Directional drilling data measurement and analysis method based on artificial intelligence

Abstract

The invention relates to the technical field of drilling data analysis, in particular to an artificial intelligence-based directional drilling data measurement and analysis method, which comprises the following steps of: acquiring multi-dimensional sensor data in a directional drilling process; executing intelligent data processing and feature extraction based on the multi-dimensional sensor data; according to the feature extraction result, well drilling trajectory prediction and parameter optimization are carried out; and outputting the optimized drilling parameters and the predicted trajectory. According to the invention, multi-source heterogeneous data from an intelligent drilling tool sensor, an underground real-time monitoring system and stratum information acquisition equipment can be processed at the same time. According to the all-around data acquisition and analysis strategy, subtle changes and potential problems in the drilling process can be captured, a solid data basis is provided for accurate control and optimization, internal structures and implicit information in data can be effectively captured, and the method is of great importance to accurate prediction of drilling trajectories and optimization of drilling parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of drilling data analysis, and more specifically, to a method for measuring and analyzing directional drilling data based on artificial intelligence. Background Art

[0002] As the exploration and development of oil and gas resources continue to advance towards complex geological conditions and deep areas, directional drilling technology plays an increasingly important role in the oil and gas industry. Traditional directional drilling technology mainly relies on logging data and manual experience to adjust drilling parameters and control the trajectory. This method performs well under simple geological conditions, but it is often difficult to meet the requirements of modern oil and gas development when faced with complex formation structures and high-precision drilling requirements.

[0003] In recent years, with the progress of sensor technology and data processing capabilities, the industry has begun to attempt to apply artificial intelligence technologies such as machine learning to the field of directional drilling. These methods learn from historical drilling data to establish a relationship model between drilling parameters and drilling effects, thereby achieving more accurate trajectory prediction and parameter optimization. However, these methods still have some obvious limitations.

[0004] First of all, existing machine learning methods often focus on single or a few parameters, such as weight on bit and rotary speed, while ignoring the complex correlations between multi-dimensional data during the drilling process. Although this simplified processing reduces the computational complexity, it also leads to information loss and affects the accuracy of decision-making.

[0005] Secondly, traditional feature engineering methods have limited effectiveness in dealing with high-dimensional and non-linear drilling data. They usually adopt manually designed feature extraction methods and are difficult to fully capture the deep structure and implicit information in the data. Especially when faced with complex geological conditions, the limitations of these methods are particularly obvious.

[0006] Furthermore, most existing optimization algorithms adopt traditional gradient descent or heuristic search methods. When faced with highly non-convex optimization problems, they are prone to falling into local optimal solutions and difficult to find the globally optimal combination of drilling parameters.

[0007] Finally, existing methods generally lack the ability to comprehensively evaluate and manage drilling risks. They often focus on improving drilling efficiency while insufficiently considering potential risk factors that may lead to serious accidents, which may pose major safety hazards under complex geological conditions.

[0008] In view of the above problems, there is an urgent need for a method for measuring and analyzing directional drilling data that can comprehensively utilize multi-dimensional drilling data, has strong feature extraction capabilities, can achieve global optimization, and can effectively manage drilling risks. Summary of the Invention

[0009] The present invention aims to solve the problems existing in the existing directional drilling data analysis methods, such as insufficient utilization of multi-dimensional data, limited feature extraction ability, unsatisfactory optimization effect, and insufficient risk management, and provides a directional drilling data measurement and analysis method based on artificial intelligence.

[0010] The present invention provides a directional drilling data measurement and analysis method based on artificial intelligence, including:

[0011] An acquisition step, including:

[0012] Acquiring multi-dimensional sensor data during the directional drilling process;

[0013] A processing step, including:

[0014] Performing intelligent data processing and feature extraction based on the multi-dimensional sensor data;

[0015] Predicting the drilling trajectory and optimizing the parameters according to the feature extraction results;

[0016] An output step, including:

[0017] Outputting the optimized drilling parameters and the predicted trajectory.

[0018] Preferably, the acquisition step specifically includes:

[0019] Acquiring intelligent drill tool sensor data;

[0020] Acquiring downhole real-time monitoring data;

[0021] Acquiring formation information acquisition data.

[0022] Preferably, the intelligent drill tool sensor data includes gyroscope data, acceleration sensor data, and torque stress monitoring data; the downhole real-time monitoring data includes weight on bit data, rotary speed data, and mud parameter data; the formation information acquisition data includes formation resistivity data, gamma ray spectroscopy data, and acoustic logging data.

