Intelligent linkage method for oil and gas exploitation process

Abstract

The invention relates to the technical field of oil and gas exploitation, in particular to an oil and gas exploitation process intelligent linkage method which comprises the following steps: S1, data acquisition and monitoring: acquiring temperature, pressure and flow data in an oil refining process in real time through data acquisition equipment; s2, model optimization and prediction; s3, intelligent control and adjustment; according to the method, the production process is optimized through data analysis, the mining efficiency is improved, the model is utilized, the operation state of the oil refining process is known, adjustment is made according to needs, efficient decision making is achieved, all units in the oil refining process are automatically controlled, the operation process is more accurate and stable, and the oil refining efficiency is improved. Model prediction can be used to accurately analyze the correlation between data so as to accurately predict the future trend, and is not influenced by subjective consciousness and personal experience, the prediction result is relatively reliable, the human input and subjective interference in the prediction process are reduced, excessive bandwidth and computing power are not consumed, and the prediction efficiency is improved. And seamless connection and sharing of data can be realized.

Description

Technical Field

[0001] This invention relates to the field of oil and gas extraction technology, and in particular to an intelligent linkage method for oil and gas extraction processes. Background Technology

[0002] Oil and natural gas are indispensable energy sources in modern society, used in power generation, transportation, chemical industry and many other industries. By extracting these resources, the energy needs of a country or region can be met, supporting economic development and improving people's living standards. The extraction and utilization of oil and gas resources can drive the development of related industries, such as petrochemicals and transportation, thereby promoting national or regional economic growth and employment.

[0003] In the existing digital oilfield architecture in the oil and gas industry, after IoT sensors collect operational data, they usually send the data back to the data center, resulting in excessive consumption of bandwidth and computing power. Furthermore, they cannot achieve seamless data integration and sharing, and cannot automatically adjust the operating status.

[0004] Therefore, to address the problem of excessive bandwidth and computing power consumption and the inability to automatically adjust the operating status, an intelligent linkage method for oil and gas extraction processes can be designed. Summary of the Invention

[0005] To overcome the problem of not being able to automatically adjust the running status.

[0006] The technical solution of this invention is: an intelligent linkage method for oil and gas extraction processes, comprising the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the oil refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the oil refining process and making adjustments as needed. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: The steps of data preprocessing include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data; S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information.

[0007] S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function. S22: The algorithms for model prediction include linear regression, support vector machine, decision tree, and random forest. The mathematical formula for linear regression is: , For dependent variable, For independent variable, For intercept, For slope, For error terms, The number of samples; the mathematical model formula for support vector machines is: , Represents the decision function, These are the parameters of the support vector machine. It is a sample label, Here, b is the kernel function, b is the offset, and x is the input vector; the mathematical model formula for the decision tree is: if ,in Input variables It is an output variable. The random forest mathematical model formula is: c represents the number of categories; c is the number of categories. , This represents the prediction result of the random forest on the input data x. Indicates the number of decision trees in a random forest. Indicates the first The prediction results of each decision tree; S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation more precise and stable.

[0008] Preferably, the data acquisition device includes a pressure sensor, a temperature sensor, and a flow sensor, which are installed on the production equipment and pipelines.

[0009] Preferably, missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; predicting missing values ​​using K-nearest neighbors or decision tree algorithms; outlier detection and handling includes using statistical methods to detect outliers; deciding whether to delete, replace, or retain outliers based on business needs; data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; normalization scales data to the range [0,1] or [-1,1]; encoding categorical variables includes one-hot encoding, label encoding, and ordinal encoding, where one-hot encoding converts categorical variables into binary columns; label encoding converts categorical variables into integers; and ordinal encoding, for ordered categorical variables, converts them into integers while retaining the order information. Information; Feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; using PCA and t-SNE methods to reduce dimensionality and reduce feature complexity; Data transformation includes logarithmic transformation and Box-Cox transformation; Multinomial feature generation; Data partitioning divides the dataset into training, validation, and test sets to evaluate model performance and generalization ability; Handling imbalanced data includes oversampling of the minority class; undersampling of the majority class; using synthetic sample techniques or cost-sensitive learning to handle imbalance problems; Text data preprocessing includes removing stop words, punctuation marks, and special characters; Stemming or lemma reconstruction; Text vectorization; Time series data preprocessing includes: extraction of date and time features; Time series stationarization; Seasonal decomposition and trend decomposition.

