A construction method for a dynamic assessment model of safety risks in petroleum engineering

Abstract

The present invention relates to the technical field of model construction, and particularly relates to a construction method of a dynamic evaluation model for oil engineering safety risks. The method includes the following steps: obtaining geological data of the oil engineering area; deploying multi-dimensional sensors for the geological data of the oil engineering area to generate an oil extraction sensor deployment network; collecting environmental sensing data based on the oil extraction sensor deployment network to obtain standard oil extraction environment collection data; performing oil extraction geographical elevation conversion on the standard oil extraction environment collection data to generate oil extraction geographical elevation data; and performing multi-level oil extraction impact assessment based on the oil extraction geographical elevation data to generate multi-level oil extraction impact data. By integrating data, dynamically monitoring, and analyzing the risk propagation path of the data in the oil engineering stage, and through the oil engineering safety risk assessment, the present invention improves the dynamic perception and instant response capabilities of oil engineering safety risks.

Description

Technical Field

[0001] The present invention relates to the technical field of model construction, and particularly to a construction method of a dynamic evaluation model for oil engineering safety risks. Background Art

[0002] In the early stage, the safety assessment of oil engineering mainly relied on experience and qualitative analysis. With the development of computing technology, methods based on probabilistic risk assessment (PRA) emerged, which improved the scientificity of assessment through quantitative analysis. However, traditional PRA methods often lack consideration of dynamic factors and are difficult to adapt to the complex and changeable oil engineering environment. As the oil engineering safety assessment model gradually develops towards dynamic and intelligent directions, the dynamic risk assessment (DRA) model introduces time series analysis and dynamic simulation technology, which can monitor and evaluate risk changes in real time. These models combine multiple data sources, such as sensor data, historical accident data, and environmental monitoring data, and improve the accuracy and timeliness of risk prediction through machine learning and data mining technologies. In recent years, with the application of big data technology and the Internet of Things (IoT), the oil engineering safety assessment model has been further optimized. Big data technology can process massive and complex data, improving data analysis and processing capabilities, while IoT technology enables real-time data collection and transmission, enhancing the dynamic response ability of the model. However, currently, traditional geological impact assessment is often limited to single-level analysis, lacking comprehensive consideration of multi-level influencing factors, and at the same time, the analysis of risk propagation paths is often limited to the surface level, lacking in-depth understanding of risk propagation at different stages, thus resulting in a relatively low dynamic perception and immediate response ability to oil engineering safety risks. Summary of the Invention

[0003] Based on this, it is necessary to provide a construction method of a dynamic evaluation model for oil engineering safety risks to solve at least one of the above technical problems.

[0004] To achieve the above object, a construction method of a dynamic evaluation model for oil engineering safety risks, the method includes the following steps:

[0005] Step S1: Obtain geological data of the oil engineering area; deploy multi-dimensional sensors for the geological data of the oil engineering area to generate an oil production sensor deployment network; collect environmental sensing data based on the oil production sensor deployment network to obtain standard oil production environment collection data;

[0006] Step S2: Perform petroleum extraction geographical elevation conversion on the data collected from the standard petroleum extraction environment to generate petroleum extraction geographical elevation data; conduct multi-level petroleum extraction impact assessment based on the petroleum extraction geographical elevation data to generate multi-level petroleum extraction impact data; perform multi-field coupling numerical simulation on the multi-level geological impact factors of petroleum extraction to generate oil and gas extraction risk impact factors;

[0007] Step S3: Conduct data security monitoring based on the oil and gas extraction risk impact factors to generate oil and gas extraction risk monitoring data; optimize the stream engine for the oil and gas extraction risk monitoring data to generate a dynamic risk index matrix; perform cluster parallel processing on the dynamic risk index matrix to generate a petroleum extraction risk eigenvector;

[0008] Step S4: Construct a dynamic assessment model for petroleum engineering safety risks; import the dynamic risk index matrix into the dynamic assessment model for petroleum engineering safety risks to conduct extraction risk prediction and generate petroleum engineering extraction risk prediction data; perform risk propagation simulation on the petroleum engineering extraction risk prediction data to generate a petroleum engineering risk propagation path diagram;

[0009] Step S5: Perform stage high-risk identification on the petroleum engineering risk propagation path diagram to generate high-risk petroleum engineering stage data; conduct dynamic risk decision-making on the high-risk petroleum engineering stage data to generate high-risk petroleum engineering stage decision-making strategies for implementing petroleum engineering safety risk adjustment operations.

[0010] By acquiring and deploying a sensor network, the present invention can collect environmental data in real time, such as temperature, pressure, humidity, etc., thereby optimizing the monitoring and management of the petroleum extraction environment. Standardized data collection helps to accurately evaluate the environmental conditions of the engineering area and provides reliable data support for subsequent steps. Through geographical elevation conversion and multi-level impact assessment, the impact of petroleum extraction on geology and the environment can be comprehensively understood. Multi-field coupling numerical simulation helps to simulate the complex interactions of different geological impact factors, thereby accurately evaluating the extraction risks. The generation of the dynamic risk index matrix and risk eigenvector enables real-time monitoring and assessment of the safety risks of petroleum extraction. Stream engine optimization and cluster parallel processing improve the data processing efficiency and response speed, helping to quickly identify and respond to potential risks. Through the dynamic assessment model and risk prediction data, various risks that occur during petroleum extraction can be identified and predicted in advance. Risk propagation simulation and the generation of the path diagram help to understand the risk diffusion mechanism and guide the formulation of more effective risk management strategies. Identifying and making dynamic decisions on high-risk stages helps to timely adjust and optimize the extraction strategy at critical moments, reduce the probability of risk occurrence, and ensure the safe and stable progress of petroleum engineering. Therefore, the present invention improves the dynamic perception and immediate response capabilities of petroleum engineering safety risks through data integration, dynamic monitoring, and risk propagation path analysis of petroleum engineering stage data and through petroleum engineering safety risk assessment.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Use GIS to obtain geological data of the petroleum engineering area;

[0013] Step S12: Analyze the geological data of the petroleum engineering area to generate petroleum extraction range data;

[0014] Step S13: Deploy multi-dimensional sensors based on the petroleum extraction range data to generate a petroleum extraction sensor deployment network;

[0015] Step S14: Collect environmental sensing data based on the petroleum extraction sensor deployment network to obtain petroleum extraction environment collection data; perform data preprocessing on the petroleum extraction environment collection data to generate standard petroleum extraction environment collection data, where data preprocessing includes data cleaning, filling missing data values, and data standardization.

[0016] Through the GIS system of the present invention, geological data of the petroleum engineering area can be obtained quickly and accurately. These data include key information such as terrain, landform, and geological structure, which helps to comprehensively understand the geological conditions of the petroleum extraction area and provides basic data support for subsequent extraction planning. By analyzing the geological data, the most suitable extraction range can be determined, maximizing resource utilization efficiency, reducing unnecessary extraction range and resource waste. At the same time, areas with poor geological conditions or potential risks can be avoided to ensure the safety and economy of extraction. Based on the extraction range data, multi-dimensional sensors are deployed to achieve comprehensive monitoring of the extraction process. The sensor deployment network can monitor geological changes, equipment operation status, and environmental conditions in real time, discover and handle abnormal situations in a timely manner, and ensure the stability and efficiency of the extraction process. By collecting environmental data through the sensor network, the environmental conditions of the extraction area can be comprehensively understood. The data preprocessing steps (data cleaning, filling missing values, and data standardization) ensure the accuracy and consistency of the data, providing a reliable data basis for subsequent analysis and decision-making.

[0017] Preferably, step S2 includes the following steps:

[0018] Step S21: Extract geological data from the standard petroleum extraction environment collection data to obtain petroleum extraction geological data, where the petroleum extraction geological data includes geological exploration data and drilling data;

[0019] Step S22: Perform petroleum extraction geographical elevation conversion on the geological exploration data and drilling data to generate petroleum extraction geographical elevation data;

[0020] Step S23: Conduct multi-level petroleum extraction impact assessment based on the petroleum extraction geographical elevation data to generate multi-level petroleum extraction impact data;

[0021] Step S24: Conduct a multi-field coupling numerical simulation on the multi-level geological impact factors of oil extraction to generate oil and gas extraction risk impact factors.

[0022] By extracting geological data, the present invention can obtain detailed geological exploration and drilling information. These data can help comprehensively understand the underground structure and the distribution of oil and gas reservoirs, provide necessary data support for precise extraction, ensure the scientificity and rationality of the extraction process. By performing geographical elevation conversion on the geological exploration data and drilling data, more accurate geographical elevation information can be obtained. This helps to accurately locate the position and depth of the oil and gas reservoirs, provide accurate guidance for drilling operations, reduce blind drilling and unnecessary costs, and improve the drilling efficiency and success rate. Through multi-level impact assessment using geographical elevation data, the impact of oil extraction on different levels of geology and the environment can be comprehensively evaluated. This helps to identify potential risks and problems, formulate corresponding countermeasures, and ensure the safety and sustainability of oil extraction activities. Through multi-field coupling numerical simulation, the interaction and comprehensive impact of different geological impact factors during the extraction process can be analyzed in detail. This helps to accurately assess the risks of oil and gas extraction, provide a scientific basis for decision-making, and thus reduce risks.

