Shaft life cycle management system and method

Abstract

The invention relates to the technical field of petroleum and natural gas engineering, and discloses a wellbore life cycle management system and method, and the system comprises multiple modules of wellbore design, construction monitoring, integrity evaluation and the like. Accurate design parameters are generated by obtaining geological exploration data, multi-sensor data are collected in real time to guarantee construction safety, integrity is evaluated according to multi-source data, the corrosion rate is predicted, a time-space correlation algorithm is used for integrating data to recognize an abnormal coupling effect, then a risk level signal and an early warning instruction set are generated, and an optimization maintenance scheme is formulated. According to the method, the design accuracy and stability of the shaft can be remarkably improved, the condition of the shaft is comprehensively and accurately evaluated, corrosion is accurately predicted, and risk early warning and maintenance decision making are scientifically carried out. Meanwhile, the management efficiency is improved through visual display, many problems of traditional shaft management are effectively solved, the operation and maintenance cost is reduced, and the safety and economical efficiency of energy exploitation are improved.

Description

Technical Field

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

[0002] In the field of energy extraction such as oil and natural gas, wellbore is the key channel connecting underground resources with ground facilities. Its safe and stable operation throughout its life cycle is crucial to the efficient extraction of energy and the continued development of production activities. However, wellbore management currently faces many severe challenges, and existing technologies have obvious shortcomings in dealing with these problems.

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

[0004] During the wellbore construction process, construction safety is directly related to the progress and quality of the entire project. At present, construction monitoring mainly focuses on the monitoring of some key parameters, such as the pressure value in the wellbore, but the monitoring of other important parameters such as temperature and vibration intensity is not comprehensive or real-time enough. When these parameters exceed the safety range, they cannot be discovered and measures cannot be taken in time, which can easily lead to construction accidents such as blowouts and collapses. In addition, the existing monitoring systems often analyze the data of each sensor in isolation, lack comprehensive consideration of the dynamic relationship between multiple parameters, cannot accurately judge the complex conditions in the construction process, and are difficult to warn of potential risks in advance.

[0005] During the long-term service, the wellbore is affected by many complex factors, such as internal fluid pressure fluctuations, external formation stress changes, corrosion, etc., and its structural integrity will gradually be threatened. Existing integrity assessment methods are usually based on single or a few monitoring data for analysis, which is difficult to fully reflect the true condition of the wellbore. For example, relying solely on the measurement data of the wellbore wall thickness to assess integrity ignores the impact of annular pressure changes and dynamic loads on the wellbore structure, which may lead to misjudgment of the safety status of the wellbore. At the same time, when considering dynamic loads, the existing assessment methods lack accurate calculation models, cannot accurately predict stress concentration and fatigue damage in various parts of the wellbore, and are 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 and seriously affect the service life of the wellbore. Most of the existing corrosion prediction methods are based on simple empirical formulas or static models, and fail to fully consider the combined effects of factors such as the dynamic changes of wellbore material properties, environmental corrosion factors, and material fatigue accumulation. For example, the effects of temperature, pressure, and fluid flow rate at different depths on the corrosion rate are ignored, resulting in a large deviation between the predicted corrosion rate and the actual situation. This makes the lack of scientific basis for the formulation of anti-corrosion measures, either excessive anti-corrosion measures cause waste of resources, or insufficient anti-corrosion measures lead to premature corrosion and damage of the wellbore.

[0007] A large amount of data is generated in each stage of the wellbore from design, construction to service, but there is currently a lack of effective technical means to integrate and analyze this data. Data from different stages are often stored in different systems, forming data islands, and it is impossible to achieve cross-stage data association and collaborative analysis. This makes it difficult to discover the potential connections and abnormal coupling effects between data at different stages, and it is impossible to comprehensively manage the wellbore from a full life cycle perspective. For example, local defects in the construction stage may interact with specific environmental factors in the service stage, causing serious safety problems, but due to the lack of data integration and analysis, it is difficult to identify and prevent this situation in advance. Due to the shortcomings 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 it is impossible to comprehensively consider the interaction of multiple risk factors. The warning results are inaccurate and prone to omissions or false alarms. In terms of maintenance decision-making, there is a lack of scientific maintenance strategy library and optimization methods. Maintenance plans are usually formulated based on experience, which makes it difficult to achieve accurate maintenance. For example, it is impossible to reasonably determine the maintenance time, maintenance location and maintenance process based on the actual risk status of the wellbore, resulting in high maintenance costs and poor results. Summary of the invention

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

[0009] To achieve the above object, the present invention provides the following technical solution: a wellbore life cycle management system, the system comprising: 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.