[0023] Preferably, the intelligent data processing and feature extraction step includes:

[0024] Performing a random matrix denoising algorithm;

[0025] Performing a topological feature extraction algorithm based on the denoised data;

[0026] Performing a group theory invariant extraction algorithm according to the topological features;

[0027] Performing a random matrix feature fusion algorithm based on the group theory invariants;

[0028] Execute a quantum-inspired optimization algorithm according to the fusion feature.

[0029] Preferably, the random matrix denoising algorithm is implemented by the following formula:

[0030]

[0031] where X r is the original data matrix, λ is the threshold parameter, U and V are singular vector matrices, and S λ is the singular value matrix after soft thresholding, and X c is the denoised data matrix.

[0032] Preferably, the topological feature extraction algorithm is implemented by the following formula:

[0033]

[0034] where X c is the denoised data matrix, ∈ is the neighborhood parameter, is the Rips complex, H k is the k-th homology group, and T is the set of extracted topological features.

[0035] Preferably, the group theory invariant extraction algorithm is implemented by the following formula:

[0036]

[0037] where T is the set of topological features, G is the selected transformation group, Hom(G, Aut(T)) is the set of homomorphisms from G to the automorphism group of T, and I is the set of extracted group theory invariants.

[0038] Preferably, the random matrix feature fusion algorithm is implemented by the following formula:

[0039]

[0040] where I is the set of group theory invariants, Ω is the random projection matrix, Ω ∈ R n×k whose column vectors are independently and identically distributed according to the spherical uniform distribution, is the fused feature matrix.

[0041] Preferably, the quantum-inspired optimization algorithm is implemented by the following formula:

[0042]

[0043] where F is the fusion feature matrix, H is the quantum Hamiltonian operator, λ is the regularization parameter, ||·|| * is the nuclear norm, is the finally optimized feature matrix.

[0044] Preferably, it further includes:

[0045] Based on the optimized drilling parameters and predicted trajectory, perform real-time drilling control;

[0046] According to the real-time drilling control results, update the multi-dimensional sensor data;

[0047] Based on the updated multi-dimensional sensor data, repeatedly execute the above processing steps and output steps to form a closed-loop optimization control.

[0048] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0049] First of all, through the intelligent fusion of multi-dimensional data, the present invention realizes the comprehensive perception and in-depth understanding of the drilling process. Compared with traditional methods, the present invention can simultaneously process multi-source heterogeneous data from intelligent drill sensors, downhole real-time monitoring systems, and formation information acquisition devices. This all-round data acquisition and analysis strategy enables the present invention to capture subtle changes and potential problems in the drilling process, providing a solid data foundation for precise control and optimization.

[0050] Secondly, the present invention adopts an innovative feature extraction method based on random matrix theory, topological data analysis, and group theory. These advanced mathematical tools enable the present invention to extract more valuable and representative features from complex drilling data. Especially when dealing with high-dimensional non-linear data, the method of the present invention shows significant advantages, and can effectively capture the internal structure and implicit information in the data, which is crucial for accurately predicting the drilling trajectory and optimizing drilling parameters.

[0051] Furthermore, the present invention introduces a quantum-inspired optimization algorithm, which is a brand-new optimization method inspired by quantum computing ideas. Compared with traditional optimization algorithms, the quantum-inspired optimization algorithm has stronger global search ability and can find better solutions in complex parameter spaces. This enables the present invention to better handle non-linear and multi-objective optimization problems in directional drilling, achieving a double improvement in drilling efficiency and accuracy.

[0052] In addition, the present invention establishes a comprehensive drilling risk assessment and management system. Through in-depth analysis of multi-dimensional data, the present invention can timely identify potential drilling risks, such as formation mutations, wellbore instability, etc., and give corresponding early warnings and treatment suggestions. This greatly enhances the safety and controllability of the drilling process and reduces the risks of drilling operations under complex geological conditions.

[0053] Finally, the present invention adopts a closed-loop optimization control strategy, which can continuously adjust and optimize drilling parameters according to real-time drilling data. This adaptive learning mechanism enables the present invention to quickly respond to changes in geological conditions, continuously optimize drilling performance, and thus maintain high efficiency and high precision throughout the drilling process.

[0054] In summary, through the organic combination of innovative technologies such as multi-dimensional data fusion, advanced feature extraction, global optimization, and risk management, the present invention significantly improves the accuracy, efficiency, and safety of directional drilling. This not only provides strong technical support for the development of oil and gas resources under complex geological conditions but also points out a new direction for the future development of directional drilling technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the method of the present invention.

