A method and device for risk assessment and early warning of oil and gas well operation

Abstract

This invention discloses a method and device for risk assessment and early warning of oil and gas well operations, relating to the field of industrial safety management technology. The method includes: acquiring multi-source risk data from six categories: personnel, equipment, management, environment, process technology, and geology; constructing a hierarchical risk assessment index system based on this data; performing prior probability modeling of leaf node risk factors by combining historical data and expert knowledge; establishing a risk network model for oil and gas well operations based on the risk assessment index system and prior probabilities; obtaining risk assessment results through Bayesian inference by inputting real-time data; and finally generating risk indicators. When a risk indicator exceeds a preset threshold, an early warning message is automatically output. This invention solves the technical problems of traditional oil and gas well operation risk assessment methods, which are unable to comprehensively cover various influencing factors, cannot dynamically reflect risk changes, and suffer from strong subjectivity and untimely early warning responses. It achieves the technical effect of improving the accuracy of risk management and adaptability to different operational scenarios.

Description

Technical Field

[0001] This invention relates to the field of industrial safety management technology, and in particular to a method and device for risk assessment and early warning of oil and gas well operations. Background Technology

[0002] As oil and gas exploration and development advances into deeper and more complex geological structures, the risks of oil and gas well operations are becoming increasingly prominent, easily leading to accidents such as blowouts and well collapses. Therefore, scientific risk assessment and real-time early warning are crucial. Existing technologies largely rely on experience-based judgment or qualitative scoring methods, such as safety checklists and the analytic hierarchy process (AHP). While these methods can identify risk factors, they have significant limitations: they lack descriptions of causal relationships between risk factors, cannot reflect the dynamic evolution of risks, expert scoring is highly subjective, and their real-time response capability is insufficient, making them unsuitable for complex operational scenarios involving multiple coupled factors. Summary of the Invention

[0003] This application provides a method and device for risk assessment and early warning of oil and gas well operations, which solves the technical problems of traditional oil and gas well operation risk assessment methods that are difficult to fully cover various influencing factors, cannot dynamically reflect risk changes, and have strong subjectivity in assessment results and untimely early warning response.

[0004] The first aspect of this application provides a method for risk assessment and early warning of oil and gas well operations. The method includes: acquiring multi-source risk data, including data on personnel factors, equipment factors, management factors, environmental factors, technological factors, and geological factors; constructing a hierarchical risk assessment index system based on the risk elements corresponding to the multi-source risk data; performing prior probability modeling on leaf node risk factors, generating leaf node prior probabilities based on historical data and expert knowledge for each leaf node risk factor; establishing a risk network model for oil and gas well operations based on the risk assessment index system and the leaf node prior probabilities; inputting real-time data from the oil and gas well operation process into the risk network model, calculating the risk probability of each node through Bayesian inference, and obtaining a risk assessment result for the oil and gas well operation; generating risk indicators based on the risk assessment results, and outputting early warning information when the risk indicators exceed a preset threshold.

[0005] A second aspect of this application provides an oil and gas well operation risk assessment and early warning device, the device comprising: a multi-source risk data acquisition module for acquiring multi-source risk data, including data on personnel factors, equipment factors, management factors, environmental factors, technological factors, and geological factors; a risk assessment index system construction module for constructing a hierarchical risk assessment index system based on the risk elements corresponding to the multi-source risk data; a leaf node prior probability acquisition module for performing prior probability modeling on leaf node risk factors, generating leaf node prior probabilities based on historical data and expert knowledge; a risk network model construction module for establishing a risk network model for oil and gas well operations based on the risk assessment index system and the leaf node prior probabilities; a risk assessment result acquisition module for inputting real-time data during oil and gas well operations into the risk network model, calculating the risk probability of each node through Bayesian inference, and obtaining the risk assessment result for oil and gas well operations; and an early warning information acquisition module for generating risk indicators based on the risk assessment results and outputting early warning information when the risk indicators exceed a preset threshold.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application collects multi-source risk data on personnel, equipment, management, environment, technology, and geology in oil and gas well operations. Through the construction of a hierarchical indicator system and leaf node prior probability modeling, it obtains quantitative data on risk factors. Bayesian inference is used to calculate the risk probability of each node and the overall risk value. The model is then optimized and adjusted by combining real-time data updates and historical early warning information. This allows for dynamic quantitative assessment of the risks throughout the entire oil and gas well operation process and precise location of potential hazards. The result is a more scientific assessment of oil and gas well operation risks and a more timely and reliable early warning response, achieving the technical effect of improving the accuracy of risk management and adaptability to different operational scenarios. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a flowchart illustrating a method for risk assessment and early warning of oil and gas well operations provided in an embodiment of this application.

[0010] Figure 2 This is a schematic diagram of the structure of an oil and gas well operation risk assessment and early warning device provided in an embodiment of this application.

