Well drilling parameter optimization method of oil and gas well

Abstract

The invention discloses a drilling parameter optimization method for an oil and gas well, and relates to the technical field of oil and gas well drilling, and the method specifically comprises the following steps: identifying whether an expansive clay layer exists in a current drilling area, and determining the position, thickness and activity characteristics of the expansive clay layer under the condition that the expansive clay layer exists in the current drilling area; dynamic change information caused by expansion of the clay layer in the drilling process is collected in real time, and the initial time period from the time when the clay layer meets water to the time when the clay layer starts to rapidly expand is determined; the collected dynamic change information is analyzed, and the influence level on drilling operation in the initial period of clay layer expansion is evaluated; and according to the evaluation result, analyzing the instant influence of the clay layer expansion on the shaft pressure, the drill bit operation load and the slurry flowing condition, and taking corresponding solutions according to the analysis result. The problem of drilling instability caused by clay layer expansion is solved, and drilling parameters are optimized in real time.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas well drilling, and in particular to a method for optimizing drilling parameters of an oil and gas well. Background Art

[0002] The drilling of oil and gas wells is a crucial initial step in the process of oil and gas extraction, which aims to form a wellbore by drilling into the formation in order to extract the oil and gas resources underground. The drilling process not only involves mechanical operations to penetrate the formation, but also needs to ensure the stability and safety of the wellbore to prevent accidents such as blowouts and well collapse. During the drilling process, multiple key parameters need to be closely monitored and adjusted, including drilling pressure (force acting on the drill bit), drilling speed (speed at which the drill bit penetrates the formation), mud density (density of mud used to cool and lubricate the drill bit), mud flow (mud circulation rate), drill bit torque (torque required for drill bit rotation), downhole pressure and temperature, etc. These parameters directly affect the efficiency, cost and safety of drilling. The purpose of optimizing drilling parameters is to maximize drilling efficiency and minimize drilling costs under complex geological conditions by accurately adjusting these parameters, while ensuring the safety and stability of the wellbore, so as to achieve the purpose of completing the drilling task efficiently and safely. Optimized drilling parameters can reduce non-productive time, lower the incidence of mechanical failures, and increase the recovery rate of oil and gas resources, making the entire drilling process more economical and controllable.

[0003] Existing oil and gas well drilling parameter optimization technologies mainly collect key parameter data in the drilling process in real time, such as drilling pressure, drilling speed, mud density, mud flow, etc., use data preprocessing technology to clean and align these data, and build complex mathematical models or machine learning algorithms to analyze the relationship between parameters and their impact on drilling results. Optimization technology sets specific drilling goals, such as reducing drilling costs or improving wellbore stability, and uses iterative optimization algorithms (such as genetic algorithms, simulated annealing, etc.) to generate the optimal parameter combination and verify its feasibility in a virtual environment or simulated well. Ultimately, through real-time monitoring and feedback systems, the parameters in the actual drilling process are dynamically adjusted to ensure efficient and safe drilling operations under changing geological conditions, thereby achieving comprehensive optimization of drilling efficiency, cost and safety.

[0004] The prior art has the following deficiencies:

[0005] When drilling in formations containing highly active swelling clay layers, when the clay layer first encounters water, it will expand rapidly within a few minutes, resulting in a reduction in the wellbore diameter, a sharp increase in wellbore pressure, and even blockage of the fluid channel. Because this expansion occurs extremely quickly, existing oil and gas well drilling parameter optimization technologies usually rely on a long data acquisition and analysis cycle, and are unable to adjust the drilling speed and mud flow in time at the beginning of the expansion, resulting in the drilling parameters failing to quickly adapt to the expansion changes. In this case, the wellbore may quickly become blocked, the drill bit may get stuck or the wellbore may deform, the mud flow may be blocked, and the wellbore may not be thoroughly cleaned, leading to wellbore instability or even wellbore collapse, which not only seriously threatens the safety of drilling operations, but may also force operations to be interrupted, increasing overall costs and non-productive time.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method for optimizing drilling parameters of oil and gas wells to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for optimizing drilling parameters of oil and gas wells, specifically comprising the following steps:

[0009] Identify, through geological data analysis and real-time logging data, whether there is an expansive clay layer in the current drilling area, and if an expansive clay layer is identified in the current drilling area, determine its location, thickness and activity characteristics, and after determination, perform the following steps;

[0010] Real-time collection of dynamic change information caused by clay layer expansion during drilling, and determination of the initial period from when the clay layer encounters water to when it begins to expand rapidly;

[0011] After determining the initial period from when the clay layer meets water to when it begins to expand rapidly, analyze the acquired dynamic change information to assess the level of impact on drilling operations during the initial period of clay layer expansion;

[0012] Based on the evaluation results, analyze the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions, and take corresponding solutions based on the analysis results;

[0013] Continuously monitor the overall stability of the wellbore, and monitor the wellbore pressure and wellbore deformation during the critical stage after the drilling rate and mud flow are adjusted;

[0014] Based on real-time monitoring and feedback information, the optimization model is dynamically updated, and the drilling parameter adjustment strategy is optimized by learning and evaluating multiple adjustment results.

[0015] Preferably, real-time acquisition of dynamic change information caused by clay layer expansion during drilling to determine the initial time period from when the clay layer encounters water to when it begins to expand rapidly specifically includes the following steps:

[0016] Real-time collection of dynamic change information caused by clay layer expansion during drilling, including pressure change data in the wellbore, micro-deformation data of the well wall, torque change data of the drill bit, and flow velocity data of the drilling fluid;

[0017] Preprocessing the dynamic change information caused by clay layer expansion during drilling;

[0018] Extract key features from the pre-processed dynamic change information, including pressure change rate, deformation rate, torque fluctuation amplitude and flow rate change rate, and quantify these feature data to generate feature vectors;

[0019] Based on the extracted feature vectors, a clay layer expansion response model is constructed. By analyzing the rate of change and cumulative change of each feature, the critical change point of each feature is determined. When the change rate and cumulative change of multiple features reach or exceed the preset threshold, the model will identify the time point when the clay layer begins to expand rapidly.

[0020] The time series of the identified time points were analyzed to determine the initial time period from when the clay layer met water to when it began to expand rapidly, which was defined as the period from the appearance of the first characteristic critical point to the time before the expansion rate of the clay layer stabilized.

[0021] Preferably, after determining the initial time period from when the clay layer meets water to when it begins to expand rapidly, the collected dynamic change information is analyzed to evaluate the impact level on the drilling operation during the initial time period of the clay layer expansion, specifically including the following steps:

[0022] After determining the initial time period from when the clay layer meets water to when it begins to expand rapidly, extract the wellbore dynamic stress information and drill bit operation corresponding information in the initial time period from the collected dynamic change information;

[0023] Preprocessing the extracted wellbore dynamic stress information and drill bit operation corresponding information in the initial time period;

[0024] Analyze the wellbore dynamic stress information and drill bit operation corresponding information in the preprocessed initial period to generate a dynamic wellbore stability coefficient and a drill bit load response index respectively;

[0025] The generated dynamic wellbore stability coefficient and drill bit load response index are used to construct an impact grade assessment model to generate an impact coefficient;

[0026] A preset threshold interval of the influence coefficient is determined, and the generated influence coefficient is compared with the threshold interval, and the influence level on the drilling operation in the initial period of clay layer expansion is evaluated according to the comparison result.