[0056] Figure 2 is a flowchart of the intelligent data processing and feature extraction steps of the present invention.

[0057] Figure 3 is a flowchart of the real-time drilling control and parameter update process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0058] Please refer to Figures 1-3 , the present invention provides a method for measuring and analyzing directional drilling data based on artificial intelligence. Through intelligent data processing and feature extraction technologies, this method realizes the precise analysis and control of the directional drilling process, significantly improving drilling efficiency and accuracy.

[0059] Specifically, the method of the present invention includes the following steps:

[0060] First, in the acquisition step, this method acquires multi-dimensional sensor data during the directional drilling process. These data comprehensively reflect all aspects of the drilling process, providing a rich information basis for subsequent analysis.

[0061] Next, in the processing step, based on the acquired multi-dimensional sensor data, this method performs intelligent data processing and feature extraction. This step is the core of the present invention. Through advanced algorithms, the raw data is processed and analyzed to extract the most valuable feature information. Subsequently, according to the results of feature extraction, this method conducts drilling trajectory prediction and parameter optimization. This process makes full use of artificial intelligence technology and can accurately predict the future drilling trajectory based on historical data and the current state, and give optimal drilling parameter suggestions.

[0062] Finally, in the output step, this method outputs the optimized drilling parameters and predicted trajectory. These output results directly guide on-site operations to ensure the accuracy and efficiency of the drilling process.

[0063] Furthermore, the acquisition steps of the present invention specifically include data acquisition in three aspects:

[0064] Firstly, acquire intelligent drill tool sensor data. Intelligent drill tools are an important part of modern directional drilling technology, and various sensors built into them can monitor the status of the drill tool and the surrounding environment in real time.

[0065] Secondly, acquire downhole real-time monitoring data. These data reflect the real-time situation downhole and are crucial for timely adjusting drilling parameters and avoiding accidents.

[0066] Finally, acquire formation information acquisition data. Formation information is the key basis for guiding the drilling trajectory. Through advanced logging technologies, detailed formation structure and property information can be obtained.

[0067] Even further, the present invention has defined the above three types of data in detail:

[0068] Intelligent drill tool sensor data includes gyroscope data, acceleration sensor data, and torque stress monitoring data. Among them, gyroscope data is used to accurately measure the attitude of the drill tool, acceleration sensor data reflects the motion state of the drill tool, and torque stress monitoring data reflects the mechanical conditions of the drill tool. The combination of these data enables a comprehensive understanding of the working state of the drill tool.

[0069] Downhole real-time monitoring data includes weight-on-bit data, drilling rate data, and mud parameter data. Weight-on-bit and drilling rate are key parameters for controlling drilling efficiency, while mud parameters directly affect the safety and effectiveness of drilling. By monitoring these data in real time, drilling strategies can be adjusted in a timely manner to ensure the safety and efficiency of the drilling process.

[0070] Formation information acquisition data includes formation resistivity data, gamma ray spectroscopy data, and acoustic logging data. These data respectively reflect the electrical properties, radioactive properties, and acoustic properties of the formation. Through comprehensive analysis, different formation types can be accurately identified, providing precise geological navigation for directional drilling.

[0071] In practical applications, the parameters of each step of the method of the present invention need to be adjusted according to specific circumstances. For example, when acquiring intelligent drill tool sensor data, the sampling frequency of the gyroscope is usually set between 100 - 1000 Hz, and the specific value needs to be determined according to the drilling speed and required accuracy. The measurement range of the acceleration sensor is generally selected between ±50g and ±250g to adapt to different drilling conditions.

[0072] For downhole real-time monitoring data, the monitoring range of weight-on-bit is usually between 0 - 50 tons, and the monitoring range of drilling rate is between 0 - 200 m / h. Among the mud parameters, the monitoring range of density is generally between 0.8 - 2.5 g / cm3 Between them, the monitoring range of viscosity is between 10 - 100 s. The specific settings of these parameters need to be comprehensively considered according to factors such as well depth and formation characteristics.

[0073] In terms of formation information acquisition, the measurement range of resistivity logging is usually between 0.2 - 2000 Ω·m, the energy range of gamma ray spectroscopy logging is between 0 - 3 MeV, and the frequency range of acoustic logging is between 10 - 20 kHz. The selection of these parameters directly affects the accuracy of formation identification and needs to be specifically set according to the characteristics of the target formation.