[0011] Figure labeling: Module 1 for multi-source risk data acquisition, Module 2 for risk assessment index system construction, Module 3 for leaf node prior probability acquisition, Module 4 for risk network model construction, Module 5 for risk assessment result acquisition, and Module 6 for early warning information acquisition. Detailed Implementation

[0012] This application provides a method and device for risk assessment and early warning of oil and gas well operations, which solves the technical problems of traditional oil and gas well operation risk assessment methods that are difficult to fully cover various influencing factors, cannot dynamically reflect risk changes, and have strong subjectivity in assessment results and untimely early warning response.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., used in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, a method for risk assessment and early warning of oil and gas well operations is provided, wherein the method includes:

[0016] Acquire multi-source risk data, which includes data on personnel factors, equipment factors, management factors, environmental factors, process technology factors, and geological factors.

[0017] Specifically, the multi-source risk data first clarifies that it covers six major categories of factors: personnel factors, including the qualifications, training, health, fatigue level, violations, and work experience of the operators; equipment factors, including the operating status, maintenance records, and fault logs of various operating equipment such as well control devices and top drive systems; management factors, including the implementation and recording of relevant systems such as work permits and construction organization designs; environmental factors, including well site-related environmental conditions such as climate, transportation, and lighting; process technology factors, including operation process-related indicators such as drilling parameters and cementing quality; and geological factors, including geological parameters such as formation pressure, temperature, and wellbore stability.

[0018] A hierarchical risk assessment indicator system is constructed based on the risk elements corresponding to the multi-source risk data.

[0019] Optionally, the risk assessment indicator system adopts a multi-level architecture design: the first-level indicators correspond one-to-one with the six types of risk elements contained in the multi-source risk data, while the second- and third-level indicators further break down and refine each type of risk element, comprehensively covering all kinds of potential risk factors in the oil and gas well operation process, forming a structured risk factor system.

[0020] Prior probability modeling is performed on the risk factors of leaf nodes, and prior probabilities of leaf nodes are generated based on historical data and expert knowledge.

[0021] In one embodiment of this application, the frequency of occurrence of each risk event is first statistically analyzed based on historical accident data to obtain the frequency probability value of the leaf node risk factor. Then, the leaf node risk factor is qualitatively scored through expert review, and the score is converted into a probability distribution using a fuzzy number aggregation method to finally form the corresponding leaf node prior probability.

[0022] Based on the risk assessment index system and the prior probability of the leaf node, a risk network model for oil and gas well operations is established.

[0023] Specifically, firstly, each node corresponds to a risk factor, where the parent node represents the overall operational risk, and the child nodes represent specific risk factors. The causal relationship between parent and child nodes is reflected through connections. Time-series nodes are introduced to describe the dynamic evolution of risk as the operation progresses. Conditional probability tables are calculated using statistical data, expert knowledge, and fuzzy number aggregation methods to provide the probability distribution for each node. Finally, by combining the conditional probability tables, causal relationships, and actual oil and gas well operation data, the risk network model is established.

[0024] Real-time data from oil and gas well operations is input into the risk network model, and the risk probability of each node is calculated through Bayesian inference to obtain the risk assessment results for oil and gas well operations.

[0025] Specifically, real-time data is input into the risk network model, and the risk probability of each risk node is calculated through a Bayesian inference mechanism that includes forward prediction and backward diagnosis. During the calculation process, the conditional probability table is dynamically updated based on real-time data, thereby obtaining the current risk status and overall risk value of each node, and finally determining the risk assessment result of oil and gas well operations.

[0026] Risk indicators are generated based on the risk assessment results, and warning information is output when the risk indicators exceed a preset threshold.

[0027] Specifically, the risk assessment results of oil and gas well operations are first converted into a visual form to generate corresponding risk indicators. Then, the risk indicators are compared with preset thresholds. When the risk indicators exceed the preset thresholds, an early warning mechanism is automatically triggered to output early warning information.

[0028] Furthermore, the method provided in this application embodiment includes:

[0029] The personnel factors include data on the operators' certification qualifications, training records, health status, fatigue level, violations, and operational experience; the equipment factors include the operating status, maintenance records, and fault logs of equipment such as well control devices, top drive systems, mud pumps, cementing equipment, logging / well logging instruments, perforating guns, and electrical power systems; the management factors include work permits, construction organization design, adherence to operating procedures, shift handover systems, emergency drills, and safety inspection records; the environmental factors include climate conditions, surface traffic conditions, lighting conditions, well site layout, and ventilation conditions; the technological factors include drilling parameters, cementing quality, perforation depth, completion technology, and mud performance indicators; and the geological factors include formation pressure, pore pressure, formation temperature, fracture development, wellbore stability, and abnormal geological bodies.

[0030] Specifically, when obtaining data related to personnel factors, it is necessary to first determine the information that needs to be collected, such as the workers' certification qualifications, training records, health status, fatigue level, violations, and work experience. The qualifications and training status are confirmed by reviewing the qualification files and training registration materials. Health status and fatigue level are recorded with the help of health monitoring equipment. Records of violations during the work process are compiled. Past work experience is clarified by retrieving past work records. Some of the relevant information is entered by staff, and some is automatically collected through the personnel management system.