[0027] Preferably, the acquisition logic of the dynamic wellbore stability coefficient is as follows:

[0028] Extract the dynamic stress information of the wellbore in the initial time period after preprocessing, including the average pressure inside the wellbore at different times in a period of time, the average density of the mud inside the wellbore, the average stress of the formation around the wellbore, the deformation of the wellbore wall and the corresponding time points, and use the functions P(t), ρM(t), YL(t) and XB(t) to represent them according to the time series, where t is the time point, the time period is defined as [t1, t2], P(t) represents the average pressure inside the wellbore at time t in a period of time, ρM(t) represents the average density of the mud inside the wellbore at time t in a period of time, YL(t) represents the average stress of the formation around the wellbore at time t in a period of time, and XB(t) represents the deformation of the wellbore wall at time t in a period of time;

[0029] The actual length of the wellbore and the gravity acceleration at the wellbore are obtained, and are calibrated as L and g respectively after being obtained;

[0030] Calculate the change in the average pressure inside the wellbore, the change in the average density of the mud inside the wellbore, and the stress intensity ratio of the wellbore during the initial period. The specific calculation formula is as follows:

[0031]

[0032]

[0033]

[0034] Where ΔP is the change in the average pressure inside the wellbore during the initial period, ΔρM is the change in the average density of the mud inside the wellbore during the initial period, and S wb is the stress intensity ratio of the wellbore in the initial period;

[0035] Calculate the dynamic wellbore stability coefficient. The specific calculation formula is as follows:

[0036]

[0037] Where DWSC is the dynamic wellbore stability coefficient.

[0038] Preferably, the acquisition logic of the drill bit load response index is as follows:

[0039] Extract the corresponding information of the drill bit operation in the initial time period after preprocessing, including the actual torque of the drill bit, the average drilling pressure of the drill bit, the drilling speed of the drill bit, the cuttings return rate and the corresponding time points at different times in a period of time, and use the functions NJ(t), W(t), VD(t) and VS(t) to represent them according to the time series, where t is the time point, the time period is defined as [t1, t2], NJ(t) represents the actual torque of the drill bit at time t in a period of time, W(t) represents the average drilling pressure of the drill bit at time t in a period of time, VD(t) represents the drilling speed of the drill bit at time t in a period of time, and VS(t) represents the cuttings return rate at time t in a period of time;

[0040] Calculate the change rate of the actual torque of the drill bit, the change rate of the average drilling pressure of the drill bit, the average drilling speed of the drill bit, and the average cuttings return rate during the initial period. The specific calculation formula is as follows:

[0041]

[0042]

[0043]

[0044]

[0045] In the formula, λ NJ is the rate of change of the actual torque of the drill bit during the initial period, λ W is the rate of change of the average bit pressure during the initial period, VD- is the average value of the drilling speed during the initial period, and VS_ is the average value of the cuttings return rate during the initial period;

[0046] Calculate the dynamic response coefficient of the drill load. The specific calculation formula is as follows:

[0047]

[0048] In the formula, R d is the dynamic response coefficient of drill bit load;

[0049] Obtain the average load response factor under normal drilling conditions and calibrate it as R d yu ;

[0050] Calculate the drill bit load response index. The specific calculation formula is as follows:

[0051]

[0052] Where DBLRI is the drill bit load response index.

[0053] Preferably, an impact level evaluation model is constructed using the generated dynamic wellbore stability coefficient DWSC and the bit load response index DBLRI, and an impact coefficient IC is generated through weighted summation.

[0054] Preferably, a preset impact coefficient threshold interval [IC1, IC2] is determined, and the generated impact coefficient IC is compared with this threshold interval [IC1, IC2]. According to the comparison result, the impact level on the drilling operation during the initial time period of clay layer swelling is evaluated. The specific comparison and analysis are as follows:

[0055] If IC < IC1, the impact level on the drilling operation during the initial time period of clay layer swelling is a low-level impact;

[0056] If IC1 ≤ IC ≤ IC2, the impact level on the drilling operation during the initial time period of clay layer swelling is a medium-level impact;

[0057] If IC > IC2, the impact level on the drilling operation during the initial time period of clay layer swelling is a high-level impact.

[0058] Preferably, according to the evaluation result, the immediate impacts of clay layer swelling on wellbore pressure, bit operation load, and mud flow conditions are analyzed, and corresponding solutions are taken according to the analysis results, specifically including:

[0059] If the evaluation result is a low-level impact, the immediate impacts of clay layer swelling on wellbore pressure, bit operation load, and mud flow conditions are all weak. Continue to maintain the current drilling parameters and keep real-time monitoring of the internal pressure of the wellbore, bit load, and mud flow status;

[0060] If the evaluation result is a medium-level impact, the immediate impacts of clay layer swelling on wellbore pressure, bit operation load, and mud flow conditions are all medium. It is necessary to adjust the drilling parameters, specifically including reducing the drilling speed parameter, increasing the mud flow rate parameter, and adjusting the mud density parameter, and real-time monitoring the adjustment effect of the system on these parameters;

[0061] If the evaluation result is a high-level impact, the immediate impacts of clay layer swelling on wellbore pressure, bit operation load, and mud flow conditions are all strong. It is urgent to adjust the drilling parameters and immediately stop the drilling operation, specifically including reducing the mud flow rate parameter to reduce the pressure load on the wellbore, conducting an emergency evaluation and adjusting the pressure parameter and mud density parameter in the wellbore, and resetting the drilling strategy.

[0062] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0063] 1. The present invention accurately identifies and determines the initial time period of clay layer expansion by real-time monitoring and analyzing the dynamic change information caused by clay layer expansion. The present invention can respond quickly in the early stage of clay layer expansion, identify changes in wellbore pressure, drill bit load and mud flow conditions through real-time data acquisition and analysis, and then quickly adjust the drilling speed and mud flow to avoid problems such as wellbore blockage, drill bit sticking or wellbore instability caused by rapid expansion of the clay layer. This efficient monitoring and response mechanism significantly improves the safety and efficiency of drilling operations, reduces non-productive time and potential operational risks.

[0064] 2. The present invention realizes the scientific and intelligent adjustment strategy of drilling parameters by constructing and applying the evaluation model of dynamic wellbore stability coefficient and drill bit load response index. By generating the influence coefficient through weighted summation and comparing it with the preset threshold interval, the system can accurately evaluate the immediate impact level of clay layer expansion on drilling operation, so as to adjust the drilling parameters in a targeted manner. This adjustment strategy based on real-time feedback and dynamic optimization not only improves the adaptability of drilling operation, but also ensures that the response measures under different impact levels are reasonable and effective, further reducing the operational risks in the drilling process.

[0065] 3. The present invention ensures the continuous stability of drilling operations through a closed-loop system of continuous monitoring and dynamic optimization. The overall stability of the wellbore is monitored in real time, especially in the stage after the key parameters are adjusted, to ensure the safety of the wellbore and the drill bit. At the same time, based on the learning and evaluation of multiple adjustment results, the system can continuously optimize the drilling parameter adjustment strategy to make the drilling operation more accurate and efficient. This technical solution of multiple learning and dynamic updating of the optimization model not only improves the reliability of drilling operations, but also provides valuable experience and data support for future drilling operations, thereby reducing operating costs in the long run and enhancing the economic benefits of oil and gas wells. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0067] Figure 1 It is a schematic flow chart of the method for optimizing drilling parameters of oil and gas wells according to the present invention. DETAILED DESCRIPTION

[0068] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0069] The present invention provides Figure 1 The drilling parameter optimization method for oil and gas wells shown in the figure specifically comprises the following steps:

[0070] Identify, through geological data analysis and real-time logging data, whether there is an expansive clay layer in the current drilling area, and if an expansive clay layer is identified in the current drilling area, determine its location, thickness and activity characteristics, and after determination, perform the following steps;

[0071] The first step in optimizing drilling parameters is to identify whether there is an expansive clay layer in the current drilling area through geological data analysis and real-time logging data. The purpose of this step is to identify the possible existence of an expansive clay layer through detailed analysis of geological conditions before or during drilling, so as to take preventive measures in advance. The purpose of identifying an expansive clay layer is to predict the expansion phenomenon that may be caused by the clay layer when it encounters water during the drilling process, thereby providing accurate geological background information for subsequent drilling operations and ensuring the safety and effectiveness of the operation.