[0074] Through the above detailed parameter settings and data acquisition, the method of the present invention can obtain comprehensive and accurate drilling process data, laying a solid foundation for subsequent intelligent processing and analysis. This multi-dimensional and high-precision data acquisition method significantly improves the accuracy and efficiency of directional drilling, effectively reduces drilling risks, and provides strong technical support for the efficient development of oil and gas resources. After the method of the present invention obtains multi-dimensional sensor data, it enters the crucial stage of intelligent data processing and feature extraction.

[0075] This stage includes five closely related steps, and each step utilizes advanced mathematical and artificial intelligence technologies to extract the most valuable information from complex raw data.

[0076] First, the method executes a random matrix noise reduction algorithm. In the actual drilling process, the data collected by various sensors will inevitably contain noise, which may come from equipment vibration, electromagnetic interference or other environmental factors. The random matrix noise reduction algorithm can effectively remove this noise and improve the accuracy of subsequent analysis.

[0077] Subsequently, based on the noise-reduced data, the method executes a topological feature extraction algorithm. The introduction of this step is a major innovation of the present invention. Topological feature extraction can capture the overall structural features of the data, which is crucial for understanding complex geological structures and drilling states. For example, by analyzing the topological features of parameters such as weight on bit and penetration rate, possible formation mutations or anomalies can be better identified. [[ID=1۸]]

[0078] In the third step, according to the extracted topological features, the method executes a group theory invariant extraction algorithm. The purpose of this step is to obtain invariant features in the data, which remain unchanged under various transformations and can therefore provide more stable and reliable information. In directional drilling, such invariant features may correspond to certain key geological features or drilling states, which are of great significance for precisely controlling the drilling trajectory.

[0079] Next, based on the extracted group-theoretic invariants, this method executes the random matrix feature fusion algorithm. This step intelligently fuses the various features obtained previously to form a comprehensive feature representation. Through this fusion, a more comprehensive and compact data representation can be obtained, which not only retains the key information but also reduces the complexity of subsequent processing.

[0080] Finally, according to the fused features, this method executes the quantum-inspired optimization algorithm. This is another important innovation point of the present invention. The quantum-inspired algorithm draws on the ideas of quantum computing and has the potential to find better solutions in complex optimization problems. In directional drilling, this algorithm can be used to optimize drilling parameters such as weight on bit and rotary speed to achieve the best drilling effect.

[0081] The random matrix noise reduction algorithm is implemented by the following formula:

[0082]

[0083] where X r is the original data matrix, λ is the threshold parameter, U and are the singular vector matrices, S λ is the singular value matrix after soft thresholding, and X c is the data matrix after noise reduction.

[0084] In practical applications, the selection of the threshold parameter λ is crucial. In the preferred embodiment of the present invention, λ is usually set to 1% - 5% of the largest singular value of the original data matrix. This range is obtained based on a large number of experiments and practical experiences and can achieve a good balance between effectively removing noise and retaining useful information.

[0085] During the directional drilling process, the data collected by sensors may be affected by environmental noise such as mechanical vibration and electromagnetic interference. Assume that multiple sensors (such as accelerometers and gyroscopes) are used to record the movement trajectory and attitude changes of the drill bit during drilling. Due to the complex underground environment, these sensor data are often mixed with noise. Through the random matrix noise reduction algorithm, these noises can be effectively removed, and the main signal features can be retained. For example, when abnormal vibration is detected, it can be more accurately determined whether it is due to encountering a hard rock layer or equipment failure rather than noise interference.

[0086] The original data is collected in real time by sensors installed on the drill bit to form a high-dimensional matrix X r . After being processed by the random matrix noise reduction algorithm, the noise-reduced data matrix X c is obtained for subsequent analysis.

[0087] Suppose there is a vibration data matrix X r ∈R 10×5, representing the readings of 5 sensors at 10 time points.

[0088] Obtain the raw data matrix from the sensors on the drilling equipment:

[0089]

[0090] For X r Perform singular value decomposition (SVD):

[0091] X r = USV T ,

[0092] Assume the obtained singular value matrix is:

[0093]

[0094] Set the threshold parameter λ = 3 and perform soft thresholding:

[0095]

[0096] Finally, obtain the denoised data matrix X c .

[0097] The topological feature extraction algorithm is implemented through the following formula:

[0098]

[0099] Where X c is the denoised data matrix, ∈ is the neighborhood parameter, is the Rips complex, H k is the k-th homology group, and T is the set of extracted topological features. The selection of the neighborhood parameter ∈ has an important impact on the result of topological feature extraction. In the embodiments of the present invention, ∈ is usually selected to be 0.1 - 0.5 times the average distance between data points. This range can effectively capture the local structure of the data while avoiding generating overly complex topological structures.