[0023] Preferably, step S23 includes the following steps:

[0024] Step S231: Based on the geographical elevation data of oil extraction, divide the extraction formation to generate extraction formation division data; conduct formation lithology analysis on the standard oil extraction environment acquisition data based on the extraction formation division data to generate formation lithology analysis data; conduct fault structure analysis on the formation lithology analysis data to generate oil extraction geological impact data;

[0025] Step S232: Conduct oil and gas extraction rock mechanics property analysis on the geographical elevation data of oil extraction to generate rock mechanics property data; conduct formation pressure analysis on the standard oil extraction environment acquisition data through the rock mechanics property data to generate oil extraction mechanical impact data;

[0026] Step S233: Calculate the distribution of oil and gas reservoirs according to the geographical elevation data of oil extraction to obtain oil and gas reservoir distribution data; conduct oil and gas fluid property evaluation based on the oil and gas reservoir distribution data to generate oil and gas fluid property evaluation data; integrate the oil extraction geological impact data, oil extraction mechanical impact data, and oil and gas fluid property evaluation data to generate multi-level impact data of oil extraction.

[0027] By accurately dividing the exploited strata, the present invention can effectively determine the geological characteristics and resource distribution of each layer, optimize the exploitation plan, maximize the resource utilization rate, and reduce the interference with irrelevant strata. Through lithology analysis, the rock types and physical properties of different strata can be understood, providing detailed geological information for drilling and exploitation to ensure the accuracy and safety of construction. Fault structure analysis can identify and evaluate potential geological risks, avoid exploitation accidents caused by fault activities, and ensure the safety and stability of the exploitation process. Understanding the mechanical properties of rocks helps to evaluate the stability and bearing capacity of strata, optimize drilling parameters and exploitation strategies, and reduce exploitation risks. Through formation pressure analysis, the changes in formation pressure can be predicted and monitored to prevent accidents such as blowouts and well collapses and ensure the safety of the exploitation process. Accurately calculating the distribution of oil and gas reservoirs can optimize well location design and exploitation plans, maximize the oil and gas recovery rate, and improve economic benefits. Evaluating the properties of oil and gas fluids can understand the physical and chemical characteristics of oil and gas, providing an important basis for processing and marketing, and improving the quality and market competitiveness of oil and gas products. Through the integrated analysis of multi-dimensional data, the multi-level impacts of oil exploitation can be comprehensively and systematically evaluated, optimizing exploitation decisions and ensuring the sustainability and efficiency of exploitation activities.

[0028] Preferably, the calculation of the distribution of oil and gas reservoirs based on the geographical elevation data of oil exploitation includes:

[0029] Conduct terrain modeling on the geographical elevation data of oil exploitation to generate oil exploitation terrain data; generate contour lines for the oil exploitation terrain data to obtain oil exploitation contour line data; calculate the curvature of the oil exploitation contour line data to obtain oil exploitation terrain change data;

[0030] Use geological exploration data to judge the reservoir level of the oil exploitation terrain change data to generate initial oil exploitation reservoir level thickness data; collect logging data through the initial oil exploitation reservoir level thickness data to generate logging data; calculate the porosity of the logging data based on the formation lithology analysis data to obtain reservoir porosity data;

[0031] Conduct reservoir characteristic analysis on the initial oil exploitation reservoir level thickness data based on the reservoir porosity data to obtain initial reservoir permeability evaluation data and initial reservoir saturation data; use the initial reservoir permeability evaluation data and initial reservoir saturation data to optimize the reservoir distribution of the oil exploitation terrain change data to generate an oil exploitation reservoir thickness map;

[0032] Conduct calculation of the distribution of oil and gas reservoirs on the oil exploitation reservoir thickness map to obtain oil and gas reservoir distribution data.

[0033] Through terrain modeling, the present invention can depict in detail the terrain features of the mining area, providing basic data for subsequent contour generation and curvature calculation, and helping to accurately locate and plan mining activities. The contour data can visually show the ups and downs of the terrain, helping to identify potential reservoir distribution areas and providing references for further geological analysis and mining plans. The curvature calculation can reveal the changing trends and complexities of the terrain, helping to identify the positions and characteristics of reservoirs and improving the mining accuracy and efficiency. Through the comprehensive analysis of geological exploration data, the layers and thicknesses of reservoirs can be accurately divided, the distribution ranges and characteristics of reservoirs can be determined, and guidance can be provided for well logging data acquisition. Through the comprehensive analysis of geological exploration data, the layers and thicknesses of reservoirs can be accurately divided, the distribution ranges and characteristics of reservoirs can be determined, and guidance can be provided for well logging data acquisition. The porosity data can reflect the oil storage capacity of the reservoir, helping to evaluate the distribution and reserves of oil and gas and providing a scientific basis for formulating mining strategies. By analyzing the permeability and saturation of the reservoir, the fluidity and content of oil and gas in the reservoir can be evaluated, providing an important reference for optimizing mining strategies. The optimization of reservoir distribution can more accurately reflect the actual situation of the reservoir, optimize the mining plan, ensure the efficiency and safety of the mining process. Through comprehensive calculation, the spatial distribution of oil and gas reservoirs can be accurately determined, and the well location design and mining plan can be optimized.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Perform data security monitoring based on oil and gas mining risk impact factors to generate oil and gas mining risk monitoring data;

[0036] Step S32: Optimize the oil and gas mining risk monitoring data with a stream engine to generate a dynamic risk index matrix;

[0037] Step S33: Split the dynamic risk index matrix into data blocks to obtain dynamic risk data blocks; perform cluster node distribution on the dynamic risk data blocks to generate distributed dynamic risk data blocks;

[0038] Step S34: Perform parallel processing on the distributed dynamic risk data blocks to obtain local risk feature data; aggregate risk factors for the local risk feature data to generate an initial risk feature set; perform data dimensionality reduction on the initial risk feature set to generate an oil mining risk feature vector.

[0039] Through the monitoring of the risk impact factors in oil and gas exploitation, the present invention can obtain the risk information during the exploitation process in real time, discover and give early warnings of potential risks in a timely manner, and ensure the safety and continuity of the exploitation activities. The optimization of the stream engine can improve the efficiency of data processing and analysis. The generated dynamic risk index matrix can reflect the real-time dynamics of risk changes, providing accurate and timely information for risk management and decision-making. The data block splitting and cluster node distribution can effectively manage and process large-scale risk data, improving the speed and efficiency of data processing through distributed processing, and realizing the efficient management and analysis of risk data. Parallel processing improves the efficiency of data processing. The aggregation of risk factors and data dimensionality reduction help to extract and simplify the key risk characteristics. The generated risk feature vector can accurately reflect the risk characteristics in the oil and gas exploitation process, helping to formulate more accurate and effective risk management strategies.

[0040] Preferably, step S32 includes the following steps:

[0041] Step S321: Perform stream data access on the oil and gas exploitation risk monitoring data to obtain the oil and gas exploitation risk monitoring data stream;

[0042] Step S322: Define the event pattern for the oil and gas exploitation risk monitoring data stream through complex time processing rules to generate the oil and gas exploitation event monitoring stream; perform key event detection on the oil and gas exploitation event detection stream to generate the key events of oil and gas exploitation risks;

[0043] Step S323: Assign risk weights to the key events of oil and gas exploitation risks to generate a dynamic risk index matrix.

[0044] Through the access of streaming data, the present invention can obtain and process the risk monitoring data in the oil and gas exploitation process in real time, ensuring the real-time nature and continuity of the data, and providing a basis for subsequent event detection and analysis. The complex time processing rules and event pattern definitions can help identify and extract important events in the risk monitoring data. Through critical event detection, potential risk points can be discovered in a timely manner, early warnings and responses can be carried out, and major accidents can be avoided. By allocating risk weights to critical events, the severity of various risks can be quantified and evaluated. The generated dynamic risk index matrix can comprehensively reflect the current risk status, providing an accurate basis for risk management and decision-making, and optimizing the risk control strategy. Through the access and real-time processing of streaming data, the real-time nature and continuity of the risk monitoring data are ensured, and potential risk events can be quickly responded to and processed. Through complex time processing rules and critical event detection, important risk events can be accurately identified and extracted, improving the accuracy of risk assessment. The dynamic risk index matrix synthesizes the weight allocation of multiple risk events, can comprehensively reflect the risk situation in the exploitation process, and provides a reliable basis for scientific decision-making. Early discovery and identification of critical risk events, risk warning and timely response can reduce the possibility of accidents and ensure the safety and stability of the exploitation process.