[0010] Preferred statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is primarily used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study the degree and direction of the influence of independent variables on the dependent variable, mainly including linear regression and nonlinear regression.

[0011] Preferred methods for model building include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict trends and periodicity in time series data, such as ARIMA models and GARCH models. Machine learning models utilize algorithms and pattern recognition to automatically learn data models, such as decision trees, support vector machines, and neural networks.

[0012] As a preferred option, the specific steps of the model optimization algorithm are: loading the model, initializing parameters, defining the loss function, selecting the optimal model, training the model, and evaluating the model. The mathematical model formula for the model optimization algorithm is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function.

[0013] As a preferred approach, intelligent control and regulation are achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues.

[0014] Preferably, the control time domain is selected as 10% to 20% of the prediction time domain.

[0015] The beneficial effects of this invention are as follows: It optimizes the production process through data analysis, improves mining efficiency, utilizes models to understand the operating status of the refining process, and makes adjustments as needed to achieve efficient decision-making. Furthermore, the automatic control of each unit in the refining process makes the operation more precise and stable. Model prediction can accurately analyze the correlation between data, thereby accurately predicting future trends without being influenced by subjective consciousness or personal experience, resulting in relatively reliable predictions. It reduces human input and subjective interference in the prediction process, and does not consume excessive bandwidth or computing power, enabling seamless data integration and sharing. Detailed Implementation

[0016] The present invention will be further described below with reference to embodiments.

[0017] Example 1 This invention provides an embodiment of an intelligent linkage method for oil and gas extraction processes, comprising the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the refining process and making adjustments as needed. The data acquisition equipment includes pressure sensors, temperature sensors, and flow sensors, which are installed on production equipment and pipelines. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: Data preprocessing steps include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. Missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; and predicting missing values ​​using K-nearest neighbors or decision tree algorithms. Outlier detection and handling includes using statistical methods to detect outliers; and deciding whether to delete, replace, or retain outliers based on business needs. Data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; and normalization scales data to the range of [0,1] or [-1,1]. Encoding categorical variables includes one-hot encoding, label encoding, and sequential encoding, where one-hot encoding converts categorical variables into binary columns. Label encoding converts categorical variables to integers; ordinal encoding converts ordered categorical variables to integers while preserving ordinal information; feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; using PCA and t-SNE methods for dimensionality reduction to reduce feature complexity; data transformation includes logarithmic transformation and Box-Cox transformation; multinomial feature generation; data partitioning divides the dataset into training, validation, and test sets to evaluate model performance and generalization ability; handling imbalanced data includes oversampling of the minority class; undersampling of the majority class; using synthetic sample techniques or cost-sensitive learning to handle imbalance problems; text data preprocessing includes removing stop words, punctuation, and special characters; stemming or lemma reconstruction; text vectorization; time series data preprocessing includes: extraction of date and time features; time series stationarization; seasonality decomposition; trend decomposition. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data. Statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is mainly used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study independent variables. The influence of variables on the dependent variable, including the degree and direction of influence, is mainly assessed using two methods: linear regression and nonlinear regression. Model building methods include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict trends and periodicity in time series data, such as ARIMA and GARCH models. Machine learning models utilize algorithms and pattern recognition to automatically learn data models, such as decision trees, support vector machines, and neural networks. S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information.

[0018] S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function. The specific steps of model optimization algorithms are: loading the model, initializing parameters, defining the loss function, selecting an optimization parameter, training the model, and evaluating the model. The mathematical model formula for model optimization algorithms is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function; S22: The model prediction algorithm uses linear regression, and the mathematical formula for linear regression is as follows: , For dependent variable, For independent variable, For intercept, For slope, For error terms, The number of samples; S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation process more precise and stable. Intelligent control and regulation is achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues. The control time domain is selected as 10% to 20% of the prediction time domain.

