A wellbore life cycle management system and method

Abstract

The present invention relates to the technical field of oil and gas engineering, and discloses a wellbore life cycle management system and method. The system covers multiple modules such as wellbore design, construction monitoring, integrity assessment, etc. By obtaining geological exploration data to generate accurate design parameters, real-time collecting multi-sensor data to ensure construction safety, evaluating integrity and predicting corrosion rate based on multi-source data, using spatio-temporal correlation algorithms to integrate data to identify abnormal coupling effects, and then generating risk level signals and early warning instruction sets, and formulating optimized maintenance plans. The invention can significantly improve the accuracy and stability of wellbore design, comprehensively and accurately evaluate its condition, accurately predict corrosion, scientifically conduct risk early warning and maintenance decision-making. At the same time, it improves management efficiency through visual display, effectively solves many problems of traditional wellbore management, reduces operation and maintenance costs, and improves the safety and economy of energy extraction.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas engineering, and specifically to a wellbore life cycle management system and method. Background Art

[0002] In the field of energy extraction such as oil and gas, the wellbore, as a key passage connecting underground resources and surface facilities, the safe and stable operation of its entire life cycle is crucial for efficient energy extraction and the continuous development of production activities. However, current wellbore management faces many severe challenges, and existing technologies have obvious deficiencies in addressing these problems.

[0003] Traditional wellbore design mainly relies on experience and simple geological data, and it is difficult to comprehensively consider the complex distribution of formation stress. Geological conditions vary greatly in different regions, and parameters such as rock density, pore pressure gradient, and in-situ stress direction angle change diversely. These factors will have a significant impact on the wellbore structure. If these factors are not accurately analyzed during the design stage, the generated wellbore structure design parameters may not match the actual requirements. For example, in the stress concentration area, if the wellbore wall thickness is designed too thin, problems such as rupture and deformation of the wellbore will occur in the initial stage of service, increasing maintenance costs and safety risks; conversely, if the wall thickness is designed too thick, it will cause material waste and increase the extraction cost. Moreover, traditional design methods cannot be dynamically adjusted according to real-time geological data and are difficult to adapt to complex and changeable geological environments.

[0004] During the wellbore construction process, construction safety is directly related to the progress and quality of the entire project. Currently, construction monitoring mainly focuses on the monitoring of some key parameters, such as the pressure value inside the wellbore, but the monitoring of other important parameters such as temperature value and vibration intensity value is not comprehensive enough or lacks real-time performance. When these parameters exceed the safe range, they cannot be detected and measures taken in a timely manner, easily leading to construction accidents such as blowouts and collapses. In addition, existing monitoring systems often analyze the data of each sensor in isolation, lacking a comprehensive consideration of the dynamic correlation between multiple parameters, unable to accurately judge the complex conditions during the construction process, and difficult to early warn of potential risks.

[0005] During the long-term service of the wellbore, affected by various complex factors such as internal fluid pressure fluctuations, external formation stress changes, and corrosion effects, its structural integrity will gradually be threatened. Existing integrity assessment methods usually analyze based on single or a few monitoring data, and it is difficult to comprehensively reflect the true situation of the wellbore. For example, relying solely on the measurement data of the wellbore wall thickness to evaluate integrity, ignoring the annulus pressure change and the impact of dynamic loads on the wellbore structure, may lead to misjudgment of the wellbore safety state. At the same time, when considering dynamic loads in existing assessment methods, there is a lack of accurate calculation models, unable to accurately predict the stress concentration and fatigue damage of each part of the wellbore, and difficult to detect potential structural defects in advance.

[0006] The underground environment where the wellbore is located usually contains various corrosive media, such as hydrogen sulfide, carbon dioxide, etc. These media will corrode the wellbore materials, seriously affecting the service life of the wellbore. Most of the existing corrosion prediction methods are based on simple empirical formulas or static models, and do not fully consider the comprehensive effects of factors such as the characteristics of wellbore materials, the dynamic changes of environmental corrosion factors, and the cumulative material fatigue. For example, the influence of temperature, pressure, and fluid flow rate at different depths on the corrosion rate is ignored, resulting in a large deviation between the predicted corrosion rate and the actual situation. This makes it lack a scientific basis when formulating anti-corrosion measures. Either the anti-corrosion measures are excessive, causing waste of resources, or the anti-corrosion measures are insufficient, leading to premature corrosion and damage of the wellbore.

[0007] A large amount of data will be generated during each stage of the wellbore from design, construction to service. However, there is currently a lack of effective technical means to integrate and analyze this data. The data at different stages are often stored in different systems, forming data islands, and it is impossible to achieve cross-stage data correlation and collaborative analysis. This makes it difficult to discover the potential connections and abnormal coupling effects between the data at different stages, and it is impossible to conduct comprehensive management of the wellbore from the perspective of the entire life cycle. For example, local defects in the construction stage may interact with specific environmental factors during the service stage, triggering serious safety problems. However, due to the lack of data integration and analysis, it is difficult to identify and prevent this situation in advance. Due to the deficiencies of existing technologies in data acquisition, analysis, and integration, there are defects in risk warning and maintenance decision-making. Risk warning is often based on a single indicator or simple threshold judgment, and cannot comprehensively consider the interaction of multiple risk factors. The warning results are inaccurate, and there are easily missed reports or false reports. In terms of maintenance decision-making, there is a lack of a scientific maintenance strategy library and optimization methods. Usually, maintenance plans are formulated based on experience, and it is difficult to achieve precise maintenance. For example, it is impossible to reasonably determine the maintenance time, maintenance location, and maintenance process according to the actual risk status of the wellbore, resulting in high maintenance costs and poor effects. Summary of the Invention

[0008] The purpose of the present invention is to provide a wellbore life cycle management system and method to solve the problems raised in the above background technology.

