A land super-deep well blowout risk analysis method, device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, equipment, and storage medium for analyzing blowout risks in ultra-deep land wells. Applied to the field of well control risk analysis, it determines the probability distribution of the duration of each well control operation sub-process based on a well control operation process model of the well control emergency response process in ultra-deep land wells. Based on the probability distribution of the duration of each well control operation sub-process, it determines the probability distribution of the duration of the overall well control process. Multiphase flow transient simulation is performed to obtain the casing pressure change law after a blowout occurs, and based on the casing pressure change law, the predicted casing pressure for the duration of the overall well control process is determined. Risk analysis is then performed based on the predicted casing pressure and probability distribution of the duration of the overall well control process to obtain the blowout risk analysis results. By determining the time probability distribution of each operation process through the well control model and determining the casing pressure at the time of well shut-in through multiphase flow transient simulation, real-time risk analysis is conducted to quickly grasp the on-site risk status and adjust well control measures in a timely manner, thereby reducing operational risks.

Description

Technical Field

[0001] This invention relates to the field of well control risk analysis, and in particular to a method for analyzing the risk of blowouts in ultra-deep land wells, a device for analyzing the risk of blowouts in ultra-deep land wells, electronic equipment, and a computer-readable storage medium. Background Technology

[0002] As oil and gas exploration and development advances into deeper and ultra-deep formations, onshore ultra-deep wells generally exhibit characteristics such as large depth, high well temperature, high formation pressure, and narrow pressure windows. If identification, decision-making, or handling is not timely after a blowout occurs, it can easily lead to well control failure and further evolve into a blowout accident, causing a chain reaction of consequences such as wellhead overpressure, casing damage, formation fracturing, and well leakage. Existing methods still have some common shortcomings in field applications of ultra-deep wells: many evaluation results deviate from linear / static parameters, offering limited characterization of the dynamic evolution of blowout risk with changing operating conditions; the quantitative expression of uncertainties (parameter fluctuations, differences in handling time, and changes in human factors and equipment status) is not uniform; risk output often remains at the level or conclusion level, making it difficult to directly correspond to the constraints and operational judgment criteria required for on-site decision-making. Summary of the Invention

[0003] The purpose of this invention is to provide a method, apparatus, equipment, and storage medium for analyzing the risk of blowouts in ultra-deep land wells. This method is applied to the field of well control risk analysis. The method determines the time probability distribution of each operation process through a well control model and determines the casing pressure at the time of well shut-in through multiphase flow transient simulation. Based on the casing pressure, real-time risk analysis is performed to quickly grasp the on-site risk status and adjust well control measures in a timely manner to reduce operational risks.

[0004] To address the aforementioned technical problems, this invention provides a method for analyzing the risk of blowouts in ultra-deep onshore wells, comprising: A well control operation process model for the well control emergency response process of an ultra-deep land well is constructed, and the probability distribution of the duration of each well control operation sub-process is determined based on the well control operation process model. The probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the well control operation sub-processes. Multiphase flow transient simulation is performed to obtain the casing pressure change law after the overflow occurs, and the predicted casing pressure for the duration of the well control process is determined based on the casing pressure change law; Based on the predicted casing pressure and probability distribution of the duration of the overall well control process, a blowout risk analysis is performed to obtain the results.

[0005] Optionally, a blowout risk analysis result is obtained by performing risk analysis based on the predicted casing pressure and probability distribution during the duration of the overall well control process, including: The duration of the overall well control process is discretized into multiple time nodes to obtain the probability distribution of each time node; Based on the predicted casing pressure and the maximum allowable shut-in casing pressure, a set of comments is constructed, a fuzzy judgment matrix of the set of comments is established, and normalization processing is performed to obtain a normalized fuzzy judgment matrix. The severity score of well control consequences at each time point is determined based on the set of comments and the normalized fuzzy evaluation matrix. A comprehensive risk score is determined based on the probability distribution of each time point and the severity score of the well control consequences. The overall risk level and well control status are determined based on the comprehensive risk score and with reference to the risk database.

[0006] Optionally, a well control operation process model for the well control emergency response process of ultra-deep onshore wells may be constructed, including: In the well control system of the emergency response process for ultra-deep wells on land, the status of personnel and equipment is mapped to storage locations, and the events that cause changes in the status are mapped to transitions. A stochastic Petri net is used for modeling, and the storage locations and transitions are connected based on the well control logic relationship to construct the sub-process model of each well control operation; The well control operation process model for the well control emergency response process is constructed based on each of the well control operation sub-process models.