[0045] Preferably, step S4 includes the following steps:

[0046] Step S41: Construct a dynamic assessment model for oil engineering safety risks; import the dynamic risk index matrix into the dynamic assessment model for oil engineering safety risks to predict the exploitation risks and generate oil engineering exploitation risk prediction data;

[0047] Step S42: Define the network nodes of the oil engineering project through the risk impact factors of oil and gas exploitation to obtain the network node data of the oil engineering project; construct the risk propagation network structure based on the network node data of the oil engineering project to obtain the initial risk propagation network structure;

[0048] Step S43: Allocate node weights to the initial risk propagation network structure according to the oil exploitation risk prediction data to generate the risk propagation network structure; simulate the propagation process of the risk propagation network structure to generate risk propagation process simulation data;

[0049] Step S44: Trace the node propagation paths of the risk propagation network structure based on the risk propagation process simulation data to generate the oil engineering risk propagation path diagram.

[0050] By constructing a dynamic evaluation model, the present invention can systematically and dynamically evaluate various risks in petroleum engineering. Importing the dynamic risk index matrix into the model for mining risk prediction can generate accurate and real-time risk prediction data, helping to timely identify and evaluate potential risks, and improving the forward-looking and effectiveness of risk management. By defining and constructing the risk propagation network structure, the various nodes and their mutual relationships in the petroleum engineering project can be comprehensively understood, which helps to systematically identify and analyze the risk propagation paths and improve the overall control ability of risks. Through node weight allocation and propagation process simulation, the importance and influence of each node in risk propagation can be quantitatively evaluated. The generated propagation process simulation data can truly reflect the dynamic process of risk propagation, helping to identify key nodes and potential risk propagation paths and formulate effective risk control strategies. Through node propagation path tracking, the specific paths and processes of risk propagation can be intuitively displayed. The generated risk propagation path diagram can help to comprehensively understand and monitor the dynamic changes of risk propagation, and timely take measures to prevent or slow down the spread of risks, ensuring the safety and stability of the petroleum engineering project. By constructing the risk dynamic evaluation model and the propagation network structure, various risks in petroleum engineering can be comprehensively and systematically evaluated and managed. Real-time prediction and simulation of the risk propagation process can identify and evaluate potential risks in advance, improving the forward-looking and initiative of risk management. Quantitative node weight allocation and propagation process simulation can accurately evaluate the importance and influence of each node, improving the accuracy of risk assessment. Through the generated risk propagation path diagram, the specific paths and processes of risk propagation can be intuitively displayed, helping to timely take measures to control and slow down the risk propagation and ensure the safety and stability of the project.

[0051] Preferably, step S41 includes the following steps:

[0052] Step S411: Split the data of the dynamic risk index matrix to obtain a model training set, a model validation set, and a model test set;

[0053] Step S412: Use the deep neural network algorithm to train the model training set to generate a risk assessment training model; evaluate the model generalization ability of the risk assessment training model through the model validation set to generate model generalization performance data;

[0054] Step S413: Adjust the model architecture and hyperparameters of the risk assessment training model through the model generalization performance data to generate a risk assessment adjusted model; perform forward propagation calculation on the risk assessment adjusted model according to the model test set to generate risk assessment test data;

[0055] Step S414: Conduct a predictive performance evaluation on the risk assessment test data to generate model predictive performance evaluation data; based on the model predictive performance evaluation data, perform model iteration optimization on the risk assessment adjustment model to generate a dynamic oil engineering safety risk assessment model; import the dynamic risk index matrix into the dynamic oil engineering safety risk assessment model for mining risk prediction to generate oil engineering mining risk prediction data.

[0056] Through data splitting, the present invention can effectively avoid the overfitting problem during model training, and at the same time ensure the data independence and representativeness during the model training, validation, and testing phases, improving the credibility and stability of model evaluation. Through the training of the deep neural network, it can learn complex patterns and rules from a large amount of data, and the generated risk assessment training model can accurately predict various risks in oil engineering mining. The model generalization ability evaluation can evaluate the performance ability of the model on new data, ensuring that the model has good generalization ability and can adapt to the risk prediction requirements under different conditions. The model architecture and hyperparameter adjustment can further optimize the performance and accuracy of the model, ensuring that the model can perform well on different data sets. The risk assessment test data generated through forward propagation calculation can evaluate the prediction ability of the model in actual applications, providing empirical support for the further optimization and improvement of the model. Through predictive performance evaluation and model iteration optimization, the prediction accuracy and stability of the model can be continuously improved and enhanced, and the generated dynamic oil engineering safety risk assessment model can effectively predict and manage various risks during the mining process. Importing the dynamic risk index matrix into the model for mining risk prediction can achieve real-time and accurate risk monitoring and early warning, helping decision-makers formulate effective risk management strategies in a timely manner.

[0057] Preferably, step S5 includes the following steps:

[0058] Step S51: Conduct oil engineering safety risk identification on the oil engineering risk propagation path diagram to obtain oil engineering stage risk identification data;

[0059] Step S52: Compare the oil engineering stage risk identification data with the preset stage risk identification data threshold. When the oil engineering stage risk identification data is greater than or equal to the preset stage risk identification threshold, high-risk oil engineering stage data is generated;

[0060] Step S53: Conduct dynamic risk decision-making on the high-risk oil engineering stage data to generate a high-risk oil engineering stage decision-making strategy to execute the oil engineering safety risk adjustment operation.

[0061] Through the identification of the risk propagation path diagram, the present invention can clearly identify and display the safety risk distribution and influence path in different stages of oil engineering, helping decision-makers comprehensively understand and analyze the risk situations in each stage. By setting the stage risk identification threshold, it can quickly identify the oil engineering stages with high risks, timely warn and respond to high-risk events, and avoid potential safety accidents and production interruptions. Through dynamic risk decision-making, safety risk management strategies and emergency measures can be formulated and adjusted according to real-time high-risk data, ensuring the safe production and operation of oil engineering. This strategic decision-making can effectively reduce the likelihood of accidents and ensure the safety of personnel and equipment. Through dynamic monitoring and risk identification, it can respond to safety risks in oil engineering in real time and avoid accidents. By setting thresholds and comparative analysis, it can warn and identify high-risk stages in advance, providing important decision-making basis for decision-makers. Through dynamic risk decision-making and adjusted operations, it can effectively control and manage various risks in oil engineering, ensuring the smooth progress and safety of production.

[0062] The beneficial effects of the present invention are as follows: By obtaining the geological data of the petroleum engineering area, key geological information can be obtained, providing accurate basic data for subsequent sensor deployment and decision-making. Through multi-dimensional sensor deployment for geological data, a sensor network covering the entire petroleum extraction area can be established for collecting environmental sensing data. Through environmental sensing data collection, standard petroleum extraction environment data can be obtained, providing a basis for subsequent evaluation and decision-making. By performing geographical elevation conversion on the collected data of the standard petroleum extraction environment, petroleum extraction geographical elevation data can be generated, further providing a basis for petroleum extraction impact assessment. Based on the petroleum extraction geographical elevation data, multi-level petroleum extraction impact assessment can be carried out to obtain multi-level impact data of petroleum extraction, helping to understand the impact of extraction on the environment. By performing multi-field coupling numerical simulation on the multi-level geological impact factors of petroleum extraction, risk impact factors for oil and gas extraction can be generated for subsequent risk monitoring and assessment. Based on the risk impact factors for oil and gas extraction, data security monitoring can be carried out to generate risk monitoring data for oil and gas extraction, used for real-time monitoring and control of extraction risks. By optimizing the flow engine for the risk monitoring data of oil and gas extraction, a dynamic risk index matrix can be generated, providing more accurate data for subsequent risk assessment and prediction. By constructing a dynamic assessment model for petroleum engineering safety risks, importing the dynamic risk index matrix into the model for extraction risk prediction, petroleum engineering extraction risk prediction data can be generated, and risk propagation simulation can be carried out to generate a petroleum engineering risk propagation path map. By performing stage high-risk identification on the petroleum engineering risk propagation path map, high-risk petroleum engineering stage data can be generated, and dynamic risk decision-making can be carried out on the high-risk petroleum engineering stage data to generate high-risk petroleum engineering stage decision-making strategies to execute petroleum engineering safety risk adjustment operations, enabling the assessment, prediction, and decision-making of petroleum engineering safety risks, thereby improving the safety and sustainability of petroleum engineering. Therefore, the present invention improves the dynamic perception and immediate response capabilities of petroleum engineering safety risks through data integration, dynamic monitoring, and risk propagation path analysis of petroleum engineering stage data, and through petroleum engineering safety risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic flow chart of the steps of a method for constructing a dynamic assessment model for petroleum engineering safety risks;

[0064] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0065] Figure 3 is Figure 1 a detailed implementation step flow chart of step S3 in

[0066] Figure 4 is Figure 1Schematic diagram of the detailed implementation steps of step S4 in

[0067] The realization of the object of the present invention, its functional features and advantages will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific embodiments

[0068] The technical method of the present invention for a patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0071] To achieve the above object, please refer to Figures 1 to 4 , a method for constructing a dynamic risk assessment model for petroleum engineering safety, the method comprising the following steps:

[0072] Step S1: Obtain geological data of the petroleum engineering area; deploy multi-dimensional sensors for the geological data of the petroleum engineering area to generate a petroleum extraction sensor deployment network; collect environmental sensing data based on the petroleum extraction sensor deployment network to obtain standard petroleum extraction environment collection data;

[0073] Step S2: Perform petroleum extraction geographical elevation conversion on the standard petroleum extraction environment collection data to generate petroleum extraction geographical elevation data; conduct multi-level petroleum extraction impact assessment based on the petroleum extraction geographical elevation data to generate multi-level petroleum extraction impact data; perform multi-field coupling numerical simulation on the multi-level geological impact factors of petroleum extraction to generate oil and gas extraction risk impact factors;

[0074] Step S3: Perform data security monitoring based on oil and gas production risk impact factors to generate oil and gas production risk monitoring data; optimize the flow engine for the oil and gas production risk monitoring data to generate a dynamic risk index matrix; perform cluster parallel processing on the dynamic risk index matrix to generate an oil production risk feature vector;

[0075] Step S4: Construct a dynamic assessment model for oil engineering safety risks; import the dynamic risk index matrix into the dynamic assessment model for oil engineering safety risks to predict production risks and generate oil engineering production risk prediction data; perform risk propagation simulation on the oil engineering production risk prediction data to generate an oil engineering risk propagation path diagram;

[0076] Step S5: Perform stage high-risk identification on the oil engineering risk propagation path diagram to generate high-risk oil engineering stage data; perform dynamic risk decision-making on the high-risk oil engineering stage data to generate a high-risk oil engineering stage decision-making strategy to execute the oil engineering safety risk adjustment operation.

[0077] By acquiring and deploying a sensor network, the present invention can collect environmental data in real time, such as temperature, pressure, humidity, etc., thereby optimizing the monitoring and management of the oil production environment. Standardized data collection helps to accurately evaluate the environmental conditions of the engineering area and provides reliable data support for subsequent steps. Through geographical elevation conversion and multi-level impact assessment, the impact of oil production on geology and the environment can be comprehensively understood. Multi-field coupling numerical simulation helps to simulate the complex interactions of different geological impact factors, thereby accurately evaluating production risks. The generation of the dynamic risk index matrix and risk feature vector enables real-time monitoring and assessment of the safety risks of oil production. Flow engine optimization and cluster parallel processing improve data processing efficiency and response speed, helping to quickly identify and respond to potential risks. Through the dynamic assessment model and risk prediction data, various risks that occur during oil production can be identified and predicted in advance. Risk propagation simulation and the generation of the path diagram help to understand the risk diffusion mechanism and guide the formulation of more effective risk management strategies. Identifying and dynamically making decisions on high-risk stages helps to timely adjust and optimize the production strategy at critical moments, reduce the probability of risk occurrence, and ensure the safe and stable progress of oil engineering. Therefore, the present invention improves the dynamic perception and immediate response capabilities of oil engineering safety risks by integrating data, dynamically monitoring, and analyzing the risk propagation path of oil engineering stage data and through oil engineering safety risk assessment.

[0078] In the embodiment of the present invention, with reference to Figure 1 As described, it is a schematic diagram of the step flow of a method for constructing a dynamic assessment model for oil engineering safety risks according to the present invention. In this example, the method for constructing a dynamic assessment model for oil engineering safety risks includes the following steps:

[0079] Step S1: Obtain the geological data of the petroleum engineering area; deploy multi-dimensional sensors for the geological data of the petroleum engineering area to generate a petroleum extraction sensor deployment network; collect environmental sensing data based on the petroleum extraction sensor deployment network to obtain standard petroleum extraction environment collection data;

[0080] In the embodiments of the present invention, relevant data, including formation information, geological structure, lithology distribution, etc., are obtained from geological exploration, geological surveys, or existing geological databases. Geological data can be obtained by means of geological surveys, geophysical exploration, drilling, and other technical means. Advanced geological exploration equipment and technologies, such as seismic exploration and electromagnetic detection, are used to obtain high-precision geological data. Sensors suitable for petroleum extraction environment monitoring, such as temperature sensors, pressure sensors, humidity sensors, etc., are selected to ensure coverage of multiple environmental parameters. The sensor deployment locations are determined according to the geological data and environmental characteristics to ensure coverage of the entire petroleum extraction area, taking into account geological heterogeneity and uneven resource distribution. The layout and topology of the sensor network are designed to ensure the communication and data transmission efficiency between sensors. Data acquisition protocols and communication protocols are formulated to ensure that sensors can transmit environmental data to the data center in real time and stably. Environmental data, including temperature, pressure, humidity, etc., are collected in real time through the sensor network, and the data are stored in the data center or cloud platform. The collected data are standardized to ensure the accuracy and comparability of the data, facilitating subsequent data analysis and decision support.

[0081] Step S2: Perform petroleum extraction geographical elevation conversion on the standard petroleum extraction environment collection data to generate petroleum extraction geographical elevation data; conduct multi-level petroleum extraction impact assessment based on the petroleum extraction geographical elevation data to generate multi-level petroleum extraction impact data; perform multi-field coupling numerical simulation on the multi-level geological impact factors of petroleum extraction to generate oil and gas extraction risk impact factors;

[0082] In the embodiments of the present invention, by using Geographic Information System (GIS) software, the collected environmental data is subjected to geodetic elevation conversion and spatial analysis. The collected environmental data is integrated with geological data, including formation information, topographic features, etc., to generate geodetic elevation data of the oil extraction area. According to the geological characteristics and resource distribution of the oil extraction area, the assessment is divided into different levels, such as the surface layer, underground levels, etc. Quantitative analysis methods, such as impact matrix analysis, terrain change simulation, etc., are used to evaluate the multi-level impacts of oil extraction on the geological environment, hydrogeology, surface morphology, etc. Numerical simulation software, such as Finite Element Analysis (FEM), Finite Difference Method (FDM), etc., is used to simulate the complex interactions of different geological impact factors in space and time. Geological parameters, surface characteristics, hydrogeological conditions, etc. of the model are set according to the actual situation to ensure the accuracy and reliability of the simulation results. The multi-field coupling results obtained by simulation are subjected to data analysis to extract risk impact factors generated during the oil and gas extraction process, such as geological deformation, groundwater level change, surface subsidence, etc. According to the analysis results, the potential impacts of different impact factors on oil engineering safety and environmental protection are quantified, providing a scientific basis for risk assessment.

[0083] Step S3: Based on the risk impact factors of oil and gas extraction, data security monitoring is carried out to generate oil and gas extraction risk monitoring data; the oil and gas extraction risk monitoring data is optimized by a stream engine to generate a dynamic risk index matrix; the dynamic risk index matrix is processed in a cluster parallel manner to generate an oil extraction risk feature vector;

[0084] In the embodiments of the present invention, environmental data and monitoring index data are collected in real time by using the deployed sensor network, such as geological deformation monitoring, hydrogeological monitoring, surface subsidence monitoring, etc. The collected monitoring data is stored in a data center or cloud platform, and real-time data quality inspection and correction are carried out to ensure the reliability and integrity of the data. A specific data stream engine or real-time data processing platform, such as Apache Kafka, Spark Streaming, etc., is selected to process real-time stream data. The data processing flow is designed and optimized, including steps such as data cleaning, real-time calculation, anomaly detection, etc., to generate a dynamic risk index matrix. A high-performance computing cluster or cloud computing platform is configured to support large-scale data processing and parallel computing requirements. A suitable parallel processing algorithm, such as MapReduce, Spark, etc., is selected and implemented to efficiently process the dynamic risk index matrix data. Key features, such as risk trends, anomaly event predictions, etc., are extracted from the processed data to form a feature vector of oil extraction risk.

[0085] Step S4: Construct a dynamic assessment model for oil engineering safety risks; import the dynamic risk index matrix into the dynamic assessment model for oil engineering safety risks to predict exploitation risks, and generate oil engineering exploitation risk prediction data; conduct a risk propagation simulation on the oil engineering exploitation risk prediction data to generate an oil engineering risk propagation path map;

[0086] In the embodiment of the present invention, by selecting a model suitable for oil engineering safety risk assessment, it can be a model based on statistical analysis, machine learning, or deep learning, such as logistic regression, decision tree, neural network, etc. Integrate the dynamic risk index matrix generated in step S3 and other relevant data into the assessment model to establish a complete risk assessment framework. Perform preprocessing steps such as data cleaning, feature selection, and standardization on the imported risk index matrix to improve the accuracy and stability of the model. Use historical data for model training and evaluate the performance of the model using methods such as cross-validation. Generate prediction results, that is, various risks occurring during the exploitation process of oil engineering. Select a specific risk propagation model, such as a Bayesian network, a propagation dynamics model, etc., to simulate the propagation paths and influence mechanisms of different risk factors in the oil engineering system. Conduct risk propagation simulation and analysis based on the prediction data, identify key risk nodes and propagation paths, and generate an oil engineering risk propagation path map. Use the generated oil engineering risk propagation path map to provide scientific basis and strategic suggestions for decision-makers to help them formulate effective risk management and response measures.