[0009] To achieve the above purpose, the present invention provides the following technical solution: A wellbore life cycle management system, the system includes:

[0010] A wellbore design module, used to obtain geological exploration data of the target area, generate wellbore structure design parameters according to a preset formation stress distribution model, and mark the wellbore structure design parameters as an initial design parameter set;

[0011] The construction monitoring module is used to collect the data of various sensors during the construction stage of the wellbore in real time. The data of various sensors include the pressure value, temperature value, and vibration intensity value in the wellbore, and dynamically compare the data of various sensors with the preset safe construction threshold range.

[0012] The integrity assessment module is used to calculate the wellbore wall thickness attenuation rate and the annulus pressure offset coefficient according to the multi-source monitoring data during the service period of the wellbore, and generate the wellbore structure integrity assessment index based on the dynamic load distribution algorithm.

[0013] The corrosion prediction module is used to obtain the corrosion factor data of the environment where the wellbore is located, and calculate the predicted corrosion rate values of each section of the wellbore in combination with the material fatigue cumulative model.

[0014] The spatio-temporal correlation module is used to integrate the wellbore design parameters, construction monitoring data, and integrity assessment index, and generate a multi-dimensional data correlation matrix through the spatio-temporal correlation algorithm to identify the abnormal coupling effect across stages.

[0015] The risk warning module is used to generate a risk level signal and the corresponding warning instruction set based on the wellbore structure integrity assessment index and the predicted corrosion rate value, in combination with the analysis result of the abnormal coupling effect of the multi-dimensional data correlation matrix.

[0016] The maintenance decision module is used to call the pre-stored maintenance strategy library according to the risk level signal and generate an optimized maintenance plan including the maintenance time, maintenance location, and maintenance process.

[0017] Preferably, the specific construction method of the formation stress distribution model includes:

[0018] Obtain the historical geological parameters of the target area from the local database, including the rock density, pore pressure gradient, and in-situ stress direction angle, and generate the formation stress distribution grid through the three-dimensional finite element discretization algorithm.

[0019] Calculate the equivalent stress values of each grid node according to the well depth and well diameter in the wellbore design parameters, and correct the initial design parameter set based on the maximum shear stress criterion.

[0020] Preferably, the specific calculation method of the dynamic load distribution algorithm is:

[0021] Obtain the historical load data during the service period of the wellbore from the local database, including the axial tension, internal pressure fluctuation value, and external extrusion pressure, and extract the load spectrum characteristics through Fourier transform.

[0022] Build the wellbore dynamic response equation based on the spectrum characteristics, and generate the stress concentration coefficient and fatigue damage cumulative amount of each section of the wellbore in combination with the Monte Carlo random simulation algorithm.

[0023] Preferably, the specific calculation method of the corrosion rate prediction value includes:

[0024] Obtain the chemical composition data of the wellbore material, the pH value and hydrogen sulfide concentration of the environmental medium from the local database, and calculate the basic corrosion rate through the electrochemical corrosion kinetics model;

[0025] Superimpose the wellbore sectional temperature gradient data and the fluid flow rate data, and correct the basic corrosion rate by using the weighted attenuation factor.

[0026] Preferably, the generation method of the maintenance strategy library is as follows:

[0027] Obtain the historical maintenance record data from the local database, including maintenance type, time consumption, cost, and effectiveness indicators, and divide the maintenance records into multiple strategy categories through the clustering analysis algorithm;

[0028] Construct a multi-objective optimization function based on the strategy categories, and solve the priority weights of each strategy category in combination with the genetic algorithm.

[0029] Preferably, the specific implementation steps of the spatio-temporal correlation algorithm include:

[0030] Encode the wellbore design parameters, construction monitoring data, and integrity evaluation indicators into a four-dimensional tensor according to the time stamp and spatial coordinates;

[0031] Extract the abnormal patterns in the tensor through the convolutional neural network, and use the attention mechanism to allocate the correlation weights of different data sources.

[0032] Preferably, the implementation steps of the risk warning module include:

[0033] Step S1: Receive the wellbore structure integrity evaluation index set , the corrosion rate prediction value set and the multi-dimensional data correlation matrix , where is the time dimension index, is the spatial coordinate index;

[0034] Step S2: Calculate the dynamic risk benchmark value using the following formula :

[0035] Where is the normalized weight of each integrity index, and are the preset index thresholds, is the corrosion sensitivity coefficient, is the material degradation time constant;

[0036] Step S3: Based on the multi-dimensional data correlation matrix , detect the intensity of the spatio-temporal abnormal coupling effect through the following formula where is the benchmark matrix predicted by the long short-term memory network LSTM, is the standard deviation within the sliding time window, is the matrix Frobenius norm;

[0037] Step S4: Use the fuzzy logic rule base to and perform joint mapping to generate the comprehensive risk level :

[0038] where , and , are the piecewise thresholds fitted from historical accident data;

[0039] Step S5: Select the early warning instruction set from the preset instruction mapping table according to the risk level , including the wellbore pressure reduction rate threshold, the injection amount of corrosion inhibitor, and the emergency maintenance response time parameter.