[0072] Identification of expansive clay layers can be achieved in the following ways: First, geological data analysis can use existing geological exploration data, including formation lithology, mineral composition, historical drilling data, etc., combined with geological models, to predict and analyze the formation characteristics in the target area. These data are processed by software to generate geological profiles to identify areas that may contain expansive clay. Secondly, the analysis of real-time logging data relies on real-time logging information obtained during drilling, such as gamma rays, sonic velocity, density measurements, etc. These logging data are processed by software algorithms, which can analyze the changes in the lithology of the well wall in real time and identify formations with high clay content or expansive characteristics. The software can automatically determine whether there is an expansive clay layer in the current area based on the characteristics of the logging signal combined with the geological analysis model.

[0073] After identifying the presence of a swelling clay layer, its specific location, thickness and activity characteristics need to be further determined. This can be achieved through detailed logging data analysis and high-resolution imaging technology. Detailed logging data analysis includes the use of resistivity logging, sonic logging and density logging. These data are integrated through software to form a detailed profile of the well wall, thereby accurately locating the depth and thickness of the clay layer. In addition, high-resolution imaging technology (such as microelectrode imaging logging) can provide high-definition images of the well wall. The software can identify the subtle structural characteristics of the clay layer, such as crack distribution and bedding structure, through image processing technology, and then evaluate the activity characteristics of the clay, including its potential risk of swelling when exposed to water. By combining these high-precision data, the software can generate a comprehensive geological report to clarify the spatial distribution and physical characteristics of the clay layer, providing a scientific basis for subsequent drilling operations.

[0074] Real-time collection of dynamic change information caused by clay layer expansion during drilling, and determination of the initial period from when the clay layer encounters water to when it begins to expand rapidly;

[0075] In this embodiment, real-time acquisition of dynamic change information caused by clay layer expansion during drilling is performed to determine the initial time period from when the clay layer encounters water to when it begins to expand rapidly, specifically including the following steps:

[0076] Real-time collection of dynamic change information caused by clay layer expansion during drilling, including pressure change data in the wellbore, micro-deformation data of the well wall, torque change data of the drill bit, and flow velocity data of the drilling fluid;

[0077] Real-time collection of dynamic change information caused by clay layer expansion during drilling can be achieved by deploying a variety of sensors in the wellbore. Specifically, pressure sensors can be installed at different depths in the wellbore to continuously monitor the pressure change data in the wellbore; deformation sensors (such as strain gauges) can be embedded in key areas of the wellbore wall to capture small deformations of the wellbore wall; torque sensors can be integrated into the drive system of the drill bit to monitor the torque changes of the drill bit in real time; and flow sensors and densitometers are installed in the mud circulation system to measure the flow rate and density of the drilling fluid. The data collected by these sensors are transmitted to the ground control center in real time through a high-speed data transmission network. The computer system of the control center runs special software to monitor, record and preliminarily process the collected data in real time, ensuring that all kinds of dynamic change information can be accurately captured and processed in a timely manner, thereby providing a reliable data basis for subsequent analysis.

[0078] Preprocessing the dynamic change information caused by clay layer expansion during drilling;

[0079] Preprocessing is performed to ensure that the dynamic change information caused by the expansion of the clay layer collected during the drilling process is highly accurate and consistent, thereby providing a reliable data basis for subsequent analysis. The first step in preprocessing is to remove noise. By using filtering algorithms (such as low-pass filters) to eliminate data noise introduced by equipment vibration, environmental interference or sensor errors; next, time series synchronization is performed. Because there may be time differences in the data collected by different sensors, the software will automatically correct the timestamp to ensure that all data are aligned on the same time axis; finally, data calibration is performed to normalize or standardize key features such as pressure, deformation, torque and flow rate to eliminate differences between different dimensions and ensure that the data is comparable on the same scale. Through these preprocessing steps, the software can generate consistent and high-quality data, laying a solid foundation for subsequent feature extraction and model analysis.

[0080] Extract key features from the pre-processed dynamic change information, including pressure change rate, deformation rate, torque fluctuation amplitude and flow rate change rate, and quantify these feature data to generate feature vectors;

[0081] Extracting key features can be achieved through the signal processing algorithm built into the software. First, the software will perform differential operations on the preprocessed data to calculate the rate of change of pressure, deformation, torque, and flow rate at each time point. For example, the rate of change of pressure can be obtained by taking the first-order differential of continuous pressure data; similarly, the deformation rate and flow rate change rate are also obtained by performing differential processing on the deformation data and flow rate data. For the torque fluctuation amplitude, the software can extract the frequency component of the torque signal through Fourier transform, analyze the amplitude change of the torque signal at different frequencies, and then determine the fluctuation amplitude. These processed data are the original change information of each dynamic feature.

[0082] After extracting the rate of change and fluctuation of each feature, the software will quantify these feature data. The first step of quantification is standardization, which is to adjust the data of each feature to the same scale (such as standardizing the data to a range with a mean of 0 and a standard deviation of 1) to eliminate the dimensional differences between the features. Then, the software will assign different weights to each standardized feature according to a specific weighting algorithm to reflect its relative importance in the clay layer expansion process. Finally, all standardized and weighted feature data will be integrated into a feature vector, which comprehensively reflects the changing trends of multiple dynamic features in the clay layer expansion process, serving as the basis for subsequent analysis and model building.

[0083] Based on the extracted feature vectors, a clay layer expansion response model is constructed. By analyzing the rate of change and cumulative change of each feature, the critical change point of each feature is determined. When the change rate and cumulative change of multiple features reach or exceed the preset threshold, the model will identify the time point when the clay layer begins to expand rapidly.

[0084] The construction of the clay layer expansion response model can be achieved through multivariate regression analysis and machine learning algorithms in the software. First, the software uses the extracted feature vector as input data, and through multivariate regression analysis, establishes a relationship model between the feature change rate and cumulative change and the degree of clay layer expansion. Then, the software introduces historical data for training and uses machine learning algorithms (such as support vector machines or decision trees) to optimize the parameters of the model so that it can accurately predict the expansion response under different conditions. The key to model construction is to use a large amount of known expansion event data to establish a mathematical model that can predict future expansion behavior, and assign different weights to each feature in the feature vector to reflect its contribution to the expansion.

[0085] After the model is built, the software will continuously analyze the feature vectors collected in real time, paying special attention to the rate of change and cumulative change of each feature. Specifically, the software calculates the instantaneous rate of change (obtained by differential operations at consecutive time points) and the cumulative change (calculated by integration or cumulative summation) of each feature. These values ​​are compared with the preset thresholds in the model. When the rate of change and cumulative change of multiple features are detected to reach or exceed these thresholds at the same time, the software will trigger an alarm mechanism, marking it as the time point when the clay layer begins to expand rapidly. At this point, the model identifies this time point as a critical moment in the early stage of expansion and uses it to guide subsequent adjustments to drilling operations.

[0086] The time series of the identified time points were analyzed to determine the initial time period from when the clay layer met water to when it began to expand rapidly, which was defined as the period from the appearance of the first characteristic critical point to the time before the expansion rate of the clay layer stabilized.