[0087] Step S5: Mark the high-risk stages on the oil engineering risk propagation path map to generate high-risk oil engineering stage data; make dynamic risk decisions on the high-risk oil engineering stage data to generate high-risk oil engineering stage decision strategies to execute oil engineering safety risk adjustment operations.

[0088] In the embodiment of the present invention, based on the risk propagation path map generated in step S4, identify the risk levels and influence degrees of each stage or node. According to the risk assessment results, divide the entire exploitation process of oil engineering into different stages and mark the high-risk stages or key nodes. Combine the risk assessment results and the propagation path map to formulate decision strategies and response measures for high-risk stages. Considering real-time data updates and risk changes, dynamically adjust the decision strategies to ensure their adaptability to the changes and complexities during the oil engineering exploitation process. According to the formulated decision strategies, execute corresponding safety risk adjustment operations, including adjusting the project progress, improving safety facilities, optimizing operation processes, etc. Continuously monitor the risk status and effects during the implementation process, and timely feedback and adjust measures to ensure the safe operation of oil engineering and environmental protection.

[0089] Preferably, step S1 includes the following steps:

[0090] Step S11: Use GIS to obtain oil engineering regional geological data;

[0091] Step S12: Analyze the exploitation scope of the regional geological data of the petroleum engineering to generate petroleum exploitation scope data;

[0092] Step S13: Deploy multi-dimensional sensors based on the petroleum exploitation scope data to generate a petroleum exploitation sensor deployment network;

[0093] Step S14: Collect environmental sensing data based on the petroleum exploitation sensor deployment network to obtain petroleum exploitation environmental collection data; perform data preprocessing on the petroleum exploitation environmental collection data to generate standard petroleum exploitation environmental collection data, where the data preprocessing includes data cleaning, filling of missing data values, and data standardization.

[0094] In the embodiments of the present invention, by determining the channels and sources of data acquisition, such as geological investigation reports, satellite remote sensing data, ground exploration data, etc. Geological maps and exploration reports can be obtained from local mineral resource bureaus, or high-resolution surface coverage information can be obtained using public satellite data platforms. Import geological data from different sources into GIS software (such as ArcGIS, QGIS) for integration and processing. Clean the data to remove duplicate or inaccurate data points to ensure the quality and accuracy of the geological data. Use GIS tools to analyze geological features, such as stratigraphic distribution, lithology, tectonic features, etc. Conduct geological terrain analysis to generate terrain models and topographic maps for subsequent analysis of the mining area and optimization of sensor layout. Based on the geological data, perform spatial analysis on the GIS platform to determine suitable geological areas for mining. Using the terrain model and topographic map, combined with geological features, analyze potential oil resource distribution areas and feasible mining ranges. Use GIS tools to create a spatial data model of the mining range, including polygon or raster layers, for describing and visualizing the mining range. Determine the boundaries and limiting conditions of the mining area, such as environmental protection areas, geological safety areas, etc., as well as the relationship with surrounding land use and resources. Evaluate the economic viability, technical feasibility, and environmental impact of different mining ranges. Through spatial analysis tools and models, optimize the mining strategy and select the optimal mining area and development plan. According to the determined mining range and geological features, select suitable multi-dimensional sensors. These include environmental monitoring sensors (such as weather stations, water quality sensors), safety monitoring devices (such as surveillance cameras, vibration sensors), geological exploration equipment (such as drilling rigs, seismographs), etc. Design the layout plan of the sensors to determine the position, installation height, and coverage range of the sensors. Consider the communication network between the sensors and select specific data transmission technologies and communication protocols to ensure real-time data transmission and effective management. Optimize the sensor deployment network to ensure that the sensors can comprehensively cover the mining area and the integrity and accuracy of the monitored data. Implement a remote monitoring and data management system to respond promptly to abnormal sensor data and ensure the stable operation and security of the system. The deployed sensor network collects environmental data in real time, such as multi-dimensional data including temperature, humidity, air pressure, groundwater level, etc. During the data collection process, ensure the timeliness and continuity of the data to avoid data loss and delay. Conduct preliminary cleaning on the collected environmental data to remove outliers and incorrect data to ensure the quality and accuracy of the data. Fill in missing data values using specific interpolation methods or statistical methods to ensure the integrity and continuity of the data. Standardize the data collected by different sensors to unify the dimension and range of the data for subsequent data analysis and comparison. Generate a standard oil mining environment collection dataset based on the standardized data for subsequent risk assessment and decision-making analysis.

[0095] As an example of the present invention, refer to Figure 2As shown, in this example, step S2 includes:

[0096] Step S21: Extract geological data from the data collected in the standard oil extraction environment to obtain oil extraction geological data, where the oil extraction geological data includes geological exploration data and drilling data;

[0097] Step S22: Perform oil extraction geographical elevation conversion on the geological exploration data and drilling data to generate oil extraction geographical elevation data;

[0098] Step S23: Conduct multi-level oil extraction impact assessment based on the oil extraction geographical elevation data to generate multi-level oil extraction impact data;

[0099] Step S24: Perform multi-field coupling numerical simulation on the multi-level geological impact factors of oil extraction to generate oil and gas extraction risk impact factors.

[0100] In the embodiment of the present invention, by extracting geological data from the data collected in the standard oil extraction environment, including geological exploration data and drilling data, the integrity and accuracy of the data are ensured, and the existing outliers or incorrect data are processed. Geological exploration data is extracted, including geological features, stratigraphic information, lithology distribution, etc. Geological analysis tools (such as geological information systems) are used to analyze and interpret the exploration data to extract key geological parameters. Geological information is extracted from the drilling data, including the stratigraphic structure, rock type, porosity, etc. data at different depths. The drilling data is interpreted and analyzed to generate geological cross-sections and stratigraphic distribution maps. The geological information in the geological exploration data and drilling data is subjected to geographical elevation conversion. Geographical information system (GIS) tools or professional geological modeling software are used to convert the original data into geographical elevation data. The converted geographical elevation data is integrated into a unified data model to ensure the consistency and comparability of the data. An oil extraction geographical elevation model is established, including the spatial distribution and change trend of various geological parameters. A multi-level impact assessment model is established based on the oil extraction geographical elevation data. Combining geological data and geographical elevation data, the multi-level impacts of oil extraction on the surrounding environment are analyzed, including geology, hydrology, ecology, etc. Numerical simulation methods or professional geological engineering software are used to calculate and analyze the impact assessment model. Multi-level oil extraction impact data is generated, including the spatial distribution, change trend, and quantitative assessment results of the impact factors. Based on the multi-level oil extraction impact data, multi-field coupling numerical simulation is performed. Multiple factors such as geological impact factors, terrain features, and hydrological environment are coupled together to simulate the comprehensive impact of oil extraction on groundwater, surface water, geological structure, etc. The numerical simulation results are used to analyze the risk impact factors of oil extraction, including geological stability, hydrological safety, ecological protection, etc. The spatial distribution map and quantitative analysis results of the oil and gas extraction risk impact factors are generated to provide a scientific basis for risk assessment and decision-making.

[0101] Preferably, step S23 includes the following steps:

[0102] Step S231: Divide the exploited formation based on the geographical elevation data of oil exploitation to generate exploited formation division data; conduct formation lithology analysis on the collected data of the standard oil exploitation environment based on the exploited formation division data to generate formation lithology analysis data; conduct fault structure analysis on the formation lithology analysis data to generate oil exploitation geological influence data;

[0103] Step S232: Conduct oil and gas exploitation rock mechanical property analysis on the geographical elevation data of oil exploitation to generate rock mechanical property data; conduct formation pressure analysis on the collected data of the standard oil exploitation environment through the rock mechanical property data to generate oil exploitation mechanical influence data;

[0104] Step S233: Calculate the distribution of oil and gas reservoirs according to the geographical elevation data of oil exploitation to obtain oil and gas reservoir distribution data; conduct oil and gas fluid property evaluation based on the oil and gas reservoir distribution data to generate oil and gas fluid property evaluation data; integrate the oil exploitation geological influence data, the oil exploitation mechanical influence data, and the oil and gas fluid property evaluation data to generate multi-level oil exploitation influence data.