[0031] When acquiring data related to equipment factors, for equipment such as well control devices, top drive systems, mud pumps, cementing equipment, logging / well logging instruments, perforating guns, and electrical power systems, the operating status parameters of the equipment are collected in real time through field sensors. Maintenance information is obtained by retrieving written or electronic records of equipment maintenance and repair, and fault logs generated during equipment operation are extracted. Some maintenance and fault information that is not automatically recorded is supplemented and entered into the equipment management system by the operation and maintenance personnel.

[0032] When acquiring data related to management factors, collect work permit approval documents, construction organization design plans, records of the implementation of operating procedures, shift handover registration information, records of emergency drill processes and results, and various materials generated from safety inspections. Enter the approval and implementation status into the production management information system, and have on-site management personnel record the results of shift handovers, emergency drills, and safety inspections to ensure the complete collection of all types of management-related data.

[0033] When acquiring data related to environmental factors, we collect climatic parameters such as temperature, wind speed, and rainfall through on-site meteorological sensors, conduct on-site surveys and record surface traffic conditions, well site layout and ventilation conditions, and obtain lighting data with the help of lighting monitoring equipment.

[0034] When acquiring data related to process technology factors, pump pressure, speed, torque, and bottom hole temperature in drilling parameters are collected in real time through on-site sensors and logging / well logging systems. Cementing construction inspection reports are retrieved to obtain cementing quality information, perforation depth and completion process execution are recorded, and mud performance indicators are collected through professional testing equipment. The relevant data are automatically transmitted to the operation monitoring system or compiled and entered by technicians.

[0035] When acquiring geological data, parameters such as formation pressure, pore pressure, and formation temperature are collected through SCADA and logging / well logging systems. Information on fracture development and wellbore stability is extracted in conjunction with the geological survey report to clarify the distribution of abnormal geological bodies such as gas-invaded layers and salt-gypsum layers. Geological technicians then organize and supplement the relevant survey and monitoring data.

[0036] The six types of data obtained through various means, including on-site sensors, logging / well logging systems, SCADA systems, production management information systems, and data input by technical personnel, are integrated to form complete multi-source risk data. This data is then uniformly transmitted to the risk assessment and early warning device for centralized storage and processing.

[0037] Furthermore, the method provided in this application embodiment includes:

[0038] The risk assessment indicator system is divided into multiple levels. The first-level indicators correspond to the six types of risk elements in the multi-source risk data. The second-level and third-level indicators further refine each type of risk element, covering various potential risk factors.

[0039] Optionally, firstly, we can review existing research findings, industry safety standards, and related technical specifications in the field of oil and gas well operation risk assessment, extract the widely recognized risk classification dimensions, and combine the six core elements covered by multi-source risk data—personnel, equipment, management, environment, process technology, and geology—to preliminarily define the scope of the primary indicators of the risk assessment indicator system. This will ensure that the primary indicators not only conform to the risk classification logic but also comprehensively correspond to the key risk sources in actual operations.

[0040] Subsequently, safety management personnel, frontline workers, and technical experts were organized to conduct in-depth field observations of oil and gas well operations, including drilling, cementing, and perforation. They recorded frequently occurring risk scenarios and potential hazards during operations. Then, using the Delphi method, several industry experts were invited to review the initially identified primary indicators, clarifying the core risk areas requiring focus under each primary indicator and breaking them down into secondary indicators. For example, regarding the primary indicator of personnel factors, combined with high-frequency risks identified during field surveys such as unqualified personnel, fatigue, and violations, secondary indicators such as qualification compliance, fatigue level, and frequency of violations were developed after expert confirmation.

[0041] Next, a combination of risk factor decomposition and historical accident data analysis was used to further refine each secondary indicator into tertiary indicators. Historical data, including past oil and gas well operation accident cases and hazard investigation records, were collected to statistically analyze the triggering factors and high-frequency risk points of various accidents. Simultaneously, experts were organized to conduct in-depth decomposition of the risk scenarios corresponding to the secondary indicators, transforming abstract risk directions into concrete and quantifiable risk factors. For example, the secondary indicator of well control device status under equipment factors was decomposed into tertiary indicators such as well control valve malfunction, seal aging, and pressure monitoring anomalies, ensuring that every potential risk factor was accurately covered.

[0042] Finally, the multi-level indicator system was verified and optimized through expert review. Experts from different fields such as oil and gas engineering, safety management, and field operations were invited to evaluate the comprehensiveness, hierarchical rationality, and logical correlation of the indicators. Redundant indicators were eliminated, missing risk factors were added, and the hierarchical division of indicators was adjusted. In the end, a hierarchical risk assessment indicator system with a clear structure and comprehensive coverage was formed, so that each risk element can be used for subsequent modeling in the form of indicator nodes.