[0087] To determine the initial time period from the time the clay layer encounters water to the time it begins to expand rapidly, the software can use time series analysis and critical point detection algorithms to achieve this. First, the software will arrange the feature data collected in real time in chronological order to form a complete time series. Then, the software will use the inflection point detection algorithm to identify the time points when significant changes occur in the feature data, namely the so-called feature critical points. These critical points usually manifest as drastic changes in pressure, deformation, torque or flow rate. After identifying the first critical point, the software will continue to track subsequent feature changes and calculate the rate of change of the feature using the rate of change smoothing algorithm. When the rate of change of the feature tends to stabilize and the cumulative change reaches a certain threshold, the software identifies this point as the time point when the expansion rate stabilizes. Finally, the software defines the time period between the first feature critical point and the expansion rate stabilization point as the initial expansion time period of the clay layer. The determination of this time period is achieved by analyzing the time series change trend of the feature data and combining the time interval between the feature critical point and the stabilization point to ensure that the initial stage of the expansion process can be accurately captured, providing a scientific basis for subsequent operations.

[0088] After determining the initial period from when the clay layer meets water to when it begins to expand rapidly, analyze the acquired dynamic change information to assess the level of impact on drilling operations during the initial period of clay layer expansion;

[0089] In this embodiment, after determining the initial time period from when the clay layer encounters water to when it begins to expand rapidly, the collected dynamic change information is analyzed to evaluate the impact level on the drilling operation during the initial time period of the clay layer expansion, specifically including the following steps:

[0090] After determining the initial time period from when the clay layer meets water to when it begins to expand rapidly, extract the wellbore dynamic stress information and drill bit operation corresponding information in the initial time period from the collected dynamic change information;

[0091] To extract the wellbore dynamic stress information and drill bit operation response information in the initial time period, the software can filter out the data belonging to the initial time period from the dynamic change information collected in real time by setting a specific time window. First, the software will automatically mark and extract the relevant data in the time period in the time series according to the start and end time of the initial time period of clay layer expansion that has been determined. For the wellbore dynamic stress information, the extracted data include the pressure change in the wellbore, the mud density change, the formation stress distribution, and the small deformation of the wellbore wall. These data can be obtained through pressure sensors, densitometers, strain gauges and other equipment deployed in the wellbore. For the drill bit operation response information, the extracted data include the torque change of the drill bit, the drilling pressure change, the drilling speed, and the cuttings return rate, which come from the drill bit torque sensor, drilling pressure sensor and flow meter. The software will automatically separate these data in the initial time period from the large data set and store them in categories for subsequent feature extraction and analysis. In this way, it is ensured that the extracted data accurately reflects the immediate impact of the initial clay layer expansion on the wellbore and drill bit.

[0092] Preprocessing the extracted wellbore dynamic stress information and drill bit operation corresponding information in the initial time period;

[0093] Preprocessing is performed to ensure that the wellbore dynamic stress information and drill bit operation response information extracted from the initial time period are highly accurate and consistent, thereby providing a reliable data basis for subsequent analysis and calculation. Preprocessing first includes noise filtering, which uses filtering algorithms (such as Kalman filtering or bandpass filtering) to eliminate environmental noise and sensor errors in the data to ensure the purity of the signal. The second is data synchronization. Since the data acquisition frequency and timestamp of different sensors may be inconsistent, the software will automatically correct the timestamp to ensure that all data are aligned on the same time axis. Next, normalization is performed to standardize data of different dimensions (for example, adjust data such as pressure, stress, torque, etc. to the same scale range) to eliminate dimensional differences and make the data comparable. Finally, the software will also perform a data integrity check to ensure the integrity of the data set by detecting and filling missing values ​​or outliers in the data. Through these preprocessing steps, the software can generate high-quality data sets to ensure the accuracy and reliability of subsequent analysis.

[0094] Analyze the wellbore dynamic stress information and drill bit operation corresponding information in the preprocessed initial period to generate a dynamic wellbore stability coefficient and a drill bit load response index respectively;

[0095] The generated dynamic wellbore stability coefficient and drill bit load response index are used to construct an impact grade assessment model to generate an impact coefficient;

[0096] A preset threshold interval of the influence coefficient is determined, and the generated influence coefficient is compared with the threshold interval, and the influence level on the drilling operation in the initial period of clay layer expansion is evaluated according to the comparison result.

[0097] The purpose of determining the pre-set threshold intervals of the impact coefficient is to provide a standardized benchmark for the impact level assessment, which can be achieved by analyzing historical drilling data and building models. First, the software collects a large amount of relevant data from historical drilling projects, especially data related to the impact of clay layer expansion on wellbore and drill bit operations, including historical records of wellbore dynamic stress coefficients and drill bit load response indexes. By performing statistical analysis on these data, the software can calculate the typical value ranges of these parameters at different impact levels (such as normal, slight impact, and severe impact). Next, the software uses cluster analysis or multivariate regression models to classify these historical data and identify the distribution characteristics of the impact coefficients under different levels of impact. Finally, by setting confidence intervals or quantile analysis, the software determines the threshold intervals of the impact coefficients corresponding to each impact level, and stores these intervals in the system as a basis for judging the impact level during real-time evaluation. Through this data-driven approach, the software can dynamically adjust the threshold intervals to adapt to drilling needs under different geological conditions.

[0098] In this embodiment, the logic for obtaining the dynamic wellbore stability coefficient is as follows:

[0099] Extract the dynamic stress information of the wellbore in the initial time period after preprocessing, including the average pressure inside the wellbore at different times in a period of time, the average density of the mud inside the wellbore, the average stress of the formation around the wellbore, the deformation of the wellbore wall and the corresponding time points, and use the functions P(t), ρM(t), YL(t) and XB(t) to represent them according to the time series, where t is the time point, the time period is defined as [t1, t2], P(t) represents the average pressure inside the wellbore at time t in a period of time, ρM(t) represents the average density of the mud inside the wellbore at time t in a period of time, YL(t) represents the average stress of the formation around the wellbore at time t in a period of time, and XB(t) represents the deformation of the wellbore wall at time t in a period of time;

[0100] To extract the dynamic stress information of the wellbore in the initial time period after preprocessing, it is first necessary to deploy sensors and measuring equipment for real-time monitoring at different depths along the wellbore. Specifically, the average pressure inside the wellbore can be collected in real time at different depths of the wellbore by pressure sensors, and the data can be transmitted to the central data processing system. The average density of the mud can be monitored by a densitometer installed inside the wellbore, which records the density changes of the mud at different depths in real time. The average stress of the formation around the wellbore is measured by a formation stress sensor and analyzed in combination with geological data. The deformation of the wellbore wall is obtained by a strain gauge installed on the wellbore wall. The data of all these sensors will be uploaded to the central control system in real time through the data transmission network. The software will extract these data according to the preset time period, integrate and preprocess them according to the time series, and generate the dynamic stress information of the wellbore in the initial time period. This process relies entirely on automated data acquisition and analysis software, without human intervention, to ensure the timeliness and accuracy of the data.

[0101] The actual length of the wellbore and the gravity acceleration at the wellbore are obtained, and are calibrated as L and g respectively after being obtained;

[0102] In order to extract the actual length of the wellbore and the gravity acceleration at the location of the wellbore, the following operations can be performed. The actual length of the wellbore is usually determined by the wellbore measurement system, which includes measuring the cable length and the cumulative length of the drill pipe connection. The data is recorded in real time through downhole sensors and measurement equipment and uploaded to the ground control system. The depth and location of the wellbore can be accurately determined through geological information and the gyroscope or inertial navigation system installed on the equipment. The gravity acceleration g is measured in real time based on the geographic coordinates of the wellbore location, combined with the local gravity field model or through a gravity sensor. All of these data will be transmitted to the central control system in real time through the data acquisition system. The software will calibrate and calibrate these data, calibrating the actual length of the wellbore as L and the gravity acceleration as g. This process is fully automated, relying on software to complete data extraction, processing and calibration, ensuring the accuracy and real-time nature of the data.