[0007] Optionally, the probability distribution of the duration of each well control operation sub-process is determined based on the well control operation process model, including: Based on the well control operation process model, the duration of operation nodes is combined to obtain the well control time model; Monte Carlo simulations are performed based on the probability distribution of the duration of each operation node and the well control time model to determine the probability distribution of the duration of each well control operation sub-process.

[0008] Optionally, the probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the well control operation sub-processes, including: Once a completed operation node is identified, the probability distribution of the duration of each well control operation sub-process is updated based on the probability distribution of the duration of the completed operation node. The probability distribution of the duration of the overall well control process is updated based on the probability distribution of the duration of each of the updated well control operation sub-processes.

[0009] Optionally, the probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the well control operation sub-processes, including: The failure probability of each operation node is determined based on the failure probability of human factors and the failure probability of equipment, and the probability distribution of the duration of each operation node is updated based on the failure probability of each operation node. Monte Carlo simulations were performed based on the probability distribution of the duration of each of the updated operation nodes and the well control time model to update the probability distribution of the duration of each of the well control operation sub-processes. The probability distribution of the duration of the overall well control process is updated based on the probability distribution of the duration of each of the updated well control operation sub-processes.

[0010] Optionally, updating the probability distribution of the duration of each operation node based on the failure probability of each operation node includes: The sum of 1 and the failure probability is determined as the correction coefficient for the operating node; The updated probability distribution of the duration of the operation node is obtained by multiplying the correction coefficient by the probability distribution of the duration of the operation node.

[0011] To solve the above-mentioned technical problems, the present invention provides a device for analyzing the risk of blowouts in ultra-deep land wells, comprising: The first module is used to construct a well control operation process model for the well control emergency response process of land-based ultra-deep wells, and to determine the probability distribution of the duration of each well control operation sub-process based on the well control operation process model. The second module is used to determine the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each of the well control operation sub-processes. The third module is used to perform multiphase flow transient simulation to obtain the casing pressure change law after the overflow occurs, and to determine the predicted casing pressure for the duration of the well control process based on the casing pressure change law; The fourth module is used to perform risk analysis based on the predicted casing pressure and probability distribution during the duration of the overall well control process to obtain the blowout risk analysis results.

[0012] To solve the above-mentioned technical problems, the present invention provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to implement the above-described method for analyzing the risk of blowouts in ultra-deep land wells when executing the computer program.

[0013] To address the aforementioned technical problems, the present invention provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are executed by a processor, they implement the aforementioned method for analyzing the risk of blowouts in ultra-deep land wells.

[0014] As can be seen, this invention constructs a well control operation process model for the well control emergency response process of ultra-deep onshore wells. Based on the well control operation process model, it determines the probability distribution of the duration of each well control operation sub-process; based on the probability distribution of the duration of each well control operation sub-process, it determines the probability distribution of the duration of the overall well control process; it performs multiphase flow transient simulation to obtain the casing pressure change law after the overflow occurs, and based on the casing pressure change law, it determines the predicted casing pressure for the duration of the overall well control process; based on the predicted casing pressure and probability distribution of the duration of the overall well control process, it performs risk analysis to obtain the blowout risk analysis results. This invention determines the time probability distribution of each operation process through a well control model, determines the casing pressure at the time of well shut-in through multiphase flow transient simulation, and performs real-time risk analysis based on the casing pressure to quickly grasp the on-site risk status and adjust well control measures in a timely manner, thereby reducing operational risks. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a method for analyzing the risk of blowouts in ultra-deep land wells, provided in an embodiment of the present invention; Figure 2 A schematic diagram of a stochastic Petri net model for an early warning subprocess of overflow provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a stochastic Petri net model of a well-closing process provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a total stochastic Petri net model for a well control emergency response process provided in an embodiment of the present invention; Figure 5 This is a structural block diagram of a land-based ultra-deep well blowout risk analysis device provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] As oil and gas exploration and development advances to deeper (4,500 to 6,000 meters) and ultra-deep (6,000 to 9,000 meters) formations, onshore ultra-deep wells generally exhibit characteristics such as large depth, high well temperature, high formation pressure, and narrow pressure windows. If identification, decision-making, or handling is not timely after a blowout occurs, it can easily lead to well control failure and further evolve into a blowout accident, causing a chain reaction of consequences such as wellhead overpressure, casing damage, formation fracturing, and well leakage.