[0010] Preferably, the specific method for constructing 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.

[0011] Preferably, 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 section of the wellbore are generated.

[0012] Preferably, 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; The wellbore segment temperature gradient data and fluid flow rate data are superimposed, and the basic corrosion rate is corrected using a weighted attenuation factor.

[0013] Preferably, 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.

[0014] Preferably, 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.

[0015] Preferably, 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 spatiotemporal anomaly coupling effect 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: According to the 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.

[0016] Preferably, it also includes a user interaction module, and 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.

[0017] Preferably, the specific implementation of the color mapping algorithm is: 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.

[0018] Preferably, the present invention further comprises a wellbore life cycle management method, comprising the following steps: 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.

[0019] Compared with the prior art, the present invention has the following beneficial effects: 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. Unlike traditional design methods that rely 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 ground stress direction angle obtained from the local database through a three-dimensional finite element discretization algorithm, and then calculates the equivalent stress value of each grid node in combination with the well depth and well diameter, and corrects the initial design parameter set based on the maximum shear stress criterion. This makes the designed wellbore structure more in line with actual geological conditions, effectively avoids problems such as wellbore rupture and deformation caused by unreasonable design, significantly improves the stability of the wellbore during service, and reduces safety risks.

[0020] The construction monitoring module collects multi-sensor data such as pressure, temperature, and vibration intensity in the wellbore in real time, and dynamically compares it with the preset safe construction threshold range. This comprehensive and real-time monitoring method overcomes the shortcomings of traditional monitoring that only focuses on some key parameters, lacks real-time performance, and analyzes multiple parameters in isolation. Once the data exceeds the threshold range, the early warning mechanism can be triggered in time to remind construction personnel to take corresponding measures, effectively preventing the occurrence of construction accidents such as blowouts and collapses, effectively ensuring the safety of the construction process and ensuring the smooth progress of the project.

[0021] The integrity assessment module calculates key indicators such as the wellbore wall thickness attenuation rate and annular pressure deviation coefficient based on multi-source monitoring data during the service life of the wellbore, and generates wellbore structural integrity assessment indicators with the help of a dynamic load distribution algorithm. The 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 stress concentration factors and fatigue damage accumulation, which fully and accurately reflects the true condition of the wellbore. Compared with traditional single or a few data evaluation methods, it can more accurately predict stress concentration and fatigue damage in various parts of the wellbore, timely discover potential structural defects, and provide a reliable basis for taking maintenance measures in advance.

[0022] The corrosion prediction module comprehensively considers the corrosion factor data of the wellbore environment and the material fatigue accumulation model to calculate the corrosion rate prediction value. By obtaining the chemical composition 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 kinetic model, and the wellbore segment temperature gradient data and fluid flow rate data are superimposed, and the weighted attenuation factor is used for correction. This method fully considers the impact of various factors on the corrosion rate, and the prediction accuracy is greatly improved compared to the traditional simple empirical formula or static model. Based on accurate corrosion prediction results, more scientific and reasonable anti-corrosion strategies can be formulated to avoid excessive or insufficient anti-corrosion, effectively extend the service life of the wellbore, and reduce maintenance costs.

[0023] The spatiotemporal correlation module integrates wellbore design parameters, construction monitoring data, and integrity assessment indicators, and generates a multidimensional data correlation matrix through a spatiotemporal correlation algorithm, which can identify abnormal coupling effects across stages. This breaks the traditional mode of isolated storage and analysis of data at 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 local defects in the construction stage interact with specific environmental factors during the service stage to cause safety problems, conduct risk intervention in advance, and improve the overall management level of the wellbore.