[0043] Furthermore, the method provided in this application embodiment includes:

[0044] The frequency of each risk event is statistically analyzed from historical accident data to obtain the frequency-based probability value of the leaf node risk factor. Based on expert review, the leaf node risk factor is qualitatively scored, and the score is converted into a probability distribution using a fuzzy number aggregation method to form the corresponding leaf node prior probability.

[0045] In this embodiment of the application, the leaf node risk factor is the most basic potential risk factor in the risk assessment index system after being refined by secondary and tertiary indicators, and is a component of the index system.

[0046] Specifically, the process begins by collecting historical accident cases, hazard investigation records, equipment failure logs, and well shutdown statistics related to oil and gas well operations. These data are then systematically organized according to the categories of leaf node risk factors. Events corresponding to the same risk factor are categorized and summarized. After clarifying the statistical period and scope, the frequency statistics method is used to calculate the ratio of the number of occurrences of each type of risk event within the statistical period to the total number of events. This yields the frequency probability value of each leaf node risk factor, providing objective data support for prior probability modeling.

[0047] Subsequently, an expert review panel composed of professionals with extensive practical experience in oil and gas engineering, safety management, and field operations was invited to conduct multiple rounds of anonymous reviews using the Delphi method. In the first round, experts were provided with definitions of leaf node risk factors, descriptions of corresponding operational scenarios, and an overview of collected historical data, clearly defining a four-level qualitative scoring standard: low risk, moderate risk, relatively high risk, and high risk. After experts independently scored each leaf node risk factor based on their professional understanding, all scoring results were compiled and fed back to each expert, informing them of the score distribution and differing opinions for their reference. A second round of scoring was then conducted, and this process was repeated until the expert scores converged, forming the final multi-group expert qualitative scoring results.

[0048] Next, a fuzzy number aggregation method is used to process the expert scoring results. First, the qualitative scores given by each expert are converted into corresponding triangular or trapezoidal fuzzy numbers. Upper and lower bounds and peak values ​​of the fuzzy numbers are set according to the scoring levels. Then, a membership function is used to define the membership relationship of each fuzzy number to different risk levels. Afterward, a weighted average method is used to calculate the aggregated value of all expert fuzzy numbers, assigning equal weight to each expert to ensure the objectivity of the results. Finally, fuzzy number defuzzification is performed to map the aggregated fuzzy numbers to specific probability distributions. Combined with the frequency-based probability values ​​obtained in the above steps, the complete prior probability of each leaf node risk factor is formed.

[0049] Furthermore, the method provided in this application embodiment includes:

[0050] Each node in the dynamic Bayesian network model corresponds to a risk factor, with the parent node representing the overall operational risk and the child nodes representing specific risk factors. The connections between parent and child nodes represent the causal relationships between these risk factors. By introducing time-series nodes, the evolution of risk as the operation progresses is described, simulating the dynamic changes in risk during oil and gas well operations. A conditional probability table is calculated using statistical data, expert knowledge, and fuzzy number aggregation methods to provide the probability distribution for each node. Based on the conditional probability table and the causal relationships, combined with actual oil and gas well operation data and the dynamic Bayesian network model, a risk network model for the oil and gas well operation is established.

[0051] Specifically, the risk factors at all levels of the risk assessment indicator system are first mapped one by one to nodes in a dynamic Bayesian network. The overall operational risk serves as the parent node of the model, while the six primary indicators (personnel, equipment, management, environment, technology, and geology) and their subordinate secondary and tertiary indicators serve as specific child nodes, ensuring that each risk factor has a unique corresponding network node. Then, historical accident case data is used to analyze the impact logic between risk factors. For example, aging equipment seals may lead to abnormal well control pressure, and the development of geological fractures may increase the risk of well collapse. These objective correlations are transformed into directed connections between parent and child nodes, forming a preliminary network topology. Industry experts are then invited to validate the topology, correcting any unreasonable causal connections to ensure that the node relationships conform to the actual operations of oil and gas wells.

[0052] Next, the entire process is divided into several continuous time segments based on the operational progress of oil and gas well operations, with each time segment corresponding to a time node. A time dimension attribute is added to key risk nodes in the risk network model, and nodes with the same name in adjacent time slices are connected by state transition edges. For example, the bottom-hole pressure node in the drilling stage is connected to the bottom-hole pressure node in the next stage. The state transition probability is determined based on historical pressure evolution data of similar well types, thereby describing the dynamic changes of risk factors as the operation progresses. This allows the model to capture the risk evolution characteristics of different operational stages, overcoming the limitation of static models that cannot reflect changes in the time dimension.

[0053] Then, a conditional probability table is constructed by integrating the data. First, the frequency of various risk events is extracted from historical accident databases and equipment failure logs to calculate the basic probability values ​​of risk factors at leaf nodes. For risk factors with missing data or difficult-to-quantify data, the Delphi method is used to invite experts in oil and gas engineering and safety management to conduct qualitative scoring, clarifying the probability levels of different risk states. Subsequently, fuzzy number aggregation is used to transform the expert qualitative scores into fuzzy numbers. The probability distribution characteristics are defined through membership functions, and after defuzzification, they are transformed into quantitative probability values. Finally, historical statistical data and expert quantitative results are integrated to compile a conditional probability table for each node, clarifying the degree of influence of different combinations of parent node states on the risk probability of child nodes.