[0040] Preferably, it further includes a user interaction module, and the user interaction module further includes:

[0041] A wellbore three-dimensional reconstruction unit, which is used to generate a dynamic three-dimensional model of the wellbore structure according to the design parameters and real-time monitoring data, and visually display the stress concentration area and the high corrosion risk area through a color mapping algorithm.

[0042] Preferably, the specific implementation method of the color mapping algorithm is:

[0043] Normalize the stress concentration coefficient and the predicted corrosion rate value to the numerical value within the range of 0-1;

[0044] Through the HSV color space conversion, map the normalized numerical value to a gradient color sequence from green to red, and superimpose it on the surface of the three-dimensional model.

[0045] Preferably, the present invention also includes a wellbore life cycle management method, including the following steps:

[0046] Obtain the geological exploration data of the target area, and generate the initial design parameters of the wellbore based on the formation stress distribution model;

[0047] Real-time collect the pressure, temperature, and vibration data during the construction stage, and dynamically compare them with the safety thresholds;

[0048] Calculate the stress concentration coefficient and the fatigue damage accumulation amount during the wellbore service period through the dynamic load distribution algorithm;

[0049] Combined with the environmental corrosion factor and the material fatigue accumulation model, predict the corrosion rate of the wellbore segments;

[0050] Generate a risk level signal based on the stress concentration factor and the corrosion rate, and match and optimize the maintenance plan;

[0051] Utilize the spatio-temporal correlation algorithm to fuse multi-source data, and display the risk assessment results through a three-dimensional visualization model.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] By obtaining detailed geological exploration data of the target area and using a preset formation stress distribution model to generate wellbore structure design parameters, the present invention fully considers the complex distribution of formation stress. Different from the traditional design method that relies on experience and simple geological data, this model generates a formation stress distribution grid based on historical geological parameters such as rock density, pore pressure gradient, and in-situ stress direction angle obtained from the local database through a three-dimensional finite element discretization algorithm, and then calculates the equivalent stress values of each grid node in combination with the well depth and well diameter, and modifies the initial design parameter set based on the maximum shear stress criterion. This makes the designed wellbore structure more in line with the actual geological conditions, effectively avoiding problems such as wellbore rupture and deformation caused by unreasonable design, significantly improving the stability of the wellbore during service, and reducing safety risks.

[0054] The construction monitoring module continuously collects multi-sensor data such as the pressure value, temperature value, and vibration intensity value in the wellbore, and dynamically compares them with the preset safe construction threshold range. This comprehensive and real-time monitoring method overcomes the drawbacks of traditional monitoring that only focuses on some key parameters, lacks real-time performance, and has isolated analysis of multiple parameters. Once the data exceeds the threshold range, it can promptly trigger the warning mechanism to remind the construction personnel to take corresponding measures, effectively preventing the occurrence of construction accidents such as blowouts and collapses, strongly guaranteeing the safety of the construction process, and ensuring the smooth progress of the project.

[0055] The integrity assessment module calculates key indicators such as the wellbore wall thickness attenuation rate and the annulus pressure offset coefficient based on the multi-source monitoring data during the service period of the wellbore, and generates a wellbore structure integrity assessment indicator by means of a dynamic load distribution algorithm. This algorithm obtains historical load data from the local database, extracts spectral features through Fourier transform, and combines the Monte Carlo random simulation algorithm to generate the stress concentration factor and the fatigue damage accumulation amount, comprehensively and accurately reflecting the true situation of the wellbore. Compared with the traditional single or few data evaluation methods, it can more accurately predict the stress concentration and fatigue damage conditions of each part of the wellbore, timely discover potential structural defects, and provide a reliable basis for taking maintenance measures in advance.

[0056] The corrosion prediction module comprehensively considers the corrosion factor data of the wellbore environment and the material fatigue accumulation model to calculate the predicted corrosion rate. By obtaining the chemical component data of the wellbore material, the pH value of the environmental medium, and the hydrogen sulfide concentration, the basic corrosion rate is calculated using the electrochemical corrosion kinetics model, and the wellbore segmented temperature gradient data and fluid flow rate data are superimposed, and corrected using a weighted attenuation factor. This method fully considers the influence of various factors on the corrosion rate, and compared with traditional simple empirical formulas or static models, the prediction accuracy is greatly improved. Based on the accurate corrosion prediction results, a more scientific and reasonable anti-corrosion strategy can be formulated, avoiding over-anti-corrosion or under-anti-corrosion situations, effectively extending the service life of the wellbore, and reducing maintenance costs.

[0057] The spatio-temporal correlation module integrates the wellbore design parameters, construction monitoring data, and integrity assessment indicators, and generates a multi-dimensional data correlation matrix through spatio-temporal correlation algorithms, which can identify cross-stage abnormal coupling effects. This breaks the traditional mode of isolated storage and analysis of data in each stage, and realizes the deep integration and analysis of multi-source data from the perspective of the entire life cycle of the wellbore. For example, it can be found that the safety problems caused by the interaction between local defects in the construction stage and specific environmental factors in the service stage, and risk intervention can be carried out in advance to improve the overall management level of the wellbore.

[0058] The risk warning module generates risk level signals and corresponding warning instruction sets based on the wellbore structure integrity assessment indicators, the predicted corrosion rate, and the analysis results of the abnormal coupling effects of the multi-dimensional data correlation matrix. By calculating the dynamic risk benchmark value, detecting the intensity of spatio-temporal abnormal coupling effects, and using a fuzzy logic rule base to generate a comprehensive risk level, the risk warning is made more scientific and accurate, effectively reducing false negatives and false positives. The maintenance decision module calls the pre-stored maintenance strategy library according to the risk level signal to generate an optimized maintenance plan, specifying the maintenance time, maintenance location, and maintenance process. This scientific risk warning and maintenance decision-making mechanism realizes precise maintenance, improves maintenance efficiency, reduces operation and maintenance costs, and ensures the safe and stable operation of the wellbore.