[0024] The risk warning module generates risk level signals and corresponding warning instruction sets based on the wellbore structural integrity assessment indicators, corrosion rate prediction values, and abnormal coupling effect analysis results of the multidimensional data association matrix. By calculating the dynamic risk baseline value, detecting the intensity of the spatiotemporal abnormal coupling effect, and using the fuzzy logic rule library to generate a comprehensive risk level, the risk warning is made more scientific and accurate, effectively reducing missed reports and false alarms. The maintenance decision module calls the pre-stored maintenance strategy library according to the risk level signal, generates an optimized maintenance plan, and clarifies the maintenance time, maintenance location, and maintenance process. This scientific risk warning and maintenance decision-making mechanism achieves precise maintenance, improves maintenance efficiency, reduces operation and maintenance costs, and ensures the safe and stable operation of the wellbore.

[0025] The wellbore 3D reconstruction unit in the user interaction module generates a dynamic 3D model of the wellbore structure based on design parameters and real-time monitoring data, and visualizes stress concentration areas and high-risk corrosion areas through a color mapping algorithm. This function presents complex data in an intuitive and easy-to-understand 3D model, making it easier for managers to quickly understand the operating status of the wellbore, identify potential risk points in a timely manner, and provide an intuitive basis for decision-making, greatly improving the efficiency and accuracy of wellbore management. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a working principle diagram of the wellbore life cycle management system of the present invention; Figure 2 Flow chart for constructing formation stress distribution model; Figure 3 A step-by-step diagram of the specific calculation method for the corrosion rate prediction value. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1-3The present invention provides a technical solution: a wellbore life cycle management system, the method comprising: Wellbore design module: The geological exploration data of the target area are obtained from various geological exploration databases or exploration equipment. These data cover key information such as rock density, pore pressure gradient, and ground stress direction angle. The acquired data is input into the preset formation stress distribution model, and the wellbore structure design parameters such as well depth, well diameter, number of casing layers and material are generated through the calculation of the model, and these parameters are marked as the initial design parameter set.

[0029] Construction monitoring module: During the wellbore construction phase, various monitoring devices such as pressure sensors, temperature sensors, and vibration sensors are installed at different locations 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. The data processing center dynamically compares the received sensor data with the preset safe construction threshold range. Once the data is found to exceed the threshold range, the corresponding early warning mechanism is immediately triggered to remind the construction personnel to take measures.

[0030] Integrity assessment module: During the service period of the wellbore, multi-source monitoring data from various monitoring equipment are collected, including but not limited to pressure monitoring data, temperature monitoring data, displacement monitoring data, etc. These data are used to calculate key indicators such as the wellbore wall thickness attenuation rate and annular pressure deviation coefficient, and the wellbore structural integrity assessment index is generated based on the dynamic load distribution algorithm to judge the structural integrity status of the wellbore during service.

[0031] Corrosion prediction module: Obtain the corrosion factor data of the wellbore environment, such as the pH value of the environmental medium, hydrogen sulfide concentration, chemical composition data of the wellbore material, etc. Combined with the material fatigue accumulation model, the corrosion rate prediction value of each section of the wellbore is calculated through a specific calculation method, providing an important basis for subsequent maintenance decisions.

[0032] Spatiotemporal correlation module: Integrate multi-source data such as wellbore design parameters, construction monitoring data and integrity assessment indicators. Encode these data by timestamp and spatial coordinates, and generate a multidimensional data correlation matrix through the spatiotemporal correlation algorithm. This matrix can effectively identify abnormal coupling effects across stages, that is, abnormal correlations between data at different stages, so as to detect potential problems in a timely manner.