[0019] Example 2 This invention provides an embodiment of an intelligent linkage method for oil and gas extraction processes, comprising the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the refining process and making adjustments as needed. The data acquisition equipment includes pressure sensors, temperature sensors, and flow sensors, which are installed on production equipment and pipelines. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: Data preprocessing steps include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. Missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; and predicting missing values ​​using K-nearest neighbors or decision tree algorithms. Outlier detection and handling includes using statistical methods to detect outliers; and deciding whether to delete, replace, or retain outliers based on business needs. Data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; and normalization scales data to the range of [0,1] or [-1,1]. Encoding categorical variables includes one-hot encoding, label encoding, and sequential encoding, where one-hot encoding converts categorical variables into binary columns. Label encoding converts categorical variables to integers; ordinal encoding converts ordered categorical variables to integers while preserving ordinal information; feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; using PCA and t-SNE methods for dimensionality reduction to reduce feature complexity; data transformation includes logarithmic transformation and Box-Cox transformation; multinomial feature generation; data partitioning divides the dataset into training, validation, and test sets to evaluate model performance and generalization ability; handling imbalanced data includes oversampling of the minority class; undersampling of the majority class; using synthetic sample techniques or cost-sensitive learning to handle imbalance problems; text data preprocessing includes removing stop words, punctuation, and special characters; stemming or lemma reconstruction; text vectorization; time series data preprocessing includes: extraction of date and time features; time series stationarization; seasonality decomposition; trend decomposition. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data. Statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is mainly used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study independent variables. The influence of variables on the dependent variable, including the degree and direction of influence, is mainly assessed using two methods: linear regression and nonlinear regression. Model building methods include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict trends and periodicity in time series data, such as ARIMA and GARCH models. Machine learning models utilize algorithms and pattern recognition to automatically learn data models, such as decision trees, support vector machines, and neural networks. S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information.

[0020] S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function. The specific steps of model optimization algorithms are: loading the model, initializing parameters, defining the loss function, selecting an optimization parameter, training the model, and evaluating the model. The mathematical model formula for model optimization algorithms is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function; S22: The model prediction algorithm uses support vector machines. The mathematical model formula for support vector machines is as follows: , Represents the decision function, These are the parameters of the support vector machine. It is a sample label, b is the kernel function, b is the offset, and x is the input vector; S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation process more precise and stable. Intelligent control and regulation is achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues. The control time domain is selected as 10% to 20% of the prediction time domain.

[0021] Example 3 This invention provides an embodiment of an intelligent linkage method for oil and gas extraction processes, comprising the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the refining process and making adjustments as needed. The data acquisition equipment includes pressure sensors, temperature sensors, and flow sensors, which are installed on production equipment and pipelines. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: Data preprocessing steps include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. Missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; and predicting missing values ​​using K-nearest neighbors or decision tree algorithms. Outlier detection and handling includes using statistical methods to detect outliers; and deciding whether to delete, replace, or retain outliers based on business needs. Data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; and normalization scales data to the range of [0,1] or [-1,1]. Encoding categorical variables includes one-hot encoding, label encoding, and sequential encoding, where one-hot encoding converts categorical variables into binary columns. Label encoding converts categorical variables to integers; ordinal encoding converts ordered categorical variables to integers while preserving ordinal information; feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; using PCA and t-SNE methods for dimensionality reduction to reduce feature complexity; data transformation includes logarithmic transformation and Box-Cox transformation; multinomial feature generation; data partitioning divides the dataset into training, validation, and test sets to evaluate model performance and generalization ability; handling imbalanced data includes oversampling of the minority class; undersampling of the majority class; using synthetic sample techniques or cost-sensitive learning to handle imbalance problems; text data preprocessing includes removing stop words, punctuation, and special characters; stemming or lemma reconstruction; text vectorization; time series data preprocessing includes: extraction of date and time features; time series stationarization; seasonality decomposition; trend decomposition. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data. Statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is mainly used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study independent variables. The influence of variables on the dependent variable, including the degree and direction of influence, is mainly assessed using two methods: linear regression and nonlinear regression. Model building methods include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict trends and periodicity in time series data, such as ARIMA and GARCH models. Machine learning models utilize algorithms and pattern recognition to automatically learn data models, such as decision trees, support vector machines, and neural networks. S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information.

[0022] S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function. The specific steps of model optimization algorithms are: loading the model, initializing parameters, defining the loss function, selecting an optimization parameter, training the model, and evaluating the model. The mathematical model formula for model optimization algorithms is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function; S22: The model prediction algorithm uses a decision tree, and the mathematical model formula for the decision tree is: if ,in Input variables It is an output variable. c represents the category, and 'c' represents the number of categories. S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation process more precise and stable. Intelligent control and regulation is achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues. The control time domain is selected as 10% to 20% of the prediction time domain.