[0019] Existing research on blowout risk analysis and assessment mainly follows these paths: First, qualitative / semi-quantitative methods based on hazard identification and risk classification, such as pre-operation risk assessment, risk matrix, work permit and critical control point identification, are used to identify blowout-related hazards and form a list of risk levels and control measures. Second, structured analysis methods based on accident chains and causal relationships, such as event trees, fault trees, and bow-tie diagrams, are used to characterize the evolution path of well kick-well control failure-blowout and the failure links of key barriers (equipment, procedures, management). Third, quantitative methods based on probabilistic inference and data-driven approaches, such as Bayesian networks / Markov processes, are used to integrate historical data, expert experience and field information to estimate and update the probability of blowout-related events. Fourth, auxiliary assessment methods combining field monitoring and engineering calculations utilize real-time monitoring data such as wellhead / risepole pressure, back pressure, flow rate, and pool fluid level, combined with wellbore hydraulic calculations or empirical criteria, to conduct well kick identification, trend judgment and auxiliary treatment.

[0020] In addition, research on the engineering characteristics of ultra-deep wells also focuses on the integrity of well control barriers and the reliability management of key equipment, emergency response organization and human factors, as well as the constraints of materials and sealing performance on well control safety under high temperature and high pressure conditions, in order to improve the coverage of blowout risk identification and the pertinence of control measures.

[0021] However, in general, existing methods still have some common shortcomings in the field application of ultra-deep wells: many evaluation results are too "offline / static," with limited characterization of the dynamic evolution of blowout risk as operating conditions change; the quantitative expression of uncertainties (parameter fluctuations, differences in response time, and changes in human factors and equipment status) is not uniform enough; risk outputs often remain at the level or conclusion level, making it difficult to directly correspond to the constraints and actionable judgment criteria required for on-site decision-making. Therefore, a blowout risk analysis and evaluation technology more suitable for onshore ultra-deep well operating conditions is needed to improve the timeliness of risk identification, quantitative consistency, and decision-making usability.

[0022] To address the aforementioned problems, this invention provides a method for analyzing the risk of blowouts in ultra-deep land wells. This method can analyze the on-site risk status in real time, allowing staff to easily grasp the on-site risk status and adjust well control measures in a timely manner, thereby reducing the overall risk.

[0023] The following combination Figure 1 , Figure 1 A flowchart of a method for analyzing the risk of blowouts in ultra-deep land wells provided in an embodiment of the present invention, the method may include: S101: Construct a well control operation process model for the well control emergency response process of ultra-deep wells on land, and determine the probability distribution of the duration of each well control operation sub-process based on the well control operation process model.

[0024] This embodiment can first construct a well control operation process model for the well control emergency response process of ultra-deep land wells. This embodiment does not limit the specific way of constructing the well control operation process model. Generally, it can be modeled using SPN (Stochastic Petri Net).

[0025] For example, the state of personnel and equipment in the well control system of the well control emergency response process of ultra-deep land wells can be mapped to storage locations, and the events that cause state changes can be mapped to transitions. Random Petri nets are used for modeling, and storage locations and transitions are connected based on well control logic relationships to construct various well control operation sub-process models. Based on the various well control operation sub-process models, the well control operation process model of the well control emergency response process is constructed.

[0026] The specific steps can be as follows: (1) Status and operation mapping: The status of personnel and equipment in the well control system is mapped to storage locations, and the events that cause status changes are mapped to transitions; the token in the storage location indicates that the corresponding personnel / equipment is in a usable or satisfied state.

[0027] (2) Logical relationship modeling: Establish corresponding subnets for sub-processes such as early warning of overflow and well shut-in; the well shut-in sub-process can include well shut-in process including hard shut-in or soft shut-in; where hard shut-in is shutting down the blowout preventer while the choke manifold is closed, and soft shut-in is opening the choke manifold first, then closing the blowout preventer, and then closing the choke manifold again. Set selection branches for optional processes such as hard shut-in and soft shut-in, and set corresponding structures for parallel, interlocking or conflict relationships to ensure that the model can cover different handling paths.