[0033] Risk warning module: Based on the wellbore structural integrity assessment index and corrosion rate prediction value, combined with the abnormal coupling effect analysis results of the multi-dimensional data association matrix, a risk level signal is generated using a specific calculation model and logic rules. According to the risk level signal, the corresponding warning instruction set is selected from the preset instruction mapping table, such as the wellbore depressurization rate threshold, corrosion inhibitor injection volume, and emergency maintenance response time parameters, to issue a warning to relevant personnel in a timely manner.

[0034] Maintenance decision module: According to the risk level signal generated by the risk warning module, the pre-stored maintenance strategy library is called. The maintenance strategy library stores a variety of maintenance strategies. Through a specific algorithm, an optimized maintenance plan including maintenance time, maintenance location, and maintenance process is selected and generated from the maintenance strategy library to guide the operation and maintenance personnel to perform wellbore maintenance work.

[0035] The present invention will be further described below in conjunction with Examples 1 to 5: Embodiment 1:

[0036] This embodiment describes in detail 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 meet the geological conditions of the target area, and improves the stability and safety of the wellbore. Specifically, it includes: The historical geological parameters of the target area are obtained from the local database, which stores the data accumulated from past geological exploration in the area. The rock 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 geostress direction angle data clarifies the direction of geostress in space.

[0037] The obtained geological parameters are processed using a three-dimensional finite element discretization algorithm. The algorithm divides the strata in the target area into many tiny three-dimensional grid cells, each of which represents a tiny part of the stratum. By analyzing and calculating the mechanical properties of these grid cells, a stratum stress distribution grid is generated. When dividing the grid, the density of the grid is reasonably determined according to the complexity of the stratum and the calculation accuracy requirements to ensure the accuracy of the calculation results.

[0038] After generating the formation stress distribution grid, further calculations are performed based on the well depth and diameter in the wellbore design parameters. Well depth and diameter are important parameters in wellbore design and have a significant impact on the formation stress distribution. The equivalent stress value of each grid node is calculated using a specific calculation formula that comprehensively considers factors such as rock density, pore pressure gradient, geostress direction angle, and well depth and diameter.

[0039] The initial design parameter set is modified based on the maximum shear stress criterion. The maximum shear stress criterion is an important criterion in material mechanics, which is used to determine whether a material will be damaged under a complex stress state. When the calculated equivalent stress value of the grid node exceeds the threshold determined by the maximum shear stress criterion, it means that the formation stress in the area may have an adverse effect 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, to improve the bearing capacity of the wellbore.

[0040] In practical applications, taking the wellbore design in a specific area as an example, the rock density range in this area is 2.2-2.8g / cm³, the pore pressure gradient is 0.009-0.012MPa / m, and the ground stress direction angle is between 30°-40° north-east. The formation stress distribution grid generated by the three-dimensional finite element discretization algorithm has a grid unit size of 1m×1m×1m. According to the equivalent stress values ​​of each grid node calculated based on the well depth of 3000m and the well diameter of 0.2m, it was found that the equivalent stress values ​​of some grid nodes close to the wellhead were close to the destruction threshold after judging by the maximum shear stress criterion. Therefore, the casing material of the initial design was upgraded from ordinary carbon steel casing to high-strength alloy steel casing, which effectively improved the safety of the wellbore. Embodiment 2:

[0041] This embodiment describes in detail the calculation process of the dynamic load distribution algorithm, through which the stress concentration factor and fatigue damage accumulation of each segment during the service of the wellbore can be accurately obtained.

[0042] The historical load data of the wellbore during its service period is obtained from the local database, which stores the axial tension, internal pressure fluctuation value, external extrusion pressure and other data borne by the wellbore in different time periods in the past. These data are monitored and recorded for a long time by various sensors installed on the wellbore, and have high accuracy and reliability.

[0043] Fourier transform is used to process the acquired historical load data. Fourier transform is a powerful mathematical tool that can convert time domain signals into frequency domain signals to extract load spectrum characteristics. Through Fourier transform, we can clearly understand the proportion of loads with different frequency components in the total load and their changes over time.