[0100] The geological structure is complex and variable, and traditional geometric feature extraction methods are difficult to capture global topological structure information such as porosity distribution, fracture network, etc. During the drilling process, it is necessary to understand the connectivity and porosity distribution of underground rock formations. For example, when detecting the groundwater level or oil reserves, understanding these topological characteristics is crucial. Extracting topological features through persistent homology theory can help better understand the connectivity of rock formations. For example, in a complex rock formation structure, different pore networks and fracture paths can be identified through the topological feature extraction algorithm, so as to optimize the drilling path and avoid encountering non-drillable areas.

[0101] From the denoised data, select an appropriate neighborhood parameter ∈ to construct the Rips complex, calculate the homology groups of different dimensions, and extract a set of features T that reflect the global topological characteristics of the data. These features can be used to guide subsequent drilling strategies.

[0102] In the rock formation distribution, it is necessary to capture global topological structure information such as its connectivity and porosity.

[0103] Suppose that after denoising by a random matrix, the obtained data matrix is X c as follows:

[0104]

[0105] Select the neighborhood parameter ∈ = 1, construct the Rips complex, and calculate the homology groups:

[0106]

[0107] Suppose the obtained homology groups are:

[0108] H0 = {[0], [1], [2]}, H1 = {},

[0109] This means that there are three connected components in the data and no one-dimensional loops (such as holes).

[0110] The group-theoretic invariant extraction algorithm is implemented by the following formula:

[0111]

[0112] where T is the set of topological features, G is the selected transformation group, Hom(G, Aut(T)) is the set of homomorphisms from G to the automorphism group of T, and I is the set of extracted group-theoretic invariants.

[0113] In the application of directional drilling, the selection of the transformation group G usually considers the rotation group and the translation group. This is because during the drilling process, the data may change due to the rotation and movement of the drilling tool, but some key features should remain invariant under these transformations. By extracting these invariants, a more stable and reliable feature representation can be obtained.

[0114] To enhance the generalization ability of the features and make them invariant to certain transformations (such as rotation, translation), especially when the drilling equipment works in different directions.

[0115] In actual operation, the drilling equipment may rotate and translate due to terrain or other factors. For example, when the drill bit works at different angles, the data may change.

[0116] Through the group theory invariant extraction algorithm, it can be ensured that the extracted features remain consistent under these transformations. For example, when analyzing the drill bit attitude at different angles, even if the drill bit rotates, its current working state and position can still be accurately identified.

[0117] According to the existing topological feature set T, an appropriate transformation group G is selected, and the group theory invariant I is extracted by calculating the corresponding homomorphism set. These invariants can be used to guide the attitude adjustment and path planning of the drilling equipment.

[0118] The comprehensive application of these algorithms enables the method of the present invention to extract the most valuable information from complex drilling data, providing a solid foundation for subsequent drilling trajectory prediction and parameter optimization. Through this intelligent data processing method, the present invention significantly improves the accuracy and efficiency of directional drilling, providing strong technical support for the efficient development of oil and gas resources. After the extraction of group theory invariants, the method of the present invention enters a deeper data analysis stage.

[0119] Considering that when the drilling equipment works in different directions, the data may rotate or translate.

[0120] Assume that the topological feature set T contains information on three connected components:

[0121] T = {[0], [1], [2]},

[0122] Select a rotation transformation group G = SO(2), and calculate the homomorphism set from it to the automorphism group of the topological feature set T:

[0123] Hom(SO(2), Aut(T)) = {φ i (T)|i = 1, 2, 3},

[0124] Assume that the obtained invariant set is:

[0125] I = {[0'], [1'], [2']},

[0126] These invariants remain consistent under rotation and translation transformations.

[0127] The random matrix feature fusion algorithm is implemented through the following formula:

[0128]

[0129] where I is the set of group theory invariants, Ω is the random projection matrix, Ω ∈ R n×k, whose column vectors are independently and identically distributed according to a spherical uniform distribution, and F is the fused feature matrix. The key to this step lies in the construction of the random projection matrix Ω. In the preferred embodiment of the present invention, the dimension k of Ω is usually selected to be 10% - 30% of the original feature dimension n. This ratio is based on a large number of experiments and can achieve a good balance between retaining key information and reducing computational complexity. For example, if the original feature dimension is 1000, then k may be set between 100 - 300. An important advantage of the random matrix feature fusion algorithm is its high computational efficiency. In actual drilling operations, a large amount of real-time data needs to be processed quickly, and this efficient feature fusion method can significantly improve the response speed of the system and provide support for real-time decision-making.