[0028] (3) Verification and optimization: After modeling is completed, the consistency of the connection between the repository and the transition is checked. The model is then verified and the necessary structural optimizations are performed based on properties such as reachability, boundedness, activity and conflict.

[0029] In this embodiment, the overflow early warning sub-process, the well shut-in sub-process, and the stochastic Petri net model of the well control emergency response process, as well as the overall stochastic Petri net model of the well control emergency response process, can be as follows: Figures 2 to 4 As shown in the figure; the meanings of the symbols in the figure can be found in Tables 1 to 3.

[0030] Table 1: Explanation of Symbols in the Model for Early Warning Subprocesses of Overflow

[0031] Table 2: Explanation of Symbols in the Guanjingzi Process Model

[0032] Table 3: Explanation of Symbols in the Well Control Emergency Response Process Model

[0033] This embodiment can determine the probability distribution of the duration of each well control operation sub-process based on the well control operation process model. For example, this embodiment can combine the durations of operation nodes based on the well control operation process model to obtain a well control time model; Monte Carlo simulation is performed based on the probability distribution of the duration of each operation node and the well control time model to determine the probability distribution of the duration of each well control operation sub-process.

[0034] The specific steps for performing Monte Carlo simulation can be as follows: (1) Establish a time mathematical model for well control process: Based on the well control operation process model, the duration of operation nodes is combined according to the rules of summation, selection or parallelism to form the calculation relationship between the total duration of well control and the duration of sub-processes.

[0035] The calculation relationships include at least the following: the duration of the early warning process for overflow is the sum of the overflow identification time, the alarm time, and the well control personnel arrival time; the duration of the hard shut-in process is the sum of the working condition judgment time, the shut-in command issuance time, the time of stopping the rotary table and raising the drill string, the time of stopping the mud pump, the time of confirming the choke valve status, the time of closing the annular blowout preventer, the time of closing the gate blowout preventer, and the time of opening the safety valve; the duration of the soft shut-in process is the sum of the working condition judgment time, the shut-in command issuance time, the time of stopping the rotary table and raising the drill string, the time of stopping the mud pump, the time of opening the choke manifold valve, the time of closing the annular blowout preventer, the time of closing the gate blowout preventer, and the time of closing the choke manifold valve.

[0036] (2) Determine the probability distribution of the duration of each operation node: Analyze the probability distribution characteristics of the duration of each operation node in the well control process and determine its distribution type. If the probability distribution of the duration of a certain operation node does not fluctuate with the actual working conditions, then the delay is taken as a constant. If the value of a certain transition delay is not fixed, then its distribution characteristics should be determined according to the well control operation procedures and relevant historical data.

[0037] (3) Monte Carlo simulation: The computer program randomly samples the probability distribution of the duration of each operation node, substitutes it into the well control time model, repeats the simulation to obtain a sample set of the duration of the sub-process, and estimates the corresponding probability distribution accordingly.

[0038] S102: Determine the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each well control operation sub-process.

[0039] This embodiment can determine the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each well control operation sub-process. Generally, the probability distribution of the duration of the overall well control process can be obtained by combining each well control operation sub-process according to the operation logic.

[0040] As well control operations progress, the time consumed by completed well control steps can be determined, and the probability distribution of the total time required to complete the entire well control process can be updated to obtain the real-time risk status on site. That is, during the well control process, if a transition corresponding to a certain stage has occurred, the probability distribution of its duration can be replaced with a fixed value, calculated using a constant distribution. Meanwhile, transitions corresponding to stages not yet performed still follow an arbitrarily specified distribution as planned. By continuously updating the distribution of transition delays and analyzing the SPN (Special Service Number) of the system at specific moments, system performance analysis results at different time points can be obtained, achieving real-time and dynamic analysis, which will better reflect actual working conditions.

[0041] For example, identify completed operation nodes, update the probability distribution of the duration of each well control operation sub-process based on the probability distribution of the duration of the completed operation nodes; update the probability distribution of the duration of the overall well control process based on the updated probability distribution of the duration of each well control operation sub-process.

[0042] This embodiment can correct and update the probability distribution of the duration of the overall well control process based on the probability of human failure and the probability of equipment failure.