[0059] The wellbore three-dimensional reconstruction unit in the user interaction module generates a dynamic three-dimensional model of the wellbore structure based on the design parameters and real-time monitoring data, and visualizes and displays the stress concentration area and high corrosion risk area through a color mapping algorithm. This function presents complex data in an intuitive and easy-to-understand three-dimensional model, facilitating managers to quickly understand the operating conditions of the wellbore, timely discover potential risk points, and provide an intuitive basis for decision-making, greatly improving the efficiency and accuracy of wellbore management. Description of the Drawings

[0060] Figure 1 It is the working principle diagram of the wellbore life cycle management system described in the present invention;

[0061] Figure 2Flow chart for constructing a formation stress distribution model;

[0062] Figure 3 Step diagram of the specific calculation method for the predicted corrosion rate. Detailed implementation manners

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0064] Please refer to Figures 1-3 , the present invention provides a technical solution: a wellbore life cycle management system, and the method includes:

[0065] Wellbore design module: Obtain geological exploration data of the target area from various geological exploration databases or exploration equipment. These data cover key information such as rock formation density, pore pressure gradient, and in-situ stress direction angle. Input the obtained data into a preset formation stress distribution model, and through the operation of the model, generate wellbore structure design parameters such as well depth, well diameter, number of casing layers and materials, etc., and mark these parameters as the initial design parameter set.

[0066] Construction monitoring module: During the wellbore construction stage, install various monitoring devices such as pressure sensors, temperature sensors, and vibration sensors at different positions in the wellbore. These sensors collect data such as pressure values, temperature values, and vibration intensity values in the wellbore in real time, and transmit the data to the data processing center in real time through wired or wireless transmission methods. The data processing center dynamically compares the received data of each sensor with the preset safe construction threshold range. Once it is found that the data exceeds the threshold range, the corresponding warning mechanism is immediately triggered to remind the construction personnel to take measures.

[0067] Integrity assessment module: During the service period of the wellbore, collect multi-source monitoring data from various monitoring devices, including but not limited to pressure monitoring data, temperature monitoring data, displacement monitoring data, etc. Use these data to calculate key indicators such as wellbore wall thickness attenuation rate and annulus pressure offset coefficient, and generate wellbore structure integrity assessment indicators based on the dynamic load distribution algorithm to judge the structural integrity status of the wellbore during the service process.

[0068] Corrosion prediction module: Obtain corrosion factor data of the wellbore environment, such as the pH value of the environmental medium, hydrogen sulfide concentration, and chemical composition data of the wellbore material. Combine the material fatigue accumulation model and calculate the predicted corrosion rate values of each section of the wellbore through a specific calculation method, providing an important basis for subsequent maintenance decisions.

[0069] Space - time correlation module: Integrate multi - source data such as wellbore design parameters, construction monitoring data, and integrity assessment indicators. Encode these data according to timestamps and spatial coordinates, and generate a multi - dimensional data correlation matrix through space - time correlation algorithms. This matrix can effectively identify cross - stage abnormal coupling effects, that is, abnormal correlation relationships between data in different stages, so as to discover potential problems in a timely manner.

[0070] Risk early - warning module: Based on the wellbore structure integrity assessment indicators and predicted corrosion rate values, combined with the analysis results of abnormal coupling effects of the multi - dimensional data correlation matrix, use specific calculation models and logical rules to generate risk - level signals. According to the risk - level signals, select the corresponding early - warning instruction set from the preset instruction mapping table, such as wellbore pressure reduction rate thresholds, corrosion inhibitor injection amounts, and emergency maintenance response time parameters, and issue early warnings to relevant personnel in a timely manner.

[0071] Maintenance decision - making module: According to the risk - level signals generated by the risk early - warning module, call the pre - stored maintenance strategy library. The maintenance strategy library stores a variety of maintenance strategies. Through specific algorithms, select and generate an optimized maintenance plan including maintenance time, maintenance location, and maintenance technology from the maintenance strategy library to guide the operation and maintenance personnel in wellbore maintenance work.

[0072] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1:

[0073] This example details the construction process of the formation stress distribution model, which provides key basic data support for wellbore design, enables the wellbore structure design to better conform to the geological conditions of the target area, and improves the stability and safety of the wellbore. Specifically, it includes:

[0074] Obtain the historical geological parameters of the target area from the local database. This database stores the data accumulated from past geological explorations in this area. Among them, the rock layer density data reflects the mass distribution characteristics of different rock layers, the pore pressure gradient data reflects the change of fluid pressure in the formation pores with depth, and the in - situ stress direction angle data clarifies the acting direction of in - situ stress in space.

[0075] Use the three - dimensional finite - element discretization algorithm to process the obtained geological parameters. This algorithm divides the formation of the target area into numerous tiny three - dimensional grid units, and each grid unit represents a tiny part of the formation. By analyzing and calculating the mechanical properties of these grid units, a formation stress distribution grid is generated. When dividing the grid, reasonably determine the density of the grid according to the complexity of the formation and the requirements of calculation accuracy to ensure the accuracy of the calculation results.