[0044] The wellbore dynamic response equation is constructed based on the extracted spectral features. This equation describes the mechanical response characteristics of the wellbore when subjected to loads of different frequencies, and comprehensively considers factors such as the material properties, geometric shape, and boundary conditions of the wellbore. When constructing the equation, relevant theories such as material mechanics and structural dynamics are used, combined with actual engineering experience, to ensure that the equation can accurately reflect the dynamic response of the wellbore.

[0045] Combined with the Monte Carlo random simulation algorithm, the stress concentration factor and fatigue damage accumulation of each section of the wellbore are generated. The Monte Carlo random simulation algorithm is a method for solving mathematical and physical problems through random sampling. In this embodiment, multiple random samplings are performed to simulate the stress distribution of the wellbore under different load conditions, so as to obtain the stress concentration factor and fatigue damage accumulation of each section. Each time a sample is drawn, a set of load values ​​is randomly generated according to the statistical characteristics of the historical load data, and substituted into the wellbore dynamic response equation for calculation. After a large number of sampling calculations, the results are statistically analyzed to obtain the probability distribution of the stress concentration factor and the fatigue damage accumulation, so as to more accurately evaluate the stress condition of the wellbore.

[0046] For example, when analyzing a wellbore in service, the axial tension range in the past year was 50-100 kN, the internal pressure fluctuation value was between 1-3 MPa, and the external extrusion pressure was 0.5-1.5 MPa. Through Fourier transform, the main load frequency components were concentrated between 0-10 Hz. The wellbore dynamic response equation constructed is: in Indicates that the wellbore is Location, time The stress at , , , It is a parameter related to the load spectrum characteristics. After 10,000 sampling calculations using the Monte Carlo random simulation algorithm, the average stress concentration factor of a key section of the wellbore was obtained to be 1.5, and the cumulative amount of fatigue damage reached 0.05 within one year. Based on these data, it can be judged that the structural integrity of the wellbore in this section has been affected to a certain extent, and monitoring needs to be strengthened. Embodiment 3:

[0047] This embodiment introduces in detail the calculation process of the corrosion rate prediction value. Accurately predicting the corrosion rate of each section of the wellbore is crucial for formulating a reasonable maintenance plan and taking anti-corrosion measures in a timely manner. It can effectively extend the service life of the wellbore and reduce maintenance costs.

[0048] The chemical composition data of the wellbore materials are obtained from the local database. This data records in detail the content of various chemical components in the materials used in the wellbore, such as the proportion of iron, carbon, alloy elements, etc. At the same time, the pH value and hydrogen sulfide concentration data of the environmental medium are obtained. These data reflect the corrosiveness of the environment in which the wellbore is located.

[0049] The basic corrosion rate is calculated using the electrochemical corrosion kinetic model. The electrochemical corrosion kinetic model is based on the principle of electrochemistry and is used to describe the corrosion process of metals in electrolyte solutions. The model takes into account the effects of factors such as the electrochemical properties of the material, the chemical composition of the environmental medium, and temperature on the corrosion rate. The basic corrosion rate is calculated by substituting the acquired chemical composition data of the wellbore material and the environmental medium data into the model.

[0050] The temperature gradient data and fluid flow rate data of the wellbore segments are superimposed, and the basic corrosion rate is corrected using the weighted attenuation factor. During the service of the wellbore, the temperature and fluid flow rate of different segments will be different, and these factors will have an important impact on the corrosion rate. The temperature gradient data reflects the temperature changes at different depths of the wellbore, and the fluid flow rate data reflects the flow rate 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 degree of influence of 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 more accurately predicted.

[0051] Assume that the carbon content in a certain section of wellbore material 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 100ppm. The basic corrosion rate calculated by the electrochemical corrosion kinetic model is 0.1mm / year. The temperature gradient of this section of wellbore is 0.05℃ / m, and the fluid flow rate is 1m / s. According to the weighted attenuation factor determined experimentally, the weight of the temperature gradient is 0.4 and the weight of the fluid flow rate is 0.6. The corrected corrosion rate prediction value is; / year. With this accurate corrosion rate prediction method, anti-corrosion measures for this section of the wellbore can be planned in advance, such as regular injection of corrosion inhibitors or replacement of corrosion-resistant materials. Embodiment 4:

[0052] This embodiment describes in detail the generation process of the maintenance strategy library, which provides a rich basis for decision-making for the maintenance decision module, and can quickly generate optimized maintenance plans according to different risk conditions of the wellbore, thereby improving the efficiency and effectiveness of maintenance work.