[0043] For example, the failure probability of each operation node is determined based on the failure probability of human factors and the failure probability of equipment. The probability distribution of the duration of each operation node is updated based on the failure probability of each operation node. Monte Carlo simulation is performed based on the updated probability distribution of the duration of each operation node and the well control time model to update the probability distribution of the duration of each well control operation sub-process. The probability distribution of the duration of the overall well control process is updated based on the updated probability distribution of the duration of each well control operation sub-process.

[0044] Human error is analyzed using the CREAM (Cognitive Reliability and Error Analysis Method), while equipment failure is analyzed using reliability analysis. The two methods are combined to obtain the reliability of each shut-in operation, thereby updating the probability distribution of the shut-in process time. During the execution of the shut-in process, the actual time taken by the completed operation nodes is used as a fixed value to replace their probability distribution. The uncompleted nodes are simulated in a rolling manner according to the updated probability distribution to update the remaining shut-in time distribution in real time.

[0045] This embodiment allows for the application of the CREAM (Cognitive Reliability and Error Analysis Method) to analyze the well shut-in process. The cognitive demand profile and the most probable cognitive function failure modes only require a single analysis, and their results remain unchanged with variations in field conditions. However, the Common Performance Conditions (CPC) for well shut-in operations continuously change with field conditions and therefore require real-time updates. Based on the actual field conditions and referring to the CPC, performance reliability, and weighting factor table, the well shut-in performance conditions under the field conditions can be obtained. The probability of human-caused failure under the current state is then calculated based on these performance conditions.

[0046] This embodiment does not limit the specific method for determining the failure probability of each operation node based on the probability of human-caused failure and the probability of equipment failure. Generally, it can be represented by the following formula: P=P e +P h -P e P h ; In the formula, P e P represents the device failure probability of the operating node. h Let P be the probability of human-caused failure of the operating node.

[0047] Furthermore, this embodiment does not limit the specific method of updating the probability distribution of the duration of the operation node based on the failure probability of each operation node. Generally, the sum of 1 and the failure probability can be determined as the correction coefficient of the operation node. Multiplying the correction coefficient by the probability distribution of the duration of the operation node yields the probability distribution of the updated duration of the operation node, as shown in the following formula: T = (1 + P)t; In the formula, T is the updated duration, t is the duration without considering human error and equipment reliability, and (1+P) is the correction coefficient.

[0048] By utilizing the failure probability of each operation step, the probability distribution of the duration of each specific stage of the well shut-in process under ideal conditions (without considering human error and equipment reliability) is corrected. Then, Monte Carlo simulation is used to obtain the probability distribution of the well shut-in time under actual working conditions.

[0049] For example, under ideal conditions, the probability distribution of the duration of a certain operation node is: the probability of the duration being 10 minutes is 0.2, and the probability distribution of the updated duration after updating the duration is: the probability of the duration being 8 minutes is 0.2.

[0050] S103: Perform multiphase flow transient simulation to obtain the casing pressure change law after the overflow occurs, and determine the predicted casing pressure based on the casing pressure change law to determine the duration of the well control process.

[0051] This embodiment can perform multiphase flow transient simulation of a scenario following oil and gas invasion during ultra-deep well drilling to determine the change in casing pressure over time after a blowout. This embodiment does not limit the specific method of multiphase flow simulation; generally, annular multiphase flow models are used to simulate the drilling gas intrusion blowout process, calculating the casing pressure values ​​at different times after the blowout, thereby establishing a quantitative relationship between the shut-in completion time and the casing pressure at shut-in completion.

[0052] S104: Based on the predicted casing pressure and probability distribution of the duration of the overall well control process, risk analysis is performed to obtain the blowout risk analysis results.

[0053] This embodiment can analyze the probability distribution of the duration of the total shut-in process obtained in real time and combined with the predicted casing pressure at the corresponding time; compare the predicted casing pressure with the maximum allowable shut-in casing pressure, calculate the comprehensive risk score based on the severity of well control consequences corresponding to different predicted casing pressures, and thus determine the risk level at the site at different times.

[0054] This embodiment first discretizes the duration of the overall well control process into multiple time nodes to obtain the probability distribution of each time node; for example, the time-probability distribution curve can be discretized into n time nodes t1-t2. n ; where t i This represents the p corresponding to the i-th time node. i Let represent the probability distribution at the i-th time point.

[0055] This embodiment uses the time t when well shut-in is completed. i Predicted pressure P i As the primary evaluation indicator, successful well shut-in requires meeting P... i ≤P max , where P max This is the maximum permissible shut-in casing pressure.