[0054] Finally, model integration and parameter calibration are performed. The network topology, time series nodes, and conditional probability tables are systematically integrated, and actual oil and gas well operation data, including typical operating parameters and past operation risk evolution data, are substituted. The model parameters are adjusted, and the degree of fit between the risk trend output by the model and the actual situation is verified. If there is a deviation, the conditional probability value or causal connection weight of the corresponding node is corrected to ensure that the causal relationship strength and probability distribution of the model fit the actual operation scenario. Finally, the oil and gas well operation risk network model is established.

[0055] Furthermore, the method provided in this application embodiment includes:

[0056] Based on real-time input data, the risk probability of each risk node in the oil and gas well operation is calculated using the risk network model. The calculation is performed through a Bayesian inference mechanism, wherein the conditional probability table is updated according to real-time data to obtain the current risk status and overall risk value of each node. The Bayesian inference mechanism includes forward prediction and reverse diagnosis. Based on the updated risk probability and overall risk value, the risk assessment result of the oil and gas well operation is calculated.

[0057] Specifically, the process begins by collecting real-time data during oil and gas well operations, including real-time personnel status, equipment operating parameters, management execution status, environmental dynamics, process adjustment parameters, and geological condition monitoring data. This real-time data is then preprocessed to remove outliers and redundant information. Data from different sources and in different formats is uniformly converted into an input format that the risk network model can recognize, ensuring the accuracy and consistency of the data. Subsequently, the preprocessed real-time data is input one by one into the relevant nodes of the risk network model.

[0058] Next, the Bayesian inference mechanism is initiated to calculate the risk probability, first executing the forward prediction process. A bottom-up inference method is adopted, using real-time data received by the leaf nodes as evidence, combined with the constructed conditional probability table, and applying Bayes' theorem to calculate the posterior probability of each leaf node. This probability value is then passed up to the corresponding second-level nodes, first-level nodes, and so on, progressively deriving to the parent node, i.e., the overall operational risk node. During this process, the probability distribution of each node is dynamically corrected based on real-time data, achieving forward prediction from specific risk factors to overall operational risk, and obtaining the current risk probability of each level of node.

[0059] When abnormal signals or potential risk signs are detected in oil and gas well operations, a reverse diagnostic process is initiated. Using the maximum a posteriori probability estimation method, the high-risk state of the parent node (i.e., the overall operational risk) is fixed as known evidence. The contribution of each child node to this high-risk state is deduced from top to bottom. By calculating the posterior probability ranking of each node, the key intermediate nodes and leaf node risk factors most likely to trigger risks are identified, clarifying potential risk sources and their impact on the entire operational system, providing precise direction for hazard identification.

[0060] During both forward prediction and reverse diagnosis, an incremental learning method is used to dynamically update the conditional probability table. Based on the changes in risk status reflected by real-time input data and combined with newly observed node status information, the probability parameters of the corresponding nodes in the conditional probability table are corrected. This ensures that the conditional probability table can accurately reflect the actual operating conditions of oil and gas wells in real time, guaranteeing the accuracy of subsequent inference calculations. Simultaneously, the parameter changes for each update are recorded, accumulating data for model optimization.

[0061] Finally, the current risk status of each node obtained from the forward prediction, the overall risk value, and the key risk source information determined by the reverse diagnosis are integrated. The weighted summation method is used to calculate the comprehensive risk assessment results of oil and gas well operations. The corresponding coefficients are assigned according to the weight of each risk factor on the overall risk to ensure that the risk assessment results can fully reflect the risk level of the entire operation process. At the same time, the risk level and contribution ranking of each risk factor are output.

[0062] Furthermore, the method provided in this application embodiment includes:

[0063] The risk assessment results are converted into visual indicators to generate corresponding risk indicators; these risk indicators are compared with preset thresholds, and an early warning mechanism is automatically triggered when the risk indicator exceeds the preset threshold.

[0064] In one embodiment, the risk assessment results are first converted into visual indicators. For risk probability values, the risk probabilities of each node calculated by Bayesian inference are directly mapped to dynamic values ​​between 0 and 1. Higher values ​​represent higher risk levels, intuitively reflecting the quantification of risk. For risk levels, an interval division method is used. Based on the impact range, severity, and control difficulty of oil and gas well operation risks, the probability values ​​between 0 and 1 are divided into four continuous intervals, corresponding to Level I (low risk), Level II (moderate risk), Level III (relatively high risk), and Level IV (high risk). Specifically, probability values ​​of 0 to 0.25 correspond to Level I, 0.25 to 0.5 to Level II, 0.5 to 0.75 to Level III, and 0.75 to 1 to Level IV, ensuring clear risk level divisions that conform to industry risk assessment practices.

