which is implemented based on the method for automatically optimizing the ratio of raw materials of petroleum proppants according to any one of claims 1 to 4, characterized in that: include: The data collection module is used to collect the geological information of each oil field during the historical exploitation of the oil field, the historical proportion data of the petroleum proppant corresponding to the geology, and the performance index data; A generative adversarial network module is used to train a conditional generative adversarial network model using geological information and historical matching data, and obtain first geological information and first matching data using the conditional generative adversarial network model; The performance indicator model training module is used to train the performance indicator prediction model using geological information, historical proportion data and performance indicator data in the historical oil field; A performance indicator prediction module is used to use the first geological information and the first ratio data as input data and predict the first performance indicator data using the performance indicator prediction model; The optimal ratio data acquisition module is used to set the standard performance index. If the first performance index data is less than the standard performance index, the first ratio data is adjusted by the optimal ratio strategy to obtain the optimal ratio data. If the first performance index data is greater than or equal to the standard performance index, the first ratio data is used as the optimal ratio data to produce the proppant. The practical application module is used to obtain the current actual geological information, generate preliminary proportioning data, use the preliminary proportioning data and the actual geological information to obtain the predicted performance index data, and adjust the preliminary proportioning data according to the optimal proportioning strategy until the standard performance index is met.

6. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for automatically optimizing the ratio of petroleum proppant raw materials as claimed in any one of claims 1 to 4 are implemented.

7. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the steps in the method for automatically optimizing the ratio of petroleum proppant raw materials as described in any one of claims 1-4.

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