[0103] Calculate the change in the average pressure inside the wellbore, the change in the average density of the mud inside the wellbore, and the stress intensity ratio of the wellbore during the initial period. The specific calculation formula is as follows:

[0104]

[0105] Where ΔP is the change in the average pressure inside the wellbore during the initial period, ΔρM is the change in the average density of the mud inside the wellbore during the initial period, and S wb is the stress intensity ratio of the wellbore in the initial period;

[0106] ΔP represents the change in the average pressure inside the wellbore over time during the initial period of time. This parameter is calculated by integrating the wellbore internal pressure P(t) at different times t and dividing it by the length of the wellbore L, reflecting the change in pressure experienced by the wellbore during a specified period of time. The change in pressure is very important for assessing the stability of the wellbore, because pressure fluctuations may lead to wellbore instability or wellbore problems.

[0107] ΔρM represents the change in the average density of the mud inside the wellbore over time during the initial period. This parameter is calculated by integrating the mud density ρM(t) at different times t and dividing it by the length of the wellbore L to obtain the change in mud density. The change in mud density directly affects the pressure balance inside the wellbore and is an important factor in maintaining wellbore stability.

[0108] S wb It represents the stress intensity ratio between the average stress of the formation around the wellbore and the internal pressure of the wellbore during the initial period of time. This parameter integrates the difference between the formation stress YL(t) around the wellbore and the internal pressure P(t) of the wellbore at different times t and divides it by the deformation of the wellbore XB(t), reflecting the relationship between the stress borne by the wellbore during the initial period of time and the deformation of the wellbore. The magnitude of the stress intensity ratio is directly related to the stability of the wellbore. A higher stress intensity ratio may lead to wellbore instability or wellbore rupture.

[0109] Calculate the dynamic wellbore stability coefficient. The specific calculation formula is as follows:

[0110]

[0111] Where DWSC is the dynamic wellbore stability coefficient.

[0112] The size of the dynamic wellbore stability coefficient directly reflects the relationship between the comprehensive stress state of the wellbore during the initial period of clay layer expansion and its structural stability. A smaller dynamic wellbore stability coefficient means that the pressure and mud density changes inside the wellbore are relatively small, and the stress intensity of the formation around the wellbore is relatively large, indicating that the wellbore structure is relatively stable and the impact of clay layer expansion on drilling operations is small. On the contrary, a larger dynamic wellbore stability coefficient indicates that the pressure and mud density changes inside the wellbore are large, the wellbore may be subject to large stress fluctuations, and the wellbore deformation increases, which means that the impact of clay layer expansion on drilling operations is high, which may lead to serious problems such as wellbore instability and wellbore wall rupture. Therefore, the dynamic wellbore stability coefficient can be used as an important indicator to evaluate the impact level of clay layer expansion on drilling operations during the initial period of time, helping to judge and control potential risks.

[0113] In this embodiment, the logic for obtaining the drill bit load response index is as follows:

[0114] Extract the corresponding information of the drill bit operation in the initial time period after preprocessing, including the actual torque of the drill bit, the average drilling pressure of the drill bit, the drilling speed of the drill bit, the cuttings return rate and the corresponding time points at different times in a period of time, and represent them by functions NJ(t), W(t), VD(t) and VS(t) respectively according to the time series, where t is the time point, the time period is defined as [t1, t2], NJ(t) represents the actual torque of the drill bit at time t in a period of time, W(t) represents the average drilling pressure of the drill bit at time t in a period of time, VD(t) represents the drilling speed of the drill bit at time t in a period of time, and VS(t) represents the cuttings return rate at time t in a period of time;

[0115] To extract the drill bit operation response information in the preprocessed initial time period, it is first necessary to install special sensors and measuring equipment on the drilling equipment. The actual torque of the drill bit can be monitored in real time by the drill bit torque sensor, and the data is transmitted to the central data processing system. The average drilling pressure of the drill bit is collected by the drilling pressure sensor installed on the drill pipe. These sensors can record the force of the drill bit at different time points in real time. The drilling speed of the drill bit is monitored by the drilling speed sensor, which is usually installed in the drive system of the drilling equipment to record the speed at which the drill bit penetrates the formation. The cuttings return rate can be measured by a flow meter installed in the mud circulation system to record the speed at which the cuttings carried in the mud return to the wellhead. The data from all these sensors will be uploaded to the central control system in real time through the data transmission network. The software will extract and integrate these data according to the set time period, and preprocess them according to the time series to generate the drill bit operation response information in the initial time period. This process is completed by the automated data acquisition system and data processing software to ensure the continuity, accuracy and timeliness of the data.

[0116] Calculate the change rate of the actual torque of the drill bit, the change rate of the average drilling pressure of the drill bit, the average drilling speed of the drill bit, and the average cuttings return rate during the initial period. The specific calculation formula is as follows:

[0117]

[0118]

[0119]

[0120]

[0121] In the formula, λ NJ is the rate of change of the actual torque of the drill bit during the initial period, λ W is the rate of change of the average drilling pressure of the drill bit in the initial period, VD_ is the average drilling speed of the drill bit in the initial period, and VS_ is the average cuttings return rate in the initial period;

[0122] λ NJ It indicates the rate of change of the actual torque of the drill bit over time during the initial period. This rate of change is obtained by integrating and averaging the drill bit torque NJ(t) at different times t. It reflects the resistance and load changes encountered by the drill bit during drilling. A large rate of change may mean that the drill bit encounters a significant change in operating load when the clay layer expands, which may affect drilling efficiency and safety.

[0123] λ W It represents the rate of change of the average drilling pressure of the drill bit over time during the initial period. By integrating the drilling pressure W(t) of the drill bit at different times t and taking the average value, the change of drilling pressure that the drill bit is subjected to during the expansion of the clay layer is calculated. The change of drilling pressure directly affects the propulsion force of the drill bit. A large change rate may lead to uneven load on the drill bit, thus affecting the stability of drilling.

[0124] VD_ represents the average drilling speed of the drill bit during the initial period. By integrating and averaging the drilling speed VD(t) at different times t, the average speed of the drill bit penetrating the formation during this period is calculated. The change in drilling speed can reflect the working efficiency of the drill bit when penetrating different formations. A lower average drilling speed may indicate that the drill bit encounters increased resistance or excessive load in the clay layer.

[0125] VS_ represents the average value of the cuttings return rate during the initial period. By integrating and averaging the cuttings return rate VS(t) at different times t, the speed at which the cuttings are brought back to the surface by the mud during drilling is calculated. Changes in the cuttings return rate are often used to evaluate the cleaning efficiency of drilling. A lower rate may lead to the accumulation of cuttings, which affects the cleaning of the wellbore and the overall efficiency of drilling.

[0126] Calculate the dynamic response coefficient of the drill load. The specific calculation formula is as follows:

[0127]

[0128] In the formula, R d is the dynamic response coefficient of drill bit load;

[0129] Drill load dynamic response coefficient R d It is a key parameter used to quantify the load response of the drill bit during the initial period of clay layer expansion. This coefficient is calculated by the actual torque change rate of the drill bit λ NJ and the bit pressure change rate λ W The formula reflects the dynamic change of the drill bit load in the face of clay layer expansion. Specifically, the larger R dThe value of R indicates that during the expansion of the clay layer, the torque and bit pressure of the drill bit change significantly, while the drilling speed and cuttings return rate are relatively low, which means that the drill bit load is unstable and there may be a greater operational risk. d A value of 0 indicates that the drill bit load is relatively stable, the drilling process is smoother, and the operation risk is lower. This coefficient provides a quantitative indicator for drilling operations and is used to evaluate the impact of clay layer expansion on drilling load.