[0056] The maximum allowable shut-in casing pressure can be calculated as follows: p max =(ρ e -ρ m )gh; In the formula, p max The maximum allowable shut-in casing pressure (unit: MPa), ρ e Drilling fluid density at formation fracture pressure equivalent (unit: g / cm³) 3 ), ρ m Density of drilling fluid in the well (unit: g / cm³) 3 g is a constant (taken as 0.00981), and h is the vertical depth of the formation fracture pressure test layer (casing shoe) (unit: m).

[0057] Furthermore, a set of comments can be constructed based on the predicted casing pressure and the maximum allowable shut-in casing pressure. A fuzzy evaluation matrix of the comment set can be established and normalized to obtain a normalized fuzzy evaluation matrix.

[0058] For example, the evaluation set can be set as V={V1,V2,V3}, where V1, V2, and V3 correspond to low risk, medium risk, and high risk, respectively. If using a percentage system, V1, V2, and V3 would correspond to 10, 50, and 90 points, respectively. The specific risk level classification can be set based on actual application and is not limited here.

[0059] In this embodiment, the pressure of the casing is used as the main indicator for risk assessment. It is a single-factor evaluation, so the weight of this indicator on the evaluation object is 1.

[0060] This embodiment uses the predicted casing pressure at the corresponding time node as a basis to divide the well control consequences that may result from shutting in the well at different casing pressure values ​​into three risk ranges: high, medium, and low. It then determines the corresponding membership function based on a trapezoidal membership function and calculates the predicted casing pressure P corresponding to the i-th time node according to the membership function. i Fuzzy evaluation vector (membership vector): ; In the formula, R i Let r be the membership vector of the predicted casing pressure corresponding to the i-th time node. i1 r i2 r i3 These represent the original membership degrees of the predicted hedging pressure at the i-th time point relative to the three risk levels: low, medium, and high.

[0061] For the fuzzy evaluation vector R i After normalization, the normalized fuzzy evaluation vector is obtained: ; In the formula, C iLet r be the normalized membership vector of the predicted pressure at the i-th time node. i1 r i2 r i3 , respectively, represent the normalized original membership degree of the predicted pressure at the i-th time node relative to the three levels of low risk, medium risk, and high risk.

[0062] The normalized original membership degree is calculated as follows: ; In the formula, c ij This is the normalized original membership degree of the predicted pressure at the i-th time node relative to the j-th comment set element (i.e., risk level). In this example, j=1,2,3.

[0063] Furthermore, the severity score of well control consequences at each time point can be determined based on the comment set and the normalized fuzzy evaluation matrix; according to the normalized fuzzy evaluation vector and the comment set score vector, the severity score of well control consequences corresponding to the i-th time point is determined as follows: ; In the formula, b i The severity of well control consequences at the i-th time point is scored.

[0064] Finally, this embodiment can determine the comprehensive risk score based on the probability distribution of each time point and the severity score of well control consequences, and determine the comprehensive risk level and well control status based on the comprehensive risk score and the risk database.

[0065] Considering the probability of different well control consequences occurring, based on the formula: Risk = Probability The consequences lead to the following formula for calculating the comprehensive risk score: ; In the formula, S represents the comprehensive risk assessment, n represents the number of discrete time points, and p i Let b represent the probability distribution at the i-th time point (i.e., the probability of completing the well control operation at the i-th time point). i The severity of well control consequences at the i-th time point is scored.

[0066] The overall risk level and well control status are determined based on the comprehensive risk score and reference risk database. The risk database in this embodiment can be shown in Table 4.

[0067] Table 4: Example of a Risk Database

[0068] This embodiment couples the randomness of well control operation procedures with wellbore pressure evolution within the same framework, and can output quantifiable indicators such as the probability of casing pressure exceeding limits. By dynamically correcting the well shut-in completion time distribution through human error and equipment reliability, the risk assessment can be updated in real time as the field conditions change. It is applicable to complex conditions such as ultra-deep wells on land, and can provide a basis for the selection of well shut-in methods and well control disposal strategies, reducing the risk of well control failure and secondary accidents.

[0069] Based on the above embodiments, the present invention determines the time probability distribution of each operation process through a well control model, and determines the casing pressure at the time of well shut-in through multiphase flow transient simulation. Based on the casing pressure, real-time risk analysis is performed to quickly grasp the on-site risk status and adjust well control measures in a timely manner to reduce operational risks.