[0105] In the embodiments of the present invention, geographic elevation data for oil extraction is imported and processed by using a Geographic Information System (GIS) tool. The depth range and resolution for the division of the exploited formation are determined, and analysis parameters are set according to actual extraction requirements. A formation division algorithm, such as an algorithm based on depth, formation characteristics, or geological conditions, is used to divide the geographic elevation data. Extraction formation division data is generated according to the division results, including the name of the formation, the depth range, and its characteristic description. Based on the extraction formation division data, lithology analysis of the data collected from the standard oil extraction environment is performed. Key parameters such as the lithology composition, rock type, and porosity of each formation are analyzed to generate lithology analysis data of the formation. Fault structure analysis is performed on the lithology analysis data of the formation. Fault characteristics affecting the formation structure and stability are identified and described to generate geological impact data for oil extraction, which is used for subsequent risk assessment and engineering design. The rock mechanics properties of the geographic elevation data for oil extraction are analyzed by using rock mechanics analysis methods. Mechanical properties such as the elastic modulus, compressive strength, and tensile strength of the rock are measured and analyzed. Based on the rock mechanics property data, formation pressure analysis of the data collected from the standard oil extraction environment is performed. The mechanical impacts on the formation during the extraction process, including formation deformation, stress distribution, etc., are calculated and evaluated. Combining the results of the rock mechanics property analysis and the formation pressure analysis data, mechanical impact data for oil extraction is generated. These data will be used to evaluate the geological and mechanical risks that occur during the extraction process and provide a basis for engineering safety management. The distribution of oil and gas reservoirs is calculated by using the geographic elevation data for oil extraction. A geological information system or professional geological modeling software is used to analyze and depict the spatial distribution of the reservoir. Based on the oil and gas reservoir distribution data, the properties of oil and gas fluids are evaluated. The evaluation and analysis include parameters such as the production potential of oil and gas, fluid properties (such as viscosity, density, etc.), and formation permeability. The geological impact data for oil extraction, the mechanical impact data for oil extraction, and the evaluation data of oil and gas fluid properties are integrated. Multilevel impact data for oil extraction is generated, reflecting the comprehensive impact of different factors during the extraction process, and providing a basis for decision-making and risk management.

[0106] Preferably, calculating the distribution of oil and gas reservoirs according to the geographic elevation data for oil extraction includes:

[0107] Performing terrain modeling on the geographic elevation data for oil extraction to generate terrain data for oil extraction; generating contour lines for the terrain data for oil extraction to obtain contour line data for oil extraction; calculating the curvature of the contour line data for oil extraction to obtain terrain change data for oil extraction;

[0108] Using geological exploration data to judge the reservoir level of the terrain change data for oil extraction to generate initial reservoir level thickness data for oil extraction; collecting logging data through the initial reservoir level thickness data for oil extraction to generate logging data; calculating the porosity of the logging data based on the lithology analysis data of the formation to obtain reservoir porosity data;

[0109] Based on the reservoir porosity data, perform reservoir characteristic analysis on the initial reservoir layer thickness data for oil production to obtain the initial reservoir permeability evaluation data and the initial reservoir saturation data; use the initial reservoir permeability evaluation data and the initial reservoir saturation data to optimize the reservoir distribution of the oil production terrain change data, and generate an oil production reservoir thickness map;

[0110] Perform oil and gas reservoir distribution calculation on the oil production reservoir thickness map to obtain oil and gas reservoir distribution data.

[0111] In an embodiment of the present invention, by using a Geographic Information System (GIS) tool or professional geological modeling software, perform terrain modeling on the oil production geographic elevation data. Determine the geomorphic features of the terrain, such as mountains, plains, rivers, etc., and generate detailed oil production terrain data. Based on the terrain data, generate oil production contour data. Determine the spacing and density of the contours to reflect the elevation changes of the terrain and provide basic data for subsequent analysis. Perform curvature calculation on the oil production contour data. The curvature calculation can evaluate the curvature and change rate of the terrain, revealing the complexity and stability of the terrain. Use geological exploration data to perform reservoir layer level judgment on the oil production terrain change data. Determine the boundaries and distributions of different strata, and generate the initial oil production reservoir layer thickness data. Use the initial oil production reservoir layer thickness data to collect logging data. Based on the logging data and formation lithology analysis data, calculate the porosity of each reservoir to obtain the reservoir porosity data. Perform reservoir characteristic analysis according to the reservoir porosity data. Calculate and evaluate the permeability and saturation of the initial reservoir, and generate the initial reservoir permeability evaluation data and the initial reservoir saturation data. Use the initial reservoir permeability evaluation data and the initial reservoir saturation data to optimize the reservoir distribution of the oil production terrain change data. The optimization process considers the spatial distribution characteristics of permeability and saturation to improve the effective production rate and output of the reservoir. According to the optimized reservoir distribution data, generate an oil production reservoir thickness map. The thickness map reflects the reservoir thickness distribution in different regions and depths, providing basic data for oil and gas reservoir distribution calculation. Based on the oil production reservoir thickness map and the terrain data, perform oil and gas reservoir distribution calculation. Use a geological information system or professional geological modeling software to analyze and simulate the distribution of oil and gas in different strata, and generate oil and gas reservoir distribution data.

[0112] As an example of the present invention, refer to Figure 3 as shown, in this example, step S3 includes:

[0113] Step S31: Based on the oil and gas production risk impact factors, perform data security monitoring to generate oil and gas production risk monitoring data;

[0114] Step S32: Optimize the oil and gas production risk monitoring data by using a stream engine to generate a dynamic risk index matrix;

[0115] Step S33: Split the dynamic risk index matrix into data blocks to obtain dynamic risk data blocks; Distribute the dynamic risk data blocks among cluster nodes to generate distributed dynamic risk data blocks;

[0116] Step S34: Process the distributed dynamic risk data blocks in parallel to obtain local risk feature data; Aggregate risk factors for the local risk feature data to generate an initial risk feature set; Perform data dimensionality reduction on the initial risk feature set to generate an oil production risk feature vector.

[0117] In the embodiment of the present invention, relevant data for oil and gas production is collected from various sensors, monitoring devices, and geological exploration data. Data from different sources, including geological data, environmental data, equipment operation data, etc., is integrated to form a complete oil and gas production monitoring data set. Key factors affecting the safety of oil and gas production, such as geological structures, groundwater conditions, seismic activities, etc., are determined. According to the actual situation, specific models or algorithms are selected to analyze and quantify these influencing factors. Data analysis techniques are used to monitor the safety risks existing in the process of oil and gas production. Monitoring data is analyzed in real time or regularly to identify abnormal situations and generate oil and gas production risk monitoring data. A stream processing engine for processing large-scale data, such as Apache Kafka, Apache Flink, etc., is designed and implemented. The configuration of the stream engine is optimized to ensure that it can handle high-speed and large-capacity data streams. The oil and gas production risk monitoring data is input into the stream engine to generate a dynamic risk index matrix. Various risk indicators, such as geological risk, operation risk, environmental risk, etc., are determined and calculated to construct a dynamic index matrix. The dynamic risk index matrix is divided into data blocks of the same size. Ensure that each data block contains sufficient information for subsequent parallel processing. The data blocks are distributed to multiple nodes of the cluster to form distributed dynamic risk data blocks. Reasonable allocation and management are carried out according to the computing power and storage resources of the cluster. The distributed dynamic risk data blocks are processed in parallel in the cluster. A distributed computing framework (such as Apache Spark) or a custom parallel processing algorithm is used to efficiently process each data block. The local risk features in the processed data blocks are aggregated. Comprehensive indicators of various risk factors are calculated to reflect the overall safety risk status of oil and gas production. Data dimensionality reduction processing is performed on the aggregated initial risk feature set. Using principal component analysis (PCA) or other dimensionality reduction techniques, the complex risk feature data is converted into a more concise oil production risk feature vector.

[0118] Preferably, step S32 includes the following steps:

[0119] Step S321: Perform streaming data access on the oil and gas production risk monitoring data to obtain the oil and gas production risk monitoring data stream;

[0120] Step S322: Define event patterns for the oil and gas production risk monitoring data stream through complex time processing rules to generate the oil and gas production event monitoring stream; perform key event detection on the oil and gas production event detection stream to generate the key events of oil and gas production risks;

[0121] Step S323: Assign risk weights to the key events of oil and gas production risks to generate a dynamic risk index matrix.

[0122] In the embodiment of the present invention, the oil and gas production risk monitoring data stream from various monitoring devices, sensors, and other data sources is accessed into the system. Ensure the stability and real-time nature of the data source to support subsequent event processing and analysis. Use a stream processing framework (such as Apache Kafka, Apache Flink, etc.) to manage and process the data stream. Configure the data stream pipeline to ensure that the data can arrive and be processed on time. Design and implement complex time processing rules for identifying and defining different types of oil and gas production event patterns. According to actual requirements and domain knowledge, formulate rules and algorithms that can capture important events. According to the defined event pattern rules, extract and generate the oil and gas production event monitoring stream from the oil and gas production risk monitoring data stream. Ensure that the identification and extraction of events can accurately reflect the actual engineering safety and operation conditions. Perform key event detection on the generated oil and gas production event monitoring stream. Use machine learning algorithms or rule engines to identify and screen out events with significant impacts to form a set of key events of oil and gas production risks. Assign risk weights to the detected key events of oil and gas production risks. According to the type, severity, and potential impact of the events, assign risk weights to each event. Combine the assigned risk weights to construct a dynamic risk index matrix. Ensure that the matrix can reflect the comprehensive impacts of different events and risk factors, providing a basis for subsequent risk assessment and management.