[0130] Obtain the average load response factor under normal drilling conditions and calibrate it as R d yu ;

[0131] In order to obtain the average load response factor R under normal drilling conditions d yu First, it is necessary to collect the operating data of the drill bit during multiple normal drilling operations, including key parameters such as the actual torque of the drill bit, drilling pressure, drilling speed and cuttings return rate. The data of each drilling operation will be collected in real time by sensors installed on the drilling equipment and transmitted to the central data processing system. The software system will pre-process and analyze this data and calculate the load response coefficient R of each drilling operation. d And record it. In order to obtain the average load response factor R d yu , the software will perform multiple R d Specifically, the software will process all the R d The values ​​are added and then divided by the total number of jobs to get a standardized average R d yu This average value is used as a benchmark to evaluate whether the change in drill bit load is normal under special conditions (such as during the expansion of clay layers). The entire process relies on automated data acquisition and analysis software to ensure the accuracy and real-time nature of the data.

[0132] Calculate the drill bit load response index. The specific calculation formula is as follows:

[0133]

[0134] Where DBLRI is the drill bit load response index.

[0135] The size of the drill bit load response index directly reflects the relationship between the load change of the drill bit during the initial period of clay layer expansion and its operational stability. A higher drill bit load response index indicates that the torque and drilling pressure of the drill bit change significantly during the clay layer expansion process, and the drilling speed and cuttings return rate may be affected, which means that the load of the drill bit is unstable at this stage, which may make the drilling operation more difficult, such as increased drill bit wear, reduced drilling efficiency, and even drill bit sticking or other mechanical failures. On the contrary, a lower drill bit load response index indicates that the drill bit load changes less, the operation is stable, the impact of clay layer expansion on drilling operations is low, and the drilling process can proceed smoothly. Therefore, the drill bit load response index is an important parameter for evaluating the impact level of clay layer expansion on drilling operations during the initial period of time, helping to identify and respond to potential drilling risks in advance.

[0136] In this embodiment, the generated dynamic wellbore stability coefficient DWSC and drill bit load response index DBLRI are used to construct an impact level assessment model, and the impact coefficient IC is generated through weighted summation.

[0137] To construct the impact level assessment model, the generated dynamic wellbore stability coefficient DWSC and drill bit load response index DBLRI need to be used as the input parameters of the model. The relative importance of these two parameters in the overall drilling operation is reflected by assigning weight coefficients to them respectively. Usually, the weight coefficients are determined based on empirical data, historical operation analysis, or are initially set by an expert system. For example, if dynamic wellbore stability is more critical than drill bit load response in the assessment, DWSC can be given a greater weight, and vice versa. In the model, the product of DWSC and DBLRI is added using the weighted summation method, that is, the impact coefficient IC = ω1*DWSC+ω2*DBLRI, where ω1 and ω2 are the weight coefficients of DWSC and DBLRI, respectively. The final generated impact coefficient IC represents the comprehensive impact level on the drilling operation during the initial period of clay layer expansion. The calculated IC can be further used for evaluation and decision-making to determine whether it is necessary to adjust drilling parameters or take preventive measures to ensure the safety and efficiency of the operation.

[0138] In this embodiment, a preset threshold interval of the influence coefficient [IC1, IC2] is determined, and the generated influence coefficient IC is compared with the threshold interval [IC1, IC2]. The influence level on the drilling operation in the initial period of clay layer expansion is evaluated according to the comparison result. The specific comparison analysis is as follows:

[0139] If IC<IC1, the impact level on drilling operations during the initial period of clay layer expansion is low level impact;

[0140] This means that during the initial period of clay layer expansion, the impact on drilling operations is small, the wellbore stability is high, and the load on the drill bit is within an acceptable range. Usually, no major adjustments to the drilling operation are required in this case, and drilling operations can continue as planned. When the impact is low, the fluctuations in wellbore pressure and mud density are small, and the stress intensity of the wellbore wall remains within a safe range, ensuring the safety and efficiency of the drilling operation.

[0141] If IC1≤IC≤IC2, the impact level on drilling operations during the initial period of clay layer expansion is medium impact;

[0142] This indicates that the impact of clay layer expansion on drilling operations is moderate in the initial period, the wellbore may begin to show some signs of instability, and the drill bit load also shows a certain degree of fluctuation. In this case, it is recommended to make appropriate adjustments to drilling parameters, such as slowing down the drilling speed or optimizing the mud formula, to avoid further deterioration of wellbore stability and potential risks caused by excessive drill bit load. This moderate impact may require more monitoring and countermeasures to ensure the safety and efficiency of operations.

[0143] If IC>IC2, the impact level on drilling operations during the initial period of clay layer expansion is high.

[0144] This indicates that during the initial period of clay layer expansion, the drilling operation was greatly affected, the wellbore stability may have significantly decreased, the drill bit load was in a high-risk state, and continued operation may lead to serious consequences, such as wellbore instability, drill bit stuck, or wellbore collapse. In this case, it is usually necessary to stop drilling operations immediately, re-evaluate the wellbore conditions, take emergency response measures, and re-plan subsequent drilling plans to avoid operation failure and equipment damage.

[0145] Based on the evaluation results, analyze the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions, and take corresponding solutions based on the analysis results;

[0146] In this embodiment, according to the evaluation results, the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions is analyzed, and corresponding solutions are taken according to the analysis results, including:

[0147] If the assessment result is a low-level impact, the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions is weak. Continue to maintain the current drilling parameters, including drilling speed, mud flow and mud density, and maintain real-time monitoring of wellbore internal pressure, drill bit load and mud flow conditions;

[0148] The low-level impact indicates that the clay layer expansion has little immediate impact on the wellbore pressure, drill bit operating load and mud flow conditions. This means that the current drilling parameter settings are relatively ideal, the pressure changes inside the wellbore, the load fluctuations on the drill bit and the mud flow conditions are all within the controllable range, and the stability of the wellbore and drill bit is good. In this case, it is feasible to continue the current drilling operation without negatively affecting the safety and efficiency of the operation. The fluctuations of the pressure changes and drill bit loads in the wellbore are low, and the mud flows smoothly. Therefore, the drilling operation can proceed as planned without major adjustments or interruptions. While maintaining the current drilling parameters, it is necessary to strengthen the monitoring of the wellbore pressure, drill bit load and mud flow conditions to ensure that these parameters remain within the safe range. The specific monitoring method includes a real-time data acquisition system, which collects real-time data on wellbore pressure, drill bit load and mud flow through sensors installed on the wellbore and drill bit. These data will be transmitted to the central control system, and the software will analyze these data according to the preset thresholds. If any abnormal fluctuations are found, the system will automatically issue an alarm to prompt the operator to make further inspections or adjustments.