[0065] Subsequently, a multi-source data fusion method was used to determine the preset thresholds. First, safety management standards for the oil and gas industry and risk control specifications for similar oil and gas well operations were collected, and clearly defined risk thresholds were extracted as basic references. Next, risk probability thresholds leading to accidents were screened from a historical accident database, and their average and median values ​​were calculated as objective data support for threshold setting. Finally, experts in oil and gas engineering safety management, on-site operation command, and risk assessment were invited to discuss and adjust the basic reference values ​​and historical statistical values ​​based on the risk characteristics of different well types, geological conditions, and operation procedures. By integrating industry standard requirements, historical accident patterns, and expert practical experience, a dual preset threshold of risk level and risk probability value was finally determined. For example, a probability value of 0.5 corresponding to the higher risk of Level III was set as the core early warning threshold, ensuring that the preset thresholds are both scientific and practical.

[0066] Finally, the transformed visualized risk indicators are dynamically compared with preset thresholds in real time, simultaneously monitoring whether the risk level reaches or exceeds Level III and whether the risk probability value exceeds the critical value of 0.5. When any indicator meets the exceeding condition, the system automatically activates the early warning mechanism, issuing on-site warning signals through audible and visual alarm devices, and simultaneously pushing early warning information containing the risk level, exceeding indicators, and relevant risk factors to the operation management platform and the terminals of relevant responsible persons, ensuring that relevant personnel receive risk alerts in a timely manner and providing a clear basis for subsequent risk handling.

[0067] Furthermore, the method provided in this application embodiment includes:

[0068] By utilizing the time-series structure of the dynamic Bayesian network model, new data is continuously received for parameter updates; based on historical accurate early warning information, the weights of key nodes are automatically optimized, enhancing the adaptability and prediction accuracy of the dynamic Bayesian network model.

[0069] Optionally, firstly, relying on the time-series structure of the dynamic Bayesian network, a fixed data acquisition cycle is set to continuously receive new data generated during oil and gas well operations, including real-time risk monitoring data, data on the handling results after early warning responses, data on changes in operating conditions, and data on newly occurring hidden dangers or accidents. A sliding window is used to define the effective range of the data, incorporating the latest real-time data into the current analysis window while removing outdated data that exceeds the window range, ensuring that the data used for updates reflects the recent operational risk characteristics. An incremental Bayesian learning method is employed to dynamically update the model's conditional probability table and state transition probabilities based on new data. Specifically, the Bayesian formula is used to use new data as observational evidence to correct the posterior probabilities of each node, and the corrected probability values ​​are then updated inversely to the conditional probability table. This ensures that the model parameters are aligned with the actual risk status of the operation site in real time, avoiding model prediction bias due to data lag.

[0070] Subsequently, a historical early warning information database was constructed. The system stores the triggering conditions, risk indicator values, early warning level, corresponding actual operational risk situation, and handling results for each early warning, forming a complete record of the entire early warning process. Confusion matrix analysis was used to evaluate the historical early warning data, calculating key indicators such as early warning accuracy, false alarm rate, and missed alarm rate, clarifying the degree of consistency between the early warning results and the actual situation for different risk nodes. Based on the evaluation results, key nodes were selected, identifying those with a significant impact on early warning accuracy and high contribution, such as nodes corresponding to risk factors that frequently cause false alarms or missed alarms. Gradient descent was used to automatically optimize the weights of key nodes, with maximizing early warning accuracy as the objective function. The connection weights of key nodes were adjusted through iterative calculations to enhance the influence of high-contribution nodes in the inference process, reduce the weight ratio of low-contribution or interfering nodes, and gradually optimize the model's inference logic.

[0071] Finally, during parameter updates and weight optimization, a model stability verification step is included. Cross-validation is used to divide some historical data into training and validation sets to verify the prediction accuracy and stability of the updated model on the validation set. If the updated model error exceeds a preset range, the parameter update magnitude or weight optimization step size is adjusted retrospectively to ensure reliable prediction performance during dynamic optimization. Simultaneously, an optimization cycle mechanism is established, combining periodic and real-time optimization based on the phased characteristics of the work process. Real-time optimization is triggered when significant changes occur in the work conditions, while periodic optimization is performed during routine work phases, achieving adaptation between model optimization and work rhythm.

[0072] In summary, the oil and gas well operation risk assessment and early warning method provided in this application has the following technical effects:

[0073] This application acquires multi-source risk data from oil and gas well operations, constructs a hierarchical evaluation index system, generates leaf node prior probabilities, establishes a dynamic Bayesian network model with time series, calculates risk probabilities and assessment results through Bayesian inference, outputs early warnings when exceeding preset thresholds, and continuously updates model parameters to optimize weights, thereby achieving the technical effect of improving the accuracy of risk management and adaptability to different operation scenarios.