[0123] As an example of the present invention, refer to Figure 4 As shown, in this example, step S4 includes:

[0124] Step S41: Construct a dynamic assessment model for oil engineering safety risks; import the dynamic risk index matrix into the dynamic assessment model for oil engineering safety risks to predict production risks and generate oil engineering production risk prediction data;

[0125] Step S42: Define the network nodes of the oil engineering project through the oil and gas production risk impact factors to obtain the oil engineering project network node data; construct the risk propagation network structure based on the oil engineering project network node data to obtain the initial risk propagation network structure;

[0126] Step S43: Assign node weights to the initial risk propagation network structure according to the oil extraction risk prediction data to generate a risk propagation network structure; simulate the propagation process of the risk propagation network structure to generate risk propagation process simulation data;

[0127] Step S44: Trace the node propagation paths of the risk propagation network structure based on the risk propagation process simulation data to generate an oil engineering risk propagation path diagram.

[0128] In the embodiment of the present invention, by collecting and preparing a dynamic risk index matrix and other relevant data, such as geological data, environmental monitoring data, etc. Perform data cleaning, missing value filling, and data standardization to ensure data quality and consistency. Select a suitable evaluation model, such as a regression model based on machine learning, a neural network model, or a statistical model. According to the data characteristics and objectives, construct the structure and algorithm of the evaluation model. Divide the prepared data set into a training set, a validation set, and a test set. Use the training set to train the evaluation model, and evaluate the generalization ability and performance of the model through the validation set. Adjust the hyperparameters and structure of the model to optimize the model to improve prediction accuracy and robustness. Use the trained and optimized evaluation model to predict the future oil engineering safety risks. Generate oil engineering extraction risk prediction data, including the possibility and impact degree of risk events occurring. Define the key nodes of the project according to the actual situation and influencing factors of the oil engineering. The nodes can include important elements such as facilities, operation activities, environmental conditions, etc., as well as the relationships and interactions between them. Based on the defined nodes and their associations, construct the risk propagation network structure of the oil engineering project. The network structure should reflect the risk propagation paths and impact diffusion methods between different nodes. According to the oil extraction risk prediction data and the importance of the nodes, assign weights to each node. The weights can be determined based on risk occurrence probability, influence range, or other quantitative indicators. Use mathematical modeling or simulation technology to simulate the propagation process of risks in the constructed risk propagation network structure. The simulation process includes information such as the diffusion path of risks from the starting node to other nodes and the change of risk levels. Based on the results of the propagation simulation, trace and record the propagation paths and impact degrees of risks in the network structure. Determine the main propagation paths and risk expansion directions, as well as the interactions between nodes. Visualize the traced propagation paths and relevant data to generate an oil engineering risk propagation path diagram. Graphically display the risk propagation process to help decision-makers understand and respond to risk events.

[0129] Preferably, step S41 includes the following steps:

[0130] Step S411: Split the data of the dynamic risk index matrix to obtain a model training set, a model validation set, and a model test set;

[0131] Step S412: Use the deep neural network algorithm to train the model training set to generate a risk assessment training model; evaluate the model generalization ability of the risk assessment training model through the model validation set to generate model generalization performance data;

[0132] Step S413: Adjust the model architecture and hyperparameters of the risk assessment training model through the model generalization performance data to generate a risk assessment adjusted model; perform forward propagation calculation on the risk assessment adjusted model according to the model test set to generate risk assessment test data;

[0133] Step S414: Evaluate the prediction performance of the risk assessment test data to generate model prediction performance evaluation data; iteratively optimize the risk assessment adjusted model based on the model prediction performance evaluation data to generate a dynamic risk assessment model for petroleum engineering safety; import the dynamic risk index matrix into the dynamic risk assessment model for petroleum engineering safety to predict the mining risk and generate petroleum engineering mining risk prediction data.

[0134] In the embodiments of the present invention, by splitting the dynamic risk index matrix according to a certain ratio (for example, the ratio of the training set, the validation set, and the test set), the sufficiency and effectiveness of the data for training the model are ensured. The model training set, the model validation set, and the model test set are respectively formed. A suitable deep neural network algorithm is selected, such as a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN), for constructing the risk assessment model. The deep neural network model is trained using the model training set, and the model parameters are optimized through the backpropagation algorithm to enable the model to better fit the training data. The generalization ability of the trained model is evaluated using the model validation set, that is, the performance of the model on unseen validation data. The performance of the model on the validation set is analyzed, including indicators such as accuracy, precision, and recall, to evaluate the generalization ability of the model and whether there are overfitting or underfitting problems. According to the model generalization performance data, the architecture of the model (such as the number of layers, the number of nodes, etc.) and the hyperparameters (such as the learning rate, the batch size, etc.) are adjusted and optimized to further improve the performance and generalization ability of the model. Combining the adjusted and optimized model architecture and hyperparameters, a final risk assessment adjustment model is generated. This model can more accurately predict the safety risks in the process of oil engineering exploitation and has good generalization ability and stability. Forward propagation calculation is performed using the model test set to generate risk assessment test data. The prediction performance of the test data is evaluated to evaluate the prediction accuracy and effect of the model. According to the prediction performance evaluation data, iterative optimization of the model is carried out to further improve the prediction ability and practicality of the model. The dynamic risk index matrix is imported into the constructed dynamic assessment model of oil engineering safety risks. This model is used to predict and evaluate the risks in oil engineering exploitation, and generate oil engineering exploitation risk prediction data, including potential risk events, their impact degrees, and occurrence probabilities.

[0135] Preferably, step S5 includes the following steps:

[0136] Step S51: Identify the oil engineering safety risks on the oil engineering risk propagation path diagram to obtain the oil engineering stage risk identification data;

[0137] Step S52: Compare the oil engineering stage risk identification data with the preset stage risk identification data threshold. When the oil engineering stage risk identification data is greater than or equal to the preset stage risk identification threshold, high-risk oil engineering stage data is generated;

[0138] Step S53: Make dynamic risk decisions on the high-risk oil engineering stage data to generate high-risk oil engineering stage decision strategies to perform oil engineering safety risk adjustment operations.

[0139] In the embodiments of the present invention, based on the oil engineering risk propagation path diagram generated in step S44, each risk propagation path is analyzed and identified in detail. The key nodes and key events on each path are determined, as well as their potential impacts on oil engineering safety. According to the analysis results, oil engineering stage risk identification data is generated to identify and classify the risk levels of each stage. This includes information such as descriptions of risks, possible impacts, cost and time risks, etc. A preset threshold for the stage risk identification data is set as the basis for determining high risks. The threshold can be set according to project characteristics, regulatory requirements, and practical experience. The generated oil engineering stage risk identification data is compared and analyzed with the set threshold. When the oil engineering stage risk identification data is greater than or equal to the preset stage risk identification threshold, this stage is identified as a high-risk oil engineering stage, and corresponding high-risk oil engineering stage data is generated. Dynamic risk decision-making analysis is performed on the high-risk oil engineering stage data. Safety risk adjustment strategies are formulated for each high-risk stage, including risk response measures, emergency response plans, and resource allocation plans. According to the formulated dynamic risk decision-making strategy, oil engineering safety risk adjustment operations are carried out. Ensure the implementation of safety risk control measures, monitor the effectiveness during the implementation process, and adjust and optimize the strategy according to the actual situation.

[0140] The beneficial effects of the present invention are as follows: By obtaining the regional geological data of the petroleum engineering area, key geological information can be obtained, providing accurate basic data for subsequent sensor deployment and decision-making. Through multi-dimensional sensor deployment for geological data, a sensor network covering the entire petroleum extraction area can be established for collecting environmental sensing data. Through environmental sensing data collection, standard petroleum extraction environmental data can be obtained, providing a basis for subsequent evaluation and decision-making. By performing geographical elevation conversion on the collected data of the standard petroleum extraction environment, petroleum extraction geographical elevation data can be generated, further providing a basis for petroleum extraction impact assessment. Based on the petroleum extraction geographical elevation data, multi-level petroleum extraction impact assessment can be carried out to obtain multi-level impact data of petroleum extraction, helping to understand the impact of extraction on the environment. By performing multi-field coupling numerical simulation on the multi-level geological impact factors of petroleum extraction, risk impact factors for oil and gas extraction can be generated for subsequent risk monitoring and assessment. Based on the risk impact factors for oil and gas extraction, data security monitoring can be carried out to generate risk monitoring data for oil and gas extraction, for real-time monitoring and control of extraction risks. By optimizing the flow engine for the risk monitoring data of oil and gas extraction, a dynamic risk index matrix can be generated, providing more accurate data for subsequent risk assessment and prediction. By constructing a dynamic assessment model for the safety risks of petroleum engineering, importing the dynamic risk index matrix into the model for extraction risk prediction, petroleum engineering extraction risk prediction data can be generated, and risk propagation simulation can be carried out to generate a petroleum engineering risk propagation path map. By performing stage high-risk identification on the petroleum engineering risk propagation path map, high-risk petroleum engineering stage data can be generated, and dynamic risk decision-making can be carried out on the high-risk petroleum engineering stage data to generate high-risk petroleum engineering stage decision-making strategies to execute petroleum engineering safety risk adjustment operations, enabling the assessment, prediction, and decision-making of petroleum engineering safety risks, thereby improving the safety and sustainability of petroleum engineering. Therefore, the present invention improves the dynamic perception and immediate response capabilities of petroleum engineering safety risks through data integration, dynamic monitoring, and risk propagation path analysis of petroleum engineering stage data, and through petroleum engineering safety risk assessment.