[0149] If the assessment result is a medium impact, the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions is medium, and drilling parameters need to be adjusted, including reducing drilling speed parameters, increasing mud flow parameters and adjusting mud density parameters, and the adjustment effect of these parameters on the system should be monitored in real time;

[0150] The medium impact means that the immediate impact of clay layer expansion on wellbore pressure, bit operating load and mud flow conditions is moderate. This means that although the drilling parameters are still within the controllable range, the wellbore pressure and bit load may begin to fluctuate, and the mud flow may also be affected to a certain extent, and adjustments need to be made to avoid escalating risks. In this case, if the drilling parameters are not adjusted in time, the wellbore may become unstable, and the bit load may further increase, resulting in increased drilling operation risks, and may even cause problems such as wellbore instability or drill bit sticking. Therefore, adjustment measures must be taken to reduce the impact of clay layer expansion on drilling operations and ensure the safety of operations. Specific adjustment measures include reducing the drilling speed parameter, increasing the mud flow parameter, and adjusting the mud density parameter. The changes in these parameters are monitored in real time by the software system. The software will automatically calculate and adjust the set values ​​of drilling speed, mud flow and mud density based on the real-time data of wellbore pressure, bit load and mud flow. These adjustments can be made through the central control system, for example, by reducing the drilling speed by reducing the rig speed, increasing the output of the mud pump to increase the mud flow, or adjusting the mud formula to change the mud density. These adjusted data will be captured by the real-time monitoring system, and the adjustment effects will be continuously evaluated to ensure that the stability of the wellbore and drill bit is maintained.

[0151] If the assessment result is a high-level impact, the clay layer expansion has a strong immediate impact on the wellbore pressure, drill bit operating load and mud flow conditions. It is urgent to adjust the drilling parameters and immediately stop the drilling operation, including lowering the mud flow parameters to reduce the pressure load on the wellbore, conducting an emergency assessment and adjusting the pressure parameters and mud density parameters in the wellbore, and resetting the drilling strategy.

[0152] High-level impact indicates that the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions is very significant. This means that the current drilling parameter settings are no longer sufficient to cope with the huge pressure and load changes caused by expansion, and there are serious operational risks. If emergency measures are not taken, it may lead to catastrophic consequences such as wellbore instability, drill bit sticking or wellbore collapse. In this case, continuing drilling operations will greatly threaten the safety of the wellbore and drill bit, and may cause damage to the wellbore structure and even cause operation interruption. High-level impact requires immediate emergency response measures to reduce wellbore pressure and drill bit load to ensure wellbore stability and safety. Drilling operations should be stopped immediately, and emergency parameter adjustments should be performed through the software system, such as reducing mud flow to reduce the pressure load on the wellbore. At this time, the software will automatically reduce the output power of the mud pump based on the real-time monitoring data to reduce the pressure of the mud on the wellbore. At the same time, the system will instruct the operator to check and adjust the pressure parameters and mud density parameters in the wellbore, and take measures such as injecting high-density mud or plugging the expansion zone to stabilize the wellbore if necessary. The software then conducts an emergency assessment of the wellbore’s stability and resets drilling strategies and parameters based on the assessment results, ensuring that drilling operations can be carried out within safe parameters after resuming operations.

[0153] Continuously monitor the overall stability of the wellbore, and monitor the wellbore pressure and wellbore deformation during the critical stage after the drilling rate and mud flow are adjusted;

[0154] Continuous monitoring of the overall stability of the wellbore can be achieved through a variety of sensors installed inside and outside the wellbore, including pressure sensors, deformation sensors, and displacement sensors. These sensors are installed on the wellbore wall, inside the wellbore, and at key mud flow locations to collect key data such as wellbore pressure and wellbore deformation in real time. The data is transmitted to the central control system on the ground via wired or wireless means. The software in the system analyzes these real-time data and generates dynamic monitoring reports. If an abnormality in the wellbore pressure or wellbore deformation is detected at a critical stage after the drilling speed and mud flow are adjusted, the software will immediately issue an alarm to prompt the operator to take countermeasures. In addition, the software system can also automatically adjust the preset thresholds according to the real-time changes in the monitoring data to adapt to dynamically changing working conditions, thereby ensuring continuous monitoring and feedback of the overall stability of the wellbore.

[0155] Continuous monitoring of the overall stability of the wellbore is essential, especially in the critical stage after the drilling rate and mud flow are adjusted, because the pressure and deformation inside the wellbore are most susceptible to impact at this time, and may even cause the risk of wellbore instability or wellbore collapse. By monitoring the wellbore pressure and wellbore deformation in real time, any potential abnormalities can be detected in time to ensure that the drilling operation is carried out within a safe parameter range. Furthermore, dynamic monitoring can respond immediately when problems arise, reduce damage to the wellbore structure, and avoid more serious accidents. In addition, real-time monitoring data is not only used for immediate warning, but also provides a scientific basis for subsequent parameter adjustments and optimization strategies to ensure the continued safety and efficiency of drilling operations. Therefore, through such monitoring methods, the risks in the drilling process can be minimized and the overall safety of the wellbore and drill bit can be improved.

[0156] Based on real-time monitoring and feedback information, the optimization model is dynamically updated, and the drilling parameter adjustment strategy is optimized by learning and evaluating multiple adjustment results.

[0157] Based on the information from real-time monitoring and feedback, the process of dynamically updating the optimization model can be achieved by establishing a data-driven adaptive optimization system. The system mainly relies on the real-time collection of key parameter data such as wellbore pressure, drill bit load, mud flow, wellbore deformation, etc., and combines previous operation records and adjustment history to use machine learning algorithms to dynamically adjust the optimization model. Specifically, the system first compares the real-time monitoring data with the preset standard parameters to identify deviations and abnormalities. Then, based on these real-time feedbacks, the system continuously adjusts the optimization strategies of parameters such as drilling speed and mud flow through machine learning models, and records the effects of each adjustment. The system accumulates data after multiple adjustments, analyzes the effects of different adjustment measures, gradually optimizes the model, and forms a more accurate drilling parameter adjustment strategy. Finally, this adaptive optimization system can automatically learn and update the optimization strategy after each parameter adjustment to better cope with changes in actual working conditions.

[0158] The purpose of dynamically updating the optimization model is to always maintain the optimal configuration of drilling parameters in a complex and changeable drilling environment. During drilling operations, changes in geological conditions and adjustments to operating parameters often trigger a series of chain reactions. If there is no timely and effective adjustment strategy, it may lead to wellbore instability, excessive drill bit load, and even serious accidents. Through real-time monitoring and feedback, it is possible to capture small changes in working conditions in a timely manner, and adaptively update the model based on these data, which can effectively reduce the impact of emergencies on drilling operations. The dynamic optimization model can not only improve the efficiency of drilling operations, but also continuously optimize and improve future drilling parameter adjustment strategies by learning from historical data. The result is that it can achieve safer and more efficient drilling operations, reduce non-productive time and the possibility of accidents, and improve the stability and economic benefits of the entire drilling process.

[0159] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0160] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0161] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0162] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0163] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0166] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for optimizing drilling parameters of oil and gas wells, characterized in that: The specific steps include: Identify, through geological data analysis and real-time logging data, whether there is an expansive clay layer in the current drilling area, and if an expansive clay layer is identified in the current drilling area, determine its location, thickness and activity characteristics, and after determination, perform the following steps; Real-time collection of dynamic change information caused by clay layer expansion during drilling, and determination of the initial period from when the clay layer encounters water to when it begins to expand rapidly; After determining the initial period from when the clay layer meets water to when it begins to expand rapidly, analyze the acquired dynamic change information to assess the level of impact on drilling operations during the initial period of clay layer expansion; Based on the evaluation results, analyze the immediate impact of clay layer expansion on wellbore pressure, drill bit operating load and mud flow conditions, and take corresponding solutions based on the analysis results; Continuously monitor the overall stability of the wellbore, and monitor the wellbore pressure and wellbore deformation during the critical stage after the drilling rate and mud flow are adjusted; Based on real-time monitoring and feedback information, the optimization model is dynamically updated, and the drilling parameter adjustment strategy is optimized by learning and evaluating multiple adjustment results.