[0076] After generating the formation stress distribution grid, further calculations are carried out based on the well depth and well diameter in the wellbore design parameters. The well depth and well diameter, as important parameters in wellbore design, have a significant impact on the formation stress distribution. Through specific calculation formulas, the equivalent stress values of each grid node are calculated, and this formula comprehensively considers factors such as rock density, pore pressure gradient, in-situ stress direction angle, as well as well depth and well diameter.

[0077] The initial design parameter set is corrected based on the maximum shear stress criterion. The maximum shear stress criterion is an important criterion in material mechanics, used to judge whether a material will fail under complex stress states. When the calculated equivalent stress value of the grid node exceeds the threshold determined by the maximum shear stress criterion, it indicates that the formation stress in this area may have an adverse impact on the wellbore structure, and the initial design parameter set needs to be adjusted, such as increasing the strength grade of the casing or changing the wellbore structure, etc., to improve the bearing capacity of the wellbore.

[0078] In practical applications, taking the wellbore design in a specific area as an example, the rock density range in this area is 2.2 - 2.8 g / cm³, the pore pressure gradient is 0.009 - 0.012 MPa / m, and the in-situ stress direction angle is between 30° - 40° east of north. The formation stress distribution grid generated by the three-dimensional finite element discretization algorithm, with the grid element size of 1m×1m×1m. According to the calculated equivalent stress values of each grid node with a well depth of 3000m and a well diameter of 0.2m, after being judged by the maximum shear stress criterion, it is found that the equivalent stress values of some grid nodes near the wellhead are close to the failure threshold, so the casing material of the initial design is upgraded, from ordinary carbon steel casing to high-strength alloy steel casing, effectively improving the safety of the wellbore. Example 2:

[0079] This example details the calculation process of the dynamic load distribution algorithm, through which the stress concentration factor and fatigue damage accumulation amount of each section during the wellbore service period can be accurately obtained.

[0080] Historical load data during the wellbore service period is obtained from the local database. The database stores data such as axial tensile force, internal pressure fluctuation value, and external extrusion pressure that the wellbore has endured at different past time periods. These data are long-term monitored and recorded by various sensors installed on the wellbore, and have high accuracy and reliability.

[0081] The obtained historical load data is processed using Fourier transform. Fourier transform is a powerful mathematical tool that can convert time-domain signals into frequency-domain signals, thereby extracting the load spectrum characteristics. Through Fourier transform, it can be clearly understood what proportion of the loads with different frequency components account for in the total load, and how they change over time.

[0082] Construct a wellbore dynamic response equation based on the extracted spectral features. This equation describes the mechanical response characteristics of the wellbore under the action of different frequency loads, comprehensively considering factors such as the material properties, geometric shape, and boundary conditions of the wellbore. When constructing the equation, relevant theories such as mechanics of materials and structural dynamics are used, combined with actual engineering experience, to ensure that the equation can accurately reflect the dynamic response of the wellbore.

[0083] Combined with the Monte Carlo stochastic simulation algorithm, generate the stress concentration coefficient and fatigue damage accumulation amount for each segment of the wellbore. The Monte Carlo stochastic simulation algorithm is a method for solving mathematical and physical problems through random sampling. In this embodiment, through multiple random samplings, the stress distribution of the wellbore under different load conditions is simulated, so as to obtain the stress concentration coefficient and fatigue damage accumulation amount for each segment. Each time of sampling, according to the statistical characteristics of historical load data, a set of load values are randomly generated and substituted into the wellbore dynamic response equation for calculation. After a large number of sampling calculations, statistical analysis is performed on the results to obtain the probability distributions of the stress concentration coefficient and fatigue damage accumulation amount, so as to more accurately evaluate the stress condition of the wellbore.

[0084] For example, when analyzing a wellbore in service, it is obtained that the axial tension range in the past year is 50 - 100 kN, the internal pressure fluctuation value is between 1 - 3 MPa, and the external extrusion pressure is 0.5 - 1.5 MPa. Through Fourier transform, it is found that the main load frequency components are concentrated between 0 - 10 Hz. The constructed wellbore dynamic response equation is:

[0085] Where represents the stress of the wellbore at the position and time , , , , are parameters related to the load spectral characteristics. After 10,000 sampling calculations using the Monte Carlo stochastic simulation algorithm, the average stress concentration coefficient of a key segment of the wellbore is 1.5, and the fatigue damage accumulation amount reaches 0.05 within one year. Based on these data, it can be judged that the structural integrity of this segment of the wellbore has been affected to a certain extent and needs to strengthen monitoring. Example 3:

[0086] This embodiment details the calculation process of the corrosion rate prediction value. Accurately predicting the corrosion rate of each segment of the wellbore is crucial for formulating a reasonable maintenance plan and taking timely anti-corrosion measures, which can effectively extend the service life of the wellbore and reduce maintenance costs.

[0087] Obtain the chemical composition data of the wellbore materials from the local database. This data details the content of various chemical components in the materials used for the wellbore, such as the proportions of iron, carbon, alloy elements, etc. At the same time, obtain the pH value and hydrogen sulfide concentration data of the environmental medium, which reflect the corrosiveness of the environment where the wellbore is located.

[0088] Calculate the basic corrosion rate using the electrochemical corrosion kinetics model. The electrochemical corrosion kinetics model is established based on electrochemical principles and is used to describe the corrosion process of metals in electrolyte solutions. This model takes into account the influence of factors such as the electrochemical properties of the materials, the chemical composition of the environmental medium, and temperature on the corrosion rate. By substituting the obtained chemical composition data of the wellbore materials and environmental medium data into the model, the basic corrosion rate is calculated.