[0141] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0142] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for constructing a dynamic assessment model for petroleum engineering safety risks, characterized in that: The following steps are involved: Step S1: Acquire geological data of the petroleum engineering region; perform multi-dimensional sensor deployment on the geological data of the petroleum engineering region to generate an oil production sensor deployment network; Based on the oil production sensor deployment network, environmental sensor data collection is carried out to obtain standard oil production environment collection data; Step S2: converting the standard oil production environment collection data into oil production geographic elevation data to generate oil production geographic elevation data; performing a multi-level oil production impact assessment based on the oil production geographic elevation data to generate oil production multi-level impact data; Conduct multi-field coupling numerical simulation on multi-level geological influencing factors of oil production to generate oil and gas production risk influencing factors; Step S2 includes the following steps: Step S21: extracting geological data from the standard oil production environment collection data to obtain oil production geological data, wherein the oil production geological data includes geological exploration data and drilling data; Step S22: converting the geological exploration data and the drilling data into oil production geographic elevation data to generate oil production geographic elevation data; Step S23: Perform a multi-level oil production impact assessment based on the oil production geographic elevation data to generate oil production multi-level impact data; Step S23 includes the following steps: Step S231: performing mining strata division based on the oil mining geographic elevation data to generate mining strata division data; performing stratum lithology analysis on the standard oil mining environment acquisition data based on the mining stratum division data to generate stratum lithology analysis data; performing fault structure analysis on the stratum lithology analysis data to generate oil mining geological impact data; Step S232: Perform oil and gas mining rock mechanical property analysis on the oil mining geographic elevation data to generate rock mechanical property data; perform formation pressure analysis on the standard oil mining environment collection data through the rock mechanical property data to generate oil mining mechanical influence data; Step S233: Calculate the distribution of oil and gas reservoirs according to the oil production geographic elevation data to obtain oil and gas reservoir distribution data; evaluate the properties of oil and gas fluids based on the oil and gas reservoir distribution data to generate oil and gas fluid property evaluation data; integrate the oil production geological impact data, the oil production mechanical impact data and the oil and gas fluid property evaluation data to generate oil production multi-level impact data; wherein, calculating the distribution of oil and gas reservoirs according to the oil production geographic elevation data includes: Carry out terrain modeling on the oil extraction geographic elevation data to generate oil extraction terrain data; generate contour lines on the oil extraction terrain data to obtain oil extraction contour line data; calculate the curvature of the oil extraction contour line data to obtain oil extraction terrain change data; Use geological exploration data to judge the reservoir level of oil production terrain change data and generate initial oil production reservoir level thickness data; collect well logging data through initial oil production reservoir level thickness data to generate well logging data; calculate the porosity of well logging data based on formation lithology analysis data to obtain reservoir porosity data; Based on the reservoir porosity data, the reservoir characteristics of the initial oil production reservoir layer thickness data are analyzed to obtain the initial reservoir permeability evaluation data and the initial reservoir saturation data; the initial reservoir permeability evaluation data and the initial reservoir saturation data are used to optimize the reservoir distribution of the oil production topographic change data to generate the oil production reservoir thickness map; Calculate the oil and gas reservoir distribution on the oil production reservoir thickness map to obtain the oil and gas reservoir distribution data; Step S24: performing multi-field coupling numerical simulation on multi-level geological influencing factors of oil production to generate oil and gas production risk influencing factors; Step S3: Perform data security monitoring based on oil and gas production risk influencing factors to generate oil and gas production risk monitoring data; perform stream engine optimization on the oil and gas production risk monitoring data to generate a dynamic risk indicator matrix; perform cluster parallel processing on the dynamic risk indicator matrix to generate an oil production risk feature vector; Step S4: construct a dynamic assessment model for oil engineering safety risks; import the dynamic risk indicator matrix into the dynamic assessment model for oil engineering safety risks to predict mining risks and generate oil engineering mining risk prediction data; perform risk propagation simulation on the oil engineering mining risk prediction data and generate a oil engineering risk propagation path map; Step S5: Mark the high-risk stages of the petroleum engineering risk propagation path map and generate high-risk petroleum engineering stage data; make dynamic risk decisions on the high-risk petroleum engineering stage data and generate high-risk petroleum engineering stage decision strategies to execute petroleum engineering safety risk adjustment operations.

2. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: using GIS to obtain regional geological data for petroleum engineering; Step S12: Analyze the mining range of the petroleum engineering regional geological data to generate petroleum mining range data; Step S13: Perform multi-dimensional sensor deployment based on the oil production range data to generate an oil production sensor deployment network; Step S14: Collect environmental sensor data based on the oil production sensor deployment network to obtain oil production environment collection data; perform data preprocessing on the oil production environment collection data to generate standard oil production environment collection data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization.

3. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Perform data security monitoring based on oil and gas production risk influencing factors to generate oil and gas production risk monitoring data; Step S32: Optimizing the oil and gas production risk monitoring data using a stream engine to generate a dynamic risk indicator matrix; Step S33: splitting the dynamic risk indicator matrix into data blocks to obtain dynamic risk data blocks; distributing the dynamic risk data blocks into cluster nodes to generate distributed dynamic risk data blocks; Step S34: parallel processing of distributed dynamic risk data blocks to obtain local risk feature data; risk factor aggregation of the local risk feature data to generate an initial risk feature set; data dimension reduction of the initial risk feature set to generate an oil production risk feature vector.

4. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 3 is characterized in that: Step S32 includes the following steps: Step S321: accessing the oil and gas production risk monitoring data through stream data to obtain the oil and gas production risk monitoring data stream; Step S322: define event patterns for the oil and gas production risk monitoring data stream through complex time processing rules to generate an oil and gas production event monitoring stream; perform key event detection on the oil and gas production event detection stream to generate oil and gas production risk key events; Step S323: Allocate risk weights to key oil and gas production risk events and generate a dynamic risk indicator matrix.

5. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 1 is characterized in that: Step S4 includes the following steps: Step S41: constructing a dynamic assessment model for oil engineering safety risks; importing the dynamic risk indicator matrix into the dynamic assessment model for oil engineering safety risks to perform mining risk prediction and generate oil engineering mining risk prediction data; Step S42: defining the network nodes of the petroleum engineering project through the oil and gas production risk influencing factors to obtain the network node data of the petroleum engineering project; constructing the risk propagation network structure based on the network node data of the petroleum engineering project to obtain the initial risk propagation network structure; Step S43: assigning node weights to the initial risk propagation network structure according to the oil production risk prediction data to generate a risk propagation network structure; simulating the propagation process of the risk propagation network structure to generate risk propagation process simulation data; Step S44: Based on the risk propagation process simulation data, the node propagation path of the risk propagation network structure is traced to generate a petroleum engineering risk propagation path diagram.

6. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 5 is characterized in that: Step S41 includes the following steps: Step S411: splitting the dynamic risk indicator matrix into data to obtain a model training set, a model verification set and a model test set; Step S412: Perform model training on the model training set using a deep neural network algorithm to generate a risk assessment training model; perform model generalization capability evaluation on the risk assessment training model using a model validation set to generate model generalization performance data; Step S413: adjusting the model architecture and hyperparameters of the risk assessment training model through the model generalization performance data, thereby generating a risk assessment adjustment model; performing forward propagation calculation on the risk assessment adjustment model according to the model test set, thereby generating risk assessment test data; Step S414: perform predictive performance evaluation on the risk assessment test data to generate model predictive performance evaluation data; perform iterative optimization on the risk assessment adjustment model based on the model predictive performance evaluation data to generate a dynamic assessment model for petroleum engineering safety risks; import the dynamic risk indicator matrix into the dynamic assessment model for petroleum engineering safety risks to perform mining risk prediction to generate petroleum engineering mining risk prediction data.

7. The method for constructing a dynamic assessment model for petroleum engineering safety risks according to claim 1 is characterized in that: Step S5 includes the following steps: Step S51: marking the petroleum engineering safety risks on the petroleum engineering risk propagation path map to obtain petroleum engineering stage risk marking data; Step S52: Compare the petroleum engineering stage risk identification data with a preset stage risk identification data threshold value, and when the petroleum engineering stage risk identification data is greater than or equal to the preset stage risk identification threshold value, high-risk petroleum engineering stage data is generated; Step S53: Perform dynamic risk decision-making on high-risk petroleum engineering stage data, generate high-risk petroleum engineering stage decision-making strategies, and execute petroleum engineering safety risk adjustment operations.

Images