[0070] The following combination Figure 5 The figure shows a structural block diagram of a land-based ultra-deep well blowout risk analysis device provided in an embodiment of the present invention. The device may include: The first module 100 is used to construct a well control operation process model for the well control emergency response process of land ultra-deep wells, and to determine the probability distribution of the duration of each well control operation sub-process based on the well control operation process model. The second module 200 is used to determine the probability distribution of the duration of the total well control process based on the probability distribution of the duration of each well control operation sub-process. The third module 300 is used to perform multiphase flow transient simulation to obtain the casing pressure change law after the overflow occurs, and to determine the predicted casing pressure for the duration of the overall well control process based on the casing pressure change law. The fourth module 400 is used to perform risk analysis based on the predicted casing pressure and probability distribution of the duration of the overall well control process to obtain the blowout risk analysis results.

[0071] Based on the above embodiments, the present invention determines the time probability distribution of each operation process through a well control model, and determines the casing pressure at the time of well shut-in through multiphase flow transient simulation. Based on the casing pressure, real-time risk analysis is performed to quickly grasp the on-site risk status and adjust well control measures in a timely manner to reduce operational risks.

[0072] Based on the above embodiments, the fourth module 400 may include: The first unit is used to discretize the duration of the overall well control process into multiple time nodes to obtain the probability distribution of each time node; The second unit is used to construct a set of comments based on the predicted casing pressure and the maximum allowable shut-in casing pressure, establish a fuzzy evaluation matrix for the set of comments, and perform normalization processing to obtain a normalized fuzzy evaluation matrix. The third unit is used to determine the severity score of well control consequences at each time point based on the comment set and the normalized fuzzy evaluation matrix; The fourth unit is used to determine the comprehensive risk score based on the probability distribution of each time point and the severity of well control consequences. The fifth unit is used to determine the overall risk level and well control status based on the risk comprehensive score and reference risk database.

[0073] Based on the above embodiments, the first module 100 may include: The sixth unit is used to map the status of personnel and equipment in the well control system of the well control emergency response process of ultra-deep land wells to storage locations, and to map events that cause status changes to transitions. The seventh unit is used for modeling with stochastic Petri nets, and the storage locations and changes are connected based on the well control logic relationship to construct the sub-process model of each well control operation. Unit 8 is used to construct a well control operation process model for the well control emergency response process based on the well control operation sub-process models.

[0074] Based on the above embodiments, the first module 100 may include: The ninth unit is used to combine the durations of operation nodes based on the well control operation process model to obtain the well control time model; Unit 10 is used for Monte Carlo simulation based on the probability distribution of the duration of each operation node and the well control time model to determine the probability distribution of the duration of each well control operation sub-process.

[0075] Based on the above embodiments, the second module 200 may include: The eleventh unit is used to determine the completed operation nodes and update the probability distribution of the duration of each well control operation sub-process based on the probability distribution of the duration of the completed operation nodes. Unit 12 is used to update the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each updated well control operation subprocess.

[0076] Based on the above embodiments, the second module 200 may include: Unit 13 is used to determine the failure probability of each operation node based on the failure probability of human factors and the failure probability of equipment, and to update the probability distribution of the duration of the operation node based on the failure probability of each operation node. Unit 14 is used to perform Monte Carlo simulations based on the probability distribution of the duration of each operation node after the update and the well control time model, and to update the probability distribution of the duration of each well control operation sub-process. Unit 15 is used to update the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each updated well control operation subprocess.

[0077] Based on the above embodiments, the thirteenth unit may include: The first sub-unit is used to determine the correction coefficient of the operating node by summing 1 with the failure probability; The second sub-unit is used to multiply the correction coefficient by the probability distribution of the duration of the operation node to obtain the probability distribution of the updated duration of the operation node.

[0078] Based on the above embodiments, the present invention also provides an electronic device, which may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, it can implement the steps provided in the above embodiments. Of course, the device may also include various necessary network interfaces, a power supply, and other components.