[0074] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides an oil and gas well operation risk assessment and early warning device, the device comprising:

[0075] Multi-source risk data acquisition module 1 is used to acquire multi-source risk data, which includes data on personnel factors, equipment factors, management factors, environmental factors, process technology factors, and geological factors.

[0076] Risk assessment indicator system construction module 2 is used to construct a hierarchical risk assessment indicator system based on the risk elements corresponding to the multi-source risk data.

[0077] Leaf node prior probability acquisition module 3 is used to perform prior probability modeling of leaf node risk factors and generate leaf node prior probabilities for each leaf node risk factor based on historical data and expert knowledge.

[0078] Risk network model construction module 4 is used to establish a risk network model for oil and gas well operations based on the risk assessment index system and the prior probability of the leaf nodes.

[0079] Risk assessment result acquisition module 5 is used to input real-time data during oil and gas well operations into the risk network model, calculate the risk probability of each node through Bayesian inference, and obtain the risk assessment result of oil and gas well operations.

[0080] The early warning information acquisition module 6 generates risk indicators based on the risk assessment results and outputs early warning information when the risk indicators exceed a preset threshold.

[0081] Furthermore, the multi-source risk data acquisition module 1 is used to perform the following steps:

[0082] The personnel factors include data on the operators' certification qualifications, training records, health status, fatigue level, violations, and operational experience; the equipment factors include the operating status, maintenance records, and fault logs of equipment such as well control devices, top drive systems, mud pumps, cementing equipment, logging / well logging instruments, perforating guns, and electrical power systems; the management factors include work permits, construction organization design, adherence to operating procedures, shift handover systems, emergency drills, and safety inspection records; the environmental factors include climate conditions, surface traffic conditions, lighting conditions, well site layout, and ventilation conditions; the technological factors include drilling parameters, cementing quality, perforation depth, completion technology, and mud performance indicators; and the geological factors include formation pressure, pore pressure, formation temperature, fracture development, wellbore stability, and abnormal geological bodies.

[0083] Furthermore, the risk assessment indicator system construction module 2 is used to perform the following steps:

[0084] The risk assessment indicator system is divided into multiple levels. The first-level indicators correspond to the six types of risk elements in the multi-source risk data. The second-level and third-level indicators further refine each type of risk element, covering various potential risk factors.

[0085] Furthermore, the leaf node prior probability acquisition module 3 is used to perform the following steps:

[0086] The frequency of each risk event is statistically analyzed from historical accident data to obtain the frequency-based probability value of the leaf node risk factor. Based on expert review, the leaf node risk factor is qualitatively scored, and the score is converted into a probability distribution using a fuzzy number aggregation method to form the corresponding leaf node prior probability.

[0087] Furthermore, the risk network model construction module 4 is used to perform the following steps:

[0088] Each node in the dynamic Bayesian network model corresponds to a risk factor, with the parent node representing the overall operational risk and the child nodes representing specific risk factors. The connections between parent and child nodes represent the causal relationships between these risk factors. By introducing time-series nodes, the evolution of risk as the operation progresses is described, simulating the dynamic changes in risk during oil and gas well operations. A conditional probability table is calculated using statistical data, expert knowledge, and fuzzy number aggregation methods to provide the probability distribution for each node. Based on the conditional probability table and the causal relationships, combined with actual oil and gas well operation data and the dynamic Bayesian network model, a risk network model for the oil and gas well operation is established.

[0089] Furthermore, the risk assessment result acquisition module 5 is used to perform the following steps:

[0090] Based on real-time input data, the risk probability of each risk node in the oil and gas well operation is calculated using the risk network model. The calculation is performed through a Bayesian inference mechanism, wherein the conditional probability table is updated according to real-time data to obtain the current risk status and overall risk value of each node. The Bayesian inference mechanism includes forward prediction and reverse diagnosis. Based on the updated risk probability and overall risk value, the risk assessment result of the oil and gas well operation is calculated.

[0091] Furthermore, the early warning information acquisition module 6 is used to perform the following steps:

[0092] The risk assessment results are converted into visual indicators to generate corresponding risk indicators; these risk indicators are compared with preset thresholds, and an early warning mechanism is automatically triggered when the risk indicator exceeds the preset threshold.

[0093] Furthermore, the early warning information acquisition module 6 is used to perform the following steps:

[0094] By utilizing the time-series structure of the dynamic Bayesian network model, new data is continuously received for parameter updates; based on historical accurate early warning information, the weights of key nodes are automatically optimized, enhancing the adaptability and prediction accuracy of the dynamic Bayesian network model.

[0095] The oil and gas well operation risk assessment and early warning device provided in the embodiments of the present invention can execute the oil and gas well operation risk assessment and early warning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0096] Although this application makes various references to certain modules in the apparatus according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not intended to limit the scope of protection of this invention.