[0089] Overlay the temperature gradient data and fluid flow rate data of the wellbore segments, and use the weighted attenuation factor to correct the basic corrosion rate. During the service life of the wellbore, the temperature and fluid flow rate in different segments will vary, and these factors will have an important impact on the corrosion rate. The temperature gradient data reflects the temperature change at different depths of the wellbore, and the fluid flow rate data reflects the flow velocity of the fluid in the wellbore. The weighted attenuation factor is determined based on actual engineering experience and experimental data and is used to reasonably adjust the influence degree of the temperature gradient and fluid flow rate on the corrosion rate. Through this correction method, the corrosion rate of each segment of the wellbore under actual working conditions can be predicted more accurately.

[0090] Suppose the carbon content in the material of a certain wellbore segment is 0.2%, the total amount of alloy elements is 5%, the pH value of the environmental medium is 5, and the hydrogen sulfide concentration is 100 ppm. The basic corrosion rate calculated by the electrochemical corrosion kinetics model is 0.1 mm / year. The temperature gradient of this wellbore segment is 0.05 °C / m, and the fluid flow rate is 1 m / s. According to the experimentally determined weighted attenuation factor, the weight of the temperature gradient is 0.4, and the weight of the fluid flow rate is 0.6. The predicted value of the corrected corrosion rate is; / year. Through this accurate corrosion rate prediction method, anti-corrosion measures for this wellbore segment can be planned in advance, such as regularly injecting corrosion inhibitors or replacing corrosion-resistant materials. Example 4:

[0091] This example details the generation process of the maintenance strategy library. The maintenance strategy library provides rich decision-making basis for the maintenance decision-making module, can quickly generate optimized maintenance plans according to different risk situations of the wellbore, and improve the efficiency and effect of maintenance work.

[0092] Retrieve historical maintenance record data from the local database. These data contain detailed information on past wellbore maintenance, such as maintenance types (including casing repair, corrosion treatment, pressure regulation, etc.), maintenance time consumption, maintenance costs, and maintenance effect indicators (such as the wellbore pressure returning to normal, corrosion rate decreasing, etc.).

[0093] Use the clustering analysis algorithm to divide the maintenance records into multiple strategy categories. The clustering analysis algorithm is an unsupervised learning algorithm that can divide data into different groups based on the similarity of the data. In this embodiment, by analyzing data in multiple dimensions such as maintenance types, maintenance time consumption, maintenance costs, and maintenance effect indicators, similar maintenance records are grouped into one category to form different strategy categories. For example, the maintenance records for minor corrosion are grouped into one category. This type of maintenance usually uses a simple method of injecting corrosion inhibitors, with short maintenance time consumption and low costs; the maintenance records for wellbore structural damage are grouped into another category. This type of maintenance may require complex operations such as casing replacement, with longer maintenance time consumption and higher costs.

[0094] Construct a multi-objective optimization function based on the strategy categories. The multi-objective optimization function takes maintenance costs, maintenance effects, and maintenance time, etc. as optimization objectives, aiming to seek a balance among multiple objectives and find the optimal maintenance strategy. For example, the constructed multi-objective optimization function can be expressed as:

[0095] where represents the maintenance cost, represents the maintenance effect indicator, represents the maintenance time.

[0096] Combine the genetic algorithm to solve the priority weights of each strategy category. The genetic algorithm is a random search algorithm that simulates the natural evolution process and has advantages such as strong global search ability and fast convergence speed. In this embodiment, use the genetic algorithm to solve the multi-objective optimization function. By simulating operations such as natural selection, crossover, and mutation, find the priority weights of each strategy category that make the multi-objective optimization function optimal. After multiple iterative calculations, obtain the priority weights of different strategy categories under different working conditions, thus completing the generation of the maintenance strategy library.

[0097] For example, when analyzing the wellbore historical maintenance records of a certain area, the maintenance records are divided into 5 strategy categories through the clustering analysis algorithm. After 100 generations of iterative calculation by the genetic algorithm for the multi-objective optimization function constructed based on these 5 strategy categories, it is obtained that under the condition of slight corrosion of the wellbore and normal pressure, the priority weight of the simple corrosion inhibitor injection strategy category is 0.8, while under the condition of serious damage to the wellbore structure and abnormal pressure, the priority weight of the combined maintenance strategy category of casing replacement and pressure regulation is 0.9. These priority weights provide an important basis for the maintenance decision-making module to quickly select appropriate maintenance strategies in practical applications. Example 5:

[0098] This example details the specific implementation steps of the spatio-temporal correlation algorithm and the risk warning module. The spatio-temporal correlation algorithm can effectively integrate multi-source data and discover cross-stage abnormal coupling effects. The risk warning module then issues risk warnings promptly and accurately based on this information, providing an important guarantee for ensuring the safe operation of the wellbore. The specific implementation steps include:

[0099] ① Implementation steps of the spatio-temporal correlation algorithm:

[0100] Encode the wellbore design parameters, construction monitoring data, and integrity assessment indicators into a four-dimensional tensor according to the time stamp and spatial coordinates. The time stamp records the time information of data acquisition, and the spatial coordinates clarify the position information of the data in the wellbore. Through this encoding method, different types of data are unified into a four-dimensional tensor space, facilitating subsequent data analysis and processing. For example, encode the pressure data, temperature data, and the wellbore wall thickness attenuation rate corresponding to this position collected at a depth of 1000m in the wellbore at a certain moment into a four-dimensional tensor element according to the time stamp and spatial coordinates.