[0023] Example 4 This invention provides an embodiment of an intelligent linkage method for oil and gas extraction processes, comprising the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the refining process and making adjustments as needed. The data acquisition equipment includes pressure sensors, temperature sensors, and flow sensors, which are installed on production equipment and pipelines. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: Data preprocessing steps include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. Missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; and predicting missing values ​​using K-nearest neighbors or decision tree algorithms. Outlier detection and handling includes using statistical methods to detect outliers; and deciding whether to delete, replace, or retain outliers based on business needs. Data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; and normalization scales data to the range of [0,1] or [-1,1]. Encoding categorical variables includes one-hot encoding, label encoding, and sequential encoding, where one-hot encoding converts categorical variables into binary columns. Label encoding converts categorical variables to integers; ordinal encoding converts ordered categorical variables to integers while preserving ordinal information; feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; using PCA and t-SNE methods for dimensionality reduction to reduce feature complexity; data transformation includes logarithmic transformation and Box-Cox transformation; multinomial feature generation; data partitioning divides the dataset into training, validation, and test sets to evaluate model performance and generalization ability; handling imbalanced data includes oversampling of the minority class; undersampling of the majority class; using synthetic sample techniques or cost-sensitive learning to handle imbalance problems; text data preprocessing includes removing stop words, punctuation, and special characters; stemming or lemma reconstruction; text vectorization; time series data preprocessing includes: extraction of date and time features; time series stationarization; seasonality decomposition; trend decomposition. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data. Statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is mainly used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study independent variables. The influence of variables on the dependent variable, including the degree and direction of influence, is mainly assessed using two methods: linear regression and nonlinear regression. Model building methods include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict trends and periodicity in time series data, such as ARIMA and GARCH models. Machine learning models utilize algorithms and pattern recognition to automatically learn data models, such as decision trees, support vector machines, and neural networks. S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information.

[0024] S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates them to minimize the loss function. The specific steps of model optimization algorithms are: loading the model, initializing parameters, defining the loss function, selecting an optimization parameter, training the model, and evaluating the model. The mathematical model formula for model optimization algorithms is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function; S22: The model prediction algorithm uses random forest, and the mathematical model formula for random forest is as follows: , This represents the prediction result of the random forest on the input data x. Indicates the number of decision trees in a random forest. Indicates the first The prediction results of each decision tree; S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation process more precise and stable. Intelligent control and regulation is achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues. The control time domain is selected as 10% to 20% of the prediction time domain.

[0025] The embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. An intelligent linkage method for oil and gas extraction processes; characterized in that, Includes the following steps: S1: Data Acquisition and Monitoring. Data on temperature, pressure, and flow rate during the oil refining process are collected in real time through data acquisition equipment and transmitted to the central control room via network for data processing. This allows for understanding the operating status of the oil refining process and making adjustments as needed. S11: Data analysis, which involves mining and integrating the collected data, specifically including data preprocessing and data predictive analysis; S111: The steps of data preprocessing include missing value handling, outlier detection and handling, data standardization / normalization, encoding categorical variables, feature selection and dimensionality reduction, data transformation, data partitioning, handling imbalanced data, text data preprocessing, and time series data and mathematical processing. S112: Predictive analysis of data utilizes statistical analysis and model building to conduct in-depth analysis and mining of data; S12: Display the processed data in the form of charts or curves, so that producers can more intuitively understand the current production status, including but not limited to output, productivity, temperature, humidity and pressure information; S2: Model Optimization and Prediction By establishing a mathematical model of the oil refining process, optimization and prediction can be carried out. Model optimization can achieve optimal operation of the oil refining process, improve product quality and production efficiency; model prediction can predict potential problems in the oil refining process and take corresponding measures in advance to reduce risks. S21: The main methods of model optimization algorithms include: gradient descent, stochastic gradient descent, and dynamic gradient descent. Gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function; stochastic gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function; dynamic gradient descent starts with randomly initialized parameters and gradually updates the parameters to minimize the loss function. S22: The algorithms for model prediction include linear regression, support vector machine, decision tree, and random forest. The mathematical formula for linear regression is: , For dependent variable, For independent variable, For intercept, For slope, For error terms, The number of samples; the mathematical model formula for support vector machines is: , Represents the decision function, These are the parameters of the support vector machine. It is a sample label, Here, b is the kernel function, b is the offset, and x is the input vector; the mathematical model formula for the decision tree is: if ,in Input variables It is an output variable. The random forest mathematical model formula is: c represents the number of categories; c is the number of categories. , This represents the prediction result of the random forest on the input data x. Indicates the number of decision trees in a random forest. Indicates the first The prediction results of each decision tree; S3: Intelligent Control and Regulation: Through automated control systems and artificial intelligence algorithms, automatic control of each unit in the oil refining process is achieved, making the operation more precise and stable.

2. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: The data acquisition equipment includes pressure sensors, temperature sensors, and flow sensors, which are installed on production equipment and pipelines.

3. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: Missing value handling includes deleting records containing missing values; filling missing values ​​with the mean, median, or mode; and predicting missing values ​​using K-nearest neighbors or decision tree algorithms. Outlier detection and handling includes using statistical methods to detect outliers; deciding whether to delete, replace, or retain outliers based on business needs. Data standardization / normalization includes standardization and normalization, where standardization converts data into a distribution with a mean of 0 and a standard deviation of 1; normalization scales data to the range [0,1] or [-1,1]. Encoding categorical variables includes one-hot encoding, label encoding, and ordinal encoding, where one-hot encoding converts categorical variables into binary columns; label encoding converts categorical variables into integers; and ordinal encoding, for ordered categorical variables, converts them into integers while preserving order information. Feature selection and dimensionality reduction include using statistical tests and model weighting methods to select important features; and using PCA and t-SNE methods for dimensionality reduction to reduce feature complexity. Data transformation includes logarithmic transformation and Box-Cox transformation. Multinomial feature generation; data partitioning: dividing the dataset into training, validation, and test sets to evaluate model performance and generalization ability; handling imbalanced data, including oversampling of the minority class and undersampling of the majority class; Use synthetic sample techniques or cost-sensitive learning to address imbalance problems; text data preprocessing includes stop word removal, punctuation removal, and special character removal; stemming or lemmatization; Text vectorization; Time series data preprocessing includes: extraction of date and time features; time series stationarization; seasonality decomposition; and trend decomposition.

4. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: Statistical analysis methods include descriptive statistical analysis, hypothesis testing, correlation analysis, and regression analysis. Descriptive statistical analysis is primarily used to describe the basic characteristics of data, including central tendency, dispersion, and distribution. Hypothesis testing is used to determine whether a hypothesis is true, inferring the properties of the population based on sample data; common hypothesis testing methods include t-tests, analysis of variance, and chi-square tests. Correlation analysis is used to analyze the strength and direction of the relationship between two or more variables; commonly used methods include Pearson correlation analysis and Spearman correlation analysis. Regression analysis is used to study the degree and direction of the influence of independent variables on the dependent variable, mainly including linear regression and nonlinear regression.

5. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: Model building methods include linear models, nonlinear models, time series models, and machine learning models. Linear models are typically used to establish linear relationships between independent and dependent variables, such as linear regression models. Nonlinear models are used to establish nonlinear relationships between independent and dependent variables, such as multinomial regression models and logistic regression models. Time series models are used to analyze and predict the trends and periodicity of time series data, such as ARIMA models and GARCH models. Machine learning models automatically learn data models using algorithms and pattern recognition, such as decision trees, support vector machines, and neural networks.

6. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: The specific steps of the model optimization algorithm are: loading the model, initializing parameters, defining the loss function, selecting the optimal loss function, training the model, and evaluating the model. The mathematical model formula for the model optimization algorithm is: ,in Indicates the updated weights, Indicates the old weight, Indicates learning rate, Represents the loss function, This represents the gradient of the loss function.

7. The intelligent linkage method for oil and gas extraction processes according to claim 1, characterized in that: Intelligent control and regulation are achieved by adjusting the prediction time domain, control time domain, sampling period, and constraint issues.

8. The intelligent linkage method for oil and gas extraction processes according to claim 7, characterized in that: The control time domain is selected to be 10% to 20% of the prediction time domain.

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