[0053] Historical maintenance record data is obtained from the local database. This data contains detailed information on past wellbore maintenance, such as maintenance type (including casing repair, corrosion treatment, pressure regulation, etc.), maintenance time, maintenance cost, and maintenance effect indicators (such as wellbore pressure returning to normal, corrosion rate reduction, etc.).

[0054] The maintenance records are divided into multiple strategy categories using a cluster analysis algorithm. The cluster 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 type, maintenance time, maintenance cost, and maintenance effect index, similar maintenance records are classified into one category to form different strategy categories. For example, maintenance records for minor corrosion are classified into one category. This type of maintenance usually uses a simple corrosion inhibitor injection method, which is time-consuming and low-cost; maintenance records for wellbore structural damage are classified into another category. This type of maintenance may require complex operations such as casing replacement, which is time-consuming and costly.

[0055] A multi-objective optimization function is constructed based on the strategy category. The multi-objective optimization function takes maintenance cost, maintenance effect and maintenance time as optimization objectives, and aims to find a balance between multiple objectives and find the optimal maintenance strategy. For example, the constructed multi-objective optimization function can be expressed as: in represents the maintenance cost, Indicates the maintenance effect index. Indicates maintenance time.

[0056] Combined with genetic algorithm, the priority weight of each strategy category is solved. Genetic algorithm is a random search algorithm that simulates the natural evolution process, which has the advantages of strong global search capability and fast convergence speed. In this embodiment, the genetic algorithm is used to solve the multi-objective optimization function, and the priority weight of each strategy category that makes the multi-objective optimization function optimal is found by simulating natural selection, crossover and mutation operations. After multiple iterative calculations, the priority weights of different strategy categories under different working conditions are obtained, thereby completing the generation of the maintenance strategy library.

[0057] For example, when analyzing the historical maintenance records of wellbore in a certain area, the maintenance records were divided into five strategy categories through cluster analysis algorithm. After 100 generations of iterative calculations of the multi-objective optimization function constructed based on these five strategy categories, it was found that under the condition of slight corrosion of the wellbore and normal pressure, the priority weight of the simple corrosion inhibitor injection strategy category was 0.8, while under the condition of severe damage to the wellbore structure and abnormal pressure, the priority weight of the casing replacement and pressure regulation joint maintenance strategy category was 0.9. These priority weights provide an important basis for the maintenance decision module to quickly select appropriate maintenance strategies in practical applications. Embodiment 5:

[0058] This embodiment describes in detail the specific implementation steps of the spatiotemporal correlation algorithm and the risk warning module. The spatiotemporal correlation algorithm can effectively integrate multi-source data and discover abnormal coupling effects across stages. The risk warning module issues risk warnings in a timely and accurate manner based on this information, providing important guarantees for the safe operation of the wellbore. The specific implementation steps include: ① Steps to implement the spatiotemporal correlation algorithm: The wellbore design parameters, construction monitoring data, and integrity assessment indicators are encoded into a four-dimensional tensor according to timestamps and spatial coordinates. The timestamp records the time information of data collection, and the spatial coordinates clarify the location information of the data in the wellbore. Through this encoding method, different types of data are unified into a four-dimensional tensor space, which is convenient for subsequent data analysis and processing. For example, the pressure data, temperature data, and wellbore wall thickness attenuation rate corresponding to the location collected at a wellbore depth of 1000m at a certain moment are encoded into a four-dimensional tensor element according to the timestamp and spatial coordinates.