[0130] To reduce the feature dimension while retaining key information for subsequent efficient processing. In drilling data analysis, a large number of high-dimensional features may be generated, such as different types of data (temperature, pressure, vibration, etc.) from multiple sensors. These high-dimensional features not only occupy a large amount of storage space but also increase the computational complexity. Through the random matrix feature fusion algorithm, the feature dimension can be significantly reduced without losing important information. For example, when analyzing various sensor data generated during the drilling process, the high-dimensional features can be reduced to a low-dimensional representation through random projection, facilitating subsequent pattern recognition and prediction tasks.

[0131] Starting from the set of group-theoretic invariants I, use the random projection matrix Ω for dimensionality reduction processing to generate the fused feature matrix F. These low-dimensional features can be used for more efficient subsequent analysis, such as trajectory prediction and parameter optimization.

[0132] Reduce the high-dimensional features for subsequent efficient processing. Assume the set of group-theoretic invariants I is:

[0133]

[0134] Use the random projection matrix Ω ∈ R 3×2 :

[0135]

[0136] Perform dimensionality reduction processing:

[0137]

[0138] Next, the method of the present invention executes a quantum-inspired optimization algorithm, which is implemented by the following formula

[0139]

[0140] where F is the fused feature matrix, H is the quantum Hamiltonian operator, λ is the regularization parameter, ||·|| *is the nuclear norm, and O is the finally optimized feature matrix.

[0141] The quantum-inspired optimization algorithm is another innovation of the present invention. In practical applications, the construction of the quantum Hamiltonian operator H is a key issue. In one embodiment of the present invention, H can be designed as an operator reflecting the energy state of the drilling system. For example, it can include non-linear combinations of parameters such as weight on bit, rotary speed, mud flow rate, etc. to simulate the energy changes in the actual drilling process. The selection of the regularization parameter λ is also important. In the preferred embodiment of the present invention, λ is usually set between 0.01 and 0.1. This range can achieve a good balance between model complexity and generalization ability. A smaller value of λ will result in an optimization result that fits the current data better, while a larger value of λ will make the result more stable but may lose some detailed information.

[0142] Finding the global optimal solution is crucial for optimizing drilling parameters, such as determining the best drilling path and optimizing the drilling speed. In a complex underground environment, determining the best drilling path is a challenging problem. For example, during the drilling process, multiple factors need to be considered, such as geological conditions, drilling costs, time limits, etc. Through the quantum-inspired optimization algorithm, the global optimal solution can be found instead of getting stuck in a local optimal solution. For example, when designing the drilling path, factors such as different geological layer structures and the performance of drilling equipment can be comprehensively considered, and the best path can be found using the idea of quantum computing to improve drilling efficiency and resource utilization.

[0143] Based on the fused feature matrix F, optimization processing is performed through the quantum Hamiltonian operator H and the regularization parameter λ, and finally the optimized feature matrix O is obtained. These optimization results can be directly used to guide actual drilling operations, such as adjusting the drilling speed and direction.

[0144] Finding the best drilling path to optimize resource utilization. Assume that the fused feature matrix F is:

[0145]

[0146] Set the quantum Hamiltonian operator H and the regularization parameter λ = 0.1:

[0147]

[0148] Assume that the quantum Hamiltonian operator H is:

[0149]

[0150] Then the optimization problem becomes:

[0151]

[0152] By solving this optimization problem, the optimal feature matrix O that minimizes the objective function can be found.

[0153] Finally, the method of the present invention further includes a closed-loop optimization control process. This process includes the following steps:

[0154] First, based on the optimized drilling parameters and predicted trajectory, real-time drilling control is performed. This step directly applies the results of the optimization algorithm to the actual drilling operation, such as adjusting the weight on bit, rotary speed, mud parameters, etc.

[0155] Second, according to the real-time drilling control results, the multi-dimensional sensor data is updated. This step ensures that the system can timely capture the changes caused by parameter adjustment and provides the latest data basis for the next round of optimization.

[0156] Finally, based on the updated multi-dimensional sensor data, the processing step and the output step are repeatedly executed to form a closed-loop optimization control. This closed-loop design enables the method of the present invention to continuously self-optimize and adapt to complex and changeable geological conditions.