[0101] Extract the abnormal patterns in the tensor through a convolutional neural network. A convolutional neural network is a deep learning model designed specifically for processing data such as images and has powerful feature extraction capabilities. In this example, input the encoded four-dimensional tensor into the convolutional neural network, and automatically extract the abnormal patterns through network structures such as convolutional layers, pooling layers, and fully connected layers. These abnormal patterns may manifest as abnormal correlation relationships between data in different stages, such as the correlation between abnormal vibrations during the construction stage and local deformations of the wellbore during the service stage.

[0102] Use the attention mechanism to allocate the correlation weights of different data sources. The attention mechanism enables the model to focus more on important information and ignore unimportant information when processing data. In the spatio-temporal correlation algorithm, the attention mechanism is used to calculate the importance of different data sources (such as design parameters, construction monitoring data, integrity assessment indicators) in identifying abnormal coupling effects and assign corresponding correlation weights. For example, if it is found that the corrosion situation of a certain wellbore area is closely related to the local temperature anomaly during the construction stage, then when analyzing the abnormal coupling effect in this area, a higher correlation weight will be assigned to the temperature data during the construction stage.

[0103] ② Steps for implementing the risk warning module:

[0104] Step S1: Receive the set of wellbore structure integrity assessment indicators , the set of predicted corrosion rate values and the multi-dimensional data correlation matrix , and these data come from the integrity assessment module, the corrosion prediction module, and the spatio-temporal correlation module respectively.

[0105] Step S2: Calculate the dynamic risk benchmark value using the following formula :

[0106] where is the normalized weight of each integrity index, determined according to the importance of each integrity index to the wellbore safety, and obtained through expert experience and historical data statistical analysis; and are the preset index thresholds, which are set according to the design standards and historical operation data of the wellbore and are used to judge the normal range of the integrity index; is the corrosion sensitivity coefficient, which reflects the sensitivity of the wellbore to corrosion in different regions or working conditions and is determined through experiments and theoretical analysis; is the material degradation time constant, which is related to the characteristics of the wellbore material and is obtained through material performance testing.

[0107] Step S3: Based on the multi-dimensional data correlation matrix , detect the intensity of the spatio-temporal abnormal coupling effect using the following formula :

[0108] where is the benchmark matrix predicted by the long short-term memory network LSTM. The long short-term memory network can effectively process time series data and learn the long-term dependence relationship of the data. By learning the historical multi-dimensional data correlation matrix, the benchmark matrix under normal conditions is predicted; is the standard deviation within the sliding time window, which is used to measure the degree of data fluctuation; is the matrix Frobenius norm, which is used to calculate the size of a matrix.

[0109] Step S4: Use the fuzzy logic rule base to and for joint mapping to generate a comprehensive risk level :

[0110] where , and , are piecewise thresholds fitted from historical accident data. The fuzzy logic rule base is established based on a large amount of historical data and expert experience. It can map continuous and values to discrete risk levels, making the risk assessment results more intuitive and easy to understand.

[0111] Step S5: Select a warning instruction set from the preset instruction mapping table according to the risk level , including the wellbore pressure reduction rate threshold, the injection volume of corrosion inhibitor, and the emergency maintenance response time parameter. The preset instruction mapping table is pre - formulated, corresponding to different warning instructions for different risk levels. After determining the risk level, the system can quickly obtain the corresponding instructions from the instruction mapping table to guide on - site workers to take correct measures and reduce the wellbore operation risk.

[0112] Meanwhile, calculate the spatio - temporal anomaly coupling effect intensity through the multi - dimensional data correlation matrix . Assume that the piecewise thresholds , , , are fitted from historical accident data. According to the fuzzy logic rule base, is greater than and less than , is greater than and less than , so the comprehensive risk level is level II. Select the corresponding warning instruction set from the preset instruction mapping table, such as setting the wellbore pressure reduction rate threshold to 0.05 MPa / min, the injection volume of corrosion inhibitor to 50 L / h, and the emergency maintenance response time parameter to 2 hours, and timely notify relevant personnel to take corresponding measures.

[0113] The present invention also includes a wellbore life - cycle management method, and the method includes:

[0114] Obtain the geological exploration data of the target area, and generate the initial wellbore design parameters based on the formation stress distribution model;

[0115] Collect the pressure, temperature, and vibration data during the construction stage in real time and dynamically compare them with the safety thresholds;

[0116] Calculate the stress concentration factor and the cumulative amount of fatigue damage during the service period of the wellbore through the dynamic load distribution algorithm;

[0117] Combine the environmental corrosion factor and the material fatigue accumulation model to predict the corrosion rate of the wellbore segments;

[0118] Generate a risk level signal based on the stress concentration factor and the corrosion rate, and match and optimize the maintenance plan;

[0119] Use the spatio-temporal correlation algorithm to fuse multi-source data and display the risk assessment results through a three-dimensional visualization model.

[0120] The implementation manner of this method refers to the above-mentioned embodiments and will not be elaborated in the description.