[0059] Abnormal patterns in tensors are extracted through convolutional neural networks. Convolutional neural networks are deep learning models designed specifically for processing data such as images, and have powerful feature extraction capabilities. In this embodiment, the encoded four-dimensional tensor is input into a convolutional neural network, and abnormal patterns are automatically extracted through network structures such as convolutional layers, pooling layers, and fully connected layers. These abnormal patterns may appear as abnormal correlations between data at different stages, such as the correlation between abnormal vibrations in the construction stage and local deformation of the wellbore in the service stage.

[0060] The attention mechanism is used to assign association weights to different data sources. The attention mechanism enables the model to pay more attention to important information and ignore minor information when processing data. In the spatiotemporal association algorithm, the importance of different data sources (such as design parameters, construction monitoring data, and integrity assessment indicators) in identifying abnormal coupling effects is calculated through the attention mechanism, and corresponding association weights are assigned to them. For example, if it is found that the wellbore corrosion in a certain 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 association weight will be assigned to the temperature data during the construction stage.

[0061] ② Risk warning module implementation steps: Step S1: Receive a set of wellbore structural integrity assessment indicators , Corrosion rate prediction value set and multidimensional data association matrix ,These data come from the integrity assessment module, corrosion prediction module and spatiotemporal correlation module respectively.

[0062] Step S2: Calculate the dynamic risk benchmark value using the following formula: : in is the normalized weight of each integrity indicator, which is determined according to the importance of each integrity indicator to wellbore safety and is obtained through expert experience and statistical analysis of historical data; and These are preset indicator thresholds, which are set according to the wellbore design standards and historical operating data and are used to determine the normal range of integrity indicators; is the corrosion sensitivity coefficient, which reflects the sensitivity of the wellbore to corrosion in different areas 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.

[0063] Step S3: Based on the multidimensional data association matrix , the intensity of the spatiotemporal anomaly coupling effect is detected by the following formula : in The benchmark matrix predicted by the long short-term memory network LSTM can effectively process time series data, learn the long-term dependencies of data, and predict the benchmark matrix under normal circumstances by learning the historical multi-dimensional data association matrix; is the standard deviation within the sliding time window, which is used to measure the volatility of the data; is the matrix Frobenius norm, which is used to calculate the size of the matrix.

[0064] 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. The fuzzy logic rule base is established based on a large amount of historical data and expert experience. and The values ​​are mapped to discrete risk levels, making the risk assessment results more intuitive and easy to understand.

[0065] Step S5: According to the risk level Select the warning instruction set from the preset instruction mapping table, including the wellbore pressure reduction rate threshold, corrosion inhibitor injection volume and emergency maintenance response time parameters. The preset instruction mapping table is pre-established, and different warning instructions correspond to different risk levels. When the risk level is determined, the system can quickly obtain the corresponding instructions from the instruction mapping table to guide on-site staff to take correct measures to reduce the risk of wellbore operation.

[0066] At the same time, through the multidimensional data association matrix The intensity of the space-time anomaly coupling effect is calculated Assume that the segmentation threshold is obtained by fitting historical accident data , , , , according to the fuzzy logic rule base, Greater than and less than , Greater than and less than , so the overall risk level It 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 0.05MPa / min, the corrosion inhibitor injection volume to 50L / h, and the emergency maintenance response time parameter to 2 hours, and promptly notify relevant personnel to take corresponding measures.

[0067] The present invention also includes a wellbore life cycle management method, the method comprising: 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.

[0068] The implementation of this method refers to the above embodiment and will not be described in detail in the specification.

[0069] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0070] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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.

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 section of the wellbore are generated.

4. The wellbore life cycle management system according to claim 3, characterized in that: 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; The wellbore segment temperature gradient data and fluid flow rate data are superimposed, and the basic corrosion rate is corrected using a weighted attenuation factor.

5. The wellbore life cycle management system according to claim 4, 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.

6. The wellbore life cycle management system according to claim 5, 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.

7. The wellbore life cycle management system according to claim 6, characterized in that: 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 spatiotemporal anomaly coupling effect 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.

8. The wellbore life cycle management system according to claim 7, 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.

9. The wellbore life cycle management system according to claim 8, 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.

10. A wellbore life cycle management method, 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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