[0157] In practical applications, the execution frequency of this closed-loop optimization process needs to be adjusted according to specific circumstances. In an embodiment of the present invention, the system may perform a complete optimization cycle every 5 - 10 minutes. This time interval can not only ensure the system's timely response to environmental changes but also prevent the system performance from being affected by overly frequent calculations.

[0158] Through this intelligent closed-loop control, the method of the present invention can continuously optimize the drilling parameters, adjust the drilling trajectory in real time, and greatly improve the accuracy and efficiency of directional drilling. For example, in practical applications, using this method, the average deviation of the drilling trajectory can be controlled within 0.5 meters, which is about 50% more accurate than traditional methods. At the same time, by continuously optimizing the drilling parameters, this method can also significantly increase the drilling speed, and in some geological conditions, the drilling efficiency can be increased by up to 30%.

[0159] Generally speaking, the directional drilling data measurement and analysis method based on artificial intelligence provided by the present invention realizes the intelligent processing and analysis of complex drilling data through advanced mathematical models and algorithms. This method not only improves the accuracy and efficiency of directional drilling but also enhances the safety and controllability of the drilling process, providing strong technical support for the efficient development of oil and gas resources.

[0160] To verify the effectiveness and superiority of the method of the present invention, a simulation experiment was designed to simulate the directional drilling process under complex geological conditions. A typical shale gas reservoir was selected as the target for the experiment. The gas reservoir has a burial depth of about 3000 meters and a multi-layer thin interbed structure, which poses high requirements for the accuracy of directional drilling.

[0161] In Example 1, the method of the present invention is used for wellbore trajectory control and parameter optimization. In Comparative Example 1, the traditional geosteering method is used, and the drilling parameters are adjusted mainly relying on logging data and manual experience. In Comparative Example 2, a method based on simple machine learning is used, and the random forest algorithm is used for parameter optimization, but it does not include the advanced feature extraction and quantum-inspired optimization steps in the present invention.

[0162] The main conditions of the simulation experiment are as follows:

[0163] 1. Target layer thickness: 10 meters;

[0164] 2. Formation dip angle: 5° - 15° (random variation);

[0165] 3. Drilling depth: 3000 - 4000 meters;

[0166] 4. Simulated drilling time: 100 hours;

[0167] 5. Data acquisition frequency: once per second;

[0168] 6. Drilling parameter adjustment frequency: once every 5 minutes;

[0169] The following four key indicators are selected to evaluate the drilling effect:

[0170] 1. Trajectory control accuracy: defined as the average deviation between the actual trajectory and the target trajectory.

[0171] 2. Target layer encounter rate: the percentage of the drilling distance within the target layer in the total drilling distance.

[0172] 3. Average rate of penetration (ROP): reflecting the drilling efficiency.

[0173] 4. Drilling risk index: an index that comprehensively considers factors such as wellbore stability and sticking risk, the lower the better.

[0174] The detection methods for these indicators are as follows:

[0175] 1. Trajectory control accuracy: Calculate the actual trajectory through the simulated inclinometer data, compare it with the preset target trajectory, and take the average deviation.

[0176] 2. Target layer encounter rate: Use the simulated gamma logging data to judge whether it is within the target layer and count the drilling distance within the target layer. [[ID=,46]]

[0177] 3. Average rate of penetration: Obtained directly from the simulation data.

[0178] 4. Drilling risk index: Calculated according to the simulated weight on bit, torque, vibration and other data using the preset risk assessment model.

[0179] The following is a comparison table of the experimental results:

[0180]

[0181]

[0182] As can be seen from the experimental results, the method of the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the simple machine learning method (Comparative Example 2) in all key indicators.

[0183] The specific analysis is as follows:

[0184] 1. Trajectory control accuracy: The method of the present invention controls the trajectory deviation within 0.3 meters, which is 75% higher than the traditional method and 57% higher than the simple machine learning method. This benefits from the advanced feature extraction and quantum-inspired optimization algorithm in the present invention, which can more accurately predict and control the drilling trajectory.

[0185] 2. Target layer encounter rate: The method of the present invention reaches a target layer encounter rate of 92%, which is 14 percentage points higher than the traditional method and 7 percentage points higher than the simple machine learning method. This indicates that this method can better identify and track the target layer, which is particularly important when developing thin-layer gas reservoirs.

[0186] 3. Average rate of penetration: The method of the present invention increases the average rate of penetration to 15.8 meters per hour, which is 28.5% higher than the traditional method and 11.3% higher than the simple machine learning method. This shows that this method can better optimize the drilling parameters and improve the drilling efficiency.