[0121] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0122] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wellbore life cycle management system, characterized in that: The system comprises: A wellbore design module is used to obtain geological exploration data of a target area, generate wellbore structure design parameters according to a preset formation stress distribution model, and mark the wellbore structure design parameters as an initial design parameter set; The construction monitoring module is used to collect the sensor data of the wellbore construction stage in real time, including the pressure value, temperature value and vibration intensity value in the wellbore, and dynamically compare the sensor data with the preset safe construction threshold range; The integrity assessment module is used to calculate the wellbore wall thickness attenuation rate and annular pressure deviation coefficient based on multi-source monitoring data during the wellbore service period, and generate wellbore structural integrity assessment indicators based on the dynamic load distribution algorithm; The corrosion prediction module is used to obtain the corrosion factor data of the wellbore environment and calculate the corrosion rate prediction value of each section of the wellbore in combination with the material fatigue accumulation model; The spatiotemporal correlation module is used to integrate wellbore design parameters, construction monitoring data and integrity assessment indicators, and generate a multidimensional data correlation matrix through a spatiotemporal correlation algorithm to identify abnormal coupling effects across stages; A risk warning module, for generating a risk level signal and a corresponding warning instruction set based on the wellbore structural integrity assessment index and the corrosion rate prediction value, combined with the abnormal coupling effect analysis result of the multidimensional data association matrix; The maintenance decision module is used to call the pre-stored maintenance strategy library according to the risk level signal to generate an optimized maintenance plan including maintenance time, maintenance location, and maintenance process; The specific calculation method of the corrosion rate prediction value includes: The chemical composition data of wellbore materials, pH value of environmental media and hydrogen sulfide concentration are obtained from the local database, and the basic corrosion rate is calculated through the electrochemical corrosion kinetic model; Superimposing wellbore segment temperature gradient data and fluid velocity data, and using a weighted attenuation factor to correct the basic corrosion rate; The implementation steps of the risk warning module include: Step S1: Receive a set of wellbore structural integrity assessment indicators , Corrosion rate prediction value set and multidimensional data association matrix ,in is the time dimension index, is the spatial coordinate index; Step S2: Calculate the dynamic risk benchmark value using the following formula: : in is the normalized weight of each integrity indicator, and is the preset indicator threshold, is the corrosion sensitivity coefficient, is the material degradation time constant; Step S3: Based on the multidimensional data association matrix , the intensity of the space-time anomaly coupling effect A is detected by the following formula: ； in is the benchmark matrix predicted by the long short-term memory network LSTM, is the standard deviation within the sliding time window, is the matrix Frobenius norm; Step S4: Use the fuzzy logic rule base to and Perform joint mapping to generate a comprehensive risk level : ； in , and , is the segmentation threshold fitted by historical accident data; Step S5: Based on risk level Select the warning instruction set from the preset instruction mapping table, including the parallel barrel pressure reduction rate threshold, corrosion inhibitor injection amount and emergency maintenance response time parameters.

2. The wellbore life cycle management system according to claim 1, characterized in that: The specific construction method of the formation stress distribution model includes: Obtain historical geological parameters of the target area from the local database, including rock density, pore pressure gradient, and geostress direction angle, and generate a formation stress distribution grid using a three-dimensional finite element discretization algorithm; According to the well depth and well diameter in the wellbore design parameters, the equivalent stress value of each grid node is calculated, and the initial design parameter set is corrected based on the maximum shear stress criterion.

3. The wellbore life cycle management system according to claim 2, characterized in that: The specific calculation method of the dynamic load distribution algorithm is: Obtain historical load data during the service life of the wellbore from the local database, including axial tension, internal pressure fluctuation value, and external extrusion force, and extract load spectrum characteristics through Fourier transform; The wellbore dynamic response equation is constructed based on the frequency spectrum characteristics, and combined with the Monte Carlo random simulation algorithm, the stress concentration factor and fatigue damage accumulation of each wellbore segment are generated.

4. The wellbore life cycle management system according to claim 3, characterized in that: The method for generating the maintenance strategy library is: Obtain historical maintenance record data from the local database, including maintenance type, time consumption, cost, and effect indicators, and divide the maintenance records into multiple strategy categories through cluster analysis algorithms; A multi-objective optimization function is constructed based on the strategy categories, and the priority weights of each strategy category are solved by combining genetic algorithm.

5. The wellbore life cycle management system according to claim 4, characterized in that: The specific implementation steps of the spatiotemporal correlation algorithm include: The wellbore design parameters, construction monitoring data, and integrity assessment indicators are encoded into a four-dimensional tensor according to timestamps and spatial coordinates; Anomalous patterns in tensors are extracted through convolutional neural networks, and the attention mechanism is used to assign association weights to different data sources.

6. The wellbore life cycle management system according to claim 1, characterized in that: Also includes a user interaction module, the user interaction module further includes: The wellbore 3D reconstruction unit is used to generate a dynamic 3D model of the wellbore structure based on design parameters and real-time monitoring data, and visualize stress concentration areas and high-risk corrosion areas through a color mapping algorithm.

7. The wellbore life cycle management system according to claim 6, characterized in that: The specific implementation of the color mapping algorithm is as follows: Normalize the stress concentration factor and corrosion rate prediction values ​​to values ​​in the range of 0-1; Through HSV color space conversion, the normalized value is mapped into a gradient color sequence from green to red and superimposed on the surface of the three-dimensional model.

8. A wellbore lifecycle management method, applied to the wellbore lifecycle management system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Obtain geological exploration data of the target area and generate initial wellbore design parameters based on the formation stress distribution model; Collect pressure, temperature, and vibration data in real time during the construction phase, and dynamically compare them with safety thresholds; The stress concentration factor and fatigue damage accumulation during the service life of the wellbore are calculated by dynamic load distribution algorithm; Combine environmental corrosion factors and material fatigue accumulation models to predict the corrosion rate of wellbore segments; Generate risk level signals based on stress concentration factor and corrosion rate, and match optimized maintenance plans; The spatiotemporal correlation algorithm is used to fuse multi-source data, and the risk assessment results are displayed through a three-dimensional visualization model.

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