[0079] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by an execution terminal or processor, can implement the method provided in the embodiments of the present invention; the storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0080] In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for analyzing the risk of blowouts in ultra-deep onshore wells, characterized in that, include: A well control operation process model for the well control emergency response process of an ultra-deep land well is constructed, and the probability distribution of the duration of each well control operation sub-process is determined based on the well control operation process model. The probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the well control operation sub-processes. Multiphase flow transient simulation is performed to obtain the casing pressure change law after the overflow occurs, and the predicted casing pressure for the duration of the well control process is determined based on the casing pressure change law; Based on the predicted casing pressure and probability distribution of the duration of the overall well control process, a blowout risk analysis is performed to obtain the results.

2. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 1, characterized in that, Based on the predicted casing pressure and probability distribution of the duration of the overall well control process, a risk analysis is performed to obtain the blowout risk analysis results, including: The duration of the overall well control process is discretized into multiple time nodes to obtain the probability distribution of each time node; Based on the predicted casing pressure and the maximum allowable shut-in casing pressure, a set of comments is constructed, a fuzzy judgment matrix of the set of comments is established, and normalization processing is performed to obtain a normalized fuzzy judgment matrix. The severity score of well control consequences at each time point is determined based on the set of comments and the normalized fuzzy evaluation matrix. A comprehensive risk score is determined based on the probability distribution of each time point and the severity score of the well control consequences. The overall risk level and well control status are determined based on the comprehensive risk score and with reference to the risk database.

3. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 1, characterized in that, A well control operation process model for the emergency response process of ultra-deep wells on land is constructed, including: In the well control system of the emergency response process for ultra-deep wells on land, the status of personnel and equipment is mapped to storage locations, and the events that cause changes in the status are mapped to transitions. A stochastic Petri net is used for modeling, and the storage locations and transitions are connected based on the well control logic relationship to construct the sub-process model of each well control operation; The well control operation process model for the well control emergency response process is constructed based on each of the well control operation sub-process models.

4. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 1, characterized in that, Based on the well control operation process model, the probability distribution of the duration of each well control operation sub-process is determined, including: Based on the well control operation process model, the duration of operation nodes is combined to obtain the well control time model; Monte Carlo simulations are performed based on the probability distribution of the duration of each operation node and the well control time model to determine the probability distribution of the duration of each well control operation sub-process.

5. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 1, characterized in that, The probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the aforementioned well control operation sub-processes, including: Once a completed operation node is identified, the probability distribution of the duration of each well control operation sub-process is updated based on the probability distribution of the duration of the completed operation node. The probability distribution of the duration of the overall well control process is updated based on the probability distribution of the duration of each of the updated well control operation sub-processes.

6. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 1, characterized in that, The probability distribution of the duration of the overall well control process is determined based on the probability distribution of the duration of each of the aforementioned well control operation sub-processes, including: The failure probability of each operation node is determined based on the failure probability of human factors and the failure probability of equipment, and the probability distribution of the duration of each operation node is updated based on the failure probability of each operation node. Monte Carlo simulations were performed based on the probability distribution of the duration of each of the updated operation nodes and the well control time model to update the probability distribution of the duration of each of the well control operation sub-processes. The probability distribution of the duration of the overall well control process is updated based on the probability distribution of the duration of each of the updated well control operation sub-processes.

7. The method for analyzing the risk of blowouts in ultra-deep onshore wells according to claim 6, characterized in that, The probability distribution of the duration of each operation node is updated based on the failure probability of each operation node, including: The sum of 1 and the failure probability is determined as the correction coefficient for the operating node; The updated probability distribution of the duration of the operation node is obtained by multiplying the correction coefficient by the probability distribution of the duration of the operation node.

8. A device for analyzing the risk of blowouts in ultra-deep land wells, characterized in that, include: The first module is used to construct a well control operation process model for the well control emergency response process of land-based ultra-deep wells, and to determine the probability distribution of the duration of each well control operation sub-process based on the well control operation process model. The second module is used to determine the probability distribution of the duration of the overall well control process based on the probability distribution of the duration of each of the well control operation sub-processes. The third module is used to perform multiphase flow transient simulation to obtain the casing pressure change law after the overflow occurs, and to determine the predicted casing pressure for the duration of the well control process based on the casing pressure change law; The fourth module is used to perform risk analysis based on the predicted casing pressure and probability distribution during the duration of the overall well control process to obtain the blowout risk analysis results.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the method for analyzing the risk of blowouts in ultra-deep land wells as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method for analyzing the risk of blowouts in ultra-deep land wells as described in any one of claims 1 to 7.

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