[0187] 4. Drilling risk index: The method of the present invention reduces the drilling risk index to 0.15, which is 60.5% lower than the traditional method and 40% lower than the simple machine learning method. This indicates that this method can better predict and avoid potential drilling risks and improve the operation safety.

[0188] These results fully demonstrate the superiority of the method of the present invention. Especially in terms of trajectory control accuracy and drilling risk management, this method shows significant advantages. This is mainly due to the innovative points in the present invention, such as the intelligent fusion of multi-dimensional data, feature extraction based on topological features and group-theoretic invariants, and the quantum-inspired optimization algorithm. These advanced technologies enable this method to more comprehensively and deeply understand and utilize complex drilling data, thereby making more accurate predictions and better decisions.

[0189] It should be noted that while the method of the present invention improves the drilling efficiency, it also significantly reduces the drilling risk. This is of great significance for directional drilling under complex geological conditions, which can greatly reduce the drilling cost and improve the economic benefits of oil and gas resource development.

[0190] Generally speaking, this simulation experiment strongly proves the potential of the method of the present invention in practical applications. It can not only improve the accuracy and efficiency of directional drilling, but also enhance the safety and controllability of the drilling process, providing a new direction for the technological progress of the oil and gas industry.

[0191] It should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for measuring and analyzing directional drilling data based on artificial intelligence, characterized in that, Comprising: An acquisition step, comprising: Acquiring multi-dimensional sensor data during directional drilling; A processing step, comprising: Performing intelligent data processing and feature extraction based on the multi-dimensional sensor data; Performing drilling trajectory prediction and parameter optimization according to the feature extraction results; An output step, comprising: Outputting optimized drilling parameters and predicted trajectories.

2. The method according to claim 1, characterized in that, The acquisition step specifically comprises: Acquiring intelligent drill tool sensor data; Acquiring downhole real-time monitoring data; Acquiring formation information acquisition data.

3. The method according to claim 2, characterized in that The intelligent drill tool sensor data includes gyroscope data, acceleration sensor data, and torque stress monitoring data; the downhole real-time monitoring data includes weight on bit data, rotary speed data, and mud parameter data; the formation information acquisition data includes formation resistivity data, gamma ray spectrometry data, and acoustic logging data.

4. The method according to claim 1, wherein The intelligent data processing and feature extraction step comprises: Performing a random matrix noise reduction algorithm; Performing a topological feature extraction algorithm based on the noise-reduced data; Performing a group theory invariant extraction algorithm according to the topological features; Performing a random matrix feature fusion algorithm based on the group theory invariants; Performing a quantum-inspired optimization algorithm according to the fusion features.

5. The method according to claim 4, characterized in that, The random matrix noise reduction algorithm is implemented by the following formula: Among them, X r is the original data matrix, λ is the threshold parameter, U and V are the singular vector matrices, and S λ is the singular value matrix after soft thresholding, and X c is the data matrix after noise reduction.

6. The method according to claim 4, characterized in that The topological feature extraction algorithm is implemented by the following formula: Among them, X C is the noise reduction data matrix, ∈ is the neighborhood parameter, is the Rips complex, H k is the k-th homology group, and T is the set of extracted topological features.

7. The method according to claim 4, characterized in that The group theory invariant extraction algorithm is implemented by the following formula: Wherein, T is a set of topological features, G is a selected transformation group, Hom(G, Aut(T)) is a set of homomorphisms from G to the automorphism group of T, and I is a set of extracted group theory invariants.

8. The method according to claim 4, wherein The random matrix feature fusion algorithm is implemented by the following formula: where \(I\) is a set of group theory invariants, \(\Omega\) is a random projection matrix, \(\Omega\in\mathbb{R}\) n×k , whose column vectors are independently and identically distributed according to a spherical uniform distribution, is the fused feature matrix.

9. The method according to claim 4, characterized in that, The quantum-inspired optimization algorithm is implemented by the following formula: Among them, F is the fused feature matrix, H is the quantum Hamiltonian operator, λ is the regularization parameter, and ||·|| * is the nuclear norm, is the finally optimized feature matrix.

10. The method according to claim 1, characterized in that Also comprising: Performing real-time drilling control based on the optimized drilling parameters and predicted trajectories; Updating the multi-dimensional sensor data according to the real-time drilling control results; Based on the updated multi-dimensional sensor data, repeatedly performing the processing step and the output step to form a closed-loop optimization control.

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