[0097] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for risk assessment and early warning of oil and gas well operations, characterized in that, The method includes: Acquire multi-source risk data, which includes data on personnel factors, equipment factors, management factors, environmental factors, process technology factors, and geological factors; A hierarchical risk assessment indicator system is constructed based on the risk elements corresponding to the multi-source risk data. Prior probability modeling is performed on the risk factors of leaf nodes, and prior probabilities of leaf nodes are generated for the risk factors of each leaf node based on historical data and expert knowledge. Based on the risk assessment index system and the prior probability of the leaf node, a risk network model for oil and gas well operations is established. Real-time data from oil and gas well operations is input into the risk network model, and the risk probability of each node is calculated through Bayesian inference to obtain the risk assessment results of oil and gas well operations. Risk indicators are generated based on the risk assessment results, and warning information is output when the risk indicators exceed a preset threshold.

2. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for acquiring multi-source risk data includes: The personnel factors include data such as the operator's certification qualifications, training records, health status, fatigue level, violations, and work experience; The equipment factors include the operating status, maintenance records, and fault logs of equipment such as well control devices, top drive systems, mud pumps, cementing equipment, logging / well logging instruments, perforating guns, and electrical power systems. The management factors include work permits, construction organization design, implementation of operating procedures, shift handover system, emergency drills, and safety inspection records; The environmental factors include climate conditions, surface transportation conditions, lighting conditions, well site layout, and ventilation conditions; The technological factors include drilling parameters, cementing quality, perforation depth, completion process, and mud performance indicators. The geological factors include formation pressure, pore pressure, formation temperature, fracture development, wellbore stability, and anomalous geological bodies.

3. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for constructing a hierarchical risk assessment indicator system includes: The risk assessment indicator system is divided into multiple levels. The first-level indicators correspond to the six types of risk elements in the multi-source risk data. The second-level and third-level indicators further refine each type of risk element, covering various potential risk factors.

4. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for generating prior probabilities of leaf nodes includes: The frequency-based probability values ​​of the leaf node risk factors are obtained by statistically analyzing the frequency of each risk event from historical accident data. Based on the expert review, the risk factors of the leaf nodes are qualitatively scored, and the scores are converted into probability distributions using the fuzzy number aggregation method to form the corresponding prior probabilities of the leaf nodes.

5. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for establishing a risk network model for oil and gas well operations includes: According to the dynamic Bayesian network model, each node corresponds to a risk factor, with the parent node representing the overall operational risk and the child nodes representing specific risk factors. The causal relationship between the risk factors is represented by the connection between parent and child nodes; By introducing time series nodes, the evolution of risk as the operation progresses is described, and the dynamic changes of risk in oil and gas well operations are simulated. The conditional probability table is calculated using statistical data, expert knowledge, and fuzzy number aggregation methods, providing the probability distribution for each node; Based on the conditional probability table and the causal relationship, and combined with the actual data of oil and gas well operations and the dynamic Bayesian network model, a risk network model for oil and gas well operations is established.

6. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for obtaining risk assessment results for oil and gas well operations includes: Based on the real-time input data, the risk probability of each risk node in the oil and gas well operation is calculated using the risk network model. The calculation is performed through a Bayesian inference mechanism, wherein the conditional probability table is updated according to the real-time data to obtain the current risk status and overall risk value of each node. The Bayesian inference mechanism includes forward prediction and reverse diagnosis; Based on the updated risk probability and overall risk value, the risk assessment results for oil and gas well operations are calculated.

7. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, The method for outputting early warning information includes: Convert risk assessment results into visual indicators to generate corresponding risk metrics; The risk indicator is compared with a preset threshold, and an early warning mechanism is automatically triggered when the risk indicator exceeds the preset threshold.

8. The document collaborative processing method based on a document processing platform architecture as described in claim 1, characterized in that, After outputting the warning information, the method includes: The dynamic Bayesian network model continuously receives new data and updates its parameters based on the time-series structure. Based on accurate historical early warning information, the weights of key nodes are automatically optimized to enhance the adaptability and prediction accuracy of the dynamic Bayesian network model.

9. A risk assessment and early warning device for oil and gas well operations, characterized in that, The step of carrying out the method according to any one of claims 1 to 8, comprising: The multi-source risk data acquisition module is used to acquire multi-source risk data, which includes data on personnel factors, equipment factors, management factors, environmental factors, process technology factors, and geological factors. The risk assessment indicator system construction module is used to construct a hierarchical risk assessment indicator system based on the risk elements corresponding to the multi-source risk data. The leaf node prior probability acquisition module is used to model the prior probability of leaf node risk factors and generate leaf node prior probabilities based on historical data and expert knowledge for the risk factors of each leaf node. The risk network model construction module is used to establish a risk network model for oil and gas well operations based on the risk assessment index system and the prior probabilities of the leaf nodes. The risk assessment result acquisition module is used to input real-time data during oil and gas well operations into the risk network model, calculate the risk probability of each node through Bayesian inference, and obtain the risk assessment result of oil and gas well operations. The early warning information acquisition module generates risk indicators based on the risk assessment results and outputs early warning information when the risk indicators exceed a preset threshold.

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