2. The method for optimizing drilling parameters of an oil and gas well according to claim 1, characterized in that: Real-time collection of dynamic change information caused by clay layer expansion during drilling, and determination of the initial period from when the clay layer encounters water to when it begins to expand rapidly, specifically includes the following steps: Real-time collection of dynamic change information caused by clay layer expansion during drilling, including pressure change data in the wellbore, micro-deformation data of the well wall, torque change data of the drill bit, and flow velocity data of the drilling fluid; Preprocessing the dynamic change information caused by clay layer expansion during drilling; Extract key features from the pre-processed dynamic change information, including pressure change rate, deformation rate, torque fluctuation amplitude and flow rate change rate, and quantify these feature data to generate feature vectors; Based on the extracted feature vectors, a clay layer expansion response model is constructed. By analyzing the rate of change and cumulative change of each feature, the critical change point of each feature is determined. When the change rate and cumulative change of multiple features reach or exceed the preset threshold, the model will identify the time point when the clay layer begins to expand rapidly. The time series of the identified time points were analyzed to determine the initial time period from when the clay layer met water to when it began to expand rapidly, which was defined as the period from the appearance of the first characteristic critical point to the time before the expansion rate of the clay layer stabilized.

3. The method for optimizing drilling parameters of an oil and gas well according to claim 2, characterized in that: After determining the initial period from when the clay layer meets water to when it begins to expand rapidly, the collected dynamic change information is analyzed to evaluate the impact level on the drilling operation during the initial period of clay layer expansion, specifically including the following steps: After determining the initial time period from when the clay layer meets water to when it begins to expand rapidly, extract the wellbore dynamic stress information and drill bit operation corresponding information in the initial time period from the collected dynamic change information; Preprocessing the extracted wellbore dynamic stress information and drill bit operation corresponding information in the initial time period; Analyze the wellbore dynamic stress information and drill bit operation corresponding information in the preprocessed initial period to generate a dynamic wellbore stability coefficient and a drill bit load response index respectively; The generated dynamic wellbore stability coefficient and drill bit load response index are used to construct an impact grade assessment model to generate an impact coefficient; A preset threshold interval of the influence coefficient is determined, and the generated influence coefficient is compared with the threshold interval, and the influence level on the drilling operation in the initial period of clay layer expansion is evaluated according to the comparison result.

4. The method for optimizing drilling parameters of an oil and gas well according to claim 3, characterized in that: The acquisition logic of the dynamic wellbore stability coefficient is as follows: Extract the dynamic stress information of the wellbore in the initial time period after preprocessing, including the average pressure inside the wellbore at different times in a period of time, the average density of the mud inside the wellbore, the average stress of the formation around the wellbore, the deformation of the wellbore wall and the corresponding time points, and use the functions P(t), ρM(t), YL(t) and XB(t) to represent them according to the time series, where t is the time point, the time period is defined as [t1, t2], P(t) represents the average pressure inside the wellbore at time t in a period of time, ρM(t) represents the average density of the mud inside the wellbore at time t in a period of time, YL(t) represents the average stress of the formation around the wellbore at time t in a period of time, and XB(t) represents the deformation of the wellbore wall at time t in a period of time; The actual length of the wellbore and the gravity acceleration at the wellbore are obtained, and are calibrated as L and g respectively after being obtained; Calculate the change in the average pressure inside the wellbore, the change in the average density of the mud inside the wellbore, and the stress intensity ratio of the wellbore during the initial period. The specific calculation formula is as follows: Where ΔP is the change in the average pressure inside the wellbore during the initial period, ΔρM is the change in the average density of the mud inside the wellbore during the initial period, and S wb is the stress intensity ratio of the wellbore in the initial period; Calculate the dynamic wellbore stability coefficient. The specific calculation formula is as follows: Where DWSC is the dynamic wellbore stability coefficient.

5. The method for optimizing drilling parameters of oil and gas wells according to claim 4, characterized in that: The acquisition logic of the drill bit load response index is as follows: Extract the corresponding information of the drill bit operation in the initial time period after preprocessing, including the actual torque of the drill bit, the average drilling pressure of the drill bit, the drilling speed of the drill bit, the cuttings return rate and the corresponding time points at different times in a period of time, and represent them by functions NJ(t), W(t), VD(t) and VS(t) respectively according to the time series, where t is the time point, the time period is defined as [t1, t2], NJ(t) represents the actual torque of the drill bit at time t in a period of time, W(t) represents the average drilling pressure of the drill bit at time t in a period of time, VD(t) represents the drilling speed of the drill bit at time t in a period of time, and VS(t) represents the cuttings return rate at time t in a period of time; Calculate the change rate of the actual torque of the drill bit, the change rate of the average drilling pressure of the drill bit, the average drilling speed of the drill bit, and the average cuttings return rate during the initial period. The specific calculation formula is as follows: In the formula, λ NJ is the rate of change of the actual torque of the drill bit during the initial period, λ W VD is the rate of change of the average bit pressure during the initial period of time. - is the average drilling speed of the drill bit during the initial period, VS - is the average value of the cuttings return rate during the initial period; Calculate the dynamic response coefficient of the drill load. The specific calculation formula is as follows: In the formula, R d is the dynamic response coefficient of drill bit load; Obtain the average load response factor under normal drilling conditions and calibrate it as R d yu ; Calculate the drill bit load response index. The specific calculation formula is as follows: Where DBLRI is the drill bit load response index.

6. The method for optimizing drilling parameters of an oil and gas well according to claim 5, characterized in that: The generated dynamic wellbore stability coefficient DWSC and drill bit load response index DBLRI are used to construct an impact level assessment model, and the impact coefficient IC is generated through weighted summation.

7. The method for optimizing drilling parameters of an oil and gas well according to claim 6, characterized in that: Determine the preset influence coefficient threshold interval [IC1, IC2], compare the generated influence coefficient IC with the threshold interval [IC1, IC2], and evaluate the influence level on the drilling operation during the initial period of clay layer swelling according to the comparison result. The specific comparison and analysis are as follows: If IC < IC1, the influence level on the drilling operation during the initial period of clay layer swelling is a low-level influence; If IC1 ≤ IC ≤ IC2, the influence level on the drilling operation during the initial period of clay layer swelling is a medium-level influence; If IC > IC2, the influence level on the drilling operation during the initial period of clay layer swelling is a high-level influence.

8. The method for optimizing drilling parameters of oil and gas wells according to claim 7, characterized in that: According to the evaluation result, analyze the immediate influence of clay layer swelling on the wellbore pressure, bit operation load, and mud flow conditions, and take corresponding solutions according to the analysis result, specifically including: If the evaluation result is a low-level influence, the immediate influence of clay layer swelling on the wellbore pressure, bit operation load, and mud flow conditions is weak. Continue to maintain the current drilling parameters and keep real-time monitoring of the internal pressure of the wellbore, bit load, and mud flow condition; If the evaluation result is a medium-level influence, the immediate influence of clay layer swelling on the wellbore pressure, bit operation load, and mud flow conditions is medium. It is necessary to adjust the drilling parameters, specifically including reducing the drilling speed parameter, increasing the mud flow rate parameter, and adjusting the mud density parameter, and real-time monitor the adjustment effect of the system on these parameters; If the evaluation result is a high-level influence, the immediate influence of clay layer swelling on the wellbore pressure, bit operation load, and mud flow conditions is strong. It is urgent to adjust the drilling parameters and immediately stop the drilling operation, specifically including reducing the mud flow rate parameter to reduce the pressure load on the wellbore, conduct an emergency evaluation and adjust the pressure parameter and mud density parameter in the wellbore, and reset the drilling strategy.

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